‘This is possibly the single largest design flaw contributing to the bad Nash equilibrium in which … many governments are stuck. Every individual high-functioning competent person knows they can’t make much difference by being one more face in that crowd.’ Eliezer Yudkowsky, AI expert, LessWrong etc.
‘[M]uch of our intellectual elite who think they have “the solutions” have actually cut themselves off from understanding the basis for much of the most important human progress.’ Michael Nielsen, physicist and one of the handful of most interesting people I’ve ever talked to.
‘People, ideas, machines — in that order.’ Colonel Boyd.
‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities.’ Charlie Munger,Warren Buffett’s partner.
‘Two hands, it isn’t much considering how the world is infinite. Yet, all the same, two hands, they are a lot.’ Alexander Grothendieck, one of the great mathematicians.
There are many brilliant people in the civil service and politics. Over the past five months the No10 political team has been lucky to work with some fantastic officials. But there are also some profound problems at the core of how the British state makes decisions. This was seen by pundit-world as a very eccentric view in 2014. It is no longer seen as eccentric. Dealing with these deep problems is supported by many great officials, particularly younger ones, though of course there will naturally be many fears — some reasonable, most unreasonable.
Now there is a confluence of: a) Brexit requires many large changes in policy and in the structure of decision-making, b) some people in government are prepared to take risks to change things a lot, and c) a new government with a significant majority and little need to worry about short-term unpopularity while trying to make rapid progress with long-term problems.
There is a huge amount of low hanging fruit — trillion dollar bills lying on the street — in the intersection of:
the selection, education and training of people for high performance
decision-making institutions at the apex of government.
We want to hire an unusual set of people with different skills and backgrounds to work in Downing Street with the best officials, some as spads and perhaps some as officials. If you are already an official and you read this blog and think you fit one of these categories, get in touch.
The categories are roughly:
Data scientists and software developers
Junior researchers one of whom will also be my personal assistant
Weirdos and misfits with odd skills
We want to improve performance and make me much less important — and within a year largely redundant. At the moment I have to make decisions well outside what Charlie Munger calls my ‘circle of competence’ and we do not have the sort of expertise supporting the PM and ministers that is needed. This must change fast so we can properly serve the public.
A. Unusual mathematicians, physicists, computer scientists, data scientists
You must have exceptional academic qualifications from one of the world’s best universities or have done something that demonstrates equivalent (or greater) talents and skills. You do not need a PhD — as Alan Kay said, we are also interested in graduate students as ‘world-class researchers who don’t have PhDs yet’.
You should have the following:
PhD or MSc in maths or physics.
Outstanding mathematical skills are essential.
Experience of using analytical languages: e.g. Python, SQL, R.
Familiarity with data tools and technologies such as Postgres, Scikit Learn, NEO4J.
A few examples of papers that you will be considering:
Complex Contagions : A Decade in Review, 2017. This looks at a large number of studies on ‘what goes viral and why?’. A lot of studies in this field are dodgy (bad maths, don’t replicate etc), an important question is which ones are worth examining.
On the frequency and severity of interstate wars, 2019. ‘How can it be possible that the frequency and severity of interstate wars are so consistent with a stationary model, despite the enormous changes and obviously non-stationary dynamics in human population, in the number of recognized states, in commerce, communication, public health, and technology, and even in the modes of war itself? The fact that the absolute number and sizes of wars are plausibly stable in the face of these changes is a profound mystery for which we have no explanation.’ Does this claim stack up?
You should be able to explain to other mathematicians, physicists and computer scientists the ideas in such papers, discuss what could be useful for our projects, synthesise ideas for other data scientists, and apply them to practical problems. You won’t be expert on the maths used in all these papers but you should be confident that you could study it and understand it.
We will be using machine learning and associated tools so it is important you can program. You do not need software development levels of programming but it would be an advantage.
We are looking for great software developers who would love to work on these ideas, build tools and work with some great people. You should also look at some of Victor’s technical talks on programming languages and the history of computing.
You will be working with data scientists, designers and others.
C. Unusual economists
We are looking to hire some recent graduates in economics. You should a) have an outstanding record at a great university, b) understand conventional economic theories, c) be interested in arguments on the edge of the field — for example, work by physicists on ‘agent-based models’ or by the hedge fund Bridgewater on the failures/limitations of conventional macro theories/prediction, and d) have very strong maths and be interested in working with mathematicians, physicists, and computer scientists.
The ideal candidate might, for example, have a degree in maths and economics, worked at the LHC in one summer, worked with a quant fund another summer, and written software for a YC startup in a third summer!
We’ve found one of these but want at least one more.
von Neumann’s foundation of game theory and ‘expected utility’,
mainstream economic theories,
modern theories about auctions,
theoretical computer science (including problems like the complexity of probabilistic inference in Bayesian networks, which is in the NP–hard complexity class),
ideas on ‘computational rationality’ and meta-reasoning from AI, cognitive science and so on.
If these sort of things are interesting, then you will find this project interesting.
It’s a bonus if you can code but it isn’t necessary.
D. Great project managers.
If you think you are one of the a small group of people in the world who are truly GREAT at project management, then we want to talk to you. Victoria Woodcock ran Vote Leave — she was a truly awesome project manager and without her Cameron would certainly have won. We need people like this who have a 1 in 10,000 or higher level of skill and temperament.
The Oxford Handbook on Megaprojects points out that it is possible to quantify lessons from the failures of projects like high speed rail projects because almost all fail so there is a large enough sample to make statistical comparisons, whereas there can be no statistical analysis of successes because they are so rare.
It is extremely interesting that the lessons of Manhattan (1940s), ICBMs (1950s) and Apollo (1960s) remain absolutely cutting edge because it is so hard to apply them and almost nobody has managed to do it. The Pentagon systematically de-programmed itself from more effective approaches to less effective approaches from the mid-1960s, in the name of ‘efficiency’. Is this just another way of saying that people like General Groves and George Mueller are rarer than Fields Medallists?
Anyway — it is obvious that improving government requires vast improvements in project management. The first project will be improving the people and skills already here.
If you want an example of the sort of people we need to find in Britain, look at this on CC Myers — the legendary builders. SPEED. We urgently need people with these sort of skills and attitude. (If you think you are such a company and you could dual carriageway the A1 north of Newcastle in record time, then get in touch!)
E. Junior researchers
In many aspects of government, as in the tech world and investing, brains and temperament smash experience and seniority out of the park.
We want to hire some VERY clever young people either straight out of university or recently out with with extreme curiosity and capacity for hard work.
One of you will be a sort of personal assistant to me for a year — this will involve a mix of very interesting work and lots of uninteresting trivia that makes my life easier which you won’t enjoy. You will not have weekday date nights, you will sacrifice many weekends — frankly it will hard having a boy/girlfriend at all. It will be exhausting but interesting and if you cut it you will be involved in things at the age of ~21 that most people never see.
I don’t want confident public school bluffers. I want people who are much brighter than me who can work in an extreme environment. If you play office politics, you will be discovered and immediately binned.
In SW1 communication is generally treated as almost synonymous with ‘talking to the lobby’. This is partly why so much punditry is ‘narrative from noise’.
With no election for years and huge changes in the digital world, there is a chance and a need to do things very differently.
We’re particularly interested in deep experts on TV and digital. We also are interested in people who have worked in movies or on advertising campaigns. There are some very interesting possibilities in the intersection of technology and story telling — if you’ve done something weird, this may be the place for you.
I noticed in the recent campaign that the world of digital advertising has changed very fast since I was last involved in 2016. This is partly why so many journalists wrongly looked at things like Corbyn’s Facebook stats and thought Labour was doing better than us — the ecosystem evolves rapidly while political journalists are still behind the 2016 tech, hence why so many fell for Carole’s conspiracy theories. The digital people involved in the last campaign really knew what they are doing, which is incredibly rare in this world of charlatans and clients who don’t know what they should be buying. If you are interested in being right at the very edge of this field, join.
We have some extremely able people but we also must upgrade skills across the spad network.
G. Policy experts
One of the problems with the civil service is the way in which people are shuffled such that they either do not acquire expertise or they are moved out of areas they really know to do something else. One Friday, X is in charge of special needs education, the next week X is in charge of budgets.
There are, of course, general skills. Managing a large organisation involves some general skills. Whether it is Coca Cola or Apple, some things are very similar — how to deal with people, how to build great teams and so on. Experience is often over-rated. When Warren Buffett needed someone to turn around his insurance business he did not hire someone with experience in insurance:‘When Ajit entered Berkshire’s office on a Saturday in 1986, he did not have a day’s experience in the insurance business’ (Buffett).
Shuffling some people who are expected to be general managers is a natural thing but it is clear Whitehall does this too much while also not training general management skills properly. There are not enough people with deep expertise in specific fields.
If you want to work in the policy unit or a department and you really know your subject so that you could confidently argue about it with world-class experts, get in touch.
It’s also the case that wherever you are most of the best people are inevitably somewhere else. This means that governments must be much better at tapping distributed expertise. Of the top 20 people in the world who best understand the science of climate change and could advise us what to do with COP 2020, how many now work as a civil servant/spad or will become one in the next 5 years?
G. Super-talented weirdos
People in SW1 talk a lot about ‘diversity’ but they rarely mean ‘true cognitive diversity’. They are usually babbling about ‘gender identity diversity blah blah’. What SW1 needs is not more drivel about ‘identity’ and ‘diversity’ from Oxbridge humanities graduates but more genuine cognitive diversity.
We need some true wild cards, artists, people who never went to university and fought their way out of an appalling hell hole, weirdos from William Gibson novels like that girl hired by Bigend as a brand ‘diviner’ who feels sick at the sight of Tommy Hilfiger or that Chinese-Cuban free runner from a crime family hired by the KGB. If you want to figure out what characters around Putin might do, or how international criminal gangs might exploit holes in our border security, you don’t want more Oxbridge English graduates who chat about Lacan at dinner parties with TV producers and spread fake news about fake news.
By definition I don’t really know what I’m looking for but I want people around No10 to be on the lookout for such people.
We need to figure out how to use such people better without asking them to conform to the horrors of ‘Human Resources’ (which also obviously need a bonfire).
Send a max 1 page letter plus CV to email@example.com and put in the subject line ‘job/’ and add after the / one of: data, developer, econ, comms, projects, research, policy, misfit.
I’ll have to spend time helping you so don’t apply unless you can commit to at least 2 years.
I’ll bin you within weeks if you don’t fit — don’t complain later because I made it clear now.
I will try to answer as many as possible but last time I publicly asked for job applications in 2015 I was swamped and could not, so I can’t promise an answer. If you think I’ve insanely ignored you, persist for a while.
I will use this blog to throw out ideas. It’s important when dealing with large organisations to dart around at different levels, not be stuck with formal hierarchies. It will seem chaotic and ‘not proper No10 process’ to some. But the point of this government is to do things differently and better and this always looks messy. We do not care about trying to ‘control the narrative’ and all that New Labour junk and this government will not be run by ‘comms grid’.
As Paul Graham and Peter Thiel say, most ideas that seem bad are bad but great ideas also seem at first like bad ideas — otherwise someone would have already done them. Incentives and culture push people in normal government systems away from encouraging ‘ideas that seem bad’. Part of the point of a small, odd No10 team is to find and exploit, without worrying about media noise, what Andy Grove called ‘very high leverage ideas’ and these will almost inevitably seem bad to most.
I will post some random things over the next few weeks and see what bounces back — it is all upside, there’s no downside if you don’t mind a bit of noise and it’s a fast cheap way to find good ideas…
‘People, ideas, machines — in that order!’ Colonel Boyd.
‘The main thing that’s needed is simply the recognition of how important seeing is, and the will to do something about it.’ Bret Victor.
‘[T]he transfer of an entirely new and quite different framework for thinking about, designing, and using information systems … is immensely more difficult than transferring technology.’ Robert Taylor, one of the handful most responsible for the creation of the internet and personal computing, and in inspiration to Bret Victor.
‘[M]uch of our intellectual elite who think they have “the solutions” have actually cut themselves off from understanding the basis for much of the most important human progress.’ Michael Nielsen, physicist.
This blog looks at an intersection of decision-making, technology, high performance teams and government. It sketches some ideas of physicist Michael Nielsen about cognitive technologies and of computer visionary Bret Victor about the creation of dynamic tools to help understand complex systems and ‘argue with evidence’, such as ‘tools for authoring dynamic documents’, and ‘Seeing Rooms’ for decision-makers — i.e rooms designed to support decisions in complex environments. It compares normal Cabinet rooms, such as that used in summer 1914 or October 1962, with state-of-the-art Seeing Rooms. There is very powerful feedback between: a) creating dynamic tools to see complex systems deeper (to see inside, see across time, and see across possibilities), thus making it easier to work with reliable knowledge and interactive quantitative models, semi-automating error-correction etc, and b) the potential for big improvements in the performance of political and government decision-making.
It is relevant to Brexit and anybody thinking ‘how on earth do we escape this nightmare’ but 1) these ideas are not at all dependent on whether you support or oppose Brexit, about which reasonable people disagree, and 2) they are generally applicable to how to improve decision-making — for example, they are relevant to problems like ‘how to make decisions during a fast moving nuclear crisis’ which I blogged about recently, or if you are a journalist ‘what future media could look like to help improve debate of politics’. One of the tools Nielsen discusses is a tool to make memory a choice by embedding learning in long-term memory rather than, as it is for almost all of us, an accident. I know from my days working on education reform in government that it’s almost impossible to exaggerate how little those who work on education policy think about ‘how to improve learning’.
Fields make huge progress when they move from stories (e.g Icarus) and authority (e.g ‘witch doctor’) to evidence/experiment (e.g physics, wind tunnels) and quantitative models (e.g design of modern aircraft). Political ‘debate’ and the processes of government are largely what they have always been — largely conflict over stories and authorities where almost nobody even tries to keep track of the facts/arguments/models they’re supposedly arguing about, or tries to learn from evidence, or tries to infer useful principles from examples of extreme success/failure. We can see much better than people could in the past how to shift towards processes of government being ‘partially rational discussion over facts and models and learning from the best examples of organisational success‘. But one of the most fundamental and striking aspects of government is that practically nobody involved in it has the faintest interest in or knowledge of how to create high performance teams to make decisions amid uncertainty and complexity. This blindness is connected to another fundamental fact: critical institutions (including the senior civil service and the parties) are programmed to fight to stay dysfunctional, they fight to stay closed and avoid learning about high performance, they fight to exclude the most able people.
I wrote about some reasons for this before the referendum (cf. The Hollow Men). The Westminster and Whitehall response was along the lines of ‘natural party of government’, ‘Rolls Royce civil service’ blah blah. But the fact that Cameron, Heywood (the most powerful civil servant) et al did not understand many basic features of how the world works is why I and a few others gambled on the referendum — we knew that the systemic dysfunction of our institutions and the influence of grotesque incompetents provided an opportunity for extreme leverage.
Since then, after three years in which the parties, No10 and the senior civil service have imploded (after doing the opposite of what Vote Leave said should happen on every aspect of the negotiations) one thing has held steady — Insiders refuse to ask basic questions about the reasons for this implosion, such as: ‘why Heywood didn’t even put together a sane regular weekly meeting schedule and ministers didn’t even notice all the tricks with agendas/minutes etc’, how are decisions really made in No10, why are so many of the people below some cognitive threshold for understanding basic concepts (cf. the current GATT A24 madness), what does it say about Westminster that both the Adonis-Remainers and the Cash-ERGers have become more detached from reality while a large section of the best-educated have effectively run information operations against their own brains to convince themselves of fairy stories about Facebook, Russia and Brexit…
This blog is hopefully useful for some of those thinking about a) improving government around the world and/or b) ‘what comes after the coming collapse and reshaping of the British parties, and how to improve drastically the performance of critical institutions?’
Some old colleagues have said ‘Don’t put this stuff on the internet, we don’t want the second referendum mob looking at it.’ Don’t worry! Ideas like this have to be forced down people’s throats practically at gunpoint. Silicon Valley itself has barely absorbed Bret Victor’s ideas so how likely is it that there will be a rush to adopt them by the world of Blair and Grieve?! These guys can’t tell the difference between courtier-fixers and people with models for truly effective action like General Groves (HERE). Not one in a thousand will read a 10,000 word blog on the intersection of management and technology and the few who do will dismiss it as the babbling of a deluded fool, they won’t learn any more than they learned from the 2004 referendum or from Vote Leave. And if I’m wrong? Great. Things will improve fast and a second referendum based on both sides applying lessons from Bret Victor would be dynamite.
NB. Bret Victor’s project, Dynamic Land, is a non-profit. For an amount of money that a government department like the Department for Education loses weekly without any minister realising it’s lost (in the millions per week in my experience because the quality of financial control is so bad), it could provide crucial funding for Victor and help itself. Of course, any minister who proposed such a thing would be told by officials ‘this is illegal under EU procurement law and remember minister that we must obey EU procurement law forever regardless of Brexit’ — something I know from experience officials say to ministers whether it is legal or not when they don’t like something. And after all, ministers meekly accepted the Kafka-esque order from Heywood to prioritise duties of goodwill to the EU under A50 over preparations to leave A50, so habituated had Cameron’s children become to obeying the real deputy prime minister…
Below are 4 sections:
The value found in intersections of fields
Some ideas of Bret Victor
Some ideas of Michael Nielsen
1. Extreme value is often found in the intersection of fields
The legendary Colonel Boyd (he of the ‘OODA loop’) would shout at audiences ‘People, ideas, machines — in that order.‘ Fundamental political problems we face require large improvements in the quality of all three and, harder, systems to integrate all three. Such improvements require looking carefully at the intersection of roughly five entangled areas of study. Extreme value is often found at such intersections.
Explore what we know about the selection, education and training of people for high performance (individual/team/organisation) in different fields. We should be selecting people much deeper in the tails of the ability curve — people who are +3 (~1:1,000) or +4 (~1:30,000) standard deviations above average on intelligence, relentless effort, operational ability and so on (now practically entirely absent from the ’50 most powerful people in Britain’). We should train them in the general art of ‘thinking rationally’ and making decisions amid uncertainty (e.g Munger/Tetlock-style checklists, exercises on SlateStarCodex blog). We should train them in the practical reasons for normal ‘mega-project failure’ and case studies such as the Manhattan Project (General Groves), ICBMs (Bernard Schriever), Apollo (George Mueller), ARPA-PARC (Robert Taylor) that illustrate how the ‘unrecognised simplicities’ of high performance bring extreme success and make them work on such projects before they are responsible for billions rather than putting people like Cameron in charge (after no experience other than bluffing through PPE then PR). NB. China’s leaders have studied these episodes intensely while American and British institutions have actively ‘unlearned’ these lessons.
Explore the frontiers of the science of prediction across different fields from physics to weather forecasting to finance and epidemiology. For example, ideas from physics about early warning systems in physical systems have application in many fields, including questions like: to what extent is it possible to predict which news will persist over different timescales, or predict wars from news and social media? There is interesting work combining game theory, machine learning, and Red Teams to predict security threats and improve penetration testing (physical and cyber). The Tetlock/IARPA project showed dramatic performance improvements in political forecasting are possible, contra what people such as Kahneman had thought possible. A recent Nature article by Duncan Watts explained fundamental problems with the way normal social science treats prediction and suggested new approaches — which have been almost entirely ignored by mainstream economists/social scientists. There is vast scope for applying ideas and tools from the physical sciences and data science/AI — largely ignored by mainstream social science, political parties, government bureaucracies and media — to social/political/government problems (as Vote Leave showed in the referendum, though this has been almost totally obscured by all the fake news: clue — it was not ‘microtargeting’).
Explore technology and tools. For example, Bret Victor’s work and Michael Nielsen’s work on cognitive technologies. The edge of performance in politics/government will be defined by teams that can combine the ancient ‘unrecognised simplicities of high performance’ with edge-of-the-art technology. No10 is decades behind the pace in old technologies like TV, doesn’t understand simple tools like checklists, and is nowhere with advanced technologies.
Explore the frontiers of communication (e.g crisis management, applied psychology). Technology enables people to improve communication with unprecedented speed, scale and iterative testing. It also allows people to wreak chaos with high leverage. The technologies are already beyond the ability of traditional government centralised bureaucracies to cope with. They will develop rapidly such that most such centralised bureaucracies lose more and more control while a few high performance governments use the leverage they bring (c.f China’s combination of mass surveillance, AI, genetic identification, cellphone tracking etc as they desperately scramble to keep control). The better educated think that psychological manipulation is something that happens to ‘the uneducated masses’ but they are extremely deluded — in many ways people like FT pundits are much easier to manipulate, their education actually makes them more susceptible to manipulation, and historically they are the ones who fall for things like Russian fake news (cf. the Guardian and New York Times on Stalin/terror/famine in the 1930s) just as now they fall for fake news about fake news. Despite the centrality of communication to politics it is remarkable how little attention Insiders pay to what works — never mind the question ‘what could work much better?’. The fact that so much of the media believes total rubbish about social media and Brexit shows that the media is incapable of analysing the intersection of politics and technology but, although it is obviously bad that the media disinforms the public, the only rational planning assumption is that this problem will continue and even get worse. The media cannot explain either the use of TV or traditional polling well, these have been extremely important for over 70 years, and there is no trend towards improvement so a sound planning assumption is surely that the media will do even worse with new technologies and data science. This will provide large opportunities for good and evil. A new approach able to adapt to the environment an order of magnitude faster than now would disorient political opponents (desperately scrolling through Twitter) to such a degree — in Boyd’s terms it would ‘collapse their OODA loops’ — that it could create crucial political space for focus on the extremely hard process of rewiring government institutions which now seems impossible for Insiders to focus on given their psychological/operational immersion in the hysteria of 24 hour rolling news and the constant crises generated by dysfunctional bureaucracies.
Explore how to re-program political/government institutions at the apex of decision-making authority so that a) people are more incentivised to optimise things we want them to optimise, like error-correction and predictive accuracy, and less incentivised to optimise bureaucratic process, prestige, and signalling as our institutions now do; b) institutions are incentivised to build high performance teams rather than make this practically illegal at the apex of government; and c) we have ‘immune systems’ based on decentralisation and distributed control to minimise the inevitable failures of even the best people and teams.
Example 1: Red Teams and pre-mortems can combat groupthink and normal cognitive biases but they are practically nowhere in the formal structure of governments. There is huge scope for a Parliament-mandated small and extremely elite Red Team operating next to, and in some senses above, the Cabinet Office to ensure diversity of opinions, fight groupthink and other standard biases, make sure lessons are learned and so on. Cost: a few million that it would recoup within weeks by stopping blunders.
Example 2: prediction tournaments/markets could improve policy and project management, with people able to ‘short’ official delivery timetables — imagine being able to short Grayling’s transport announcements, for example. In many areas new markets could help — e.g markets to allow shorting of house prices to dampen bubbles, as Chris Dillow and others have suggested. The way in which the IARPA/Tetlock work has been ignored in SW1 is proof that MPs and civil servants are not actually interested in — or incentivised to be interested in — who is right, who is actually an ‘expert’, and so on. There are tools available if new people do want to take these things seriously. Cost: a few million at most, possibly thousands, that it would recoup within a year by stopping blunders.
Example 3: we need to consider projects that could bootstrap new international institutions that help solve more general coordination problems such as the risk of accidental nuclear war. The most obvious example of a project like this I can think of is a manned international lunar base which would be useful for a) basic science, b) the practical purposes of building urgently needed near-Earth infrastructure for space industrialisation, and c) to force the creation of new practical international institutions for cooperation between Great Powers. George Mueller’s team that put man on the moon in 1969 developed a plan to do this that would have been built by now if their plans had not been tragically abandoned in the 1970s. Jeff Bezos is explicitly trying to revive the Mueller vision and Britain should be helping him do it much faster. The old institutions like the UN and EU — built on early 20th Century assumptions about the performance of centralised bureaucracies — are incapable of solving global coordination problems. It seems to me more likely that institutions with qualities we need are much more likely to emerge out of solving big problems than out of think tank papers about reforming existing institutions. Cost = 10s/100s of billions, return = trillions, or near infinite if shifting our industrial/psychological frontiers into space drastically reduces the chances of widespread destruction.
A) Some fields have fantastic predictive models and there is a huge amount of high quality research, though there is a lot of low-hanging fruit in bringing methods from one field to another.
B) We know a lot about high performance including ‘systems management’ for complex projects but very few organisations use this knowledge and government institutions overwhelmingly try to ignore and suppress the knowledge we have.
C) Some fields have amazing tools for prediction and visualisation but very few organisations use these tools and almost nobody in government (where colour photocopying is a major challenge).
D) We know a lot about successful communication but very few organisations use this knowledge and most base action on false ideas. E.g political parties spend millions on spreading ideas but almost nothing on thinking about whether the messages are psychologically compelling or their methods/distribution work, and TV companies spend billions on news but almost nothing understanding what science says about how to convey complex ideas — hence why you see massively overpaid presenters like Evan Davis babbling metaphors like ‘economic takeoff’ in front of an airport while his crew films a plane ‘taking off’, or ‘the economy down the plughole’ with pictures of — a plughole.
E) Many thousands worldwide are thinking about all sorts of big government issues but very few can bring them together into coherent plans that a government can deliver and there is almost no application of things like Red Teams and prediction markets. E.g it is impossible to describe the extent to which politicians in Britain do not even consider ‘the timetable and process for turning announcement X into reality’ as something to think about — for people like Cameron and Blair the announcement IS the only reality and ‘management’ is a dirty word for junior people to think about while they focus on ‘strategy’. As I have pointed out elsewhere, it is fascinating that elite business schools have been collecting billions in fees to teach their students WRONGLY that operational excellence is NOT a source of competitive advantage, so it is no surprise that politicians and bureaucrats get this wrong.
But I can see almost nobody integrating the very best knowledge we have about A+B+C+D with E and I strongly suspect there are trillion dollar bills lying on the ground that could be grabbed for trivial cost — trillion dollar bills that people with power are not thinking about and are incentivised not to think about. I might be wrong but I would remind readers that Vote Leave was itself a bet on this proposition being right and I think its success should make people update their beliefs on the competence of elite political institutions and the possibilities for improvement.
Here I want to explore one set of intersections — the ideas of Bret Victor and Michael Nielsen.
2. Bret Victor: Cognitive technologies, dynamic tools, interactive quantitative models, Seeing Rooms — making it as easy to insert facts, data, and models in political discussion as it is to insert emoji
In the 1960s visionaries such as Joseph Licklider, Robert Taylor and Doug Engelbart developed a vision of networked interactive computing that provided the foundation not just for new technologies (the internet, PC etc) but for whole new industries. Licklider, Sutherland,Taylor et al provided a model (ARPA) for how science funding can work. Taylor provided a model (PARC) of how to manage a team of extremely talented people who turned a profound vision into reality. The original motivation for the vision of networked interactive computing was to help humans make good decisions in a complex world — or, ‘augmenting human intelligence’ and ‘man-machine symbiosis’. This story shows how to make big improvements in the world with very few resources if they are structured right: PARC involved ~25 key people and tens of millions over roughly a decade and generated trillions of dollars in value. If interested in the history and the super-productive processes behind the success of ARPA-PARC read THIS.
It’s fascinating that in many ways the original 1960s Licklider vision has still not been implemented. The Silicon Valley ecosystem developed parts of the vision but not others for complex reasons I don’t understand (cf. The Future of Programming). One of those who is trying to implement parts of the vision that have not been implemented is Bret Victor. Bret Victor is a rare thing: a genuine visionary in the computing world according to some of those ‘present at the creation’ of ARPA-PARC such as Alan Kay. His ideas lie at critical intersections between fields sketched above. Watch talks such as Inventing on Principle and Media for Thinking the Unthinkable and explore his current project, Dynamic Land in Berkeley.
Victor has described, and now demonstrates in Dynamic Land, how existing tools fail and what is possible. His core principle is that creators need an immediate connection to what they are creating. Current programming languages and tools are mostly based on very old ideas before computers even had screens and there was essentially no interactivity — they date from the era of punched cards. They do not allow users to interact dynamically. New dynamic tools enable us to think previously unthinkable thoughts and allow us to see and interact with complex systems: to see inside, see across time, and see across possibilities.
I strongly recommend spending a few days exploring his his whole website but I will summarise below his ideas on two things:
His ideas about how to build new dynamic tools for working with data and interactive models.
His ideas about transforming the physical spaces in which teams work so that dynamic tools are embedded in their environment — people work inside a tool.
Applying these ideas would radically improve how people make decisions in government and how the media reports politics/government.
Language and writing were cognitive technologies created thousands of years ago which enabled us to think previously unthinkable thoughts. Mathematical notation did the same over the past 1,000 years. For example, take a mathematics problem described by the 9th Century mathematician al-Khwarizmi (who gave us the word algorithm):
Once modern notation was invented, this could be written instead as:
x2 + 10x = 39
Michael Nielsen uses a similar analogy. Descartes and Fermat demonstrated that equations can be represented on a diagram and a diagram can be represented as an equation. This was a new cognitive technology, a new way of seeing and thinking: algebraic geometry. Changes to the ‘user interface’ of mathematics were critical to its evolution and allowed us to think unthinkable thoughts (Using Artificial Intelligence to Augment Human Intelligence, see below).
Similarly in the 18th Century, there was the creation of data graphics to demonstrate trade figures. Before this, people could only read huge tables. This is the first data graphic:
The Jedi of data visualisation, Edward Tufte, describes this extraordinary graphic of Napoleon’s invasion of Russia as ‘probably the best statistical graphic ever drawn’. It shows the losses of Napoleon’s army: from the Polish-Russian border, the thick band shows the size of the army at each position, the path of Napoleon’s winter retreat from Moscow is shown by the dark lower band, which is tied to temperature and time scales (you can see some of the disastrous icy river crossings famously described by Tolstoy). NB. The Cabinet makes life-and-death decisions now with far inferior technology to this from the 19th Century (see below).
If we look at contemporary scientific papers they represent extremely compressed information conveyed through a very old fashioned medium, the scientific journal. Printed journals are centuries old but the ‘modern’ internet versions are usually similarly static. They do not show the behaviour of systems in a visual interactive way so we can see the connections between changing values in the models and changes in behaviour of the system. There is no immediate connection. Everything is pretty much the same as a paper and pencil version of a paper. In Media for Thinking the Unthinkable, Victor shows how dynamic tools can transform normal static representations so systems can be explored with immediate feedback. This dramatically shows how much more richly and deeply ideas can be explored. With Victor’s tools we can interact with the systems described and immediately grasp important ideas that are hidden in normal media.
Picture: the very dense writing of a famous paper (by chance the paper itself is at the intersection of politics/technology and Watts has written excellent stuff on fake news but has been ignored because it does not fit what ‘the educated’ want to believe)
Picture: the same information presented differently. Victor’s tools make the information less compressed so there’s less work for the brain to do ‘decompressing’. They not only provide visualisations but the little ‘sliders’ over the graphics are to drag buttons and interact with the data so you see the connection between changing data and changing model. A dynamic tool transforms a scientific paper from ‘pencil and paper’ technology to modern interactive technology.
Victor explains in detail how policy analysis and public debate of climate change could be transformed. Leave aside the subject matter — of course it’s extremely important, anybody interested in this issue will gain from reading the whole thing and it would be great material for a school to use for an integrated science / economics / programming / politics project, but my focus is on his ideas about tools and thinking, not the specific subject matter.
Climate change is a great example to consider because it involves a) a lot of deep scientific knowledge, b) complex computer modelling which is understood in detail by a tiny fraction of 1% (and almost none of the social science trained ‘experts’ who are largely responsible for interpreting such models for politicians/journalists, cf HERE for the science of this), c) many complex political, economic, cultural issues, d) very tricky questions about how policy is discussed in mainstream culture, and e) the problem of how governments try to think about and act on important, complex, and long-term problems. Scientific knowledge is crucial but it cannot by itself answer the question: what to do? The ideas BV describes to transform the debate on climate change apply generally to how we approach all important political issues.
In the section Languages for technical computing, BV describes his overall philosophy (if you look at the original you will see dynamic graphics to help make each point but I can’t make them play on my blog — a good example of the failure of normal tools!):
‘The goal of my own research has been tools where scientists see what they’re doing in realtime, with immediate visual feedback and interactive exploration. I deeply believe that a sea change in invention and discovery is possible, once technologists are working in environments designed around:
ubiquitous visualization and in-context manipulation of the system being studied;
actively exploring system behavior across multiple levels of abstraction in parallel;
visually investigating system behavior by transforming, measuring, searching, abstracting;
seeing the values of all system variables, all at once, in context;
dynamic notations that embed simulation, and show the effects of parameter changes;
visually improvising special-purpose dynamic visualizations as needed.’
He then describes how the community of programming language developers have failed to create appropriate languages for scientists, which I won’t go into but which is fascinating.
He then describes the problem of how someone can usefully get to grips with a complex policy area involving technological elements.
‘How can an eager technologist find their way to sub-problems within other people’s projects where they might have a relevant idea? How can they be exposed to process problems common across many projects?… She wishes she could simply click on “gas turbines”, and explore the space:
What are open problems in the field?
Who’s working on which projects?
What are the fringe ideas?
What are the process bottlenecks?
What dominates cost? What limits adoption?
Why make improvements here? How would the world benefit?
‘None of this information is at her fingertips. Most isn’t even openly available — companies boast about successes, not roadblocks. For each topic, she would have to spend weeks tracking down and meeting with industry insiders. What she’d like is a tool that lets her skim across entire fields, browsing problems and discovering where she could be most useful…
‘Suppose my friend uncovers an interesting problem in gas turbines, and comes up with an idea for an improvement. Now what?
Is the improvement significant?
Is the solution technically feasible?
How much would the solution cost to produce?
How much would it need to cost to be viable?
Who would use it? What are their needs?
What metrics are even relevant?
‘Again, none of this information is at her fingertips, or even accessible. She’d have to spend weeks doing an analysis, tracking down relevant data, getting price quotes, talking to industry insiders.
‘What she’d like are tools for quickly estimating the answers to these questions, so she can fluidly explore the space of possibilities and identify ideas that have some hope of being important, feasible, and viable.
‘Consider the Plethora on-demand manufacturing service, which shows the mechanical designer an instant price quote, directly inside the CAD software, as they design a part in real-time. In what other ways could inventors be given rapid feedback while exploring ideas?’
Victor then describes a public debate over a public policy. Ideas were put forward. Everybody argued.
‘Who to believe? The real question is — why are readers and decision-makers forced to “believe” anything at all? Many claims made during the debate offered no numbers to back them up. Claims with numbers rarely provided context to interpret those numbers. And never — never! — were readers shown the calculations behind any numbers. Readers had to make up their minds on the basis of hand-waving, rhetoric, bombast.’
And there was no progress because nobody could really learn from the debate or even just be clear about exactly what was being proposed. Sound familiar?!! This is absolutely normal and Victor’s description applies to over 99% of public policy debates.
Victor then describes how you can take the policy argument he had sketched and change its nature. Instead of discussing words and stories, DISCUSS INTERACTIVE MODELS.
‘The reader can explore alternative scenarios, understand the tradeoffs involved, and come to an informed conclusion about whether any such proposal could be a good decision.
‘This is possible because the author is not just publishing words. The author has provided a model — a set of formulas and algorithms that calculate the consequences of a given scenario… Notice how the model’s assumptions are clearly visible, and can even be adjusted by the reader.
‘Readers are thus encouraged to examine and critique the model. If they disagree, they can modify it into a competing model with their own preferred assumptions, and use it to argue for their position. Model-driven material can be used as grounds for an informed debate about assumptions and tradeoffs.
‘Modeling leads naturally from the particular to the general. Instead of seeing an individual proposal as “right or wrong”, “bad or good”, people can see it as one point in a large space of possibilities. By exploring the model, they come to understand the landscape of that space, and are in a position to invent better ideas for all the proposals to come. Model-driven material can serve as a kind of enhanced imagination.‘
Victor then looks at some standard materials from those encouraging people to take personal action on climate change and concludes:
‘These are lists of proverbs. Little action items, mostly dequantified, entirely decontextualized. How significant is it to “eat wisely” and “trim your waste”? How does it compare to other sources of harm? How does it fit into the big picture? How many people would have to participate in order for there to be appreciable impact? How do you know that these aren’t token actions to assauge guilt?
‘And why trust them? Their rhetoric is catchy, but so is the horrific “denialist” rhetoric from the Cato Institute and similar. When the discussion is at the level of “trust me, I’m a scientist” and “look at the poor polar bears”, it becomes a matter of emotional appeal and faith, a form of religion.
‘Climate change is too important for us to operate on faith. Citizens need and deserve reading material which shows context — how significant suggested actions are in the big picture — and which embeds models — formulas and algorithms which calculate that significance, for different scenarios, from primary-source data and explicit assumptions.’
Even the supposed ‘pros’ — Insiders at the top of research fields in politically relevant areas — have to scramble around typing words into search engines, crawling around government websites, and scrolling through PDFs. Reliable data takes ages to find. Reliable models are even harder to find. Vast amounts of useful data and models exist but they cannot be found and used effectively because we lack the tools.
‘Authoring tools designed for arguing from evidence’
Why don’t we conduct public debates in the way his toy example does with interactive models? Why aren’t paragraphs in supposedly serious online newspapers written like this? Partly because of the culture, including the education of those who run governments and media organisations, but also because the resources for creating this sort of material don’t exist.
‘In order for model-driven material to become the norm, authors will need data, models, tools, and standards…
‘Suppose there were good access to good data and good models. How would an author write a document incorporating them? Today, even the most modernwritingtools are designed around typing in words, not facts. These tools are suitable for promoting preconceived ideas, but provide no help in ensuring that words reflect reality, or any plausible model of reality. They encourage authors to fool themselves, and fool others…
‘Imagine an authoring tool designed for arguing from evidence. I don’t mean merely juxtaposing a document and reference material, but literally “autocompleting” sourced facts directly into the document. Perhaps the tool would have built-in connections to fact databases and model repositories, not unlike the built-in spelling dictionary. What if it were as easy to insert facts, data, and models as it is to insert emoji and cat photos?
‘Furthermore, the point of embedding a model is that the reader can explore scenarios within the context of the document. This requires tools for authoring “dynamic documents” — documents whose contents change as the reader explores the model. Such tools are pretty much non-existent.’
These sorts of tools for authoring dynamic documents should be seen as foundational technology like the integrated circuit or the internet.
‘Foundational technology appears essential only in retrospect. Looking forward, these things have the character of “unknown unknowns” — they are rarely sought out (or funded!) as a solution to any specific problem. They appear out of the blue, initially seem niche, and eventually become relevant to everything.
‘They may be hard to predict, but they have some common characteristics. One is that they scale well. Integrated circuits and the internet both scaled their “basic idea” from a dozen elements to a billion. Another is that they are purpose-agnostic. They are “material” or “infrastructure”, not applications.’
Victor ends with a very potent comment — that much of what we observe is ‘rearranging app icons on the deck of the Titanic’. Commercial incentives drive people towards trying to create ‘the next Facebook’ — not fixing big social problems. I will address this below.
If you are an arts graduate interested in these subjects but not expert (like me), here is an example that will be more familiar… If you look at any big historical subject, such as ‘why/how did World War I start?’ and examine leading scholarship carefully, you will see that all the leading books on such subjects provide false chronologies and mix facts with errors such that it is impossible for a careful reader to be sure about crucial things. It is routine for famous historians to write that ‘X happened because Y’ when Y happened after X. Part of the problem is culture but this could potentially be improved by tools. A very crude example: why doesn’t Kindle make it possible for readers to log factual errors, with users’ reliability ranked by others, so authors can easily check potential errors and fix them in online versions of books? Even better, this could be part of a larger system to develop gold standard chronologies with each ‘fact’ linked to original sources and so on. This would improve the reliability of historical analysis and it would create an ‘anti-entropy’ ratchet — now, entropy means that errors spread across all books on a subject and there is no mechanism to reverse this…
‘Seeing Rooms’: macro-tools to help make decisions
Victor also discusses another fundamental issue: the rooms/spaces in which most modern work and thinking occurs are not well-suited to the problems being tackled and we could do much better. Victor is addressing advanced manufacturing and robotics but his argument applies just as powerfully, perhaps more powerfully, to government analysis and decision-making.
Now, ‘software based tools are trapped in tiny rectangles’. We have very sophisticated tools but they all sit on computer screens on desks, just as you are reading this blog.
In contrast, ‘Real-world tools are in rooms where workers think with their bodies.’ Traditional crafts occur in spatial environments designed for that purpose. Workers walk around, use their hands, and think spatially. ‘The room becomes a macro-tool they’re embedded inside, an extension of the body.’ These rooms act like tools to help them understand their problems in detail and make good decisions.
Picture: rooms designed for the problems being tackled
The wave of 3D printing has developed ‘maker rooms’ and ‘Fab Labs’ where people work with a set of tools that are too expensive for an individual. The room is itself a network of tools. This approach is revolutionising manufacturing.
Why is this useful?
‘Modern projects have complex behavior… Understanding requires seeing and the best seeing tools are rooms.’ This is obviously particularly true of politics and government.
Here is a photo of a recent NASA mission control room. The room is set up so that all relevant people can see relevant data and models at different scales and preserve a common picture of what is important. NASA pioneered thinking about such rooms and the technology and tools needed in the 1960s.
Here are pictures of two control rooms for power grids.
Here is a panoramic photo of the unified control centre for the Large Hadron Collider – the biggest of ‘big data’ projects. Notice details like how they have removed all pillars so nothing interrupts visual communication between teams.
Now contrast these rooms with rooms from politics.
Here is the Cabinet room. I have been in this room. There are effectively no tools. In the 19th Century at least Lord Salisbury used the fireplace as a tool. He would walk around the table, gather sensitive papers, and burn them at the end of meetings. The fire is now blocked. The only other tool, the clock, did not work when I was last there. Over a century, the physical space in which politicians make decisions affecting potentially billions of lives has deteriorated.
British Cabinet room practically as it was July 1914
Here are JFK and EXCOM making decisions during the Cuban Missile Crisis that moved much faster than July 1914, compressing decisions leading to the destruction of global civilisation potentially into just minutes.
Here is the only photo in the public domain of the room known as ‘COBRA’ (Cabinet Office Briefing Room) where a shifting set of characters at the apex of power in Britain meet to discuss crises.
Notice how poor it is compared to NASA, the LHC etc. There has clearly been no attempt to learn from our best examples about how to use the room as a tool. The screens at the end are a late add-on to a room that is essentially indistinguishable from the room in which Prime Minister Asquith sat in July 1914 while doodling notes to his girlfriend as he got bored. I would be surprised if the video technology used is as good as what is commercially available cheaper, the justification will be ‘security’, and I would bet that many of the decisions about the operation of this room would not survive scrutiny from experts in how to construct such rooms.
I have not attended a COBRA meeting but I’ve spoken to many who have. The meetings, as you would expect looking at this room, are often normal political meetings. That is:
aims are unclear,
assumptions are not made explicit,
there is no use of advanced tools,
there is no use of quantitative models,
discussions are often dominated by lawyers so many actions are deemed ‘unlawful’ without proper scrutiny (and this device is routinely used by officials to stop discussion of options they dislike for non-legal reasons),
there is constant confusion between policy, politics and PR then the cast disperses without clarity about what was discussed and agreed.
It has a few more screens but the picture is essentially the same: there are no interactive tools beyond the ability to speak and see someone at a distance which was invented back in the 1950s/1960s in the pioneering programs of SAGE (automated air defence) and Apollo (man on the moon). Tools to help thinking in powerful ways are not taken seriously. It is largely the same, and decisions are made the same, as in the Cuban Missile Crisis. In some ways the use of technology now makes management worse as it encourages Presidents and their staff to try to micromanage things they should not be managing, often in response to or fear of the media.
Individual ministers’ officers are also hopeless. The computers are old and rubbish. Even colour printing is often a battle. Walls are for kids’ pictures. In the DfE officials resented even giving us paper maps of where schools were and only did it when bullied by the private office. It was impossible for officials to work on interactive documents. They had no technology even for sharing documents in a way that was then (2011) normal even in low-performing organisations. Using GoogleDocs was ‘against the rules’. (I’m told this has slightly improved.) The whole structure of ‘submissions’ and ‘red boxes’ is hopeless. It is extremely bureaucratic and slow. It prevents serious analysis of quantitative models. It reinforces the lack of proper scientific thinking in policy analysis. It guarantees confusion as ministers scribble notes and private offices interpret rushed comments by exhausted ministers after dinner instead of having proper face-to-face meetings that get to the heart of problems and resolve conflicts quickly. The whole approach reinforces the abject failure of the senior civil service to think about high performance project management.
Of course, most of the problems with the standards of policy and management in the civil service are low or no-tech problems — they involve the ‘unrecognised simplicities’ that are independent of, and prior to, the use of technology — but all these things negatively reinforce each other. Anybody who wants to do things much better is scuppered by Whitehall’s entangled disaster zone of personnel, training, management, incentives and tools.
Dynamic Land: ‘amazing’
I won’t go into this in detail. Dynamic Land is in a building in Berkeley. I visited last year. It is Victor’s attempt to turn the ideas above into a sort of living laboratory. It is a large connected set of rooms that have computing embedded in surfaces. For example, you can scribble equations on a bit of paper, cameras in the ceiling read your scribbles automatically, turn them into code, and execute them — for example, by producing graphics. You can then physically interact with models that appear on the table or wall while the cameras watch your hands and instantly turn gestures into new code and change the graphics or whatever you are doing. Victor has put these cutting edge tools into a space and made it open to the Berkeley community. This is all hard to explain/understand because you haven’t seen anything like it even in sci-fi films (it’s telling the media still uses the 15 year-old Minority Report as its sci-fi illustration for such things).
This video gives a little taste. I visited with a physicist who works on the cutting edge of data science/AI. I was amazed but I know nothing about such things — I was interested to see his reaction as he scribbled gravitational equations on paper and watched the cameras turn them into models on the table in real-time, then he changed parameters and watched the graphics change in real-time on the table (projected from the ceiling): ‘Ohmygod, this is just obviously the future, absolutely amazing.’ The thought immediately struck us: imagine the implications of having policy discussions with such tools instead of the usual terrible meetings. Imagine discussing HS2 budgets or possible post-Brexit trading arrangements with the models running like this for decision-makers to interact with.
Video of Dynamic Land: the bits of coloured paper are ‘code’, graphics are projected from the ceiling
In his essay Thought as a Technology, Nielsen describes the feedback between thought and interfaces:
‘In extreme cases, to use such an interface is to enter a new world, containing objects and actions unlike any you’ve previously seen. At first these elements seem strange. But as they become familiar, you internalize the elements of this world. Eventually, you become fluent, discovering powerful and surprising idioms, emergent patterns hidden within the interface. You begin to think with the interface, learning patterns of thought that would formerly have seemed strange, but which become second nature. The interface begins to disappear, becoming part of your consciousness. You have been, in some measure, transformed.’
He describes how normal language and computer interfaces are cognitive technologies:
‘Language is an example of a cognitive technology: an external artifact, designed by humans, which can be internalized, and used as a substrate for cognition. That technology is made up of many individual pieces – words and phrases, in the case of language – which become basic elements of cognition. These elements of cognition are things we can think with…
‘In a similar way to language, maps etc, a computer interface can be a cognitive technology. To master an interface requires internalizing the objects and operations in the interface; they become elements of cognition. A sufficiently imaginative interface designer can invent entirely new elements of cognition… In general, what makes an interface transformational is when it introduces new elements of cognition that enable new modes of thought. More concretely, such an interface makes it easy to have insights or make discoveries that were formerly difficult or impossible. At the highest level, it will enable discoveries (or other forms of creativity) that go beyond all previous human achievement.’
Nielsen describes how powerful ways of thinking among mathematicians and physicists are hidden from view and not part of textbooks and normal teaching.
‘The reason is that traditional media are poorly adapted to working with such representations… If experts often develop their own representations, why do they sometimes not share those representations? To answer that question, suppose you think hard about a subject for several years… Eventually you push up against the limits of existing representations. If you’re strongly motivated – perhaps by the desire to solve a research problem – you may begin inventing new representations, to provide insights difficult through conventional means. You are effectively acting as your own interface designer. But the new representations you develop may be held entirely in your mind, and so are not constrained by traditional static media forms. Or even if based on static media, they may break social norms about what is an “acceptable” argument. Whatever the reason, they may be difficult to communicate using traditional media. And so they remain private, or are only discussed informally with expert colleagues.’
If we can create interfaces that reify deep principles, then ‘mastering the subject begins to coincide with mastering the interface.’ He gives the example of Photoshop which builds in many deep principles of image manipulation.
‘As you master interface elements such as layers, the clone stamp, and brushes, you’re well along the way to becoming an expert in image manipulation… By contrast, the interface to Microsoft Word contains few deep principles about writing, and as a result it is possible to master Word‘s interface without becoming a passable writer. This isn’t so much a criticism of Word, as it is a reflection of the fact that we have relatively few really strong and precise ideas about how to write well.’
He then describes what he calls ‘the cognitive outsourcing model’: ‘we specify a problem, send it to our device, which solves the problem, perhaps in a way we-the-user don’t understand, and sends back a solution.’ E.g we ask Google a question and Google sends us an answer.
This is how most of us think about the idea of augmenting the human intellect but it is not the best approach. ‘Rather than just solving problems expressed in terms we already understand, the goal is to change the thoughts we can think.’
‘One challenge in such work is that the outcomes are so difficult to imagine. What new elements of cognition can we invent? How will they affect the way human beings think? We cannot know until they’ve been invented.
‘As an analogy, compare today’s attempts to go to Mars with the exploration of the oceans during the great age of discovery. These appear similar, but while going to Mars is a specific, concrete goal, the seafarers of the 15th through 18th centuries didn’t know what they would find. They set out in flimsy boats, with vague plans, hoping to find something worth the risks. In that sense, it was even more difficult than today’s attempts on Mars.
‘Something similar is going on with intelligence augmentation. There are many worthwhile goals in technology, with very specific ends in mind. Things like artificial intelligence and life extension are solid, concrete goals. By contrast, new elements of cognition are harder to imagine, and seem vague by comparison. By definition, they’re ways of thinking which haven’t yet been invented. There’s no omniscient problem-solving box or life-extension pill to imagine. We cannot say a priori what new elements of cognition will look like, or what they will bring. But what we can do is ask good questions, and explore boldly.
In another essay, Using Artificial Intelligence to Augment Human Intelligence, Nielsen points out that breakthroughs in creating powerful new cognitive technologies such as musical notation or Descartes’ invention of algebraic geometry are rare but ‘modern computers are a meta-medium enabling the rapid invention of many new cognitive technologies‘ and, further, AI will help us ‘invent new cognitive technologies which transform the way we think.’
Further, historically powerful new cognitive technologies, such as ‘Feynman diagrams’, have often appeared strange at first. We should not assume that new interfaces should be ‘user friendly’. Powerful interfaces that repay mastery may require sacrifices.
‘The purpose of the best interfaces isn’t to be user-friendly in some shallow sense. It’s to be user-friendly in a much stronger sense, reifying deep principles about the world, making them the working conditions in which users live and create. At that point what once appeared strange can instead becomes comfortable and familiar, part of the pattern of thought…
‘Unfortunately, many in the AI community greatly underestimate the depth of interface design, often regarding it as a simple problem, mostly about making things pretty or easy-to-use. In this view, interface design is a problem to be handed off to others, while the hard work is to train some machine learning system.
‘This view is incorrect. At its deepest, interface design means developing the fundamental primitives human beings think and create with. This is a problem whose intellectual genesis goes back to the inventors of the alphabet, of cartography, and of musical notation, as well as modern giants such as Descartes, Playfair, Feynman, Engelbart, and Kay. It is one of the hardest, most important and most fundamental problems humanity grapples with.
‘As discussed earlier, in one common view of AI our computers will continue to get better at solving problems, but human beings will remain largely unchanged. In a second common view, human beings will be modified at the hardware level, perhaps directly through neural interfaces, or indirectly through whole brain emulation.
‘We’ve described a third view, in which AIs actually change humanity, helping us invent new cognitive technologies, which expand the range of human thought. Perhaps one day those cognitive technologies will, in turn, speed up the development of AI, in a virtuous feedback cycle:
‘It would not be a Singularity in machines. Rather, it would be a Singularity in humanity’s range of thought… The long-term test of success will be the development of tools which are widely used by creators. Are artists using these tools to develop remarkable new styles? Are scientists in other fields using them to develop understanding in ways not otherwise possible?’
I would add: are governments using these tools to help them think in ways we already know are more powerful and to explore new ways of making decisions and shaping the complex systems on which we rely?
Nielsen also wrote this fascinating essay ‘Augmenting long-term memory’. This involves a computer tool (Anki) to aid long-term memory using ‘spaced repetition’ — i.e testing yourself at intervals which is shown to counter the normal (for most people) process of forgetting. This allows humans to turn memory into a choice so we can decide what to remember and achieve it systematically (without a ‘weird/extreme gift’ which is how memory is normally treated). (It’s fascinating that educated Greeks 2,500 years ago could build sophisticated mnemonic systems allowing them to remember vast amounts while almost all educated people now have no idea about such techniques.)
‘[It] incorporates new user interface ideas to help you remember what you read… this essay isn’t just a conventional essay, it’s also a new medium, a mnemonic medium which integrates spaced-repetition testing. The medium itself makes memory a choice… This essay will likely take you an hour or two to read. In a conventional essay, you’d forget most of what you learned over the next few weeks, perhaps retaining a handful of ideas. But with spaced-repetition testing built into the medium, a small additional commitment of time means you will remember all the core material of the essay. Doing this won’t be difficult, it will be easier than the initial read. Furthermore, you’ll be able to read other material which builds on these ideas; it will open up an entire world…
‘Mastering new subjects requires internalizing the basic terminology and ideas of the subject. The mnemonic medium should radically speed up this memory step, converting it from a challenging obstruction into a routine step. Frankly, I believe it would accelerate human progress if all the deepest ideas of our civilization were available in a form like this.’
This obviously has very important implications for education policy. It also shows how computers could be used to improve learning — something that has generally been a failure since the great hopes at PARC in the 1970s. I have used Anki since reading Nielsen’s blog and I can feel it making a big difference to my mind/thoughts — how often is this true of things you read? DOWNLOAD ANKI NOW AND USE IT!
We need similarly creative experiments with new mediums that are designed to improve standards of high stakes decision-making.
We could create systems for those making decisions about m/billions of lives and b/trillions of dollars, such as Downing Street or The White House, that integrate inter alia:
Cognitive toolkits compressing already existing useful knowledge such as checklists for rational thinking developed by the likes of Tetlock, Munger, Yudkowsky et al.
A Nielsen/Victor research program on ‘Seeing Rooms’, interface design, authoring tools, and cognitive technologies. Start with bunging a few million to Victor immediately in return for allowing some people to study what he is doing and apply it in Whitehall, then grow from there.
An alpha data science/AI operation — tapping into the world’s best minds including having someone like David Deutsch or Tim Gowers as a sort of ‘chief rationalist’ in the Cabinet (with Scott Alexander as deputy!) — to support rational decision-making where this is possible and explain when it is not possible (just as useful).
Tetlock/Hanson prediction tournaments could easily and cheaply be extended to consider ‘clusters’ of issues around themes like Brexit to improve policy and project management.
Groves/Mueller style ‘systems management’ integrated with the data science team.
Legally entrenched Red Teams where incentives are aligned to overcoming groupthink and error-correction of the most powerful. Warren Buffett points out that public companies considering an acquisition should employ a Red Team whose fees are dependent on the deal NOT going ahead. This is the sort of idea we need in No10.
Researchers could see the real operating environment of decision-makers at the apex of power, the sort of problems they need to solve under pressure, and the constraints of existing centralised systems. They could start with the safe level of ‘tools that we already know work really well’ — i.e things like cognitive toolkits and Red Teams — while experimenting with new tools and new ways of thinking.
Hedge funds like Bridgewater and some other interesting organisations think about such ideas though without the sophistication of Victor’s approach. The world of MPs, officials, the Institute for Government (a cheerleader for ‘carry on failing’), and pundits will not engage with these ideas if left to their own devices.
This is not the place to go into how to change this. We know that the normal approach is doomed to produce the normal results and normal results applied to things like repeated WMD crises means disaster sooner or later. As Buffett points out, ‘If there is only one chance in thirty of an event occurring in a given year, the likelihood of it occurring at least once in a century is 96.6%.’ It is not necessary to hope in order to persevere: optimism of the will, pessimism of the intellect…
A final thought…
A very interesting comment that I have heard from some of the most important scientists involved in the creation of advanced technologies is that ‘artists see things first’ — that is, artists glimpse possibilities before most technologists and long before most businessmen and politicians.
Pixar came from a weird combination of George Lucas getting divorced and the visionary Alan Kay suggesting to Steve Jobs that he buy a tiny special effects unit from Lucas, which Jobs did with completely wrong expectations about what would happen. For unexpected reasons this tiny unit turned into a huge success — as Jobs put it later, he was ‘sort of snookered’ into creating Pixar. Now Alan Kay says he struggles to get tech billionaires to understand the importance of Victor’s ideas.
The same story repeats: genuinely new ideas that could create huge value always seem so odd that almost all people in almost all organisations cannot see new possibilities. If this is true in Silicon Valley, how much more true is it in Whitehall or Washington…
If one were setting up a new party in Britain, one could incorporate some of these ideas. This would of course also require recruiting very different types of people to the norm in politics. The closed nature of Westminster/Whitehall combined with first-past-the-post means it is very hard to solve the coordination problem of how to break into this system with a new way of doing things. Even those interested in principle don’t want to commit to a 10-year (?) project that might get them blasted on the front pages. Vote Leave hacked the referendum but such opportunities are much rarer than VC-funded ‘unicorns’. On the other hand, arguably what is happening now is a once in 50 or 100 year crisis and such crises also are the waves that can be ridden to change things normally unchangeable. A second referendum in 2020 is quite possible (or two referendums under PM Corbyn, propped up by the SNP?) and might be the ideal launchpad for a completely new sort of entity, not least because if it happens the Conservative Party may well not exist in any meaningful sense (whether there is or isn’t another referendum). It’s very hard to create a wave and it’s much easier to ride one. It’s more likely in a few years you will see some of the above ideas in novels or movies or video games than in government — their pickup in places like hedge funds and intelligence services will be discrete — but you never know…
Ps. While I have talked to Michael Nielsen and Bret Victor about their ideas, in no way should this blog be taken as their involvement in anything to do with my ideas or plans or agreement with anything written above. I did not show this to them or even tell them I was writing about their work, we do not work together in any way, I have just read and listened to their work over a few years and thought about how their ideas could improve government.
‘Lewis Fry Richardson argued that the frequency and severity of deadly conflicts of all kinds, from homicides to interstate wars and everything in between, followed universal statistical patterns: their frequency followed a simple Poisson arrival process and their severity followed a simple power-law distribution. Although his methods and data in the mid-20th century were neither rigorous nor comprehensive, his insights about violent conflicts have endured. In this chapter, using modern statistical methods and data, we show that Richardson’s original claims appear largely correct, with a few caveats. These facts place important constraints on our understanding of the underlying mechanisms that produce individual wars and periods of peace, and shed light on the persistent debate about trends in conflict…
Fifty years or more of relatively few large wars is thus entirely typical, given the empirical distribution of war sizes, and observing a long period of peace is not necessarily evidence of a changing likelihood for large wars [12, 13]. Even periods comparable to the great violence of the World Wars are not statistically rare under Richardson’s model… Under the model, the 100-year probability of at least one war with 16, 634, 907 or more battle deaths (the size of the Second World War) is 0.43 ± 0.01, implying about one such war per 161 years, on average…
[Simulation to test how unusual the long peace without very big war since 1945 is…] It is not until 100 years into the future [from 2003] that the long peace becomes statistically distinguishable from a large but random fluctuation in an otherwise stationary process… Our modeling effort here cannot rule out the existence of a change in the rules that generate interstate conflicts, but if it occurred, it cannot have been a dramatic shift. The results here are entirely consistent with other evidence of genuine changes in the international system, but they constrain the extent to which such changes could have genuinely impacted the global production of interstate wars…
The agreement between the historical record of inter- state wars and Richardson’s simple model of their frequency and severity is truly remarkable, and it stands as a testament to Richardson’s lasting contribution to the study of violent political conflict…
The lower portion of the distribution is slightly more curved than expected for a simple power law, which suggests potential differences in the processes that generate wars above and below this threshold [7k deaths].
How can it be possible that the frequency and severity of interstate wars are so consistent with a stationary model, despite the enormous changes and obviously non-stationary dynamics in human population, in the number of recognized states, in commerce, communication, public health, and technology, and even in the modes of war itself? The fact that the absolute number and sizes of wars are plausibly stable in the face of these changes is a profound mystery for which we have no explanation.
Our results here indicate that the post-war efforts to reduce the likelihood of large inter- state wars have not yet changed the observed statistics enough to tell if they are working.
The long peace pattern is sometimes described only in terms of peace among largely European powers, who fell into a peaceful configuration after the great violence for well understood reasons. In parallel, however, conflicts in other parts of the world, most notably Africa, the Middle East, and Southeast Asia, have became more common, and these may have statistically balanced the books globally against the decrease in frequency in the West, and may even be causally dependent on the drivers of European war and then peace.’
Please leave comments below and links to other work that may throw light on this…
Complexity and prediction II: controlled skids and immune systems (September 2014). Why is the world so hard to predict? Nonlinearity and Bismarck. How to humans adapt? The difference between science and political predictions. Feedback and emergent properties. Decentralised problem-solving in the immune system and ant colonies.
Complexity and prediction IV: The birth of computational thinking (September 2014). Leibniz and computational thinking. The first computers. Punched cards. Optical data networks. Wireless. The state of the field by the time of Turing’s 1936 paper… These sketches may help in trying to understand 1) contemporary discussions about complex systems in general, 2) new tools that are being developed, and 3) contemporary debates concerning scientific, technological, economic, and political issues which depend on computers – from algorithmic high frequency trading to ‘agent based models’, machine intelligence, and military robots.
‘People, ideas, machines — in that order!’ Colonel Boyd
‘[R]ational systems exhibit universal drives towards self-protection, resource acquisition, replication and efficiency. Those drives will lead to anti-social and dangerous behaviour if not explicitly countered. The current computing infrastructure would be very vulnerable to unconstrained systems with these drives.’ Omohundro.
‘For progress there is no cure…’ von Neumann
This blog sketches a few recent developments connecting AI and issues around ‘systems management’ and government procurement.
The biggest problem for governments with new technologies is that the limiting factor on applying new technologies is not the technology but management and operational ideas which are extremely hard to change fast. This has been proved repeatedly: eg. the tank in the 1920s-30s or the development of ‘precision strike’ in the 1970s. These problems are directly relevant to the application of AI by militaries and intelligence services. The Pentagon’s recent crash program, Project Maven, discussed below, was an attempt to grapple with these issues.
‘The good news is that Project Maven has delivered a game-changing AI capability… The bad news is that Project Maven’s success is clear proof that existing AI technology is ready to revolutionize many national security missions… The project’s success was enabled by its organizational structure.‘
This blog sketches some connections between:
The example of ‘precision strike’ in the 1970s, Marshal Ogarkov and Andy Marshall, implications for now — ‘anti-access / area denial’ (A2/AD), ‘Air-Sea Battle’ etc.
Development of ‘precision strike’ to lethal autonomous cheap drone swarms hunting humans cowering underground.
Adding AI to already broken nuclear systems and doctrines, hacking the NSA etc — mix coke, Milla Jovovich and some alpha engineers and you get…?
A few thoughts on ‘systems management’ and procurement, lessons from the Manhattan Project etc.
The Chinese attitude to ‘systems management’ and Qian Xuesen, combined with AI, mass surveillance, ‘social credit’ etc.
A few recent miscellaneous episodes such as an interesting DARPA demo on ‘self-aware’ robots.
Charts on Moore’s Law: what scale would a ‘Manhattan Project for AGI’ be?
AGI safety — the alignment problem, the dangers of science as a ‘blind search algorithm’, closed vs open security architectures etc.
A theme of this blog since before the referendum campaign has been that thinking about organisational structure/dynamics can bring what Warren Buffett calls ‘lollapalooza’ results. What seems to be very esoteric and disconnected from ‘practical politics’ (studying things like the management of the Manhattan Project and Apollo) turns out to be extraordinarily practical (gives you models for creating super-productive processes).
Part of the reason lollapalooza results are possible is that almost nobody near the apex of power believes the paragraph above is true and they actively fight to stop people learning from extreme successes so there is gold lying on the ground waiting to be picked up for trivial costs. Nudging reality down an alternative branch of history in summer 2016 only cost ~£106 so the ‘return on investment’ if you think about altered GDP, technology, hundreds of millions of lives over decades and so on was truly lollapalooza. Politics is not like the stock market where you need to be an extreme outlier like Buffett/Munger to find such inefficiencies and results consistently. The stock market is an exploitable market where being right means you get rich and you help the overall system error-correct which makes it harder to be right (the mechanism pushes prices close to random, they’re not quite random but few can exploit the non-randomness). Politics/government is not like this. Billionaires who want to influence politics could get better ‘returns on investment’ than from early stage Amazon.
This blog is not directly about Brexit at all but if you are thinking — how could we escape this nightmare and turn government institutions from hopeless to high performance and what should we focus on to replace the vision of ‘influencing the EU’ that has been blown up by Brexit? — it will be of interest. Lessons that have been lying around for over half a century could have pushed the Brexit negotiations in a completely different direction and still could do but require an extremely different ‘model of effective action’ to dominant models in Westminster.
Project Maven: new organisational approaches for rapid deployment of AI to war / hybrid-war
The quotes below are from a piece in The Bulletin of Atomic Scientists about a recent AI project by the Pentagon. The most interesting aspect is not the technical details but the management approach and implications for Pentagon-style bureaucraties.
‘Project Maven is a crash Defense Department program that was designed to deliver AI technologies … to an active combat theater within six months from when the project received funding… Technologies developed through Project Maven have already been successfully deployed in the fight against ISIS. Despite their rapid development and deployment, these technologies are getting strong praise from their military intelligence users. For the US national security community, Project Maven’s frankly incredible success foreshadows enormous opportunities ahead — as well as enormous organizational, ethical, and strategic challenges.
‘In late April, Robert Work — then the deputy secretary of the Defense Department — wrote a memo establishing the Algorithmic Warfare Cross-Functional Team, also known as Project Maven. The team had only six members to start with, but its small size belied the significance of its charter… Project Maven is the first time the Defense Department has sought to deploy deep learning and neural networks, at the level of state-of-the-art commercial AI, in department operations in a combat theater…
‘Every day, US spy planes and satellites collect more raw data than the Defense Department could analyze even if its whole workforce spent their entire lives on it. As its AI beachhead, the department chose Project Maven, which focuses on analysis of full-motion video data from tactical aerial drone platforms… These drone platforms and their full-motion video sensors play a major role in the conflict against ISIS across the globe. The tactical and medium-altitude video sensors of the Scan Eagle, MQ-1C, and MQ-9 produce imagery that more or less resembles what you see on Google Earth. A single drone with these sensors produces many terabytes of data every day. Before AI was incorporated into analysis of this data, it took a team of analysts working 24 hours a day to exploit only a fraction of one drone’s sensor data.
‘The Defense Department spent tens of billions of dollars developing and fielding these sensors and platforms, and the capabilities they offer are remarkable. Whenever a roadside bomb detonates in Iraq, the analysts can simply rewind the video feed to watch who planted it there, when they planted it, where they came from, and where they went. Unfortunately, most of the imagery analysis involves tedious work—people look at screens to count cars, individuals, or activities, and then type their counts into a PowerPoint presentation or Excel spreadsheet. Worse, most of the sensor data just disappears — it’s never looked at — even though the department has been hiring analysts as fast as it can for years… Plenty of higher-value analysis work will be available for these service members and contractors once low-level counting activity is fully automated.
‘The six founding members of Project Maven, though they were assigned to run an AI project, were not experts in AI or even computer science. Rather, their first task was building partnerships, both with AI experts in industry and academia and with the Defense Department’s communities of drone sensor analysts… AI experts and organizations who are interested in helping the US national security mission often find that the department’s contracting procedures are so slow, costly, and painful that they just don’t want to bother. Project Maven’s team — with the help of Defense Information Unit Experimental, an organization set up to accelerate the department’s adoption of commercial technologies — managed to attract the support of some of the top talent in the AI field (the vast majority of which lies outside the traditional defense contracting base). Figuring out how to effectively engage the tech sector on a project basis is itself a remarkable achievement…
‘Before Maven, nobody in the department had a clue how to properly buy, field, and implement AI. A traditional defense acquisition process lasts multiple years, with separate organizations defining the functions that acquisitions must perform, or handling technology development, production, or operational deployment. Each of these organizations must complete its activities before results are handed off to the next organization. When it comes to digital technologies, this approach often results in systems that perform poorly and are obsolete even before they are fielded.
‘Project Maven has taken a different approach, one modeled after project management techniques in the commercial tech sector: Product prototypes and underlying infrastructure are developed iteratively, and tested by the user community on an ongoing basis. Developers can tailor their solutions to end-user needs, and end users can prepare their organizations to make rapid and effective use of AI capabilities. Key activities in AI system development — labeling data, developing AI-computational infrastructure, developing and integrating neural net algorithms, and receiving user feedback — are all run iteratively and in parallel…
‘In Maven’s case, humans had to individually label more than 150,000 images in order to establish the first training data sets; the group hopes to have 1 million images in the training data set by the end of January. Such large training data sets are needed for ensuring robust performance across the huge diversity of possible operating conditions, including different altitudes, density of tracked objects, image resolution, view angles, and so on. Throughout the Defense Department, every AI successor to Project Maven will need a strategy for acquiring and labeling a large training data set…
‘From their users, Maven’s developers found out quickly when they were headed down the wrong track — and could correct course. Only this approach could have provided a high-quality, field-ready capability in the six months between the start of the project’s funding and the operational use of its output. In early December, just over six months from the start of the project, Maven’s first algorithms were fielded to defense intelligence analysts to support real drone missions in the fight against ISIS.
‘The good news is that Project Maven has delivered a game-changing AI capability… The bad news is that Project Maven’s success is clear proof that existing AI technology is ready to revolutionize many national security missions…
‘The project’s success was enabled by its organizational structure: a small, operationally focused, cross-functional team that was empowered to develop external partnerships, leverage existing infrastructure and platforms, and engage with user communities iteratively during development. AI needs to be woven throughout the fabric of the Defense Department, and many existing department institutions will have to adopt project management structures similar to Maven’s if they are to run effective AI acquisition programs. Moreover, the department must develop concepts of operations to effectively use AI capabilities—and train its military officers and warfighters in effective use of these capabilities…
‘Already the satellite imagery analysis community is working on its own version of Project Maven. Next up will be migrating drone imagery analysis beyond the campaign to defeat ISIS and into other segments of the Defense Department that use drone imagery platforms. After that, Project Maven copycats will likely be established for other types of sensor platforms and intelligence data, including analysis of radar, signals intelligence, and even digital document analysis… In October 2016, Michael Rogers (head of both the agency and US Cyber Command) said “Artificial Intelligence and machine learning … [are] foundational to the future of cybersecurity. … It is not the if, it’s only the when to me.”
‘The US national security community is right to pursue greater utilization of AI capabilities. The global security landscape — in which both Russia and China are racing to adapt AI for espionage and warfare — essentially demands this. Both Robert Work and former Google CEO Eric Schmidt have said that leadership in AI technology is critical to the future of economic and military power and that continued US leadership is far from guaranteed. Still, the Defense Department must explore this new technological landscape with a clear understanding of the risks involved…
‘The stakes are relatively low when AI is merely counting the number of cars filmed by a drone camera, but drone surveillance data can also be used to determine whether an individual is directly engaging in hostilities and is thereby potentially subject to direct attack. As AI systems become more capable and are deployed across more applications, they will engender ever more difficult ethical and legal dilemmas.
‘US military and intelligence agencies will have to develop effective technological and organizational safeguards to ensure that Washington’s military use of AI is consistent with national values. They will have to do so in a way that retains the trust of elected officials, the American people, and Washington’s allies. The arms-race aspect of artificial intelligence certainly doesn’t make this task any easier…
‘The Defense Department must develop and field AI systems that are reliably safe when the stakes are life and death — and when adversaries are constantly seeking to find or create vulnerabilities in these systems.
‘Moreover, the department must develop a national security strategy that focuses on establishing US advantages even though, in the current global security environment, the ability to implement advanced AI algorithms diffuses quickly. When the department and its contractors developed stealth and precision-guided weapons technology in the 1970s, they laid the foundation for a monopoly, nearly four decades long, on technologies that essentially guaranteed victory in any non-nuclear war. By contrast, today’s best AI tech comes from commercial and academic communities that make much of their research freely available online. In any event, these communities are far removed from the Defense Department’s traditional technology circles. For now at least, the best AI research is still emerging from the United States and allied countries, butChina’s national AI strategy, released in July, poses a credible challenge to US technology leadership.’
Project Maven shows recurring lessons from history. Speed and adaptability are crucial to success in conflict and can be helped by new technologies. So is the capacity for new operational ideas about using new technologies. These ideas depend on unusual people. Bureaucracies naturally slow things down (for some good but mostly bad reasons), crush new ideas, and exclude unusual people in order to defend established interests. The limiting factor for the Pentagon in deploying advanced technology to conflict in a useful time period was not new technical ideas — overcoming its own bureaucracy was harder than overcoming enemy action. This is absolutely normal in conflict (e.g it was true of the 2016 referendum where dealing with internal problems was at least an order of magnitude harder and more costly than dealing with Cameron).
As Colonel Boyd used to shout to military audiences, ‘People, ideas, machines — in that order!’
DARPA, ‘precision strike’, the ‘Revolution in Military Affairs’ and bureaucracies
The Project Maven experience is similar to the famous example of the tank. Everybody could see tanks were possible from the end of World War I but over 20 years Britain and France were hampered by their own bureaucracies in thinking about the operational implications and how to use them most effectively. Some in Britain and France did point out the possibilities but the possibilities were not absorbed into official planning. Powerful bureaucratic interests reinforced the normal sort of blindness to new possibilities. Innovative thinking flourished, relatively, in Germany where people like Guderian and von Manstein could see the possibilities for a very big increase in speed turning into a huge nonlinear advantage — possibilities applied to the ‘von Manstein plan’ that shocked the world in 1940. This was partly because the destruction of German forces after 1918 meant everything had to be built from scratch and this connects to another lesson about successful innovation: in the military, as in business, it is more likely if a new entity is given the job, as with the Manhattan Project to develop nuclear weapons. The consequences were devastating for the world in 1940 but, lucky for us, the nature of the Nazi regime meant that it made very similar errors itself, e.g regarding the importance of air power in general and long range bombers in particular. (This history is obviously very complex but this crude summary is roughly right about the main point)
There was a similar story with the technological developments mainly sparked by DARPA in the 1970s including stealth (developed in a classified program by the legendary ‘Skunk Works’, tested at ‘Area 51’), global positioning system (GPS), ‘precision strike’ long-range conventional weapons, drones, advanced wide-area sensors, computerised command and control (C2), and new intelligence, reconnaissance and surveillance capabilities (ISR). The hope was that together these capabilities could automate the location and destruction of long-range targets and greatly improve simultaneously the precision, destructiveness, and speed of operations.
The approach became known in America as ‘deep-strike architectures’ (DSA) and in the Soviet Union as ‘reconnaissance-strike complexes’ (RUK).The Soviet Marshal Ogarkov realised that these developments, based on America’s superior ability to develop micro-electronics and computers, constituted what he called a ‘Military-Technical Revolution’ (MTR) and was an existential threat to the Soviet Union. He wrote about them from the late 1970s. (The KGB successfully stole much of the technology but the Soviet system still could not compete.) His writings were analysed in America particularly by Andy Marshall at the Pentagon’s Office of Net Assessment (ONA) and others. ONA’s analyses of what they started calling the Revolution in Military Affairs (RMA) in turn affected Pentagon decisions. In 1991 the Gulf War demonstrated some of these technologies just as the Soviet Union was imploding. In 1992 the ONA wrote a very influential report (The Military-Technical Revolution) which, unusually, they made public (almost all ONA documents remain classified).
The ~1978 Assault Breaker concept
Soviet depiction of Assault Breaker (Sergeyev, ‘Reconnaissance-Strike Complexes,’ Red Star, 1985)
In many ways Marshal Ogarkov thought more deeply about how to develop the Pentagon’s own technologies than the Pentagon did, hampered by the normal problems that the operationalising of new ideas threatened established bureaucratic interests, including the Pentagon’s procurement system. These problems have continued. It is hard to overstate the scale of waste and corruption in the Pentagon’s horrific procurement system (see below).
China has studied this episode intensely. It has integrated lessons into their ‘anti-access / area denial’ (A2/AD) efforts to limit American power projection in East Asia. America’s response to A2/AD is the ‘Air-Sea Battle’ concept. As Marshal Ogarkov predicted in the 1970s the ‘revolution’ has evolved into opposing ‘reconnaissance-strike complexes’ facing each other with each side striving to deploy near-nuclear force using extremely precise conventional weapons from far away, all increasingly complicated by possibilities for cyberwar to destroy the infrastructure on which all this depends and information operations to alter the enemy population’s perception (very Sun Tzu!).
Graphic: Operational risks of conventional US approach vs A2/AD (CSBA, 2016)
The penetration of the CIA by the KGB, the failure of the CIA to provide good predictions, the general American failure to understand the Soviet economy, doctrine and so on despite many billions spent over decades, the attempts by the Office of Net Assessment to correct institutional failings, the bureaucratic rivalries and so on — all this is a fascinating subject and one can see why China studies it so closely.
From experimental drones in the 1970s to drone swarms deployed via iPhone
The next step for reconnaissance-strike is the application of advanced robotics and artificial intelligence which could bring further order(s) of magnitude performance improvements, cost reductions, and increases in tempo.This is central to the US-China military contest. It will also affect everyone else as much of the technology becomes available to Third World states and small terrorist groups.
I wrote in 2004 about the farce of the UK aircraft carrier procurement story (and many others have warned similarly). Regardless of elections, the farce has continued to squander billions of pounds, enriching some of the worst corporate looters and corrupting public life via the revolving door of officials/lobbyists. Scrutiny by our MPs has been contemptible. They have built platforms that already cannot be sent to a serious war against a serious enemy. A teenager will be able to deploy a drone from their smartphone to sink one of these multi-billion dollar platforms. Such a teenager could already take out the stage of a Downing Street photo op with a little imagination and initiative, as I wrote about years ago
The drone industry is no longer dependent on its DARPA roots and is no longer tied to the economics of the Pentagon’s research budgets and procurement timetables. It is driven by the economics of the extremely rapidly developing smartphone market including Moore’s Law, plummeting costs for sensors and so on. Further, there are great advantages of autonomy including avoiding jamming counter-measures. Kalashnikov has just unveiled its drone version of the AK-47: a cheap anonymous suicide drone that flies to the target and blows itself up — it’s so cheap you don’t care. So you have a combination of exponentially increasing capabilities, exponentially falling costs, greater reliability, greater lethality, greater autonomy, and anonymity (if you’re careful and buy them through cut-outs etc). Then with a bit of added sophistication you add AI face recognition etc. Then you add an increasing capacity to organise many of these units at scale in a swarm, all running off your iPhone — and consider how effective swarming tactics were for people like Alexander the Great.
This is why one of the world’s leading AI researchers, Stuart Russell (professor of computer science at Berkeley) has made this warning:
‘The capabilities of autonomous weapons will be limited more by the laws of physics — for example, by constraints on range, speed and payload — than by any deficiencies in the AI systems that control them. For instance, as flying robots become smaller, their manoeuvrability increases and their ability to be targeted decreases… Despite the limits imposed by physics, one can expect platforms deployed in the millions, the agility and lethality of which will leave humans utterly defenceless…
‘A very, very small quadcopter, one inch in diameter can carry a one- or two-gram shaped charge. You can order them from a drone manufacturer in China. You can program the code to say: “Here are thousands of photographs of the kinds of things I want to target.” A one-gram shaped charge can punch a hole in nine millimeters of steel, so presumably you can also punch a hole in someone’s head. You can fit about three million of those in a semi-tractor-trailer. You can drive up I-95 with three trucks and have 10 million weapons attacking New York City. They don’t have to be very effective, only 5 or 10% of them have to find the target.
‘There will be manufacturers producing millions of these weapons that people will be able to buy just like you can buy guns now, except millions of guns don’t matter unless you have a million soldiers. You need only three guys to write the program and launch them. So you can just imagine that in many parts of the world humans will be hunted. They will be cowering underground in shelters and devising techniques so that they don’t get detected. This is the ever-present cloud of lethal autonomous weapons… There are really no technological breakthroughs that are required. Every one of the component technologies is available in some form commercially… It’s really a matter of just how much resources are invested in it.’
There is some talk in London of ‘what if there is an AI arms race’ but there is already an AI/automation arms race between companies and between countries — it’s just that Europe is barely relevant to the cutting edge of it. Europe wants to be a world player but it has totally failed to generate anything approaching what is happening in coastal America and China. Brussels spends its time on posturing, publishing documents about ‘AI and trust’, whining, spreading fake news about fake news (while ignoring experts like Duncan Watts), trying to damage Silicon Valley companies rather than considering how to nourish European entities with real capabilities, and imposing bad regulation like GDPR (that ironically was intended to harm Google/Facebook but actually helped them in some ways because Brussels doesn’t understand them).
Britain had a valuable asset, Deep Mind, and let Google buy it for trivial money without the powers-that-be in Whitehall understanding its significance — it is relevant but it is not under British control. Britain has other valuable assets — for example, it is a potential strategic asset to have the AI centre, financial centre, and political centre all in London, IF politicians cared and wanted to nourish AI research and companies. Very obviously, right now we have a MP/official class that is unfit to do this even if they had the vaguest idea what to do, which almost none do (there is a flash of hope on genomics/AI).
Unlike during the Cold War when the Soviet Union could not compete in critical industries such as semi-conductors and consumer electronics, China can compete, is competing, and in some areas is already ahead.
The automation arms race is already hitting all sorts of low skilled jobs from baristas to factory cleaning, some of which will be largely eliminated much more quickly than economists and politicians expect. Many agricultural jobs are being rapidly eliminated as are jobs in fields like mining and drilling. Look at a modern mine and you will see driverless trucks on the ground and drones overhead. The implications for millions who make a living from driving is now well known. (This also has obvious implications for the wisdom of allowing millions of un-skilled immigrants and one of the oddities of Silicon Valley is that people there simultaneously argue a) politicians are clueless about the impact of automation on unskilled people and b) politicians should allow millions more unskilled immigrants into the country — an example of how technical people are not always as rational about politics as they think they are.)
This automation arms race will affect different countries at different speeds depending on their exposure to fields that are ripe for disruption sooner or later. If countries cannot tax those companies that lead in AI, they will have narrower options. They may even be forced into a sort of colony status. Those who think this is an exaggeration should look at China’s recent deals in Africa where countries are handing over vast amounts of data to China on extremely unfavourable terms. Huge server farms in China are processing facial recognition data on millions of Africans who have no idea their personal data has been handed over. The western media focuses on Facebook with almost no coverage of these issues.
In the extreme case, a significant lead in AI for country X could lead to a self-reinforcing cycle in which it increasingly dominates economically, scientifically, and militarily and perhaps cannot be caught as Ian Hogarth has argued and to which Putin recently alluded.
China’s investment in AI — more data = better product = more users = more revenue = better talent + more data in a beautiful flywheel…
China has about x3 number of internet users than America but the gap in internet and mobile usage is much larger. ‘In China, people use their mobile phones to pay for goods 50 times more often than Americans. Food delivery volume in China is 10 times more than that of the United States. And shared bicycle usage is 300 times that of the US. This proliferation of data — with more people generating far more information than any other country – is the fuel for improving China’s AI’ (report).
China’s AI policy priority is clear. The ‘Next Generation Artificial Intelligence Development Plan‘ announced in July 2017 states that China should catch America by 2020 and be the global leader by 2030. Xi Jinping emphasises this repeatedly.
Some implications for entangling AI with WMD — take a Milla Jovovich lookalike then add some alpha engineers…
It is important to consider nuclear safety when thinking about AI safety.
This matches research just published in the Bulletin of Atomic Scientists on the most secure (Level 3/enhanced and Level 4) bio-labs. It is now clear that laboratories conducting research on viruses that could cause a global pandemic are extremely dangerous. I am not aware of any mainstream media in Britain reporting this (story here).
Further, the systems for coping with nuclear crises have failed repeatedly. They are extremely vulnerable to false alarms, malicious attacks or even freaks like, famously, a bear (yes, a bear) triggering false alarms. We have repeatedly escaped accidental nuclear war because of flukes such as odd individuals not passing on ‘launch’ warnings or simply refusing to act. The US National Security Adviser has sat at the end of his bed looking at his sleeping wife ‘knowing’ she won’t wake up while pondering his advice to the President on a counterattack that will destroy half the world, only to be told minutes later the launch warning was the product of a catastrophic error. These problems have not been dealt with. We don’t know how bad this problem is: many details are classified and many incidents are totally unreported.
Further, the end of the Cold War gave many politicians and policy people in the West the completely false idea that established ideas about deterrence had been vindicated but they have not been vindicated (cf. Payne’s Fallacies of Cold War deterrence and The Great American Gamble). Senior decision-makers are confident that their very dangerous ideas are ‘rational’.
US and Russian nukes remain on ‘launch on warning’ — i.e a hair trigger — so the vulnerabilities could recur any time. Threats to use them are explicitly contemplated over crises such as Taiwan and Kashmir. Nuclear weapons have proliferated and are very likely to proliferate further. There are now thousands of people, including North Korean and Pakistani scientists, who understand the technology. And there is a large network of scientists involved in the classified Soviet bio-weapon programme that was largely unknown to western intelligence services before the end of the Cold War and has dispersed across the world.
Yes, you’re right to ask ‘why don’t I read about this stuff in the mainstream media?’. There is very little media coverage of reports on things like nuclear safety and pretty much nobody with real power pays any attention to all this. If those at the apex of power don’t take nuclear safety seriously, why would you think they are on top of anything? Markets and science have done wondrous things but they cannot by themselves fix such crazy incentive problems with government institutions.
Government procurement — ‘the horror, the horror’
The problem of ‘rational procurement’ is incredibly hard to solve and even during existential conflicts problems with incentives recur. If state agencies, out of fear of what opponents might be doing, create organisations that escape most normal bureaucratic constraints, then AI will escalate in importance to the military and intelligence services even more rapidly than it already is. It is possible that China will build organisations to deploy AI to war/pseudo-war/hybrid-war faster and better than America.
In January 2017 I wrote about systems engineering and systems management — an approach for delivering extremely complex and technically challenging projects. (It was already clear the Brexit negotiations were botched, that Heywood, Hammond et al had effectively destroyed any sort of serious negotiating position, and I suggested Westminster/Whitehall had to learn from successful management of complex projects to avert what would otherwise be a debacle.) These ideas were born with the Manhattan Project to build the first nuclear bomb, the ICBMs project in the 1950s, and the Apollo program in the 1960s which put man on the moon. These projects combined a) some of the most astonishing intellects the world has seen of which a subset were also brilliant at navigating government (e.g von Neumann) and b) phenomenally successful practical managers: e.g General Groves on Manhattan Project, Bernard Schriever on ICBMs and George Mueller on Apollo.
The story we are told about the Manhattan Project focuses almost exclusively on the extraordinary collection of physicists and mathematicians at Los Alamos but they were a relatively small part of the whole story which involved an engineer building an unprecedented operation at multiple sites across America in secret and with extraordinary speed while many doubted the project was possible — then coordinating multiple projects, integrating distributed expertise and delivering a functioning bomb.
If you read Groves’ fascinating book, Now It Can Be Told, and read a recent biography of him, in many important ways you will acquire what is effectively cutting-edge knowledge today about making huge endeavours work — ‘cutting-edge’ because almost nobody has learned from this (see below). If you are one of the many MPs aspiring to be not just Prime Minister but a Prime Minister who gets important things done, there are very few books that would repay careful study as much as Groves’. If you do then you could avoid joining the list of Major, Blair, Brown, Cameron and May who bungle around for a few years before being spat out to write very similar accounts about how they struggled to ‘find the levers of power’, couldn’t get officials to do what they want, and never understood how to get things done.
Systems management is generally relevant to the question: how best to manage very big complex projects? It was relevant to the referendum (Victoria Woodcock was Vote Leave’s George Mueller). It is relevant to the Brexit negotiations and the appalling management process between May/Hammond/Heywood/Robbins et al, which has been a case study in how not to manage a complex project (Parliament also deserves much blame for never scrutinising this process). It is relevant to China’s internal development and the US-China geopolitical struggle. It is relevant to questions like ‘how to avoid nuclear war’ and ‘how would you build a Manhattan Project for safe AGI?’. It is relevant to how you could develop a high performance team in Downing Street that could end the current farce. The same issues and lessons crop up in every account of a Presidency and the role of the Chief of Staff. If you want to change Whitehall from 1) ‘failure is normal’ to 2) ‘align incentives with predictive accuracy, operational excellence and high performance’, then systems management provides an extremely valuable anti-checklist for Whitehall.
Given vital principles were established more than half a century ago that were proved to do things much faster and more effectively than usual, it would be natural to assume that these lessons became integrated in training and practice both in the worlds of management and politics/government. This did not happen. In fact, these lessons have been ‘unlearned’.
General Groves was pushed out of the Pentagon (‘too difficult’). The ICBM project, conducted in extreme panic post-Sputnik, had to re-create an organisation outside the Pentagon and re-learn Groves’ lessons a decade later. NASA was a mess until Mueller took over and imported the lessons from Manhattan and ICBMs. After Apollo’s success in 1969, Mueller left and NASA reverted to being a ‘normal’ organisation and forgot his successful approach. (The plans Mueller left for developing a manned lunar base, space commercialisation, and man on Mars by the end of the 1980s were also tragically abandoned.)
While Mueller was putting man on the moon, MacNamara’s ‘Whizz Kids’ in the Pentagon, who took America into the Vietnam War, were dismantling the successful approach to systems management claiming that it was ‘wasteful’ and they could do it ‘more efficiently’. Their approach was a disaster and not just regarding Vietnam. The combination of certain definitions of ‘efficiency’ and new legal processes ensured that procurement was routinely over-budget, over-schedule, over-promising, and generated more and more scandals. Regardless of failure the MacNamara approach metastasised across the Pentagon. Incentives are so disastrously misaligned that almost every attempt at reform makes these problems worse and lawyers and lobbyists get richer. Of course, if lawmakers knew how the Manhattan Project and Apollo were done — the lack of ‘legal process’, things happening with a mere handshake instead of years of reviews enriching lawyers! — they would be stunned.
Successes since the 1960s have often been freaks (e.g the F-16, Boyd’s brainchild) or ‘black’ projects (e.g stealth) and often conducted in SkunkWorks-style operations outside normal laws. It is striking that US classified special forces, JSOC (equivalent to SAS/SBS etc), routinely use a special process to procure technologies outside the normal law to avoid the delays. This connects to George Mueller saying late in life that Apollo would be impossible with the current legal/procurement system and it could only be done as a ‘black’ program.
The lessons of success have been so widely ‘unlearned’ throughout the government system that when Obama tried to roll out ObamaCare, it blew up. When they investigated, the answer was: we didn’t use systems management so the parts didn’t connect and we never tested this properly. Remember: Obama had the support of the vast majority of Silicon Valley expertise but this did not avert disaster. All anyone had to do was read Groves’ book and call Sam Altman or Patrick Collison and they could have provided the expertise to do it properly but none of Obama’s staff or responsible officials did.
The UK is the same. MPs constantly repeat the absurd SW1 mantra that ‘there’s no money’ while handing out a quarter of a TRILLION pounds every year on procurement and contracting. I engaged with this many times in the Department for Education 2010-14. The Whitehall procurement system is embedded in the dominant framework of EU law (the EU law is bad but UK officials have made it worse). It is complex, slow and wasteful. It hugely favours large established companies with powerful political connections — true corporate looters. The likes of Carillion and lawyers love it because they gain from the complexity, delays, and waste. It is horrific for SMEs to navigate and few can afford even to try to participate. The officials in charge of multi-billion processes are mostly mediocre, often appalling. In the MoD corruption adds to the problems.
Because of mangled incentives and reinforcing culture, the senior civil service does not care about this and does not try to improve. Total failure is totally irrelevant to the senior civil service and is absolutely no reason to change behaviour even if it means thousands of people killed and many billions wasted. Occasionally incidents like Carillion blow up and the same stories are written and the same quotes given — ‘unbelievable’, ‘scandal’, ‘incompetence’, ‘heads will roll’. Nothing changes. The closed and dysfunctional Whitehall system fights to stay closed and dysfunctional. The media caravan soon rolls on. ‘Reform’ in response to botches and scandals almost inevitably makes things even slower and more expensive — even more focus on process rather than outcomes, with the real focus being ‘we can claim to have acted properly because of our Potemkin process’. Nobody is incentivised to care about high performance and error-correction. The MPs ignore it all. Select Committees issue press releases about ‘incompetence’ but never expose the likes of Heywood to persistent investigation to figure out what has really happened and why. Nobody cares.
This culture has been encouraged by the most senior leaders. The recent Cabinet Secretary Jeremy Heywood assured us all that the civil service could easily cope with Brexit and the civil service would handle Brexit fine and ‘definitely on digital, project management we’ve got nothing to learn from the private sector’. His predecessor, O’Donnell, made similar asinine comments.The fact that Heywood could make such a laughable claim after years of presiding over expensive debacle after expensive debacle and be universally praised by Insiders tells you all you need to know about ‘the blind leading the blind’ in Westminster. Heywood was a brilliant courtier-fixer but he didn’t care about management and operational excellence. Whitehall now incentivises the promotion of courtier-fixers, not great managers like Groves and Mueller. Management, like science, isregarded contemptuously as something for the lower orders to think about, not the ‘strategists’ at the top.
Long-term leadership from the likes of O’Donnell and Heywood is why officials know that practically nobody is ever held accountable regardless of the scale of failure. Being in charge of massive screwups is no barrier to promotion. Operational excellence is no requirement for promotion. You will often see the official in charge of some debacle walking to the tube at 4pm (‘compressed hours’ old boy) while the debacle is live on TV (I know because I saw this regularly in the DfE). The senior civil service now operates like a protected caste to preserve its power and privileges regardless of who the ignorant plebs vote for.
You can see how crazy the incentives are when you consider elections. If you look back at recent British elections the difference in the spending plans between the two sides has been a tiny fraction of the £250 billion p/a procurement and contracting budget — yet nobody ever really talks about this budget, it is the great unmentionable subject in Westminster! There’s the odd slogan about ‘let’s cut waste’ but the public rightly ignores this and assumes both sides will do nothing about it out of a mix of ignorance, incompetence and flawed incentives so big powerful companies continue to loot the taxpayer. Look at both parties now just letting the HS2 debacle grow and grow with the budget out of control, the schedule out of control, officials briefing ludicrously that the ‘high speed’ rail will be SLOWED DOWN to reduce costs and so on, all while an army of privileged looters, lobbyists, and lawyers hoover up taxpayer cash.
And now, when Brexit means the entire legal basis for procurement is changing, do these MPs, ministers and officials finally examine it and see how they could improve? No of course not! The top priority for Heywood et al viz Brexit and procurement has been to get hapless ministers to lock Britain into the same nightmare system even after we leave the EU — nothing must disrupt the gravy train! There’s been a lot of talk about £350 million per week for the NHS since the referendum. I could find this in days and in ways that would have strong public support. But nobody is even trying to do this and if some minister took a serious interest, they would soon find all sorts of things going wrong for them until the PermSec has a quiet word and the natural order is restored…
‘[T]he management community may have badly underestimated the benefits of core management practices [and] it’s unwise to teach future leaders that strategic decision making and basic management processes are unrelated.’ [!]
The study of management, like politics, is not a field with genuine expertise. Like other social sciences there is widespread ‘cargo cult science’, fads and charlatans drowning out core lessons. This makes it easier to understand the failure of politicians: when elite business schools now teach students NOT to value operational excellence, when supposed management gurus like MacNamara actually push things in a worse direction, then it is less surprising people like Cameron and Heywood don’t know know which way to turn. Imagine the normal politician or senior official in Washington or London. They have almost no exposure to genuinely brilliant managers or very well run organisations. Their exposure is overwhelmingly to ‘normal’ CEOs of public companies and normal bureaucracies. As the most successful investors in world history, Buffett and Munger, have pointed out for over 50 years, many of these corporate CEOs, the supposedly ‘serious people’, don’t know what they are doing and have terrible incentives.
There is almost no research funded on ARPA-PARC principles worldwide. ARPA was deliberately made less like what it was like when it created the internet. The man most responsible for PARC’s success, Robert Taylor, was fired and the most effective team in the history of computing research was disbanded. XEROX notoriously could not overcome its internal incentive problems and let Steve Jobs and Bill Gates develop the ideas. Although politicians love giving speeches about ‘innovation’ and launching projects for PR, governments subsequently almost completely ignored the lessons of how to create superproductive processes and there are almost zero examples of the ARPA-PARC approach in the world today (an interesting partial exception is Janelia). Whitehall, as a subset of its general vandalism towards science, has successfully resisted all attempts at learning from ARPA for decades and this has been helped by the attitude of leading scientists themselves whose incentives push them toward supporting objectively bad funding models. In science as well as politics, incentives can be destructive and stop learning. As Alan Kay, one of the crucial PARC researchers, wrote:
‘The most interesting thing has been the contrast between appreciation/exploitation of the inventions/contributions versus the almost complete lack of curiosity and interest in the processes that produced them… [I]n most processes today — and sadly in most important areas of technology research — the administrators seem to prefer to be completely in control of mediocre processes to being “out of control” with superproductive processes.They are trying to “avoid failure” rather than trying to “capture the heavens”.’’
Or as George Mueller said later in life about the institutional imperative and project failures:
‘Fascinating that the same problems recur time after time, in almost every program, and that the management of the program, whether it happened to be government or industry, continues to avoid reality.’
So, on one hand, radical improvements in non-military spheres would be a wonderful free lunch. We simply apply old lessons, scale them up with technology and there are massive savings for free.
But wouldn’t it be ironic if we don’t do this — instead, we keep our dysfunctional systems for non-military spheres and carry on the waste, failure and corruption but we channel the Cold War and, in the atmosphere of an arms race, America and China apply the lessons from Groves, Schreiver and Mueller but to military AI procurement?!
Not everybody has unlearned the lessons from Groves and Mueller…
China: a culture of learning from systems management
‘All stable processes we shall predict. All unstable processes we shall control.’ von Neumann.
In Science there was an interesting article on Qian Xuesen, the godfather of China’s nuclear and space programs which also had a profound affect on ideas about government. Qian studied in California at Caltech where he worked with the Hungarian mathematician Theodore von Kármán who co-founded the Jet Propulsion Laboratory (JPL) which worked on rockets after 1945.
‘In the West, systems engineering’s heyday has long passed. But in China, the discipline is deeply integrated into national planning. The city of Wuhan is preparing to host in August the International Conference on Control Science and Systems Engineering, which focuses on topics such as autonomous transportation and the “control analysis of social and human systems.” Systems engineers have had a hand in projects as diverse as hydropower dam construction and China’s social credit system, a vast effort aimed at using big data to track citizens’ behavior. Systems theory “doesn’t just solve natural sciences problems, social science problems, and engineering technology problems,” explains Xue Huifeng, director of the China Aerospace Laboratory of Social System Engineering (CALSSE) and president of the China Academy of Aerospace Systems Science and Engineering in Beijing. “It also solves governance problems.”
‘The field has resonated with Chinese President Xi Jinping, who in 2013 said that “comprehensively deepening reform is a complex systems engineering problem.” So important is the discipline to the Chinese Communist Party that cadres in its Central Party School in Beijing are required to study it. By applying systems engineering to challenges such as maintaining social stability, the Chinese government aims to “not just understand reality or predict reality, but to control reality,” says Rogier Creemers, a scholar of Chinese law at the Leiden University Institute for Area Studies in the Netherlands…
‘In a building flanked by military guards, systems scientists from CALSSE sit around a large conference table, explaining to Science the complex diagrams behind their studies on controlling systems. The researchers have helped model resource management and other processes in smart cities powered by artificial intelligence. Xue, who oversees a project named for Qian at CALSSE, traces his work back to the U.S.-educated scientist. “You should not forget your original starting point,” he says…
‘The Chinese government claims to have wired hundreds of cities with sensors that collect data on topics including city service usage and crime. At the opening ceremony of China’s 19th Party Congress last fall, Xi said smart cities were part of a “deep integration of the internet, big data, and artificial intelligence with the real economy.”… Xue and colleagues, for example, are working on how smart cities can manage water resources. In Guangdong province, the researchers are evaluating how to develop a standardized approach for monitoring water use that might be extended to other smart cities.
‘But Xue says that smart cities are as much about preserving societal stability as streamlining transportation flows and mitigating air pollution. Samantha Hoffman, a consultant with the International Institute for Strategic Studies in London, says the program is tied to long-standing efforts to build a digital surveillance infrastructure and is “specifically there for social control reasons” (Science, 9 February, p. 628). The smart cities initiative builds on 1990s systems engineering projects — the “golden” projects — aimed at dividing cities into geographic grids for monitoring, she adds.
‘Layered onto the smart cities project is another systems engineering effort: China’s social credit system. In 2014, the country’s State Council outlined a plan to compile data on individuals, government officials, and companies into a nationwide tracking system by 2020. The goal is to shape behavior by using a mixture of carrots and sticks. In some citywide and commercial pilot projects already underway, individuals can be dinged for transgressions such as spreading rumors online. People who receive poor marks in the national system may eventually be barred from travel and denied access to social services, according to government documents…
‘Government documents refer to the social credit system as a “social systems engineering project.” Details about which systems engineers consulted on the project are scant. But one theory that may have proved useful is Qian’s “open complex giant system,” Zhu says. A quarter-century ago, Qian proposed that society is a system comprising millions of subsystems: individual persons, in human parlance. Maintaining control in such a system is challenging because people have diverse backgrounds, hold a broad spectrum of opinions, and communicate using a variety of media, he wrote in 1993 in the Journal of Systems Engineering and Electronics. His answer sounds like an early road map for the social credit system: to use then-embryonic tools such as artificial intelligence to collect and synthesize reams of data. According to published papers, China’s hard systems scientists also use approaches derived from Qian’s work to monitor public opinion and gauge crowd behavior…
‘Hard systems engineering worked well for rocket science, but not for more complex social problems, Gu says: “We realized we needed to change our approach.” He felt strongly that any methods used in China had to be grounded in Chinese culture.
‘The duo came up with what it called the WSR approach: It integrated wuli, an investigation of facts and future scenarios; shili, the mathematical and conceptual models used to organize systems; and renli. Though influenced by U.K. systems thinking, the approach was decidedly eastern, its precepts inspired by the emphasis on social relationships in Chinese culture. Instead of shunning mathematical approaches, WSR tried to integrate them with softer inquiries, such as taking stock of what groups a project would benefit or harm. WSR has since been used to calculate wait times for large events in China and to determine how China’s universities perform, among other projects…
‘Zhu … recently wrote that systems science in China is “under a rationalistic grip, with the ‘scientific’ leg long and the democratic leg short.” Zhu says he has no doubt that systems scientists can make projects such as the social credit system more effective. However, he cautions, “Systems approaches should not be just a convenient tool in the expert’s hands for realizing the party’s wills. They should be a powerful weapon in people’s hands for building a fair, just, prosperous society.”’
In Open Complex Giant System (1993), Qian Xuesen compares the study of physics, where large complex systems can be studied using the phenomenally successful tools of statistical mechanics, and the study of society which has no such methods. He describes an overall approach in which fields spanning physical sciences, study of the mind, medicine, geoscience and so on must be integrated in a sort of uber-field he calls ‘social systems engineering‘.
‘Studies and practices have clearly proved that the only feasible and effective way to treat an open complex giant system is a metasynthesis from the qualitative to the quantitative, i.e. the meta—synthetic engineering method. This method has been extracted, generalized and abstracted from practical studies…’
This involves integrating: scientific theories, data, quantitative models, qualitative practical expert experience into ‘models built from empirical data and reference material, with hundreds and thousands of parameters’ then simulated.
‘This is quantitative knowledge arising from qualitative understanding. Thus metasynthesis from qualitative to quantitative approach is to unite organically the expert group, data, all sorts of information, and the computer technology, and to unite scien- tific theory of various disciplines and human experience and knowledge.’
He gives some examples and gives this diagram as a high level summary:
So, China is combining:
A massive ~$150 billion data science/AI investment program with the goal of global leadership in the science/technology and economic dominance.
A massive investment program in associated science/technology such as quantum information/computing.
A massive domestic surveillance program combining AI, facial recognition, genetic identification, the ‘social credit system’ and so on.
A massive anti-access/area denial military program aimed at America/Taiwan.
A massive technology espionage program that, for example, successfully stole the software codes for the F-35.
The use of proven systems management techniques for integrating principles of effective action to predict and manage complex systems at large scale.
America led the development of AI technologies and has the huge assets of its universities, a tradition (weakening) of welcoming scientists (since they opened Princeton to Einstein, von Neumann and Gödel in the 1930s), and the ecosystem of places like Silicon Valley.
It is plausible that China could find a way within 15 years to find some nonlinear asymmetries that provide an edge while, channeling Marshal Ogarkov, it outthinks the Pentagon in management and operations.
A few interesting recent straws in the AI/robotics wind
I blogged recently about Judea Pearl. He is one of the most important scholars in the field of causal reasoning. He wrote a short paper about the limits of state-of-the-art AI systems using ‘deep learning’ neural networks — such as the AlphaGo system which recently conquered the game of GO — and how these systems could be improved. Humans can interrogate stored representations of their environment with counter-factual questions: how to instantiate this in machines? (Also economists, NB. Pearl’s statement that ‘I can hardly name a handful (<6) of economists who can answer even one causal question posed in ucla.in/2mhxKdO‘.)
In an interview he said this about self-aware robots:
‘If a machine does not have a model of reality, you cannot expect the machine to behave intelligently in that reality. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans. The next step will be that machines will postulate such models on their own and will verify and refine them based on empirical evidence. That is what happened to science; we started with a geocentric model, with circles and epicycles, and ended up with a heliocentric model with its ellipses.
‘We’re going to have robots with free will, absolutely. We have to understand how to program them and what we gain out of it. For some reason, evolution has found this sensation of free will to be computationally desirable… Evidently, it serves some computational function.
‘I think the first evidence will be if robots start communicating with each other counterfactually, like “You should have done better.” If a team of robots playing soccer starts to communicate in this language, then we’ll know that they have a sensation of free will. “You should have passed me the ball — I was waiting for you and you didn’t!” “You should have” means you could have controlled whatever urges made you do what you did, and you didn’t.
[When will robots be evil?] When it appears that the robot follows the advice of some software components and not others, when the robot ignores the advice of other components that are maintaining norms of behavior that have been programmed into them or are expected to be there on the basis of past learning. And the robot stops following them.’
A DARPA project recently published this on self-aware robots.
‘A robot that learns what it is, from scratch, with zero prior knowledge of physics, geometry, or motor dynamics. Initially the robot does not know if it is a spider, a snake, an arm—it has no clue what its shape is. After a brief period of “babbling,” and within about a day of intensive computing, their robot creates a self-simulation. The robot can then use that self-simulator internally to contemplate and adapt to different situations, handling new tasks as well as detecting and repairing damage in its own body…
‘Initially, the robot moved randomly and collected approximately one thousand trajectories, each comprising one hundred points. The robot then used deep learning, a modern machine learning technique, to create a self-model. The first self-models were quite inaccurate, and the robot did not know what it was, or how its joints were connected. But after less than 35 hours of training, the self-model became consistent with the physical robot to within about four centimeters…
‘Lipson … notes that self-imaging is key to enabling robots to move away from the confinements of so-called “narrow-AI” towards more general abilities. “This is perhaps what a newborn child does in its crib, as it learns what it is,” he says. “We conjecture that this advantage may have also been the evolutionary origin of self-awareness in humans. While our robot’s ability to imagine itself is still crude compared to humans, we believe that this ability is on the path to machine self-awareness.”
‘Lipson believes that robotics and AI may offer a fresh window into the age-old puzzle of consciousness. “Philosophers, psychologists, and cognitive scientists have been pondering the nature self-awareness for millennia, but have made relatively little progress,” he observes. “We still cloak our lack of understanding with subjective terms like ‘canvas of reality,’ but robots now force us to translate these vague notions into concrete algorithms and mechanisms.”
‘Lipson and Kwiatkowski are aware of the ethical implications. “Self-awareness will lead to more resilient and adaptive systems, but also implies some loss of control,” they warn. “It’s a powerful technology, but it should be handled with care.”’
‘… a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization — all without task-specific training… The model is chameleon-like — it adapts to the style and content of the conditioning text. This allows the user to generate realistic and coherent continuations about a topic of their choosing… Our model is capable of generating samples from a variety of prompts that feel close to human quality and show coherence over a page or more of text… These samples have substantial policy implications: large language models are becoming increasingly easy to steer towards scalable, customized, coherent text generation, which in turn could be used in a number of beneficial as well as malicious ways.’ (bold added).
OpenAI has not released the full model yet because they take safety issues seriously. Cf. this for a discussion of some safety issues and links. As the author says re some of the complaints about OpenAI not releasing the full model, when you find normal cyber security flaws you do not publish the problem immediately — that is a ‘zero day attack’ and we should not ‘promote a norm that zero-day threats are OK in AI.’ Quite. It’s also interesting that it would probably only take ~$100,000 for a resourceful individual to re-create the full model quite quickly.
A few weeks ago, Deep Mind showed that their approach to beating human champions at GO can also beat the world’s best players at StarCraft, a game of IMperfect information which is much closer to real life human competitions than perfect information games like chess and GO. OpenAI has shown something similar with a similar game, DOTA.
Moore’s Law: what if a country spends 1-10% GDP pushing such curves?
The march of Moore’s Law is entangled in many predictions. It is true that in some ways Moore’s Law has flattened out recently…
… BUT specialised chips developed for machine learning and other adaptations have actually kept it going. This chart shows how it actually started long before Moore and has been remarkably steady for ~120 years (NVIDIA in the top right is specialised for deep learning)…
NB. This is a logarithmic scale so makes progress seem much less dramatic than the ~20 orders of magnitude it represents.
Since Von Neumann and Turing led the development of the modern computer in the 1940s, the price of computation has got ~x10 cheaper every five years (so x100 per decade), so over ~75 years that’s a factor of about a thousand trillion (1015).
The industry seems confident the graph above will continue roughly as it has for at least another decade, though not because of continued transistor doubling rates which has reached such a tiny nanometer scale that quantum effects will soon interfere with engineering. This means ~100-fold improvement before 2030 and combined with the ecosystem of entrepreneurs/VC/science investment etc this will bring many major disruptions even without significant progress with general intelligence.
Dominant companies like Apple, Amazon, Google, Baidu, Alibaba etc (NB. no big EU players) have extremely strong incentives to keep this trend going given the impact of mobile computing / the cloud etc on their revenues.
Computers will be ~10,000 times more powerful than today for the same price if this chart holds for another20 years and ~1 million times more powerful for the same price than today if it holds for another 30 years. Today’s multi-billion dollar supercomputer performance would be available for ~$1,000, just as the supercomputer power of a few decades ago is now available in your smartphone.
But there is another dimension to this trend. Look at this graph below. It shows the total amount of compute, in petaflop/s-days, that was used to train some selected AI projects using neural networks / deep learning.
‘Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore’s Law had an 18-month doubling period). Since 2012, this metric has grown by more than 300,000x (an 18-month doubling period would yield only a 12x increase)… The chart shows the total amount of compute, in petaflop/s-days, that was used to train selected results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used. A petaflop/s-day (pfs-day) consists of performing 1015neural net operations per second for one day, or a total of about 1020operations. ‘ (Cf. OpenAI blog.)
The AlphaZero project in the top right is the recent Deep Mind project in which an AI system (a successor to the original AlphaGo that first beat human GO champions) zoomed by centuries of human knowledge on GO and chess in about one day of training.
Many dramatic breakthroughs in machine learning, particularly using neural networks (NNs), are open source. They are scaling up very fast. They will be networked together into ‘networks of networks’ and will become x10, x100, x1,000 more powerful. These NNs will keep demonstrating better than human performance in relatively narrowly defined tasks (like winning games) but these narrow definitions will widen unpredictably.
OpenAI’s blog showing the above graph concludes:
‘Overall, given the data above, the precedent for exponential trends in computing, work on ML specific hardware, and the economic incentives at play, we think it’d be a mistake to be confident this trend won’t continue in the short term. Past trends are not sufficient to predict how long the trend will continue into the future, or what will happen while it continues. But even the reasonable potential for rapid increases in capabilities means it is critical to start addressing both safety and malicious use of AI today. Foresight is essential to responsible policymaking and responsible technological development, and we must get out ahead of these trends rather than belatedly reacting to them.’ (Bold added)
This recent analysis of the extremely rapid growth of deep learning systems tries to estimate how long this rapid growth can continue and what interesting milestones may fall. It considers 1) the rate of growth of cost, 2) the cost of current experiments, and 3) the maximum amount that can be spent on an experiment in the future. Its rough answers are:
‘The cost of the largest experiments is increasing by an order of magnitude every 1.1 – 1.4 years.
‘The largest current experiment, AlphaGo Zero, probably cost about $10M.’
On the basis of the Manhattan Project costing ~1% of GDP, that gives ~$200 billion for one AI experiment. Given the growth rate, we could expect a $200B experiment in 5-6 years.
‘There is a range of estimates for how many floating point operations per second are required to simulate a human brain for one second. Those collected by AI Impacts have a median of 1018 FLOPS (corresponding roughly to a whole-brain simulation using Hodgkin-Huxley neurons)’. [NB. many experts think 1018 is off by orders of magnitude and it could easily be x1,000 or more higher.]
‘So for the shortest estimates … we have already reached enough compute to pass the human-childhood milestone. For the median estimate, and the Hodgkin-Huxley estimates, we will have reached the milestone within 3.5 years.’
We will not reach the bigger estimates (~1025FLOPS) within the 10 year window.
‘The AI-Compute trend is an extraordinarily fast trend that economic forces (absent large increases in GDP) cannot sustain beyond 3.5-10 more years. Yet the trend is also fast enough that if it is sustained for even a few years from now, it will sweep past some compute milestones that could plausibly correspond to the requirements for AGI, including the amount of compute required to simulate a human brain thinking for eighteen years, using Hodgkin Huxley neurons.’
I can’t comment on the technical aspects of this but one political/historical point. I think this analysis is wrong about the Manhattan Project (MP). His argument is the MP represents a reasonable upper-bound for what America might spend. But the MP was not constrained by money — it was mainly constrained by theoretical and engineering challenges, constraints of non-financial resources and so on. Having studied General Groves’ book (who ran the MP), he does not say money was a problem — in fact, one of the extraordinary aspects of the story is the extreme (to today’s eyes) measures he took to ensure money was not a problem. If more than 1% GDP had been needed, he’d have got it (until the intelligence came in from Europe that the Nazi programme was not threatening).
This is an important analogy. America and China are investing very heavily in AI but nobody knows — are there places at the edge of ‘breakthroughs with relatively narrow applications’ where suddenly you push ‘a bit’ and you get lollapalooza results with general intelligence? What if someone thinks — if I ‘only’ need to add some hardware and I can muster, say, 100 billion dollars to buy it, maybe I could take over the world? What if they’re right?
I think it is therefore more plausible to use the US defence budget at the height of the Cold War as a ‘reasonable estimate’ for what America might spend if they feel they are in an existential struggle. Washington knows that China is putting vast resources into AI research. If it starts taking over from Deep Mind and OpenAI as the place where the edge-of-the-art is discovered, then it WILL soon be seen as an existential struggle and there would rapidly be political pressures for a 1950s/1960s style ‘extreme’ response. So a reasonable upper bound might be at least 5-8 times bigger than 1% of GDP.
Further, unlike the nuclear race, an AGI race carries implications of not just ‘destroy global civilisation and most people’ but ‘potentially destroys ABSOLUTELY EVERYTHING not just on earth but, given time and the speed of light, everywhere’ — i.e potentially all molecules re-assembled in the pursuit of some malign energy-information optimisation process. Once people realise just how bad AGI could go if the alignment problem is not solved (see below), would it not be reasonable to assume that even more money than ~8% GDP will be found if/when this becomes a near-term fear of politicians?
Some in Silicon Valley who already have many billions at their disposal are already calculating numbers for these budgets. Surely people in Chinese intelligence are doodling the same as they listen to the week’s audio of Larry talking to Demis…?
General intelligence and safety
‘[R]ational systems exhibit universal drives towards self-protection, resource acquisition, replication and efficiency. Those drives will lead to anti-social and dangerous behaviour if not explicitly countered. The current computing infrastructure would be very vulnerable to unconstrained systems with these drives.’ Omohundro.
Shane Legg, co-founder and chief scientist of Deep Mind, said publicly a few years ago that there is a 50% probability that we will achieve human level AI by 2028, a 90% probability by 2050, and ‘I think human extinction will probably occur‘. Given Deep Mind’s progress since he said this it is surely unlikely he thinks the odds now are lower than 50% by 2028. Some at the leading edge of the field agree.
‘I think that within a few years we’ll be able to build an NN-based [neural network] AI (an NNAI) that incrementally learns to become at least as smart as a little animal, curiously and creatively learning to plan, reason and decompose a wide variety of problems into quickly solvable sub-problems. Once animal-level AI has been achieved, the move towards human-level AI may be small: it took billions of years to evolve smart animals, but only a few millions of years on top of that to evolve humans. Technological evolution is much faster than biological evolution, because dead ends are weeded out much more quickly. Once we have animal-level AI, a few years or decades later we may have human-level AI, with truly limitless applications. Every business will change and all of civilisation will change…
‘In 2050 there will be trillions of self-replicating robot factories on the asteroid belt. A few million years later, AI will colonise the galaxy. Humans are not going to play a big role there, but that’s ok. We should be proud of being part of a grand process that transcends humankind.’ Schmidhuber, one of the pioneers of ML, 2016.
Others have said they believe that estimates of AGI within 15-30 years are unlikely to be right. Two of the smartest people I’ve ever spoken to are physicists who understand the technical details and know the key researchers and think that dozens of Nobel Prize scale ideas will probably be needed before AGI happens and it is more likely that the current wave of enthusiasm with machine learning/neural networks will repeat previous cycles in science (e.g with quantum computing 20 years ago) — great enthusiasm, the feeling that all barriers are quickly falling, then an increasingly obvious plateau, spreading disillusion, a search for new ideas, then a revival of hope and so on. They would bet more on a 50-80 year than a 20 year scale.
Of top people I have spoken to and/or followed their predictions, it’s clear that there is a consensus that mainstream economic analysis (which is the foundation of politicians’ and media discussion) seriously underestimates the scale and speed of social/economic/military/political disruption that narrow AI/automation will soon cause. But predictions on AGI are unsurprisingly all over the place.
Many argue there even if Moore’s Law continues for 30 years (millionfold performance improvement) this may mean nothing significant for general intelligence, even if narrow AI transforms the world in many ways. Some experts think that estimates of the human brain’s computational capacity widely believed in the computer science world are actually orders of magnitude wrong. We still don’t know much about basics of the brain such as how long-term memories are formed. Maybe the brain’s processes will be much more resistant to understanding than ‘optimists’ assume.
But maybe relatively few big new ideas are needed to create world-changing capabilities. ‘Just’ applying great engineering and more resources to existing ideas allowed Deep Mind to blow past human performance metrics. I obviously cannot judge competing expert views but from a political perspective we know for sure that there is inherent uncertainty about how we discover new knowledge and this means we are bound to be surprised in all sorts of ways. We know that even brilliant researchers working right at the edge of progress are often clueless about what will happen quite soon and cannot reliably judge ‘is it less than 1% or more like 20% probability?’ questions. For example:
‘In 1901, two years before helping build the first heavier-than-air flyer, Wilbur Wright told his brother that powered flight was fifty years away. In 1939, three years before he personally oversaw the first critical chain reaction in a pile of uranium bricks, Enrico Fermi voiced 90% confidence that it was impossible to use uranium to sustain a fission chain reaction.’ (Yudkowsky)
Fermi’s experience suggests we should be extremely careful and put more resources into thinking very hard about how to minimise risks viz both narrow and general AI.
Those right at the edge of genetic engineering, such as George Church and Kevin Esvelt, are pushing for their field to be forcibly opened up to make it safer. As they argue, the current scientific approach and incentive system is essentially a ‘blind search algorithm’ in which small teams work in secret without being able to predict the consequences of their work and cannot be warned by those who do understand. A blind search algorithm is a bad approach for things like bioweapons that can destroy billions of lives and it is what we now have. The same argument applies to AGI.
We also know that political people and governments are slow to cope with major technological disruptions. Just look at TV. It’s been dominating politics since the 1950s. It is roughly 70 years old. Many politicians still do not understand it well. The UK state and political parties are in many ways much less sophisticated in its use of TV than groups like Hezbollah. This is even more true of social media. Also look at how unfounded conspiracy theories about fake news and social media viz the referendum and Trump have gripped much of the ‘educated’ class that thinks they see through fake news that fools the uneducated! Journalists are awarded THE ORWELL AWARD(!) for spreading fake news about fake news (and it’s not ‘lies’, they actually believe what they say)! (My experience is it’s much easier to fool people about politics if they have a degree than if they don’t because those with a degree tend to spend so much more energy fooling themselves.) This is not encouraging particularly if one considers that politicians are directly incentivised to understand technologies like TV and internet polling for their own short-term interests yet most don’t.
From cars to planes it has taken time for us to work out how to adapt to new things that can kill us. Given that 1) conventional research is ‘a blind search algorithm’, 2) our politicians are behind the curve on 70 year-old technologies and 3) there is little prospect of this changing without huge changes to conventional models of politics, we must ask another question about secrecy v openness and centralised vs decentralised architectures.
One of the leaders of the 3D printing / FabLab revolution wrote this comparing the closed v open models of security:
‘The history of the Internet has shown that security through obscurity doesn’t work. Systems that have kept their inner workings a secret in the name of security have consistently proved more vulnerable than those that have allowed themselves to be examined — and challenged — by outsiders. The open protocols and programs used to protect Internet communications are the result of ongoing development and testing by a large expert community. Another historical lesson is that people, not technology, are the most common weakness when it comes to security. No matter how secure a system is, someone who has access to it can always be corrupted, wittingly or otherwise. Centralized control introduces a point of vulnerability that is not present in a distributed system.’ (Bold added)
As we saw above, the centralised approach has been a disaster for nuclear weapons and we survived by fluke. Overall the history of nuclear security is surely a very relevant and bad signal for AI safety. I would bet a lot that Deep Mind et al are all hacked and spied on by China and Russia (at least) so I think it’s safest to plan on the assumption that dangerous breakthroughs will leak almost instantly and could be applied by the sort of people who spy for intel agencies. So it is natural to ask, should we take an open/decentralised approach towards possible AGI?
(Tangential thought experiment: if you were in charge of an organisation like the KGB, why would you not hack hedge funds like Renaissance Technologies and use the information for your own ‘black’ hedge fund and thus dodge the need for arguments over funding (a ‘virtuous’ circle of espionage, free money, resources for more effective R&D and espionage plus it minimises the need for irritating interactions with politicians)? How hard would it be to detect such activity IF done with intelligent modesty? Given someone can hack the NSA without their identity being revealed, why would they not be hacking Renaissance and Deep Mind, with a bit of help from a Milla Jovovich lookalike whose reading a book on n-dimensional string theory at the bar when that exhausted physics PhD with the access codes staggers in to relax?)
This seems to collide with another big problem — the alignment problem.
Stuart Russell, one of the world’s leading researchers, is one of those who has been very forceful about the fundamental importance of this: how do we GUARANTEE that entities more intelligent than us are aligned with humanity’s interests?
‘One [view] is: It’ll never happen, which is like saying we are driving towards the cliff but we’re bound to run out of gas before we get there. And that doesn’t seem like a good way to manage the affairs of the human race. And the other [view] is: Not to worry — we will just build robots that collaborate with us and we’ll be in human-robot teams. Which begs the question: If your robot doesn’t agree with your objectives, how do you form a team with it?’ .
Eliezer Yudkowsky, one of the few working on the alignment problem, described the difficulty:
‘How do you encode the goal functions of an A.I. such that it has an Off switch and it wants there to be an Off switch and it won’t try to eliminate the Off switch and it will let you press the Off switch, but it won’t jump ahead and press the Off switch itself? And if it self-modifies, will it self-modify in such a way as to keep the Off switch? We’re trying to work on that. It’s not easy… When you’re building something smarter than you, you have to get it right on the first try.’
So, we know centralised systems are very vulnerable and decentralised systems have advantages, but with AGI we also have to fear that we have no room for the trial-and-error of decentralised internet style security architectures — ‘you have to get it right on the first try’. Are we snookered?! And of course there is no guarantee it is even possible to solve the alignment problem. When you hear people in this field describing ideas about ‘abstracting human ethics and encoding them’ one wonders if solving the alignment problem might prove even harder than AGI — maybe only an AGI could solve it…
Given the media debate is dominated by endless pictures of the Terminator and politicians are what they are, researchers are, understandably, extremely worried about what might happen if the political-media system makes a sudden transition from complacency to panic. After all, consider the global reaction if reputable scientists suddenly announced they have discovered plausible signals that super-intelligent aliens will arrive on earth within 30 years: even when softened by caveats, such a warning would obviously transform our culture (in many ways positively!). As Peter Thiel has said, creating true AGI is a close equivalent to the ‘super-intelligent aliens arriving on earth’ scenario and the most important questions are not economic but political, and in particular: are they friendly and can we stop them eliminating us by design, bad luck, or indifference?
Further, in my experience extremely smart technical people are often naive about politics. They greatly over-estimate the abilities of prime ministers and presidents. They greatly under-estimate the incentive problems and the degree of focus that is required to get ANYTHING done in politics. They greatly exaggerate the potential for ‘rational argument’ to change minds and wrongly assume somewhere at the top of power ‘there must be’ a group of really smart people working on very dangerous problems who have real clout. Further, everybody thinks they understand ‘communication’ but almost nobody does. We can see from recent events that even the very best engineering companies like Facebook and Google can not just make huge mistakes with the political/communication world but not learn (Facebook hiring Clegg was a sign of deep ignorance inside Facebook about their true problems). So it’s hard to be optimistic about the technical people educating the political people even assuming the technical people make progress with safety.
Hypothesis: 1) minimising nuclear/bio/AI risks and the potential for disastrous climate change requires a few very big things to change roughly simultaneously (‘normal’ political action will not be enough) and 2) this will require a weird alliance between a) technical people, b) political ‘renegades’, c) the public to ‘surround’ political Insiders locked into existing incentives:
Different ‘models for effective action’ among powerful people, which will only happen if either (A) some freak individual/group pops up, probably in a crisis environment or (B) somehow incentives are hacked. (A) can’t be relied on and (B) is very hard.
A new institution with global reach that can win global trust and support is needed. The UN is worse than useless for these purposes.
Public opinion will have to be mobilised to overcome the resistance of political Insiders, for example, regarding the potential for technology to bring very large gains ‘to me’ and simultaneously avert extreme dangers. This connects to the very widespread view that a) the existing economic model is extremely unfair and b) this model is sustained by a loose alliance of political elites and corporate looters who get richer by screwing the rest of us.
I have an idea about a specific project, mixing engineering/economics/psychology/politics, that might do this and will blog on it separately.
I suspect almost any idea that could do 1-3 will seem at least weird but without big changes, we are simply waiting for the law of averages to do its thing. We may have decades for AGI and climate change but we could collide with the WMD law of averages tomorrow so, impractical as this sounds, it seems to me people have to try new things and risk failure and ridicule.
Autonomous technology and the greater human good. Omohundro. ‘Military and economic pressures are driving the rapid development of autonomous systems. We show that these systems are likely to behave in anti-social and harmful ways unless they are very carefully designed. Designers will be motivated to create systems that act approximately rationally and rational systems exhibit universal drives towards self-protection, resource acquisition, replication and efficiency. Those drives will lead to anti-social and dangerous behaviour if not explicitly countered. The current computing infrastructure would be very vulnerable to unconstrained systems with these drives. We describe the use of formal methods to create provably safe but limited autonomous systems. We then discuss harmful systems and how to stop them. We conclude with a description of the ‘Safe-AI Scaffolding Strategy’ for creating powerful safe systems with a high confidence of safety at each stage of development.’ I strongly recommend reading this paper if interested in this blog.
Read this 1955 essay by von Neumann ‘Can we survive technology?‘. VN was involved in the Manhattan Project, inventing computer science, game theory and much more. This essay explored the essential problem that the scale and speed of technological change have suddenly blown past political institutions. ‘For progress there is no cure…’
Below is a short summary of some basic principles of the ARPA/PARC project that created the internet and the personal computer. I wrote it originally as part of an anniversary blog on the referendum but it is also really part of this series on effective action.
One of the most interesting aspects of this project, like Mueller’s reforms of NASA, is the contrast between 1) extreme effectiveness, changing the world in a profound way, and 2) the general reaction to the methods was not only a failure to learn but a widespread hostility inside established bureaucracies (public and private) to the successful approach: NASA dropped Mueller’s approach when he left and has never been the same, and XEROX closed PARC and fired Bob Taylor. Changing the world in a profound and beneficial way is not enough to put a dint in bureaucracies which operate on their own dynamics.
Warren Buffet explained decades ago how institutions actively fight against learning and fight to stay in a closed and vicious feedback loop:
‘My most surprising discovery: the overwhelming importance in business of an unseen force that we might call “the institutional imperative”. In business school, I was given no hint of the imperative’s existence and I did not intuitively understand it when I entered the business world. I thought then that decent, intelligence, and experienced managers would automatically make rational business decisions. But I learned the hard way that isn’t so. Instead rationality frequently wilts when the institutional imperative comes into play.
‘For example, 1) As if governed by Newton’s First Law, any institution will resist any change in its current direction. 2) … Corporate projects will materialise to soak up available funds. 3) Any business craving of the leader, however foolish, will quickly be supported by … his troops. 4) The behaviour of peer companies … will be mindlessly imitated.’
Many of the principles behind ARPA/PARC could be applied to politics and government but they will not be learned from ‘naturally’ inside the system. Dramatic improvements will only happen if a group of people force ‘system’ changes on how government works so it is open to learning.
I have modified the below very slightly and added some references.
ARPA/PARC and ‘capturing the heavens’: The best way to predict the future is to invent it
The panic over Sputnik brought many good things such as a huge increase in science funding. America also created the Advanced Research Projects Agency (ARPA, which later added ‘Defense’ and became DARPA). Its job was to fund high risk / high payoff technology development. In the 1960s and 1970s, a combination of unusual people and unusually wise funding from ARPA created a community that in turn invented the internet, or ‘the intergalactic network’ as Licklider originally called it, and the personal computer. One of the elements of this community was PARC, a research centre working for Xerox. As Bill Gates said, he and Steve Jobs essentially broke into PARC, stole their ideas, and created Microsoft and Apple.
The ARPA/PARC project is an example of how if something is set up properly then a tiny number of people can do extraordinary things.
PARC had about 25 people and about $12 million per year in today’s money.
The breakthroughs from the ARPA/PARC project created over 35 TRILLION DOLLARS of value for society and counting.
The internet architecture they built, based on decentralisation and distributed control, has scaled up over ten orders of magnitude (1010) without ever breaking and without ever being taken down for maintenance since 1969.
The whole story is fascinating in many ways. I won’t go into the technological aspects. I just want to say something about the process.
What does a process that produces ideas that change the world look like?
One of the central figures was Alan Kay. One of the most interesting things about the project is that not only has almost nobody tried to repeat this sort of research but the business world has even gone out of its way to spread mis-information about it because it was seen as so threatening to business-as-usual.
I will sketch a few lessons from one of Kay’s pieces but I urge you to read the whole thing.
‘This is what I call “The power of the context” or “Point of view is worth 80 IQ points”. Science and engineering themselves are famous examples, but there are even more striking processes within these large disciplines. One of the greatest works of art from that fruitful period of ARPA/PARC research in the 60s and 70s was the almost invisible context and community that catalysed so many researchers to be incredibly better dreamers and thinkers. That it was a great work of art is confirmed by the world-changing results that appeared so swiftly, and almost easily. That it was almost invisible, in spite of its tremendous success, is revealed by the disheartening fact today that, as far as I’m aware, no governments and no companies do edge-of-the-art research using these principles.’
‘[W]hen I think of ARPA/PARC, I think first of good will, even before brilliant people… Good will and great interest in graduate students as “world-class researchers who didn’t have PhDs yet” was the general rule across the ARPA community.
‘[I]t is no exaggeration to say that ARPA/PARC had “visions rather than goals” and “funded people, not projects”. The vision was “interactive computing as a complementary intellectual partner for people pervasively networked world-wide”. By not trying to derive specific goals from this at the funding side, ARPA/PARC was able to fund rather different and sometimes opposing points of view.
‘The pursuit of Art always sets off plans and goals, but plans and goals don’t always give rise to Art. If “visions not goals” opens the heavens, it is important to find artistic people to conceive the projects.
‘Thus the “people not projects” principle was the other cornerstone of ARPA/PARC’s success. Because of the normal distribution of talents and drive in the world, a depressingly large percentage of organizational processes have been designed to deal with people of moderate ability, motivation, and trust. We can easily see this in most walks of life today, but also astoundingly in corporate, university, and government research. ARPA/PARC had two main thresholds: self-motivation and ability. They cultivated people who “had to do, paid or not” and “whose doings were likely to be highly interesting and important”. Thus conventional oversight was not only not needed, but was not really possible. “Peer review” wasn’t easily done even with actual peers. The situation was “out of control”, yet extremely productive and not at all anarchic.
‘”Out of control” because artists have to do what they have to do. “Extremely productive” because a great vision acts like a magnetic field from the future that aligns all the little iron particle artists to point to “North” without having to see it. They then make their own paths to the future. Xerox often was shocked at the PARC process and declared it out of control, but they didn’t understand that the context was so powerful and compelling and the good will so abundant, that the artists worked happily at their version of the vision. The results were an enormous collection of breakthroughs.
‘Our game is more like art and sports than accounting, in that high percentages of failure are quite OK as long as enough larger processes succeed… [I]n most processes today — and sadly in most important areas of technology research — the administrators seem to prefer to be completely in control of mediocre processes to being “out of control” with superproductive processes. They are trying to “avoid failure” rather than trying to “capture the heavens”.
‘All of these principles came together a little over 30 years ago to eventually give rise to 1500 Altos, Ethernetworked to: each other, Laserprinters, file servers and the ARPAnet, distributed to many kinds of end-users to be heavily used in real situations. This anticipated the commercial availability of this genre by 10-15 years. The best way to predict the future is to invent it.
‘[W]e should realize that many of the most important ARPA/PARC ideas haven’t yet been adopted by the mainstream. For example, it is amazing to me that most of Doug Engelbart’s big ideas about “augmenting the collective intelligence of groups working together” have still not taken hold in commercial systems. What looked like a real revolution twice for end-users, first with spreadsheets and then with Hypercard, didn’t evolve into what will be commonplace 25 years from now, even though it could have. Most things done by most people today are still “automating paper, records and film” rather than “simulating the future”. More discouraging is that most computing is still aimed at adults in business, and that aimed at nonbusiness and children is mainly for entertainment and apes the worst of television. We see almost no use in education of what is great and unique about computer modeling and computer thinking. These are not technological problems but a lack of perspective. Must we hope that the open-source software movements will put things right?
‘The ARPA/PARC history shows that a combination of vision, a modest amount of funding, with a felicitous context and process can almost magically give rise to new technologies that not only amplify civilization, but also produce tremendous wealth for the society. Isn’t it time to do this again by Reason, even with no Cold War to use as an excuse? How about helping children of the world grow up to think much better than most adults do today? This would truly create “The Power of the Context”.’
Note how this story runs contrary to how free market think tanks and pundits describe technological development. The impetus for most of this development came from government funding, not markets.
Also note that every attempt since the 1950s to copy ARPA and JASON (the semi-classified group that partly gave ARPA its direction) in the UK has been blocked by Whitehall. The latest attempt was in 2014 when the Cabinet Office swatted aside the idea. Hilariously its argument was ‘DARPA has had a lot of failures’ thus demonstrating extreme ignorance about the basic idea — the whole point is you must have failures and if you don’t have lots of failures then you are failing!
People later claimed that while PARC may have changed the world it never made any money for XEROX. This is ‘absolute bullshit’ (Kay). It made billions from the laser printer alone and overall Xerox made 250 times what they invested in PARC before they went bust. In 1983 they fired Bob Taylor, the manager of PARC and the guy who made it all happen.
‘They hated [Taylor] for the very reason that most companies hate people who are doing something different, because it makes middle and upper management extremely uncomfortable. The last thing they want to do is make trillions, they want to make a few millions in a comfortable way’ (Kay).
Someone finally listened to Kay recently. ‘YC Research’, the research arm of the world’s most successful (by far) technology incubator, is starting to fund people in this way. I am not aware of any similar UK projects though I know that a small network of people are thinking again about how something like this could be done here. If you can help them, take a risk and help them! Someone talk to science minister Jo Johnson but be prepared for the Treasury’s usual ignorant bullshit — ‘what are we buying for our money, and how can we put in place appropriate oversight and compliance?’ they will say!
As we ponder the future of the UK-EU relationship shaped amid the farce of modern Whitehall, we should think hard about the ARPA/PARC example: how a small group of people can make a huge breakthrough with little money but the right structure, the right ways of thinking, and the right motives.
Those of us outside the political system thinking ‘we know we can do so much better than this but HOW can we break through the bullshit?’ need to change our perspective and gain 80 IQ points.
This real picture is a metaphor for the political culture: ad hoc solutions that are either bad or don’t scale.
ARPA said ‘Let’s get rid of all the wires’. How do we ‘get rid of all the wires’ and build something different that breaks open the closed and failing political cultures? Winning the referendum was just one step that helps clear away dead wood but we now need to build new things.
The ARPA vision that aligned the artists ‘like little iron filings’ was:
‘Computers are destined to become interactive intellectual amplifiers for everyone in the world universally networked worldwide’ (Licklider).
We need a motivating vision aimed not at tomorrow but at changing the basic wiring of the whole system, a vision that can align ‘the little iron filings’, and then start building for the long-term.
I will go into what I think this vision could be and how to do it another day. I think it is possible to create something new that could scale very fast and enable us to do politics and government extremely differently, as different to today as the internet and PC were to the post-war mainframes. This would enable us to build huge long-term value for humanity in a relatively short time (less than 20 years). To create it we need a process as well suited to the goal as the ARPA/PARC project was and incorporating many of its principles.
We must try to escape the current system with its periodic meltdowns and international crises. These crises move 500-1,000 times faster than that of summer 1914. Our destructive potential is at least a million-fold greater than it was in 1914. Yet we have essentially the same hierarchical command-and-control decision-making systems in place now that could not even cope with 1914 technology and pace. We have dodged nuclear wars by fluke because individuals made snap judgements in minutes. Nobody who reads the history of these episodes can think that this is viable long-term, and we will soon have another wave of innovation to worry about with autonomous robots and genetic engineering. Technology gives us no option but to try to overcome evolved instincts like destroying out-group competitors.
Watch Alan Kay explain how to invent the future HERE and HERE.
Part II on the emergence of ‘systems management’, how George Mueller used it to put man on the moon, and a checklist of how successful management of complex projects is systematically different to how Whitehall (and other state bureaucracies) work HERE.
Ps. Kay also points out that the real computer revolution won’t happen until people fulfil the original vision of enabling children to use this powerful way of thinking:
‘The real printing revolution was a qualitative change in thought and argument that lagged the hardware inventions by almost two centuries. The special quality of computers is their ability to rapidly simulate arbitrary descriptions, and the real computer revolution won’t happen until children can learn to read, write, argue and think in this powerful new way. We should all try to make this happen much sooner than 200 or even 20 more years!’
Almost nobody in education policy is aware of the educational context for the ARPA/PARC project which also speaks volumes about the abysmal field of ‘education research/policy’. People rightly say ‘education tech has largely failed’ but very few are aware that many of the original ideas from Licklider, Engelbart et al have never been tried and the Apple and MS versions are not the original vision.
Before the referendum I started a series of blogs and notes exploring the themes of complexity and prediction. This was part of a project with two main aims: first, to sketch a new approach to education and training in general but particularly for those who go on to make important decisions in political institutions and, second, to suggest a new approach to political priorities in which progress with education and science becomes a central focus for the British state. The two are entangled: progress with each will hopefully encourage progress with the other.
I was working on this paper when I suddenly got sidetracked by the referendum and have just looked at it again for the first time in about two years.
The paper concerns a fascinating episode in the history of ideas that saw the most esoteric and unpractical field, mathematical logic, spawn a revolutionary technology, the modern computer. NB. a great lesson to science funders: it’s a great mistake to cut funding on theory and assume that you’ll get more bang for buck from ‘applications’.
Apart from its inherent fascination, knowing something of the history is helpful for anybody interested in the state-of-the-art in predicting complex systems which involves the intersection between different fields including: maths, computer science, economics, cognitive science, and artificial intelligence. The books on it are either technical, and therefore inaccessible to ~100% of the population, or non-chronological so it is impossible for someone like me to get a clear picture of how the story unfolded.
Further, there are few if any very deep ideas in maths or science that are so misunderstood and abused as Gödel’s results. As Alan Sokal, author of the brilliant hoax exposing post-modernist academics, said, ‘Gödel’s theorem is an inexhaustible source of intellectual abuses.’ I have tried to make clear some of these using the best book available by Franzen, which explains why almost everything you read about it is wrong. If even Stephen Hawking can cock it up, the rest of us should be particularly careful.
I sketched these notes as I tried to pull together the story from many different books. I hope they are useful particularly for some 15-25 year-olds who like chronological accounts about ideas. I tried to put the notes together in the way that I wish I had been able to read at that age. I tried hard to eliminate errors but they are inevitable given how far I am from being competent to write about such things. I wish someone who is competent would do it properly. It would take time I don’t now have to go through and finish it the way I originally intended to so I will just post it as it was 2 years ago when I got calls saying ‘about this referendum…’
The only change I think I have made since May 2015 is to shove in some notes from a great essay later that year by the man who wrote the textbook on quantum computers, Michael Nielsen, which would be useful to read as an introduction or instead, HERE.
As always on this blog there is not a single original thought and any value comes from the time I have spent condensing the work of others to save you the time. Please leave corrections in comments.
‘Gödel’s achievement in modern logic is singular and monumental – indeed it is more than a monument, it is a land mark which will remain visible far in space and time.’ John von Neumann.
‘Einstein had often told me that in the late years of his life he has continually sought Gödel’s company in order to have discussions with him. Once he said to me that his own work no longer meant much, that he came to the Institute merely in order to have the privilege of walking home with Gödel.’ Oskar Morgenstern (co-author with von Neumann of the first major work on Game Theory).
A few months ago I put a paper on my blog: The unrecognised simplicities of effective action #2: ‘Systems engineering’ and ‘systems management’ — ideas from the Apollo programme for a ‘systems politics’.
It examined the history of the classified programme to build ICBMs and the way in which George Mueller turned the failing NASA bureaucracy into an organisation that could put man on the moon. The heart of the paper is about the principles behind effective management of complex projects. These principles are relevant to Government, politics, and campaigns.
The paper is long as I thought it worthwhile to tell some of the detailed story. At the suggestion of various spads, ministers, hacks and so on I have cut and pasted the conclusion below particularly for those starting new jobs today. This is in the form of a crude checklist that compares a) the principles of Mueller’s systems management and b) how Whitehall actually works.
You will see that Whitehall operates on exactly opposite principles to those organisations where high performance creates real value. You will also soon see that you are now in a culture in which almost nobody is aware of this and anybody who suggests it sinks their career. In your new department, failure is so normal it is not defined as ‘failure’. Officials lose millions and get a gong. There is little spirit of public service or culture of responsibility. The most political people are promoted and the most competent people, like Victoria Woodcock, leave. The very worst officials are often put in charge of training the next generation. For most powerful officials, the most important thing is preserving the system, closed and impregnable. Unlike for ministers, the TV blaring with DISASTER is of no concern – provided it is the Minister in the firing line not them – and the responsible officials will happily amble to the tube at 4pm while political careers hang in the balance and you draft statements taking ‘full responsibility’ for things you knew nothing about and would have been prohibited from fixing if you had.
For all those spads in particular who are moving into new jobs, it is worth reflecting on the deep principles that actually determine why things work and do not work. Nobody will explain these to you or talk to you about them. Sadly, few MPs these days understand the crucial role of management – they tend to think of it like science as a rather lowly skill beneath their Olympian status – so you will also probably have to cope with the fact that your minister is more interested in keeping one step ahead of Simon Walters (they won’t). The thing that officials will try hardest to do is convey to you that you have no role in personnel decisions and/or management.
If you accept that, you are accepting at the start that you will achieve very little. The reason why Gove’s team got much more done than ANY insider thought was possible – including Cameron and the Perm Sec – was because we bent or broke the rules and focused very hard on a) replacing rubbish officials and bringing in people from outside and b) project management.
You cannot reform the way the civil service works. Only a PM can do that and there is no chance of May doing it – she blew her chance and her reward is to be pushed around by Heywood and Sue Gray until her colleagues pull the plug and start the leadership campaign. You should assume that won’t be long so focus, manage a few priorities with daily and weekly timetables, and use embarrassing errors to negotiate secret deals with the Perm Sec to move rubbish officials out of your priority areas – trust me, Perm Secs understand this game and will do deals with alacrity to make their lives easier. Officials are less politically biased than you probably have been told – they are much more concerned with avoiding hard work and protecting the system than in resisting specific policies, and you can exploit this. Make alliances with the good officials who still have hope and have not been broken by the system, there are surprisingly many who will pop up if they think you actually care about the public rather than party interests.
You will also notice that fundamental issues of organisational culture described below explain the shambles of CCHQ over the past 8 weeks: the lack of information sharing, the lack of orientation, the culture of blaming juniors for the failures of overpaid senior people, bottlenecks preventing fast decisions, endless small errors compounding into a broken organisation because nobody knows who is responsible for what and so on. Every failing organisation has the same stories, people find it very hard to learn from the most successful organisations and people.
To the extent Vote Leave was successful, it was partly because I consciously tried to copy Mueller in various ways, though given my own severe limitations this was patchy. If you ever get the chance to exercise leadership, try to copy people like Mueller who tried to make the world better and build an organisation that people were proud to serve.
Finally, consider the basic condition that allows Westminster and Whitehall to be so rubbish and get away with it: they are not just monopolies, they set the rules of the game, and both the civil service and the parties make it almost impossible for outsiders to influence anything. But a) the combination of the 2008 crisis, Brexit, and extreme unhappiness about politics as usual provides a potentially powerful fuel for an insurgency, and b) technology provides opportunities for startups to catch public imagination and scale extremely fast. I’ve always been sceptical of the idea of a new UK party of any sort but I increasingly think there is a chance that a handful of entrepreneurs could start a sort of anti-party to exploit the broken system and create something which confounds the right/centre/left broken mental model that dominates SW1 and which combines Mueller’s principles with Silicon Valley technology.
If the Tory Party does not make some profound changes fast, then it faces being blamed for the disintegration of Brexit talks and the election of Corbyn after which it is possible that, rather than attempting a coup to take them over, entrepreneurs may decide it is more rational to build something that ploughs them into the earth next to Corbyn.
I said since last summer that if the Tory Party tried to carry on with Brexit and government using the same broken Downing Street operation, which spends its time on crap spin and has almost no capacity for serious management, and the same broken political operation, dominated by people who have failed to persuade the country convincingly for many years, then they would blow up. They failed to change Downing Street and they ran yet another fundamentally misconceived campaign that blew massive structural advantages. Kaboom.
[[Within minutes of publishing this blog I got the following email from someone I haven’t met but who I know was inside CCHQ with the para above highlighted and these words: ‘This is exactly my depressing experience – shit show run by people who don’t care about anything other than their jobs.’]]
MPs of all parties need to realise that the referendum makes it impossible to carry on with your usual bullshit – it forces changes upon you even though you want to carry on with the old games. The first set of MPs that realise this and change their operating principles will quickly overwhelm the others: there is a huge first-mover advantage especially in a field characterised by institutional incompetence that is susceptible to external shocks (terror, financial collapse) and which is opening up to technological disruption. And you will only get on top of Brexit if you realise that leaving the EU is a systems problem requiring a systems response and this means a radically different organisation of the UK negotiating team. The challenge is not far short of the political equivalent of the Apollo program and it needs similarly imaginative management.
For those who do want to do something better, the below will be useful. I encourage you to read the whole history HERE but for those rushing through a sandwich on Day 1 this summary will help you think of the big picture. If you want a detailed tutorial on how the civil service works then read The Hollow Men HERE…
[Added later… It is also very instructive that despite the triumph of Mueller’s methods, NASA itself abandoned them after he left and has never recovered. Even spectacular success on a world-changing project is not enough to beat bureaucratic inertia. Also, the US Government passed so many laws that Mueller himself said in later life it would be impossible to repeat Apollo without making it a classified ‘black’ project to evade the regulations. JSOC, US classified special forces, has to run a lot of its standard procurement via ‘black’ procurement processes just to get anything done. The abysmal procurement rules imposed under the Single Market are just one of the good reasons for us to get out of the SM as well as the EU. I had to deal with them a lot in the DfE and had to find ways to cheat them a lot to get things done faster and cheaper. They add billions to costs every year and Whitehall refused for years even to assess this huge area to avoid undermining support for the EU.]
Excerpt from The unrecognised simplicities of effective action #2 (p.28ff)
Core lessons [of Mueller’s systems management] for politics?
Finally, I will summarise some of the core lessons of systems management that could be applied to re-engineering political institutions such as Downing Street. Mueller’s approach meant an extreme focus on some core principles:
Organisation-wide orientation. Everybody in a large organisation must understand as much about the goals and plans as possible. Whitehall now works on opposite principles: I doubt a single department has proper orientation across most of the organisation (few will have it even across the top 10 people), never mind a whole government. This is partly because most ministers fail at the first hurdle — developing coherent goals — so effective orientation is inherently impossible.
Integration. There must be an overall approach in which the most important elements fit together, including in policy, management, and communications. Failures in complex projects, from renovating your house to designing a new welfare system, often occur at interfaces between parts. Whitehall now works on opposite principles: for example, Cameron and Osborne approached important policy on immigration/welfare in the opposite way by 1) promising to reduce immigration to less than 100,000 while simultaneously 2) having no legal tools to do this (and even worse promising to change this then failing in the EU renegotiation) and 3) having welfare policies that incentivised more immigration then 4) announcing a new living wage thus increasing incentives further for immigration. They emphasised each element as part of short-term political games and got themselves into a long-term inescapable mess.
Extreme transparency and communication, horizontally as well as hierarchically. More, richer, deeper communication so that ‘all of us understand what was going on throughout the program… [C]ommunications on a level that is free and easy and not constrained by the fact that you’re the boss… [This was] the secret of the success of the program, because so many programs fail because everybody doesn’t know what it is they are supposed to do’ (Mueller). Break information and management silos — a denser network of information and commands is necessary and much of it must be decentralised and distributed between different teams, but with leadership having fast and clear information flow at the centre so problems are seen and tackled fast (a virtuous circle). There is very little that needs to be kept secret in government and different processes can easily be developed for that very small number of things. As McChrystal says of special forces operations generally the advantages of communication hugely outweigh the dangers of leaks. Whitehall now works on opposite principles: it keeps information secret that does not need to be secret in order to hide its own internal processes from scrutiny, thus adding to its own management failures and distrust (a vicious circle).
‘Configuration management’. There must be a process whereby huge efforts go into the initial design of a complex system then there is a process whereby changes are made in a disciplined way such that a) interdependencies are tested where possible by relevant people before a change is agreed and b) then everybody relevant knows about the change. This ties together design, engineering, management, scheduling, cost, contracts, and allows the coordination of interdisciplinary teams. Test, learn, communicate results, change where needed, communicate… Whitehall now works on opposite principles: it does not put enough effort into the initial design then makes haphazard changes then fails to communicate changes effectively.
Physical and information structures should reinforce open communication. From Mueller’s NASA to JSOC, organisations that have coped well with complexity have built novel control centres to reinforce extreme communication. Spend money and time on new technologies and processes to help spread orientation and learning through the organisation. Whitehall now works on opposite principles: e.g. its antiquated committee structure and ‘red box’ system are ludicrously inefficient regarding management but are kept because they give officials huge control over ministers.
Long-term budgets. Long-term budgets save money. Whitehall now works on opposite principles: normal government budget processes do not value speed and savings from doing things fast. They are focused on what Parliament thinks this year. This makes it very hard to plan wisely and wastes money in the long-term (see below).
You need a complex mix of centralisation and decentralisation. While overall vision, goals, and strategy usually comes from the top, it is vital that extreme decentralisation dominates operationally so that decisions are fast and unbureaucratic. Information must be shared centrally and horizontally across the organisation — it is not either/or. Big complex projects must empower people throughout the network and cannot rely on issuing orders through a hierarchy. Whitehall now works on opposite principles: it is a centralising ratchet. E.g. Budgets and spending reviews are the exact opposite of Mueller’s approach. 1) They are short-term with almost no long-term elements. 2) They do not balance off priorities in any serious way. 3) They involve totally fake numbers — every department lies to the Treasury and provides fake numbers. Treasury officials dig into these. There are rounds of these games. Officials never stop lying. To maintain the charade the Chancellor never says to the SoS ‘stop your officials lying to us’ — candour would break the system. 4) The Treasury does not have the expertise to evaluate most of what they are looking at. The idea it is a department staffed by brilliant whiz kids is a joke. I saw DfE officials with very modest abilities routinely cheat the Treasury.
Extreme focus on errors. Schriever had ‘Black Saturdays’ and Mueller had similar meetings focused not on ‘reporting progress’ but making clear the problems. Simple as it sounds this is very unusual. Whitehall now works on opposite principles: routinely nobody is held responsible for errors and most management works on the basis of ‘give me good news not bad news’. Neither the culture nor incentives focus effort on eliminating errors. Most don’t care and you see those responsible for disaster ambling to the tube at 4pm or going on holiday amid meltdown.
Spending on redundancy to improve resilience. Whitehall now works on opposite principles: it tends to treat redundancy as ‘waste’ and its short-term budget processes reinforce decisions that mean out-of-control long-term budgets. By the time the long-term happens, the responsible people have all moved on to better paid jobs and nobody is accountable.
Important knowledge is discovered but then the innovation is standardised and codified so it can be easily learned and used by others. Whitehall now works on opposite principles: for example, in the Department for Education officials systematically destroyed its own library. The DfE operated with almost no institutional memory. By the time I left in 2014, after David Cameron banned me from entering any department officials would ask to meet me outside to find out why decisions had been taken in 2011 because three years later almost everybody had moved on to other things. The Foreign Office similarly destroyed its own library.
Systems management means lots of process and documentation but at its best it is fluid and purposeful — it is not process for ass-covering. The crucial ‘Gillette Procedures’ swept away red tape and Schriever battled the system to maintain freedom from normal government processes. When asked how he would do a similar programme to Apollo now (1990s) Mueller responded that the only way to do it would be as a classified ‘black’ project to escape the law on issues like procurement. Whitehall now works on opposite principles: its obsession is bullshit process for buck-passing and it fights with all its might against simplification and focus.
Saving time saves money. Schriever and Mueller focused on speed and saving time. Whitehall now works on opposite principles: its default mode is to go slower and those who advocate speed are denounced as reckless. Repeatedly in the DfE I was told it was ‘impossible’ to do things in the period I demanded — often less than half what senior officials wanted — yet we often achieved this and there was practically no example of failure that came because my time demands were inherently unreasonable. The system naturally pushes for the longest periods they can get away with to give themselves what they think of as a chance to beat ‘expectations’ but then they often fail on absurdly long timetables. In the DfE we often had a better record of hitting timetables that were ‘impossibly’ short than on those that were traditionally long. Also in many areas there is no downside to pushing fast — the worst that happens is minor and irrelevant embarrassment while the cumulative gains from trying to go fast are huge.
The ‘systems’ approach is inherently interdisciplinary ‘because its function is to integrate the specialized separate pieces of a complex of apparatus and people — the system — into a harmonious ensemble that optimally achieves the desired end’ (Ramo). Whitehall now works on opposite principles: it is hopeless at assembling interdisciplinary teams and elevates legal advice over everything in relation to practically any problem, causing huge delays and cost overruns.
The ‘matrix management’ system allowed coordination across different departments and different projects. Whitehall now works on opposite principles. It is stuck with antiquated departments, an antiquated Cabinet Office system, and antiquated project management. Anything ‘cross-government’ is an immediate clue to the savvy that it is doomed and rarely worth wasting time on. A ‘matrix’ approach could and should be applied to break existing hierarchies and speed everything up.
People and ideas were more important than technology. Computers and other technologies can help but the main ideas came in the 1950s before personal computers. JSOC applied all sorts of technologies but Colonel Boyd’s dictum holds: people, ideas, technology — in that order. Whitehall now works on opposite principles: for example, the former Cabinet Secretary, Gus O’Donnell, recently blamed a ‘lack of investment’ in IT and a shortage of staff for a huge range of Whitehall blunders. This is really deluded. The central problem is known to all experts and is shown in almost every inquiry: IT projects fail repeatedly in the same ways because of failures of management, not ‘lack of investment’, and adding people to flawed projects is not a solution.
Ministers have little grip of departments and little power to change their direction. They can’t hire or fire and they can’t set incentives. They are almost never in a job long enough to acquire much useful knowledge and they almost never have the sort of management skills that provide alternative value to specific knowledge. They have little chance to change anything and officials ensure this little chance becomes almost no chance.
This story shows how to do things much better than normal. It shows that the principles underlying Mueller’s success are naturally in extreme competition with the principles of management that dominate all normal bureaucracies, public or private. People have been able to read about these principles for decades yet today in Whitehall almost everything runs on exactly the opposite principles: incentives operate to suppress learning. The institutional and policy changes inherent in leaving the EU are a systems problem requiring a systems response. Implementing Mueller’s principles would mean changes to most of the antiquated and failing foundations of Whitehall and bring big improvements and cost savings. Such changes are likely to be resisted by most MPs as well as Whitehall given few of them understand or have experience in high performance teams and would regard Mueller’s approach as a threat to their career prospects.
Because Whitehall is a system failure in which different failures are entangled, its inhabitants tend to potter around in an uncomprehending fog of confusion without understanding why things fail every day and therefore they do not support changes that could improve things even though these changes would be personally advantageous particularly for the first mover.
What is the minimum needed to break bureaucratic resistance and spark a virtuous circle?
How can people outside the system affect mission critical political institutions protected from market competition and resistant to major reforms?
How can we replace many traditional centralised bureaucracies with institutions that mimic successful biological systems such as the immune system that a) use distributed information processing to identify useful structure in the environment, b) find ‘good enough’ solutions in a vast search space of possibilities, and c) move at least ten times faster than existing systems?
[If you find this interesting and/or useful, then the PDF of the whole story is here. It involves some of the cleverest people of the 20th Century, such as John von Neumann.]
This paper concerns a very interesting story combining politics, management, institutions, science and technology. When high technology projects passed a threshold of complexity post-1945 amid the extreme pressure of the early Cold War, new management ideas emerged. These ideas were known as ‘systems engineering’ and ‘systems management’. These ideas were particularly connected to the classified program to build the first Intercontinental Ballistic Missiles (ICBMs) in the 1950s and successful ideas were transplanted into a failing NASA by George Mueller and others from 1963 leading to the successful moon landing in 1969.
These ideas were then applied in other mission critical teams and could be used to improve government performance. Urgently needed projects to lower the probability of catastrophes for humanity will benefit from considering why Mueller’s approach was 1) so successful and 2) so un-influential in politics. Could we develop a ‘systems politics’ that applies the unrecognised simplicities of effective action?
For those interested, it also looks briefly at an interesting element of the story – the role of John von Neumann, the brilliant mathematician who was deeply involved in the Manhattan Project, the project to build ICBMs, the first digital computers, and subjects like artificial intelligence, artificial life, possibilities for self-replicating machines made from unreliable components, and the basic problem that technological progress ‘gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we have known them, cannot continue.’
An obvious project with huge inherent advantages for humanity is the development of an international manned lunar base as part of developing space for commerce and science. It is the sort of thing that might change political dynamics on earth and could generate enormous support across international boundaries. After 23 June 2016, the UK has to reorient national policy on many dimensions. Developing basic science is one of the most important dimensions (for example, as I have long argued we urgently need a civilian version of DARPA similarly operating outside normal government bureaucratic systems including procurement and HR). Supporting such an international project would be a great focus for UK efforts and far more productive than our largely wasted decades of focus on the dysfunctional bureaucracy in Brussels that is dominated by institutions that fail the most important test – the capacity for error-correction the importance of which has been demonstrated over long periods and through many problems by the Anglo-American political system and its common law.
Please leave comments or email dmc2.cummings at gmail.com
‘The combination of physics and politics could render the surface of the earth uninhabitable.’ John von Neumann.
This series of blogs considers:
the difference between fields with genuine expertise, such as fighting and physics, and fields dominated by bogus expertise, such as politics and economic forecasting;
the big big problem we face – the world is ‘undersized and underorganised’ because of a collision between four forces: 1) our technological civilisation is inherently fragile and vulnerable to shocks, 2) the knowledge it generates is inherently dangerous, 3) our evolved instincts predispose us to aggression and misunderstanding, and 4) there is a profound mismatch between the scale and speed of destruction our knowledge can cause and the quality of individual and institutional decision-making in ‘mission critical’ institutions – our institutions are similar to those that failed so spectacularly in summer 1914 yet they face crises moving at least ~103 times faster and involving ~106 times more destructive power able to kill ~1010 people;
what classic texts and case studies suggest about the unrecognised simplicities of effective action to improve the selection, education, training, and management of vital decision-makers to improve dramatically, reliably, and quantifiably the quality of individual and institutional decisions (particularly 1) the ability to make accurate predictions and b) the quality of feedback);
how we can change incentives to aim a much bigger fraction of the most able people at the most important problems;
what tools and technologies can help decision-makers cope with complexity.
[I’ve tweaked a couple of things in response to this blog by physicist Steve Hsu.]
Summary of the big big problem
The investor Peter Thiel (founder of PayPal and Palantir, early investor in Facebook) asks people in job interviews: what billion (109) dollar business is nobody building? The most successful investor in world history, Warren Buffett, illustrated what a quadrillion (1015) dollar business might look like in his 50th anniversary letter to Berkshire Hathaway investors.
‘There is, however, one clear, present and enduring danger to Berkshire against which Charlie and I are powerless. That threat to Berkshire is also the major threat our citizenry faces: a “successful” … cyber, biological, nuclear or chemical attack on the United States… The probability of such mass destruction in any given year is likely very small… Nevertheless, what’s a small probability in a short period approaches certainty in the longer run. (If there is only one chance in thirty of an event occurring in a given year, the likelihood of it occurring at least once in a century is 96.6%.) The added bad news is that there will forever be people and organizations and perhaps even nations that would like to inflict maximum damage on our country. Their means of doing so have increased exponentially during my lifetime. “Innovation” has its dark side.
‘There is no way for American corporations or their investors to shed this risk. If an event occurs in the U.S. that leads to mass devastation, the value of all equity investments will almost certainly be decimated.
‘No one knows what “the day after” will look like. I think, however, that Einstein’s 1949 appraisal remains apt: “I know not with what weapons World War III will be fought, but World War IV will be fought with sticks and stones.”’
Politics is profoundly nonlinear. (I have written a series of blogs about complexity and prediction HERE which are useful background for those interested.) Changing the course of European history via the referendum only involved about 10 crucial people controlling ~£107 while its effects over ten years could be on the scale of ~108 – 109 people and ~£1012: like many episodes in history the resources put into it are extremely nonlinear in relation to the potential branching histories it creates. Errors dealing with Germany in 1914 and 1939 were costly on the scale of ~100,000,000 (108) lives. If we carry on with normal human history – that is, international relations defined as out-groups competing violently – and combine this with modern technology then it is extremely likely that we will have a disaster on the scale of billions (109) or even all humans (~1010). The ultimate disaster would kill about 100 times more people than our failure with Germany. Our destructive power is already much more than 100 times greater than it was then: nuclear weapons increased destructiveness by roughly a factor of a million.
Even if we dodge this particular bullet there are many others lurking. New genetic engineering techniques such as CRISPR allow radical possibilities for re-engineering organisms including humans in ways thought of as science fiction only a decade ago. We will soon be able to remake human nature itself. CRISPR-enabled ‘gene drives’ enable us to make changes to the germ-line of organisms permanent such that changes spread through the entire wild population, including making species extinct on demand. Unlike nuclear weapons such technologies are not complex, expensive, and able to be kept secret for a long time. The world’s leading experts predict that people will be making them cheaply at home soon – perhaps they already are. These developments have been driven by exponential progress much faster than Moore’s Law reducing the cost of DNA sequencing per genome from ~$108 to ~$103 in roughly 15 years.
It is already practically possible to deploy a cheap, autonomous, and anonymous drone with facial-recognition software and a one gram shaped-charge to identify a relevant face and blow it up. Military logic is driving autonomy. For example, 1) the explosion in the volume of drone surveillance video (from 71 hours in 2004 to 300,000 hours in 2011 to millions of hours now) requires automated analysis, and 2) jamming and spoofing of drones strongly incentivise a push for autonomy. It is unlikely that promises to ‘keep humans in the loop’ will be kept. It is likely that state and non-state actors will deploy low-cost drone swarms using machine learning to automate the ‘find-fix-finish’ cycle now controlled by humans. (See HERE for a video just released for one such program and imagine the capability when they carry their own communication and logistics network with them.)
In the medium-term, many billions are being spent on finding the secrets of general intelligence. We know this secret is encoded somewhere in the roughly 125 million ‘bits’ of information that is the rough difference between the genome that produces the human brain and the genome that produces the chimp brain. This search space is remarkably small – the equivalent of just 25 million English words or 30 copies of the King James Bible. There is no fundamental barrier to decoding this information and it is possible that the ultimate secret could be described relatively simply (cf. this great essay by physicist Michael Nielsen). One of the world’s leading experts has told me they think a large proportion of this problem could be solved in about a decade with a few tens of billions and something like an Apollo programme level of determination.
Not only is our destructive and disruptive power still getting bigger quickly – it is also getting cheaper and faster every year. The change in speed adds another dimension to the problem. In the period between the Archduke’s murder and the outbreak of World War I a month later it is striking how general failures of individuals and institutions were compounded by the way in which events moved much faster than the ‘mission critical’ institutions could cope with such that soon everyone was behind the pace, telegrams were read in the wrong order and so on. The crisis leading to World War I was about 30 days from the assassination to the start of general war – about 700 hours. The timescale for deciding what to do between receiving a warning of nuclear missile launch and deciding to launch yourself is less than half an hour and the President’s decision time is less than this, maybe just minutes. This is a speedup factor of at least 103.
Economic crises already occur far faster than human brains can cope with. The financial system has made a transition from people shouting at each other to a a system dominated by high frequency ‘algorithmic trading’ (HFT), i.e. machine intelligence applied to robot trading with vast volumes traded on a global spatial scale and a microsecond (10-6) temporal scale far beyond the monitoring, understanding, or control of regulators and politicians. There is even competition for computer trading bases in specific locations based on calculations of Special Relativity as the speed of light becomes a factor in minimising trade delays (cf. Relativistic statistical arbitrage, Wissner-Gross). ‘The Flash Crash’ of 9 May 2010 saw the Dow lose hundreds of points in minutes. Mini ‘flash crashes’ now blow up and die out faster than humans can notice. Given our institutions cannot cope with economic decisions made at ‘human speed’, a fortiori they cannot cope with decisions made at ‘robot speed’. There is scope for worse disasters than 2008 which would further damage the moral credibility of decentralised markets and provide huge chances for extremist political entrepreneurs to exploit. (* See endnote.)
What about the individuals and institutions that are supposed to cope with all this?
Our brains have not evolved much in thousands of years and are subject to all sorts of constraints including evolved heuristics that lead to misunderstanding, delusion, and violence particularly under pressure. There is a terrible mismatch between the sort of people that routinely dominate mission critical political institutions and the sort of people we need: high-ish IQ (we need more people >145 (+3SD) while almost everybody important is between 115-130 (+1 or 2SD)), a robust toolkit for not fooling yourself including quantitative problem-solving (almost totally absent at the apex of relevant institutions), determination, management skills, relevant experience, and ethics. While our ancestor chiefs at least had some intuitive feel for important variables like agriculture and cavalry our contemporary chiefs (and those in the media responsible for scrutiny of decisions) generally do not understand their equivalents, and are often less experienced in managing complex organisations than their predecessors.
The national institutions we have to deal with such crises are pretty similar to those that failed so spectacularly in summer 1914 yet they face crises moving at least ~103 times faster and involving ~106 times more destructive power able to kill ~1010 people. The international institutions developed post-1945 (UN, EU etc) contribute little to solving the biggest problems and in many ways make them worse. These institutions fail constantly and do not – cannot – learn much.
If we keep having crises like we have experienced over the past century then this combination of problems pushes the probability of catastrophe towards ‘overwhelmingly likely’.
What Is To be Done? There’s plenty of room at the top
‘In a knowledge-rich world, progress does not lie in the direction of reading information faster, writing it faster, and storing more of it. Progress lies in the direction of extracting and exploiting the patterns of the world… And that progress will depend on … our ability to devise better and more powerful thinking programs for man and machine.’ Herbert Simon, Designing Organizations for an Information-rich World, 1969.
‘Fascinating that the same problems recur time after time, in almost every program, and that the management of the program, whether it happened to be government or industry, continues to avoid reality.’ George Mueller, pioneer of ‘systems engineering’ and ‘systems management’ and the man most responsible for the success of the 1969 moon landing.
Somehow the world has to make a series of extremely traumatic and dangerous transitions over the next 20 years. The main transition needed is:
Embed reliably the unrecognised simplicities of high performance teams (HPTs), including personnel selection and training, in ‘mission critical’ institutions while simultaneously developing a focused project that radically improves the prospects for international cooperation and new forms of political organisation beyond competing nation states.
Big progress on this problem would automatically and for free bring big progress on other big problems. It could improve (even save) billions of lives and save a quadrillion dollars (~$1015). If we avoid disasters then the error-correcting institutions of markets and science will, patchily, spread peace, prosperity, and learning. We will make big improvements with public services and other aspects of ‘normal’ government. We will have a healthier political culture in which representative institutions, markets serving the public (not looters), and international cooperation are stronger.
Can a big jump in performance – ‘better and more powerful thinking programs for man and machine’ – somehow be systematised?
Feynman once gave a talk titled ‘There’s plenty of room at the bottom’ about the huge performance improvements possible if we could learn to do engineering at the atomic scale – what is now called nanotechnology. There is also ‘plenty of room at the top’ of political structures for huge improvements in performance. As I explained recently, the victory of the Leave campaign owed more to the fundamental dysfunction of the British Establishment than it did to any brilliance from Vote Leave. Despite having the support of practically every force with power and money in the world (including the main broadcasters) and controlling the timing and legal regulation of the referendum, they blew it. This was good if you support Leave but just how easily the whole system could be taken down should be frightening for everybody .
Creating high performance teams is obviously hard but in what ways is it really hard? It is not hard in the same sense that some things are hard like discovering profound new mathematical knowledge. HPTs do not require profound new knowledge. We have been able to read the basic lessons in classics for over two thousand years. We can see relevant examples all around us of individuals and teams showing huge gains in effectiveness.
The real obstacle is not financial. The financial resources needed are remarkably low and the return on small investments could be incalculably vast. We could significantly improve the decisions of the most powerful 100 people in the UK or the world for less than a million dollars (~£106) and a decade-long project on a scale of just ~£107 could have dramatic effects.
The real obstacle is not a huge task of public persuasion – quite the opposite. A government that tried in a disciplined way to do this would attract huge public support. (I’ve polled some ideas and am confident about this.) Political parties are locked in a game that in trying to win in conventional ways leads to the public despising them. Ironically if a party (established or new) forgets this game and makes the public the target of extreme intelligent focus then it would not only make the world better but would trounce their opponents.
The real obstacle is not a need for breakthrough technologies though technology could help. As Colonel Boyd used to shout, ‘People, ideas, machines – in that order!’
The real obstacle is that although we can all learn and study HPTs it is extremely hard to put this learning to practical use and sustain it against all the forces of entropy that constantly operate to degrade high performance once the original people have gone. HPTs are episodic. They seem to come out of nowhere, shock people, then vanish with the rare individuals. People write about them and many talk about learning from them but in fact almost nobody ever learns from them – apart, perhaps, from those very rare people who did not need to learn – and nobody has found a method to embed this learning reliably and systematically in institutions that can maintain it. The Prussian General Staff remained operationally brilliant but in other ways went badly wrong after the death of the elder Moltke. When George Mueller left NASA it reverted to what it had been before he arrived – management chaos. All the best companies quickly go downhill after the departure of people like Bill Gates – even when such very able people have tried very very hard to avoid exactly this problem.
Charlie Munger, half of the most successful investment team in world history, has a great phrase he uses to explain their success that gets to the heart of this problem:
‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities… It’s a community of like-minded people, and that makes most decisions into no-brainers. Warren [Buffett] and I aren’t prodigies. We can’t play chess blindfolded or be concert pianists. But the results are prodigious, because we have a temperamental advantage that more than compensates for a lack of IQ points.’
The simplicities that bring high performance in general, not just in investing, are largely unrecognised because they conflict with many evolved instincts and are therefore psychologically very hard to implement. The principles of the Buffett-Munger success are clear – they have even gone to great pains to explain them and what the rest of us should do – and the results are clear yet still almost nobody really listens to them and above average intelligence people instead constantly put their money into active fund management that is proved to destroy wealth every year!
Most people think they are already implementing these lessons and usually strongly reject the idea that they are not. This means that just explaining things is very unlikely to work:
‘I’d say the history that Charlie [Munger] and I have had of persuading decent, intelligent people who we thought were doing unintelligent things to change their course of action has been poor.’ Buffett.
Even more worrying, it is extremely hard to take over organisations that are not run right and make them excellent.
‘We really don’t believe in buying into organisations to change them.’ Buffett.
If people won’t listen to the world’s most successful investor in history on his own subject, and even he finds it too hard to take over failing businesses and turn them around, how likely is it that politicians and officials incentivised to keep things as they are will listen to ideas about how to do things better? How likely is it that a team can take over broken government institutions and make them dramatically better in a way that outlasts the people who do it? Bureaucracies are extraordinarily resistant to learning. Even after the debacles of 9/11 and the Iraq War, costing many lives and trillions of dollars, and even after the 2008 Crash, the security and financial bureaucracies in America and Europe are essentially the same and operate on the same principles.
Buffett’s success is partly due to his discipline in sticking within what he and Munger call their ‘circle of competence’. Within this circle they have proved the wisdom of avoiding trying to persuade people to change their minds and avoiding trying to fix broken institutions.
This option is not available in politics. The Enlightenment and the scientific revolution give us no choice but to try to persuade people and try to fix or replace broken institutions. In general ‘it is better to undertake revolution than undergo it’. How might we go about it? What can people who do not have any significant power inside the system do? What international projects are most likely to spark the sort of big changes in attitude we urgently need?
This is the first of a series. I will keep it separate from the series on the EU referendum though it is connected in the sense that I spent a year on the referendum in the belief that winning it was a necessary though not sufficient condition for Britain to play a part in improving the quality of government dramatically and improving the probability of avoiding the disasters that will happen if politics follows a normal path. I intended to implement some of these ideas in Downing Street if the Boris-Gove team had not blown up. The more I study this issue the more confident I am that dramatic improvements are possible and the more pessimistic I am that they will happen soon enough.
Please leave comments and corrections…
* A new transatlantic cable recently opened for financial trading. Its cost? £300 million. Its advantage? It shaves 2.6 milliseconds off the latency of financial trades. Innovative groups are discussing the application of military laser technology, unmanned drones circling the earth acting as routers, and even the use of neutrino communication (because neutrinos can go straight through the earth just as zillions pass through your body every second without colliding with its atoms) – cf. this recent survey in Nature.
Forecasts have been fundamental to mankind’s journey from a small tribe on the African savannah to a species that can sling objects across the solar system with extreme precision. In physics, we developed models that are extremely accurate across vastly different scales from the sub-atomic to the visible universe. In politics we bumbled along making the same sort of errors repeatedly.
Until the 20th century, medicine was more like politics than physics. Its forecasts were often bogus and its record grim. In the 1920s, statisticians invaded medicine and devised randomised controlled trials. Doctors, hating the challenge to their prestige, resisted but lost. Evidence-based medicine became routine and saved millions of lives. A similar battle has begun in politics. The result could be more dramatic.
In 1984, Philip Tetlock, a political scientist, did something new – he considered how to assess the accuracy of political forecasts in a scientific way. In politics, it is usually impossible to make progress because forecasts are so vague as to be useless. People don’t do what is normal in physics – use precise measurements – so nobody can make a scientific judgement in the future about whether, say, George Osborne or Ed Balls is ‘right’.
Tetlock established a precise measurement system to track political forecasts made by experts to gauge their accuracy. After twenty years he published the results. The average expert was no more accurate than the proverbial dart-throwing chimp on many questions. Few could beat simple rules like ‘always predict no change’.
Tetlock also found that a small fraction did significantly better than average. Why? The worst forecasters were those with great self-confidence who stuck to their big ideas (‘hedgehogs’). They were often worse than the dart-throwing chimp. The most successful were those who were cautious, humble, numerate, actively open-minded, looked at many points of view, and updated their predictions (‘foxes’). TV programmes recruit hedgehogs so the more likely an expert was to appear on TV, the less accurate he was. Tetlock dug further: how much could training improve performance?
In the aftermath of disastrous intelligence forecasts about Iraq’s WMD, an obscure American intelligence agency explored Tetlock’s ideas. They created an online tournament in which thousands of volunteers would make many predictions. They framed specific questions with specific timescales, required forecasts using numerical probability scales, and created a robust statistical scoring system. Tetlock created a team – the Good Judgement Project (GJP) – to compete in the tournament.
The results? GJP beat the official control group by 60% in year 1 and by 78% in year 2. GJP beat all competitors so easily the tournament was shut down early.
How did they do it? GJP recruited a team of hundreds, aggregated the forecasts, gave extra weight to the most successful, and applied a simple statistical rule. A few hundred ordinary people and simple maths outperformed a bureaucracy costing tens of billions.
Tetlock also found ‘superforecasters’. These individuals outperformed others by 60% and also, despite a lack of subject-specific knowledge, comfortably beat the average of professional intelligence analysts using classified data (the size of the difference is secret but was significant).
Superforecasters explores the nature of these unusual individuals. Crucially, Tetlock has shown that training programmes can yield big improvements. Even a mere sixty minute tutorial on some basics of statistics improves performance by 10%. The cost:benefit ratio of training forecasting is huge.
It would be natural to assume that this work must be the focus of intense thought and funding in Whitehall. Wrong. Whitehall has ignored this entire research programme. Whitehall experiences repeated predictable failure while simultaneously seeing no alternative to their antiquated methods, like 1950s doctors resisting randomised control trials that threaten prestige.
This may change. Early adopters could use Tetlock’s techniques to improve performance. Success sparks mimicry. Everybody reading this could do one simple thing: ask their MP whether they have done Tetlock’s training programme. A website could track candidates’ answers before the next election. News programmes could require quantifiable predictions from their pundits and record their accuracy.
We now expect that every medicine is tested before it is used. We ought to expect that everybody who aspires to high office is trained to understand why they are so likely to make mistakes forecasting complex events. The cost is tiny. The potential benefits run to trillions of pounds and millions of lives. Politics is harder than physics but Tetlock has shown that it doesn’t have to be like astrology.
Superforecasting: the art and science of prediction, by Philip Tetlock (Random House, 352 pages)
Ps. When I wrote this (August/September 2015) I was assembling the team to fight the referendum. One of the things I did was hire people with very high quantitative skills, as I describe in this blog HERE.