Surveillance capitalism and anti-capitalism

In the last few years, the computer scientists and entrepreneurs who fuel Silicon Valley have gone through a bewildering series of transformations. Once upon a time they were ostracised nerds. Then they were the lovable geeks of the Big Bang Theory TV show, and for a short while they were superheroes. (In case you’re wondering, geeks wonder what sex in zero gravity is like; nerds wonder what sex is like.) Then it all went wrong, and now they are the tech bros; the anti-heroes in the dystopian saga of society’s descent into algorithmic rule by Big Brother, soon to be followed by extermination by Terminators.

Techlash is in full swing, and Shoshana Zuboff is its latest high priestess. She is professor emerita at Harvard Business School, and author of “Surveillance Capitalism”, a 600-page book on how the tech giants, especially Google and Facebook, have developed a “rogue mutation of capitalism” which threatens our personal autonomy, and democracy.

Zuboff is beyond scathing about Google and Facebook: even favourable reviewers agree she is extreme. She likens tech giant executives to the Spanish conquistadores, with the rest of us as the indigenous populations of South America, and rivers of blood as the consequence. (She doesn’t specify which countries have lost 90% of their populations as a result of their citizens using Facebook.) She describes Sheryl Sandberg, Facebook’s COO, as the “Typhoid Mary” of this socio-economic plague.

Apparently, the goal of the tech giants is not just to understand our behaviour so they can enable other organisations to sell things to us. It is to control us and turn us into robots, “to automate us”. She quotes a data scientist: “We are learning how to write the music, and then we let the music make [our victims] dance.”

Zuboff wants governments to “interrupt and outlaw surveillance capitalism’s data supplies and revenue flows … outlawing the secret theft of private experience.” After all, “We already outlaw markets that traffic in slavery or human organs.” The old phrase (which pre-dates the Web) “if you’re not paying for it, you’re the product” isn’t extreme enough for Zuboff: she compares the social media platforms to elephant poachers who kill us in order to steal our ivory tusks. “You are not the product … You are the abandoned carcass.”

Dead elephant, white

Zuboff claims that Google’s founders are fully aware of the harms their company causes, and that originally, they swore off using our personal data so perniciously. They were effectively bullied into exploiting the opportunity – and into becoming billionaires – by the demands of the stock market.

She also claims that surveillance capitalism would not have evolved if there had not been a corresponding rise in state surveillance. She claims that in 2000, the FTC was poised to regulate the tech giants, but the war on terror prompted by the 9/11 attacks drained away any support for privacy campaigns in US government circles.

If we give Zuboff the benefit of the doubt, and push the hyperbole to one side, is her thesis reasonable? Do the tech giants steal our data and sell it to new breeds of capitalists who use it to control us? If we take it literally, much of it is simply mistaken. In general, Google and Facebook do not steal our data. You have to accept their terms and conditions in order for them to access and use it, although of course, none of us read those conditions, and most of us have no detailed knowledge of what they contain. The tech giants could and should do a much better job of explaining that.

It is also untrue that Google and Facebook have spawned new types of capitalists: for decades, firms have spent significant sums of money to obtain data about their customers. In the bad old days when junk mail clogged up hallways, companies desperately wanted to avoid wasting money sending mailers about lawnmowers to people living in high-rise apartments. Direct marketing was a large and growing industry well before the invention of the Web.

Nevertheless, there is clearly a genuine need for debate about whether Google, Facebook, and other tech giants are harming us with the ways they use our data. Certainly it can be disconcerting when you search for information about a product category, and then notice that ads for companies selling that product are following you around the internet for several hours or even days. Many people find this exploitative, dishonest, creepy, and intrusive.

There are plenty of instances where the tech giants, and indeed many other organisations, have obtained personal data improperly, mis-used it, and / or failed as its custodians. The FTC has just imposed its largest-ever fine on Facebook for allowing its customers data to be mis-used by Cambridge Analytica, although some people felt that $5 billion was too trivial a sum.

Cambridge Analytica
But does that mean that the business model is illegitimate? An important test of that is whether consumers want it. It is patronising and simply wrong to say that the population as a whole does not know what is going on. Most users do know that companies sell access to our data to companies that want to show us adverts, and in return we get free stuff. “Take my data and give me free shit”, as one consumer put it. We might be foolish to accept this trade-off (it might even be “false consciousness”, as the Marxists like to say) but governments would ban it at their peril – and the ones subject to elections don’t.

Zuboff claims that “research over the past decade suggests that when users are informed of surveillance capitalism’s backstage operations, they want protection, and they want alternatives,” but most of the evidence points to the contrary. Erik Brynjolffson, a professor of economics at MIT, ran a survey in 2018 to assess how much Americans would have to be paid to avoid using the products provided for free by the tech giants. Facebook and other social media were valued at $322 a year, and search was valued at an eye-opening $17,500. Globally, Facebook makes $80 per person for using our data, so on the face of it, the deal is not too shabby. (Americans are more profitable, at $105 per head, and Europeans rather less so, at $35.)

Duck Duck Go
Those who find the trade-off unacceptable are in no way obliged to engage in it. Duck Duck Go is by all accounts a pretty good substitute for Google Search, and sells itself on not using your data. I have never used Facebook, not because of privacy concerns, but because I reckon I would spend too much time looking at cat videos.

The term “Surveillance capitalism” was invented by Zuboff in a 2014 essay. It’s a great phrase, but it is deliberately misleading. The Cambridge Dictionary defines surveillance as “the careful watching of a person or placeespecially by the police or army, because of a crime that has happened or is expected.” That is not what Google is doing. It is trying to figure out what makes me and a thousand other people like me choose to buy a particular type of car and when, and then sell that information to a firm that sells cars. The data about me is useless unless combined in that way, and it is data that I could not possibly sell on my own.

An alternative to the phrase surveillance capitalism would be personalised capitalism. It would be more accurate, but of course it wouldn’t be as scary, or generate as many headlines.

The place we should look for dangerous surveillance is not the capitalists, but the state. China’s developing Social Credit system shows clearly where the real threat lies. Capitalists just want to sell us fizzy black water and cars. Governments provide security and a welfare safety net, but in order to do this they lay claim to between a third and a half of our income, they send some of us to war, and they lock some of us up. It seems that many Chinese are intensely relaxed about Social Credit: they say it improves public behaviour, and they argue that there is nothing to worry about if you have done nothing wrong. This is a very poor argument. State surveillance leads to self-censorship, and if the levers of state power fall into malign hands – which from time to time they do – then a powerful surveillance network becomes a disaster for everyone.

A lot of the current wave of techlash is actually anti-capitalism. The real problem with the tech giants in the eyes of many of their critics is they are too big, too powerful, and above all, they make too much profit. And profit is a Bad Thing. This may not be not true of Zuboff, who declares herself a fan of good old-fashioned capitalism, but it is certainly true of Jeremy Corbyn and Bernie Sanders. Corbyn and Sanders are just as populist as the alt-right, and just as dangerous. They are wilfully ignorant of the huge benefits delivered by modern capitalism, and they seek to wreck it.

It is ironic that the tech giants are currently among the most hated targets of the left, since their founders and staff are so clearly left-leaning themselves. In attacking the tech giants for spreading fake news they are surely missing the most egregious culprits. For instance the blatant lies told about the EU by Murdoch’s News International, the Telegraph, and the Daily Mail are what gave us Brexit, and gave permission to racists and homophobes to re-emerge blinking into the daylight.

In any discussion of the future, timing is important. The data being hoovered up and exploited by the tech giants today is mostly about our shopping habits. We are on the verge of an era when we will generate tsunamis of data about our health. Apple Watches are showing the way, and before long most of us will wear devices which take readings of our pulse, our sweat, our eye fluids, our electrical impulses, analyse some of it on the device and stream more of it to the cloud. Even those of us who are relatively relaxed about Google’s privacy terms today should be thinking about who we want to be custodians of our minute-by-minute health data.

And perhaps further ahead, when AI, biotech, and other technologies are powerful and cheap enough to enable a gruntled teenager to slaughter people in their thousands, what price privacy then? When a megadeath is priced in the mere hundreds of dollars, can we avoid the universal panopticon?

Batman's Panopticon

“Calum’s Rule”

Forecasts should specify the timeframe

Time dispute

Disagreements which suggest profound differences of philosophy sometimes turn out to be merely a matter of timing: the parties don’t actually disagree about whether a thing will happen or not, they just disagree over how long it will take. For instance, timing is at the root of apparently fundamental differences of opinion about the technological singularity.

Elon Musk is renowned for his warnings about superintelligence:

With artificial intelligence, we are summoning the demon. You know all those stories where there’s the guy with the pentagram and the holy water and he’s like, yeah, he’s sure he can control the demon? Doesn’t work out.” We are the biological boot-loader for digital super-intelligence.”

Comments like this have attracted fierce criticism:

I don’t work on not turning AI evil today for the same reason I don’t worry about the problem of overpopulation on the planet Mars.” (Andrew Ng)

We’re very far from having machines that can learn the most basic things about the world in the way humans and animals can do. Like, yes, in particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat. This makes a lot of questions people are asking themselves premature.” (Yann LeCun)

Superintelligence is beyond the foreseeable horizon.”  (Oren Etzioni)

Surviving cover, compressed
If you look closely, these people don’t disagree with Musk that superintelligence is possible – even likely, and that its arrival could be an existential threat for humans. What they disagree about is the likely timing, and the difference isn’t as great as you might think. Ng thinks “There could be a race of killer robots in the far future,” but he doesn’t specify when. LeCun seems to think it could happen this century: “if there were any risk of [an “AI apocalypse”], it wouldn’t be for another few decades in the future.” And Etzioni’s comment was based on a survey where most respondents set the minimum timeframe as a mere 25 years. As Stephen Hawking famously wrote, “If a superior alien civilisation sent us a message saying, ‘We’ll arrive in a few decades,’ would we just reply, ‘OK, call us when you get here—we’ll leave the lights on’? Probably not.”

Although it is less obvious, I suspect a similar misunderstanding is at play in discussions about the other singularity – the economic one: the possibility of technological unemployment and what comes next. Martin Ford is one of the people warning us that we may face a jobless future:

A lot of people assume automation is only going to affect blue-collar people, and that so long as you go to university you will be immune to that … But that’s not true, there will be a much broader impact.”

The opposing camp includes most of the people running the tech giants:

People keep saying, what happens to jobs in the era of automation? I think there will be more jobs, not fewer.” “… your future is you with a computer, not you replaced by a computer…” “[I am] a job elimination denier.” – Eric Schmidt

Schmidt and bot
There are many things AI will never be able to do… When there is a lot of artificial intelligence, real intelligence will be scarce, real empathy will be scarce, real common sense will be scarce. So, we can have new jobs that are actually predicated on those attributes.” – Satya Nadella

For perfectly good reasons, these people mainly think in time horizons of up to five years, maybe ten at a stretch. And in that time period they are surely right to say that technological unemployment is unlikely. For machines to throw us out of a job, they have to be able to do it cheaper, better, and / or faster. Automation has been doing that for centuries: elevator operator and secretary are very niche occupations these days. When a job is automated, the employer’s process becomes more efficient. This creates wealth, and wealth creates demand, and thus new jobs. This will continue to happen – unless and until the day arrives when the machines can do almost all the work that we do for money.

Time horizon

If and when that day arrives, any new jobs which are created as old jobs are destroyed will be taken by machines, not humans. And our most important task as a species at that point will be to figure out a happy ending to that particular story.

Will that day arrive, and if so, when? People often say that Moore’s Law is dead or dying, but it isn’t true. It has been evolving ever since Gordon Moore noticed, back in 1965, that his company was putting twice as many transistors on each chip every year. (In 1975 he adjusted the time to two years, and shortly afterwards it was adjusted again, to eighteen months.) The cramming of transistors has slowed recently, but we are seeing an explosion of new types of chips, and Chris Bishop, the head of Microsoft Research in the UK, argues that we are seeing the start of a Moore’s Law for software: “I think we’re seeing … a similar, singular moment in the history of software … The rate limiting step now is … the data, and what’s really interesting is the amount of data in the world is – guess what – it’s growing exponentially! And that’s set to continue for a long, long time to come.”

So there is plenty more Moore, and plenty more exponential growth. The machines we have in 10 years time will be 128 times more powerful than the ones we have today. In 20 years time they will be 8,000 times more powerful, and in 30 years time, a million times more powerful. If you take the prospect of exponential growth seriously, and you look far enough ahead, it becomes hard to deny the possibility that machines will do pretty much all the things we do for money cheaper, better and faster than us.

New rule
So I would like to propose a new rule, and with no superfluous humility I’m calling it Calum’s Rule:

Forecasts should specify the time frame.”

If we all follow this injunction, I suspect we will disagree much less, and we can start to address the issue more constructively.

Has AI ethics got a bad name?


Amid all the talk of robots and artificial intelligence stealing our jobs, there is one industry that is benefiting mightily from the dramatic improvements in AI: the AI ethics industry. Members of the AI ethics community are very active on Twitter and the blogosphere, and they congregate in real life at conferences in places like Dubai and Puerto Rico. Their task is important: they want to make the world a better place, and there is a pretty good chance that they will succeed, at least in part. But have they chosen the wrong name for their field?

Artificial intelligence is a technology, and a very powerful one, like nuclear fission. It will become increasingly pervasive, like electricity. Some say that its arrival may even turn out to be as significant as the discovery of fire. Like nuclear fission, electricity, and fire, AI can have positive impacts and negative impacts, and given how powerful it is and it will become, it is vital that we figure out how to promote the positive outcomes and avoid the negative outcomes.

Bias - 6 or 9

This is what concerns people in the AI ethics community. They want to minimise the amount of bias in the data which informs the decisions that AI systems help us to make – and ideally, to eliminate the bias altogether. They want to ensure that tech giants and governments respect our privacy at the same time as they develop and deliver compelling products and services. They want the people who deploy AI to make their systems as transparent as possible, so that in advance or in retrospect, we can check for sources of bias, and other forms of harm.

But if AI is a technology like fire or electricity, why is the field called “AI ethics”? We don’t have “fire ethics” or “electricity ethics”, so why should we have AI ethics? There may be a terminological confusion here, and it could have negative consequences.

One possible downside is that people outside the field may get the impression that some sort of moral agency is being attributed to the AI, rather than to the humans who develop AI systems. The AI we have today is narrow AI: superhuman in certain narrow domains, like playing chess and Go, but useless at anything else. It makes no more sense to attribute moral agency to these systems than it does to a car, or a rock. It will probably be many years before we create an AI which can reasonably be described as a moral agent.

Sophia citizen

It is ironic that people who regard themselves as AI ethicists are falling into this trap, because many of them get very heated when robots are anthorpomorphised, as when the humanoid Sophia was given citizenship by Saudi Arabia.

There is a more serious potential downside to the nomenclature. People are going to disagree about the best way to obtain the benefits of AI and minimise or eliminate its harms. That is the way it should be: science, and indeed most types of human endeavour, advance by the robust exchange of views. People and groups will have different ideas about what promotes benefit and minimises harm. These ideas should be challenged and tested against each other. But if you think your field is about ethics rather than about what is most effective, there is a danger that you start to see anyone who disagrees with you as not just mistaken, but actually morally bad. You are in danger of feeling righteous, and unwilling or unable to listen to people who take a different view. You are likely to seek the company of like-minded people, and to fear and despise the people who disagree with you. This is again ironic, as AI ethicists are generally (and rightly) keen on diversity.

Bridge failThe issues explored in the field of AI ethics are important, but it would help to clarify them if some of the heat was taken out of the discussion. It might help if instead of talking about AI ethics, we talked about beneficial AI, and AI safety. When an engineer designs a bridge she does not finish the design and then consider how to stop it falling down. The ability to remain standing in all foreseeable circumstances is part of the design criteria, not a separate discipline called “bridge ethics”. Likewise, if an AI system has deleterious effects it is simply a badly designed AI system.

Interestingly, this change has already happened in the field of AGI research, the study of whether and how to create artificial general intelligence, and how to avoid the potential downsides of that development, if and when it does happen. Here, researchers talk about AI safety. Why not make the same move in the field of shorter-term AI challenges?

This article first appeared in Forbes magazine on 7th March 2019

The greatest generations

Greatest generation 2

Every generation thinks the challenges it faces are more important than what has gone before. American journalist Tom Brokaw bestowed the name “the greatest generation” on the people who grew up in the Great Depression and went on to fight in the Second World War. As a late “baby boomer” myself, I certainly take my hat off to that generation.

The Boomers were named for demography: they were a bulge in the population (“the pig in the python”) caused by soldiers returning from the war. They saw themselves as special, and maybe they were. They invented sex in the 1960s, apparently, along with rock and roll, the counter-culture, the civil rights movement and the second wave of feminism.

Generation X was the first to take a letter as its title, although that happened late in their history, with the publication in 1991 of Canadian author Douglas Coupland’s novel, “Generation X: tales for an accelerated culture”. Cynical Boomers said Generation X got its name because its members were cyphers: their role in the world was less clear, their contribution to it was doubtful. Early on they were accused of being lazy and disaffected: they were the MTV generation, and their musical styles were grunge and hip hop. But these are accusations that most parents hurl at their successors. Later on, Generation X showed high levels of entrepreneurship, and appeared to be above averagely happy, with a good work-life balance. Their profile may be lower because there are fewer of them: they were the first generation whose parents had access to the contraceptive pill.

Generation Y

Generation Y, 2
Generation X was followed, naturally enough, by Generation Y, also known as the Millennials, since they were born between the early 1981 and 2000. (There are no generally agreed dates for the generations; I like 1941-60 for Boomers, 1961-80 for Generation X, and 1981-2000 for Millennials.) Following Generation X, and still being born, is Generation Z. Whatever their predecessors may think, it is these two generations which will face the biggest challenges yet presented to humanity.

Speaking at the United Nations in 1963, John F Kennedy said something which would not be out of place today: “Never before has man had such capacity to control his own environment, to end thirst and hunger, to conquer poverty and disease, to banish illiteracy and massive human misery. We have the power to make this the best generation of mankind in the history of the world – or make it the last.”i

The members of Generation Y and Z have been born at the best time ever to be a human, in terms of life expectancy, health, wealth, access to education, information, and entertainment. They have also been born at the most interesting time, and the most important. Whether they like it or not, they have the task of navigating us through the economic singularity of mass unemployment, and then the technological singularity of super-intelligence.

The economic singularity will arrive when Generation Y is running the show, which makes their name apposite, since one of the challenges the economic singularity will raise is to ensure that everyone finds meaning in a life without jobs. Generation Y will have to come up with a great new answer to the question of “why” we are here.

Generation Z

Generation Z
Generation Z is, if anything, even better named, although again, entirely by accident. One way or another, they are likely to be the last generation of humans to reach old age in a form their ancestors would recognise. These timings are of course uncertain and tendentious, but Generation Z is likely to be the dominant force in politics and business when the first superintelligence appears, and humanity becomes the second-smartest species on the planet. The consequences for humans will be staggering. If things go well, and the superintelligence really likes us, then at a minimum, humans will quickly be augmented to dispense with many of the limitations and frailties which have afflicted us since life on earth began: ageing, vulnerability, and probably even death. These augmentations will render us barely recognisable, and hard to continue to classify as human. If things go very well, perhaps we will merge with the machines we have created, and travel the universe together to wonder at its marvels, immune to the ravages of vacuum and radiation. If things go less well, generation Z could be the last generation of humans for less cheerful reasons.

Generations Y and Z are destined to be our greatest generations. If either of them fails in their respective tasks, humanity’s future could be bleak. But if they succeed, it could be almost incredibly good. They must succeed.

Stories from 2045


Sparky, a NAO robot who lives at Queen Mary University, helped launch a book this week.  In the very whooshy surroundings of the Reform Club on London’s Pall Mall, she read out a story written by an AI.  It’s not a very good story, to be honest, but it’s impressive that an AI can write stories at all.

The other stories, written by humans, are very good indeed.  You’ll find them at an Amazon site near you. They speculate on what life might be like during and after the economic singularity.  Two-thirds of them are positive, which is what we need – Hollywood has given us more than enough dystopias already.

red cover 27 nov
In the coming decades, artificial intelligence (AI) and related technologies will have enormous impacts on the job market. At the moment, no-one can predict exactly what will happen or when. The outcomes could be anywhere from very good to very bad, and which ones we get will depend significantly on the actions taken (and not taken) by governments and others in the coming few years.

The Economic Singularity Club think tank (ESC) was set up to discuss these issues, and try to influence their outcome positively. This book is our first tangible project.

esc key
One possible impact of the AI revolution is that many people will be unemployable within a relatively short space of time – maybe in two or three decades. If this does happen, and if we are smart and perhaps a bit lucky, the outcome could be wonderful, and we should certainly try to make it so.

The book is intended to encourage political leaders, policy makers, and everyone else to bring a much more serious level of attention and investment to the possibility of technological unemployment. The idea is to make the prospect of technological unemployment seem more real and less academic to people who have not previously given the idea much thought. It should also stimulate readers to devise their own solutions, and to decide what actions they can take to help ensure that we get a good outcome and not one of the bad ones.

ar screengrab
The book has an augmented reality cover.  Download an app, point your phone at the book, and a gaggle of TV screens emerge, and hover in front of you.  Select one, and click it to watch a short video.

All proceeds from this book will go to a charitable foundation set up by the ESC to promote its objectives. We hope you find it enjoyable and stimulating. 

charity jar
Finally, if you feel inspired to write your own story from 2045, you can submit it to the book’s website,  If it’s shorter than 1,000 words, and not illegal or hateful, we’ll publish it there.  It we get enough great stories, we’ll publish a sequel book.

stories webpage

Road rage against the machines? Self-driving cars in 2018 and 2019

Self-driving image

Self-driving cars – or Autos, as I hope we’ll call them – passed several important milestones in 2018, and they will pass several more in 2019. The big one came at the end of the year, on 5th December: Google’s Autos spin-out Waymo launched the world’s first commercial self-driving taxi service, open to citizens in Phoenix, Arizona, who are not employees of the company, and not bound by confidentiality agreements.

This service, branded Waymo One, was an extension of the company’s EasyRider programme, which was launched back in April. In that programme, selected members of the public who were willing to sign non-disclosure agreements (NDAs) got free rides in cars where sometimes no-one sat up front: no driver, no supervising engineer. There is much debate about how often the cars in both these programmes run with the front seats empty. Google and Waymo won’t say, but the answer seems to be sometimes, but not often. Some people argue this means that self-driving cars won’t be ready for prime time for years to come. Others see it as commendable caution.

Waymo is the clear front-runner in this business. In October it announced that its test cars had driven 10 million miles, and they have not been the unambiguous cause of a single accident. In simulations, they drive that many miles every single day.

General Motors, America’s biggest car maker by volume, is determined not to lag far behind, and has said for some time that it will launch a fleet of self-driving taxis during 2019. In October it announced a $2.75bn JV with Honda in Cruise, its self-driving car unit, which added to the earlier $2.25bn investment by Softbank to bring the valuation of Cruise to $14bn,i which is almost half the parent company’s equity value.

The rest of America’s car industry is also in hot pursuit, especially its newest and most valuable participant, Tesla Motors, which is pursuing the contrarian strategy of offering more and more driver assistance rather than jumping straight to full automation.

Tesla dog driver

Autos are still expensive, not least because production volumes of their LIDAR sensors are still low. So for some years to come, these vehicles will probably only be sold to commercial fleets, especially taxis and trucks. Unless, of course, Tesla’s Elon Musk is proved right, and Autos can operate solely with cameras, and don’t need LIDAR. So far he’s in a small minority, but his contrarian views have been vindicated before. Even if Musk is wrong, city dwellers in particular may well stop buying cars and start using Auto taxis. In which case, how long would the switch take? A famous pair of photographs taken on the same New York street on the same day in 1900 and 1913 shows that it took just 13 years to effect a complete swap in that city from horse-drawn carriages to automobiles. The switch took longer in rural areas of the US, and much longer again in less developed countries.

Self-driving adoption - from horse to car

In short, anyone who thinks that self-driving vehicles will not be in widespread use by the mid-2020s is probably in for a shock.

The US is in the vanguard of the Autos revolution, but other countries are keen to catch up. Both the UK government and London’s leading private hire company (Addison Lee) have stated their intention to have Autos operating in London by 2021. Driving in London is a whole different proposition to driving in Phoenix, so this two-year delay does not denote a lack of ambition.

But as usual in AI, it is China which is most likely to catch the US if there is a race to deploy self-driving technology. Baidu, often described as China’s Google, is the leader so far, with more than 100 partners involved in its Apollo project, including car manufacturers like Ford and Hyundai, and technology providers. The Chinese government is keeping close tabs on these developments, not least in obliging foreign companies to source their maps from Chinese companies.

Baidu self-driving car

Are we ready for the arrival of Autos? Can our infrastructures cope? The belief that Autos require modifications to our road infrastructure is a misapprehension. Waymo’s cars don’t need smart lane dividers, special traffic light telematics, or dedicated local area networks. They drive on ordinary roads, just like you and me. No doubt Autos will lead to our cities and towns becoming smarter and more intelligible, but they don’t require it to get started.

What about resistance? Will there be road rage against the machines? The most tragic thing to happen in the self-driving car industry this year was also perhaps the most revealing. In April, an Uber Auto ran over and killed a woman walking a bicycle across a busy road. There is still disagreement about what caused the accident, and Uber stopped its self-driving test programme immediately. But the most interesting thing is that no other company followed suit – and there are over 40 companies trialling self-driving cars in the US alone. Despite this, and despite blanket press coverage, there was no popular protest against Autos. It seems that people have already “discounted” the arrival of Autos: it’s a done deal.

Even if the arrival of Autos is a done deal for society as a whole, there may well be pockets of resistance. On a low level, this will come from petrol heads who find themselves banned from more and more roads because they are much more dangerous drivers than machines. Eventually they will only be allowed to drive on designated racetracks, after signing detailed indemnifications. We should welcome this, not resist it: right now, we kill 1.2 million people around the world each year by running them over, and we maim another 50 million. We are sending humans to do a machine’s job, and there is a holocaust taking place on our roads. We should hurry to embrace Autos. And anyone tempted to vandalise Autos will quickly find that they are bristling with cameras: if people start spray-painting their LIDARS to disable them, they will find themselves on the wrong end of a criminal prosecution.

Cross Clarkson

But there is another form of resistance which may not be so easy to assuage. In June, I gave a talk about AI to a room full of senior US police officers – just outside Phoenix, Arizona, appropriately enough. When I argued that a million Americans who currently earn a reasonable living driving trucks are going to be out of a job fairly soon because the economics of truck driving is going to flip, there was an audible gulp in the hall. They didn’t need me to point out that many of these people have guns.

One of the most significant impacts of Autos may well be to play the role of the canary in the coal mine: they could alert people to the likelihood that technological unemployment is coming – not now, and not in five years, but in a generation. If it is coming, we had better have a plan for how to cope. Otherwise there could be a panic which makes the current wave of populism look mild. At the moment we have no plan, and we’re not even thinking about developing a plan because so many influential people are saying that it cannot happen. They might be right to say that it will not happen. But to say that it cannot happen is dangerous complacency.

So what of 2019? Assuming success in Phoenix, Google is likely to roll out its pilot to other US cities – we could maybe see a dozen of them start during 2019. GM will be anxious not be seen as lagging, and no doubt Tesla will make startling announcements followed by almost-as-startling achievements. I’ll be surprised if there aren’t some significant pilots in China by the end of 2019 as well. And who knows, maybe all this will spur Europe into getting more serious about AI in general. Here’s hoping. 

This article was first published by Forbes magazine

Reviewing last year’s AI-related forecasts

Robodamus 3

As usual, I made some forecasts this time last year about how AI would change, and how it would change us. It’s time to look back and see how those forecasts for 2018 panned out. The result: a 50% success rate, by my reckoning. Better than the previous year, but lots of room for improvement. Here are the forecasts, with my verdicts in italics.

1. Non-tech companies will work hard to deploy AI – and to be seen to be doing so. One consequence will be the growth of “insights-as-a-service”, where external consultants are hired to apply machine learning to corporate data. Some of these consultants will be employees of Google, Microsoft and Amazon, looking to make their open source tools the default option (e.g. Google’s TensorFlow, Microsoft’s CNTK, Amazon’s MXNet).

Yes. The conversation among senior business people at the events I speak at has moved from “What is this AI thing?” to “Are we moving fast enough?”

2. The first big science breakthrough that could not have been made without AI will be announced. (I stole this from DeepMind’s Demis Hassabis. Well, I want to get at least one prediction right!)

Yes. In May, an AI system called Eve helped researchers at Manchester University discover that triclosan, an ingredient commonly found in toothpaste, could be a powerful anti-malarial drug. The research was published in the journal Scientific Reports (here).

3. There will be media reports of people being amazed to discover that a customer service assistant they have been exchanging messages with is a chatbot.

Yes. Google Duplex

4. Voice recognition won’t be quite good enough for most of us to use it to dictate emails and reports – but it will become evident that the day is not far off.

Yes. Alexa is pretty good, but not yet a reliable stenographer. (Other brands of AI assistant are available.)

5. Some companies will appoint Chief Artificial Intelligence Officers (CAIOs).

Not sure. I don’t know of any, but I bet some exist.

6. Capsule networks will become a buzz word. These are a refinement of deep learning, and are being hailed as a breakthrough by Geoff Hinton, the man who created the AI Big Bang in 2012.

Not as far as I know.

7. Breakthroughs will be announced in systems that transfer learning from one domain to another, avoiding the issue of “catastrophic forgetting”, and also in “explainable AI” – systems which are not opaque black boxes whose decision-making cannot be reverse engineered. These will not be solved problems, but encouraging progress will be demonstrated.

I think I’ve seen reports of progress, but nothing that could fairly be described as a major breakthrough.

8. There will be a little less Reverse Luddite Fallacism, and a little more willingness to contemplate the possibility that we are heading inexorably to a post-jobs world – and that we have to figure out how to make that a very good thing. (I say this more in hope than in anticipation.)

No, dammit.

Book review: “21 Lessons for the 21st Century”, by Yuval Harari


The title of Yuval Harari’s latest best-seller is a misnomer: it asks many questions, but offers very few answers, and hardly any lessons. It is the least notable of his three major books, since most of its best ideas were introduced in the other two. But it is still worth reading. Harari delights in grandiloquent sweeping generalisations which irritate academics enormously, and part of the fun is precisely that you can so easily picture his colleagues seething with indignation that he is trampling on their turf. More important, some of his generalisations are acutely insightful.

The insight at the heart of “Sapiens”, his first book, was that humans dominate the planet not because we are logical, but because 70,000 or so years ago we developed the ability to agree to believe stories that we know are untrue. These stories are about religion, and political and economic organisation. The big insight in his second book, “Homo Deus” is that artificial intelligence and other technologies are about to transform our lives far more – and far more quickly – than almost anyone realises. Both these key ideas are reprised in “21 Lessons”, but they are big ideas which bear repeating.

Happily, he has toned down his idiosyncratic campaigns about religion and vegetarianism. In the previous books he encountered religion everywhere: capitalism and communism have passionate adherents, but they are not religions. The first third of “Homo Deus” is religious in a different way: it is a lengthy sermon about vegetarianism.

Sapiens and Homo Deus

21 Lessons” is divided into five parts, of which the first is the most coherent and the best. It concerns the coming technological changes, which Harari first explored in “Homo Deus”. “Most people in Birmingham, Istanbul, St Petersburg and Mumbai are only dimly aware, if at all, of the rise of artificial intelligence and its potential impact on their lives. It is undoubtable, however, that the technological revolutions will gather momentum in the next few decades, and will confront humankind with the hardest trials we have ever encountered.”

He is refreshingly blunt about the possibility of technological unemployment: “It is dangerous just to assume that enough new jobs will appear to compensate for any losses. The fact that this has happened during previous waves of automation is absolutely no guarantee that it will happen again under the very different conditions of the twenty-first century. The potential social and political disruptions are so alarming that even if the probability of systemic mass unemployment is low, we should take it very seriously.”

Very well said, but this part of the book would be much more powerful if he had offered a fully worked-through argument for this claim, which in the last couple of years has been sneeringly dismissed by a procession of tech giant CEOs, economists, and politicians. Perhaps next year, the World Economic Forum could organise a debate on this question between Harari and a leading sceptic, such as David Autor.

It is also a shame that he offers no prescriptions, beyond categorising them: “Potential solutions fall into three main categories: what to do in order to prevent jobs from being lost; what to do in order to create enough new jobs; and what to do if, despite our best efforts, job losses significantly outstrip job creation.” Fair enough, but this should be the start of the discussion, not the end. Still, at least he doesn’t fall back on the usual panacea of universal basic income, and his warning about what happens if we fail to develop a plan is clear: “as the masses lose their economic importance … the state might lose at least some of the incentive to invest in their health, education and welfare. It’s very dangerous to be redundant.”

Harari is also more clear-sighted than most about the risk of algocracy – the situation which arises when we delegate decisions to machines because they make better ones than we do. “Once we begin to count on AI to decide what to study, where to work, and who to marry, human life will cease to be a drama of decision-making. … Imagine Anna Karenina taking out her smartphone and asking the Facebook algorithm whether she should stay married to Karenin or elope with the dashing Count Vronsky.” Warning about technological unemployment, he coined the brutal phrase “the gods and the useless”. Warning about algocracy, he suggests that humans could become mere “data cows”.

Data cows

The remaining four parts of the book contain much less that is original and striking. Harari is a liberal and an unapologetic globalist, pointing out reasonably enough that global problems like technological disruption require global solutions. He describes the EU as a “miracle machine”, which Brexit is throwing a spanner into. He does not see nationalism as a problem in itself, although he observes that for most of our history we have not had nations, and they are unnatural things and hard to build. In fact he thinks they can be very positive, but “the problem starts when benign patriotism morphs into chauvinistic ultra-nationalism.”

Although he sees nationalism as a possible problem, he also thinks it has already lost the game: “we are all members of a single rowdy global civilisation … People still have different religions and national identities. But when it comes to the practical stuff – how to build a state, an economy, a hospital, or a bomb –almost all of us belong to the same civilisation.” He supports this claim by pointing out that the Olympic Games, currently “organised by stable countries, each with boringly similar flags and national anthems,” could not have happened in mediaeval times, when there were no such things as nation states. And he argues that this is a very good thing: “For all the national pride people feel when their delegation wins a gold medal and their flag is raised, there is far greater reason to feel pride that humankind is capable of organising such an event.”


He is even more dismissive of religion – especially monotheism – despite his obsession with it. “From an ethical perspective, monotheism was arguably one of the worst ideas in human history … What monotheism undoubtedly did was to make many people far more intolerant than before … the late Roman Empire was as diverse as Ashoka’s India, but when Christianity took over, the emperors adopted a very different approach to religion.” Religion, he says, has no answers to any of life’s important questions, which is why there is no great following for a Christian version of agriculture, or a Muslim version of economics. “We don’t need to invoke God’s name in order to live a moral life. Secularism can provide us with all the values we need.”

He seems to be applying for membership of the “new atheists” club, in which Richard Dawkins and Stephen Pinker deliberately goad the religious by diagnosing religion as a disease which can be cured. Harari suggests that “when a thousand people believe some made-up story for one month, that’s fake news. When a billion people believe it for a thousand years, that’s a religion.”

New atheists

Oddly, given his perceptive take on the future of AI, Harari is weak on science fiction, displaying a fundamental misunderstanding of both The Matrix and Ex Machina. He is stronger on terrorism, pointing out that it is much less of a threat than it seems, contrary to the deliberate mis-representations by populists: “Since 11 September 2001, every year terrorists have killed about fifty people in the European Union, about ten people in the USA, about seven people in China, and up to 25,000 people globally (mostly in Iraq, Afghanistan, Pakistan, Nigeria and Syria).  In contrast, each year traffic accidents kill about 80,000 Europeans, 40,000 Americans, 270,000 Chinese, and 1.25 million people altogether.” Terrorists “challenge the state to prove it can protect all its citizens all the time, which of course it can’t.” They are trying to make the state over-react, and populists are their eager accomplices.

The book seems to be building to a climax when it addresses the meaning of life. Here and elsewhere, Harari has said that humans create meaning – or at least the basis of power – by telling ourselves stories. So is he going to give us a story which will help us navigate the challenges of the 21st century?

Sadly not. The closest we get is a half-baked version of Buddhism.

The Buddha taught that the three basic realities of the universe are that everything is constantly changing, nothing has any enduring essence, and nothing is completely satisfying.  Suffering emerges because people fail to appreciate this … The big question facing humans isn’t ‘what is the meaning of life?’ but rather, ‘how do we get out of suffering?’ … If you really know the truth about yourself and about the world, nothing can make you miserable. But that is of course much easier said than done.” Indeed.


Harari has worked out his own salvation: “Having accepted that life has no meaning, I find meaning in explaining this truth to others.” Given his six-figure speaking fees, this makes perfect sense.

Harari also finds solace in meditation, which he practices for two hours every day, and a whole month or two every year. “21 Lessons” is a collection of essays written for newspapers and in response to questions. This shows in its disjointed, discursive, and inconclusive nature. If Harari had spent less time meditating, maybe he would have found more time to answer the questions he raises. It’s still definitely worth reading, though. 

This article first appeared in Forbes Magazine

Shooting the Messenger

Facebook evilIt was Facebook wot dunnit.”

Select the unpleasantness of your choice, and Facebook is almost certainly being blamed for it by someone, and probably a lot of someones. Also in the dock are YouTube and Twitter, with Instagram and Snapchat lurking about, keeping their heads down and hoping that nobody notices them.

The charge sheet is long. Facebook and the other social media have shortened our attention spans, leaving us easy prey to slick salesmen with plausible one-liners. They have corralled us all into echo chambers, so that we only ever hear voices telling us what we already think. We are now all isolated from the wider community. They sneakily deploy algorithms of such breathtaking sophistication that they can delve inside our neurons and suck out the information that constitutes their marrow, and then use that information against us like master hypnotists. OK, maybe I made that last bit up. But they definitely plug into our neocortex and inject a sort of digital heroin, forcing us, like enslaved machines, to spend hours online, clicking away to generate ad money.

It is because of all this skull-duggery (pun intended) that political discussion has become so heated, and people aren’t listening to each other any more.

Well, it has to be someone’s fault, doesn’t it, and the tech giants who own the social media are uniquely well-placed to attract universal opprobrium. For people on the political left, they are large companies which make a lot of money. For many on the the left, capitalism is a conspiracy against the masses, profit is a Bad Thing, and a large profit is a Very Bad Thing Indeed. For people on the political right, the tech giants are run by suspiciously hippy-ish types, who give away their money and talk about Universal Basic Income. Their employees are a bunch of snowflake lefties who cannot bear to work with the military, and who excoriate anyone who doesn’t share their hatred of the patriarchy, and who dares to question the wisdom of affirmative action hiring and training policies. And people from both political wings can hold hands while condemning the tech giants for not paying enough tax.


The mainstream media is also furious with the tech giants, and understandably so: Google and Facebook stole their lunch. Local newspapers grew fat on a diet of classified ads, but these were the first casualty of the web. National newspapers depended much less on classified ads and more on display ads and cover prices, but these have also dwindled, as advertisers have discovered the charms of paying only for eyeballs which have actually scanned their messages, and which can be micro-targeted to make those messages more relevant.

This is not something to make light of. If today’s febrile political atmosphere tells us anything, it is that we need professional journalists who have genuinely mastered their craft, and who care about getting the story right as well as getting it first. We are far from figuring out all the business models we need in order to pay for this, and one way or another, pay we must.

I’m not here to defend the tech giants. They are very rich, they hire great lobbyists, and they can look after themselves. (By way of disclosure, I have never used Facebook, as I don’t trust myself not to lose whole afternoons, chatting with friends and looking at cat videos. I think Twitter and Reddit are fabulous, and LinkedIn is handy, although inexplicably clumsy.) But mis-diagnosing major social problems simply allows those problems to fester while causing new ones, and it is mis-diagnosis on a grand scale to blame social media for today’s vicious style of political debate. It implies that there was a halcyon past when the electorate took great care to inform itself of the arguments from both sides, thought deeply about the philosophical underpinnings of each position, and arrived at a sophisticated understanding of the issues of the day. In reality, before social media came along, people lived in the political echo chamber constructed by their newspaper of choice. Guardian readers didn’t check out the talking points promoted by the Daily Telegraph or vice versa, and likewise the Sun and the Daily Mirror. And if you’re looking for examples of the blatant exploitation of human appetites to boost sales, remember it was only in 2015 that The Sun stopped publishing photos of topless young women on page three.

The real reason for the bitterness of today’s political conversation is not the arrival of social media. It is the considerable success of the liberal social agenda.

The left is always angry. That is as it should be: if the left isn’t angry, then it’s not doing its job. The job of the left is to make people discontented with the current state of affairs, and to agitate to improve it. The world can never be equal enough or just enough, and it is the left’s job to keep pushing it in the right direction. The right, traditionally, is more relaxed. By and large it thinks that the world is in pretty good condition, that the institutions are doing a reasonable job, and that throwing all the cards in the air and risking anarchy is a terrible idea. Generally, both have a very good point. Societies should change: they should look for ways to solve problems, but they should do so in ways that will actually improve the lives of citizens, not to accommodate ideological whims. They should recognise that ordinary citizens today live better lives than the kings of a couple of centuries ago, and that over-zealous radicalism has caused at least as much misery as any other social force. Apart from religion, of course.

JS Mill On Liberty
But something changed at the end of the first decade of the twenty-first century. Left-wingers complain that global economic policy has long been dominated by right-wing neoclassical orthodoxy. Be that as it may, during the end of the 20th century and the start of the 21st century, social policy in the developed world was driven strongly in a liberal direction. Strongly and quickly. Governments in developed countries now mostly spend between a third and two-fifths of GDP, and much of that spend is on social and welfare support. The treatment of homosexuals, women, and minority races has been greatly improved. Citizens were relieved of interference in their intimate lives by church and state, while on the other hand, health and safety officers worked to make construction sites and consumer goods less likely to kill and maim people.

As the economies of developed countries grew, their people became wealthier. This made them have fewer children, and made them less willing to do menial work for low wages. This spurred new waves of immigration, and innumerable studies have shown that economic migration is a boon for both the country receiving the immigrant and the one they came from.

All this means change, and change is uncomfortable. And when immigration is from countries with sharply different cultures, and perhaps different skin colours, it is more obvious, and more uncomfortable. If women, gays, and ethnic minorities are advancing, the previously privileged populations might not do worse in absolute terms, but their privilege is undermined, and less reassuring.

Tea partyIt is no coincidence that the creation of the Tea Party in the US, the moment when the right started to get cross, was in 2009, the year of Obama’s inauguration. The election of a black president was perhaps the apogee of liberal values. It also coincided with the credit crunch and the start of the prolonged recession which that caused – a source of further discontent all round.

The extremists, the alt right and the Saundernistas, have a spring in their step, and the squishy middle which is more-or-less neoclassical in economics and liberal in social values, is looking weak. This is an important battle, and it will take some years for its fog to lift.

Meanwhile, what of social media? If we accept they are a messenger that shouldn’t be shot, we shouldn’t just sit back and relax. We can do much better than leaving the present undifferentiated mess of facts and lies, thoughtful opinion and rabid conspiracy theory to contend on equal terms for the attention of the unwary. Editorials in the mainstream media call for social media platforms to be treated like media and to be regulated as such. But media regulation, whether by government or by industry bodies, is ponderous and generally timid. Thanks to the magic of the web, we can do better.

Reddit gives an idea of how. Many of the posts of Reddit are based on links to newspapers, magazines and other outlets, to which readers add comments – sometimes dumb, often insightful, and occasionally hilarious. Reddit automatically rates each source, and readers vote each others’ comments up or down. The most highly up-voted posts appear at the top of the page. Wikipedia is another site that crowd-sources opinion very effectively to fact-check and verify.

Using these ideas and new ones, and with the thousands of clever technologists and user experience designers they employ, the tech giants can semi-automate fact-checking, and over time, make social media better than any media or platform we have known so far. The process of getting there won’t be fast, and it will be messy: the likes of TrustPilot and TripAdviser are afflicted with false reviews on an almost industrial scale. But if problems like this are soluble, and they probably are, then we can have media and platforms where readers can easily assess the veracity of any piece of content, guided by judgements which even out and transcend partisan opinion.

Looking ahead, the tech giants are going to have the mother of all PR problems when AI-powered automation starts causing job churn, but for the time being, if we can refrain from shooting the messenger, maybe we can take the mess out of the message.

Fact check


Grace JonesPowerful new technologies can produce great benefits, but they can often produce great harm. Artificial intelligence is no exception. People have numerous concerns about AI, including privacy, transparency, security, bias, inequality, isolation, oligopoly, and killer robots. One which perhaps gets less attention than it deserves is algocracy.

Decisions about the allocation of resources are being made all the time in societies, on scales both large and small. Because markets are highly efficient systems for allocating resources in economies characterised by scarcity, capitalism has proved highly effective at raising the living standards of societies which have adopted it. Paraphrasing Churchill, it is the worst possible economic system except for all the others.

Historically, markets have consisted of people. There may be lots of people on both sides of the transaction (flea markets are one example, eBay is another). Or there may be few buyers and many sellers (farmers selling to supermarket chains) or vice versa (supermarket chains selling to consumers). But typically, both buyers and sellers were humans. That is changing. 

Machine-made decisions

Robot bossAlgorithms now take many decisions which were formerly the responsibility of humans. They initiate and execute many of the trades on stock and commodity exchanges. They manage resources within organisations providing utilities like electricity, gas and water. They govern important parts of the supply chains which put food on supermarket shelves. This phenomenon will only increase.

As our machines get smarter, we will naturally delegate decisions to them which would seem surprising today. Imagine you walk into a bar and see two attractive people at the counter. Your eye is drawn to the blond but your digital assistant (located now in your glasses rather than your phone) notices that and whispers to you, “hang on a minute: I’ve profiled them both, and the red-head is a much better match for you. You share a lot of interests. Anyway, the blond is married.”

In his 2006 book “Virtual Migration”, Indian-American academic A. Aneesh coined the term “algocracy”. The difficulty with it has been explored in detail by the philosopher John Danaher, who sets the problem up as follows. Legitimate governance requires transparent decision-making processes which allow for involvement by the people affected. Algorithms are often not transparent and their decision-making processes do not admit human participation. Therefore algorithmic decision-making should be resisted.i

Danaher thinks that algocracy poses a threat to democratic legitimacy, but does not think that it can be, or should be, resisted. He thinks there will be important costs to embracing algocracy and we need to decide whether we are comfortable with those costs.

What not to delegate?

Robot diplomatOf course many of the decisions being delegated to algorithms are ones we would not want returned to human hands – partly because the machines make the decisions so much better, and partly because the intellectual activity involved is deathly boring. It is not particularly ennobling to be responsible for the decision whether to switch a city’s street lights on at 6.20 or 6.30 pm, but the decision could have a significant impact. The additional energy cost may or may not be offset by the improvement in road safety, and determining that equation could involve collating and analysing millions of data points. Much better work for a machine than a human, surely.

Other applications make us much less sanguine. Take law enforcement: a company called Intrado provides an AI scoring system to the police in Fresno, California. When an emergency call names a suspect, or a house, the police can “score” the danger level of the person or the location and tailor their response accordingly.i Other forces use a “predictive policing” system called PredPol which forecasts the locations within a city where crime is most likely to be carried out in the coming few hours.ii Optimists would say this is an excellent way to deploy scarce resources. Pessimists would reply that Big Brother has arrived.

AI is already helping to administer justice after the event. In 2016 the San Francisco Superior Court began using an AI system called PSA to determine whether parole should be given to alleged offenders. They got the tool free from the John and Laura Arnold Foundation, a Texas-based charity focused on criminal justice reform. Academics studying this area have found it very hard to obtain information about how these systems work: they are often opaque by their nature, and they are also often subject to commercial confidentiality.iii

There are many decisions which machines could make better than humans, but we might feel less comfortable having them do so. The allocation of new housing stock, the best date for an important election, the cost ceiling for a powerful new drug, for instance. Arguments about which decisions should be made by machines, and which should be reserved for humans are going to become increasingly commonly and increasingly vehement. Regardless whether they make better decisions than we do, not everyone is going to be content (to paraphrase Grace Jones) to be a slave to the algorithm.

Information is power. Machines may intrude on our freedom without actually making decisions. In September 2017 a research team from Stanford University was reported to have developed an AI system which could do considerably more than just recognise faces. It could tell whether their owners were straight or gay. The idea of a machine with “gaydar” is startling; it becomes shocking when you consider the uses it might be put to – in countries where homosexuals are persecuted and even prosecuted, for instance.iv The Stanford professor who led the research later said that the technology would probably soon be able to predict with reasonable accuracy a person’s IQ, their political inclination, or their predisposition towards criminality.

Things are getting weird.