Surveillance capitalism and anti-capitalism

Nerd_Dork_Geek_Venn_Diagram
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
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.

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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

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“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?

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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

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.

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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.

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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, https://storiesfrom2045.com.  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.