This article was taken from the December 2014 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online.
A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence.
This was the home of Watson, the electronic genius that conquered US quiz show Jeopardy! in 2011. The original Watson is still here -- it's about the size of a bedroom, with ten upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines' backs. It is surprisingly warm inside, as if the cluster were alive.
Today's Watson is very different. It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred "instances" of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops or their own data servers.
This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be immediately transferred to the others. And instead of one single program, it's an aggregation of diverse software engines -- its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations -- all are cleverly integrated into a unified stream of intelligence.
Consumers can tap into that always-on intelligence directly but also through third-party apps that harness the power of this AI cloud. Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that one of the apps under development is a medical-diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is Giardia -- the correct answer.
This expertise isn't yet available to patients directly; IBM provides access to Watson's intelligence to partners, helping them develop user-friendly interfaces for subscribing doctors and hospitals. "I believe something like Watson will soon be the world's best diagnostician -- whether machine or human," says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trekmedical tricorder and powered by a cloud AI. "At the rate AI technology is improving, by the time they are an adult a kid born today will rarely need to see a doctor to get a diagnosis."
Medicine is only the beginning. All the major cloud companies, plus dozens of startups, are in a mad rush to launch a Watson-like cognitive service. According to quantitative analysis firm Quid, AI has attracted more than $17 billion (£10.5bn) in investments since 2009. Last year alone, more than $2 billion was invested in 322 companies with AI-like technology. Facebook and Google have recruited researchers to join their in-house AI research teams.
Yahoo!, Intel, Dropbox, LinkedIn, Pinterest and Twitter have all purchased AI companies since last year. Private investment in the AI sector
has been expanding 62 per cent a year on average for the past four years, a rate that is expected to continue.
Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000 -- a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness -- or a Singularitan rapture of superintelligence.
The AI on the horizon looks more like Amazon Web Services -- cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. Like all utilities, AI will be supremely boring, even as it transforms the internet, the global economy and civilisation. It will enliven inert objects, much as electricity did more than a century ago. Everything we electrified we will now cognitise. This new, utilitarian AI will also augment us individually as people (deepening our memory, speeding our recognition) and collectively as a species. There is almost nothing we can think of that cannot be made new, different or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: take X and add AI.
This is a big deal, and now it's here.
Around 2002 I attended a small party for Google -- before its IPO, when it focused only on search. I struck up a conversation with Larry Page, Google's cofounder, who became the company's CEO in 2011. "Larry, I still don't get it. There are so many search companies. Web search, for free? Where does that get you?" My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defence this was before Google had ramped up its ad-auction scheme to generate real income, long before YouTube or any other major acquisitions. I was not the only avid user of its search site who thought it would not last long. But Page's reply has always stuck with me: "Oh, we're really making an AI."
I've thought a lot about that conversation over the past few years as Google has bought 14 AI and robotics companies. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search contributes 80 per cent of its revenue. But I think that's backwards. Rather than use AI to make its search better, Google is using search to make its AI better. Every time you type a query, click on a search-generated link or create a link on the web, you are training the Google AI.
When you type "Easter bunny" into the image search bar and then click on the most Easter bunny-looking image, you are teaching the AI what an Easter bunny looks like. Each of the 12.1 billion queries that Google's 1.2 billion searchers conduct each day tutor the deep-learning AI over and over again. With another ten years of steady improvements to its AI algorithms, plus a thousand-fold more data and 100 times more computing resources, Google will have an unrivalled AI. My prediction: by 2024, Google's main product will not be search but AI.
This is the point where it is entirely appropriate to be sceptical. For almost 60 years, AI researchers have predicted that AI is right around the corner, yet until a few years ago it seemed as stuck in the future as ever. There was even a term coined to describe this era of meagre results and even more meagre research funding: the AI winter. Has anything really changed?
Yes. Three recent breakthroughs have unleashed the long-awaited arrival of artificial intelligence:
1/ Cheap parallel computation
Thinking is an inherently parallel process, billions of neurons firing simultaneously to create synchronous waves of cortical computation. To build a neural network -- the primary architecture of AI software -- also requires many processes to take place simultaneously. Each node of a neural network loosely imitates a neuron in the brain -- mutually interacting with its neighbours to make sense of the signals it receives. To recognise a spoken word, a program must be able to hear all the phonemes in relation to one another; to identify an image, it needs to see every pixel in the context of the pixels around it -- both deeply parallel tasks. But, until recently, the typical computer processor could only ping one thing at a time.
That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual -- and parallel -- demands of video games, in which millions of pixels had to be recalculated many times a second. That required a specialised parallel computing chip, which was added as a supplement to the motherboard. The parallel graphical chips worked, and gaming soared. By 2005, GPUs were being produced in such quantities that they became much cheaper. In 2009, Andrew Ng and a team at Stanford realized that GPU chips could run
neural networks in parallel.
That discovery unlocked possibilities for neural networks, which can include millions of connections between their nodes.
Traditional processors required several weeks to calculate all the cascading possibilities in a 100 million-parameter neural net. Ng found that a cluster of GPUs could accomplish that in a day. Today, neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or, in the case of Netflix, to make reliable recommendations for 50 million subscribers.
2/ Big data
Every intelligence has to be taught. A human brain, which is genetically primed to categorise things, still needs to see a dozen examples before it can distinguish between cats and dogs. That's even truer for artificial minds. Even the best-programmed computer has to play at least a thousand games of chess before it gets good at it. Part of the AI breakthrough lies in the incredible avalanche of collected data about our world, which provides the schooling AIs need. Massive databases, self-tracking, web cookies, online footprints, terabytes of storage, decades of search results, Wikipedia and the entire digital universe became the teachers making AI smart.
3/ Better algorithms
Digital neural nets were invented in the 50s, but it took decades for computer scientists to learn how to tame the astronomically huge combinatorial relationships between a million -- or 100 million -- neurons. The key was to organise neural nets into stacked layers. Take the relatively simple task of recognising that a face is a face. When a group of bits in a neural net are found to trigger a pattern -- the image of an eye, say -- that result is moved up to another level in the neural net for further parsing. The next level might group two eyes together and pass that meaningful chunk on to another level of hierarchical structure that associates it with the pattern of a nose. It can take many millions of these nodes (each producing a calculation feeding others around it), stacked up to 15 levels high, to recognise a human face. In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed "deep learning". He was able to optimise results from each layer so that the learning accumulated faster as it proceeded up the layers. Deep-learning algorithms accelerated enormously a few years later when they were ported to GPUs. The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBM's Watson, Google's search engine and Facebook's algorithms.
This perfect storm of parallel computation, bigger data and deeper algorithms generated the 60-years-in-the-making overnight success of AI. As AI improves, this cloud-based AI will become an ingrained part of our life. But at a price. Cloud computing obeys the law of increasing returns, sometimes called the network effect, which holds that a network's value increases much faster as it grows bigger. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people use it. The more people that use it, the smarter it gets. Once a company enters this virtuous cycle, it tends to grow so big, so fast that it overwhelms any competitors.
As a result, our AI future is likely to be ruled by an oligarchy of two or three large, general-purpose cloud-based commercial intelligences.
In 1997, Watson's precursor, IBM's Deep Blue, beat the reigning chess grandmaster Garry Kasparov in a famous man-versus-machine match. After machines repeated their victories in a few more matches, humans largely lost interest in such contests. You might think that was the end of the story (if not the end of human history), but Kasparov realised that he might have performed better if he'd had, as Deep Blue did, the same instant access to a massive database of all previous chess moves. If this database tool was fair for an AI, why not for a human? To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them.
Now called freestyle chess matches, these are like mixed-martial-arts fights, where players use whatever combat techniques they want. You can play as your unassisted human self, or you can act as the hand for your supersmart
chess computer, merely moving its board pieces, or you can play as a "centaur", which is the human/AI cyborg that Kasparov advocated. A centaur player will listen to the moves whispered by the AI but will occasionally override them - much the way we use GPS in our cars. In the championship Freestyle Battle in 2014, open to all modes of players, pure chess AI engines won 42 games but centaurs won 53 games. Today the best chess player alive is a centaur: Intagrand, a British team of humans and several different chess programs.
But here's the even more surprising part: the advent of AI didn't diminish the performance of purely human chess players.
Quite the opposite. Cheap, supersmart chess programs inspired more people than ever to play chess, at more tournaments than ever, and the players got better than ever. There are more than twice as many grandmasters now as there were when Deep Blue first beat Kasparov.
The top-ranked human player today, Magnus Carlsen, trained with AIs and has been deemed the most computer-like of all human chess players. He also has the highest
human grandmaster rating of all time.
If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers. Most of the commercial work completed by AI will be done by special-purpose, narrowly focused software brains that can, for example, translate any language into any other language, but do little else. Drive a car, but not converse. Or recall every pixel of every video on YouTube, but not anticipate your work routines. In the next ten years, 99 per cent of the AI that you will interact with, directly or indirectly, will be nerdily autistic, supersmart specialists.
In fact, this won't really be intelligence, at least not as we've come to think of it. Indeed, intelligence may be a liability -- especially if by "intelligence" we mean our peculiar self-awareness, all our frantic loops of introspection and messy currents of self-consciousness. We want our self-driving car to be inhumanly focused on the road, not obsessing over an argument it had with the garage. The synthetic Dr Watson at our hospital should be maniacal in its work, never wondering whether it should have majored in English instead. As AIs develop, we might have to engineer ways to prevent consciousness in them -- and our most premium AI services will likely be advertised as consciousness-free.
What we want instead of intelligence is artificial smartness.
Unlike general intelligence, smartness is focused, measurable, specific. It also can think in ways completely different from human cognition. A cute example of this nonhuman thinking is a stunt performed at the South by Southwest festival in Austin, Texas, in March. IBM researchers overlaid Watson with a culinary database comprising online recipes, US Department of Agriculture nutritional facts and flavour research on what makes compounds taste pleasant.
From this pile of data, Watson dreamed up novel dishes based on flavour profiles and patterns from existing dishes and willing human chefs cooked them. One crowd favourite generated from Watson's mind was a tasty version of fish and chips using ceviche and fried plantains. For lunch at the IBM labs in Yorktown Heights, I slurped down that and another tasty Watson invention: Swiss/Thai asparagus quiche. Not bad! It's unlikely that either one would ever have occurred to humans.
Non-human intelligence is not a bug, it's a feature. The chief virtue of AIs will be their alien intelligence. An AI will think about food differently than any chef, allowing us to think about food differently. Or to think about manufacturing materials differently. Or clothes. Or any branch of science and art. The alienness will become more valuable to us than its speed or power.
As it does, it will help us better understand what we mean by intelligence in the first place. In the past, we would have said only a superintelligent AI could drive a car or beat a human at chess. But once AI did those things, we considered that achievement obviously mechanical and hardly worth the label of true intelligence. Every success in AI redefines it.
But we haven't just been redefining AI -- we're redefining what it means to be human. As mechanical processes have replicated behaviours and talents we thought were unique to humans, we've had to change our minds about what sets us apart. As we invent more species of AI, we'll be forced to surrender more of what is "unique" about us. We'll spend the next decade in a permanent identity crisis, asking ourselves what humans are for. With the grandest irony, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science -- although all those will happen. The greatest benefit of the arrival of AI is that they will help define humanity. They will tell us who we are.
Kevin Kelly is senior maverick at US WIRED. He wrote about Stewart Brand in 10.12
This article was originally published by WIRED UK
