Are we headed for an AI winter?

[Bloomberg] [Fully Charged]( From [Bloomberg](   [FOLLOW US [Facebook Share]]([Twitter Share]( [SUBSCRIBE [Subscribe]](  Greetings folks. This is Jeremy in London. Artificial Intelligence is having a moment in this city. Last week, to coincide with [London Tech Week](, an annual showcase of the city’s digital prowess, it hosted [CogX](, a 6,000-person-strong event that bills itself the “Festival of All Things AI,” and the [AI Summit London](, which lays claim to the mantle of “the world’s largest AI event for business.” The events have non-stop panels, parties, and big-name sponsors like SoftBank, Accenture, IBM and Google. Underpinning much of the buzz over artificial intelligence in London and elsewhere is the implicit premise that AI is the transformative technology of the moment, or maybe of the decade, or even of the century or, well, just about ever. Promises like the AI Summit’s claim that the technology goes “beyond the hype” to “deliver real value in business” only drives the corporate feeding frenzy among executives desperate not to be left behind. But there is something else going on “beyond the hype,” something that ought to be disconcerting for AI boosters: Among those closest to the cutting edge of machine learning, there is a sense–perhaps a faint but creeping suspicion—that deep learning, the techniques which underpin most of what people think of as ground-breaking AI, may not deliver on its promise. For one thing, there's a growing consensus among AI researchers that deep learning alone will probably not get us to artificial general intelligence (the idea of a single piece of software that is more intelligent than humans at a wide variety of tasks). But there’s also a growing fear that AI may not create systems that are reliably useful for even a narrower set of real-world challenges, like autonomous driving or making investment decisions. Filip Piekniewski, an expert on computer vision in San Diego who is currently the principal AI scientist for Koh Young Technology Inc, a company the builds 3D measurement devices, recently kicked off a debate with a [viral blog post]( predicting a looming “[AI Winter](,” a period of disillusionment and evaporating funding for AI-related research. "(The field has experienced several such periods over the past half-century.) Piekniewski’s evidence? The pace of AI breakthroughs seems to be slowing and those breakthroughs that are occurring seem to require ever-larger amounts of data and computer power. Several top AI researchers—such as Andrew Ng and Yann LeCun—who had been hired by big tech firms (Baidu and Facebook respectively) to head in-house AI labs have [left]( (Ng) or moved into slightly [less prominent roles]( (LeCun). And most importantly, Piekniewski argues that the recent [crashes]( of self-driving cars point to fundamental issues with the ability of deep learning to handle the complexity of the real world. Even more notable than the crashes, he says, is how often machines lose confidence in their ability to make safe decisions and cede control back to human drivers. Piekniewski also references the work of New York University’s Gary Marcus, who earlier this year published [a much-discussed paper]( critiquing the failings of today’s deep learning systems. This software, Marcus argues, can identify objects in images, but lacks any model of the real world. As a result, they often can’t handle new situations, even if they are very similar to the ones they’ve been trained to perform. For instance, the DeepMind algorithm that performs so well at the Atari game “Breakout”—and which the company often highlights in public presentations—does terribly if it is suddenly presented with a different-sized paddle, whereas a top human player would likely find the larger paddle wasn’t much of a handicap. Piekniewski’s AI winter warning has drummed up much protest from AI enthusiasts, but there are other signs a touch of frost in the air. John Langford, a machine learning researcher at Microsoft, recently wrote a blog warning that AI research was in a bubble, concluding “there is a chance for something traumatic for both the people and field when/where there is a sudden cut-off.” Martin Daum, chief executive officer of Daimler Trucks, [lowered expectations]( for how soon autonomous trucks would be commercially viable. And an index of hedge funds using AI-driven strategies has badly [lagged]( the S&P 500 since the start of the year—with many speculating that these systems, often trained on data drawn from our recent ultra-low interest rate environment, cannot handle a market where rates are starting to rise. Even LeCun thinks a winter could be coming if endowing AI with more human-like generality “takes longer than the people funding our research expect,” he told me at a [Bloomberg conference]( in late May. But he also [said]( that if it did happen, it wouldn’t be as severe as previous funding droughts. That’s because existing machine learning systems can actually perform a wide-range of fairly narrow but very useful tasks better than people. “There is a huge industry right now around the current technology of machine learning and that is not going away,” LeCun said. AI tools are already having an impact in industries like precision farming, insurance underwriting and even medicine, Paul Daugherty, Accenture’s chief technology and innovation officer, told me last week. “These systems don’t have be smarter than a human,” he said. “They just have to be able to solve a problem that hasn’t been solved before.” If the technology lives up to even a fraction of its promise, perhaps the goosebumps some in the industry are feeling will turn out to be the sun just momentarily passing behind a cloud, and not the north winds of winter starting to gather. —[Jeremy Kahn](mailto:jkahn21@bloomberg.net)  And here’s what you need to know in global technology news Scooter startup Bird is cashing in on the hype. Just weeks ago it raised money with an eye-popping $1 billion valuation. Now, it’s raising [twice the money at twice the valuation](—an unprecedented fundraising sprint for such a young company.  Another partner has left venture firm Social Capital. Arjun Sethi [will depart the firm](, co-founded by early Facebook employee Chamath Palihapitiya, in a year’s time. The announcement follows the resignations of two of the firm's other co-founders last year.  Norway, one of Tesla’s biggest markets, wants to become a [pioneer]( of electric planes.    You received this message because you are subscribed to the Bloomberg Technology newsletter Fully Charged. You can tell your friends to [sign up here](.  [Unsubscribe]( | [Bloomberg.com]( | [Contact Us]( Bloomberg L.P. 731 Lexington, New York, NY, 10022