DICK POUNTAIN
The hottest show in town isn’t about music or film: it’s about top AI researchers begging for our help
The hottest show in town isn’t about music or film: it’s about top AI researchers begging for our help.
Perhaps you suspect, as I did beforehand, that the content would be all hype, but you would be very wrong indeed
Supermarkets can now use facial recognition software to divine the emotional state of shoppers and change the prices on the fly
Ireckoned I’d served my time in the purgatory that is the computer show. For 15 years, I spent whole weeks at CeBIT in Hannover, checking out new tech for Byte magazine; I’ve done Comdex in Vegas (I stayed in Bugsy Siegel’s horrendous Flamingo, where you walked through a mile of slots to get to your room); I’ve flown steerage to Taipei to help judge product awards at Computex.
So I thought I was done, but the CogX 2019 festival had two irresistible attractions: it brought together the world’s top AI researchers for three days of serious talk, and it was in King’s Cross, a short walk down the canal from my house.
CogX was the first proper show to be held in the futuristic Coal Drops Yard development and it was big – 500 speakers on 12 stages, some in large geodesic tents. It was also silly expensive at £2,000 for three days and all events, or £575 per day (naturally I had a press pass). Rain poured down on the first day, causing one wag to call it “Glastonbury for nerds”, but it was packed with standing room only at every talk I attended. A strikingly young, trendy and diverse crowd, most I imagine being paid for by red-hot Old Street startups. Smart young people who don’t aspire to be DJs or film stars now seem to aim at AI instead. This made me feel like a relic, doffing my cap on the way out, but in a nice way.
Perhaps you suspect, as I did beforehand, that the content would be all hype, but you would be very wrong indeed. It was tough to choose a dozen from the 500. I went mostly for the
highly techie or political, skipping the marketing and entrepreneurial ones, and the standard of talks, panel discussions and organisation was mightily impressive. This wasn’t a conference about how machine learning (ML) or deep learning work – that’s now sorted. These folk have their supercomputers and ML tools that deliver the goods: it’s now about what they’re delivering, whether they should be and who is going to tell them.
David Ferrucci (formerly of IBM Watson, now of Elemental Cognition) works on natural language processing and making ML decisions more transparent, using a strategy that combines deep learning and database search with interactive tuition. For example, two women buy mint plants: one puts hers on her windowsill where it thrives, the other in a dark room where it doesn’t. His system threw out guesses and questions until it understood that plants need light, and that light comes through windows. Second story: two people buy wine, one stores it in the fridge, the other on the windowsill where it spoils. More guesses, more questions. His system remembers what it learned from the mint story, deduces that light is good for plants but bad for wine. To make machines really smart, teach them like kids.
Maja Pantić, professor of affective and behavioural computing at Imperial College London and head of Samsung AI lab in Cambridge, told a nice story about autism and a nasty one about supermarkets. It’s been found that autistic children lack the ability to integrate human facial signals (such as mouth, eyes and voice), becoming overwhelmed and
terrified. An AI robotic face can separate these signals into a format the child can cope with. On the other hand, supermarkets can now use facial recognition software to divine the emotional state of shoppers and change the prices they see on the fly. Deep creepiness.
Eric Beinhocker of the Oxford Martin School and César Hidalgo of MIT Media Lab gave mind-boggling presentations on the way AI is now used to build colossal arrays of virtual environments – rat mazes, economic simulations, war games – on which to train other ML systems, thus exponentially reducing training times and improving accuracy. Michigan State University’s Stephen Hsu described how ML is now learning from hundreds of thousands of actual human genomes to identify disease mutations with great accuracy, while Helen O’Neill, lecturer in reproductive and molecular genetics at UCL, explained that combining this with CRISPR will permit choosing not merely the gender but many other traits of unborn babies, maybe within five years. The theme that emerged everywhere was: “we are already doing unprecedented things that are morally ambiguous, even godlike, but the law and the politicians haven’t a clue. Please regulate us, please tell us what you want done with this stuff.”
As you can tell, CogX contained way too much for just one column. More next month about extraordinary AI hardware developments and what it’s all going to mean. And no doubt even more in the 299 columns after that.