Geoff Hinton is a true artificial intelligence lifer. Since receiving his Ph.D. in AI from the University of Edinburgh in 1978, Hinton has spent ample time teaching, researching, and innovating within the field. He is a pioneer of artificial neural networks, which with technological advancements have become not only functional, but vital for major tech companies. Hinton’s company, DNNresearch, was acquired by Google in 2013 after delivering a significant improvement in object recognition accuracy in photos, though neural nets have application in speech recognition, language processing, and more.
With vast experience in the field and an executive position at one of tech’s biggest and most important companies, Hinton is uniquely qualified to discuss the future of AI (as he did recently with Wired). The technology’s recent rise to prominence has brought with it ethical, philosophical, and practical questions. Hinton believes that there “…should be something like a Geneva Convention banning [AI in lethal autonomous weapons], like there is for chemical weapons.” An agreement would then function as a “sort of moral flag post” for nations around the world – whether nations chose to sign or not, people would know where they stand. Hinton was among those who expressed reservations to Google co-founder Sergey Brin about the company’s Pentagon contract related to machine learning with drone imagery (which was completed but not renewed) – the company has since released guidelines on how to use AI, including a “…pledge not to use it in weapons.”
Hinton expressed reservations about dictating how policy should function, calling himself “…an expert on trying to get the technology to work, not an expert on social policy.” But he did say his technical expertise has led him to believe that it “…would be a complete disaster” if regulators forced people to explain the workings of their AI systems, equating it to “…forcing them to make up a story.” Hinton says trust should instead be dictated by performance, with experiments identifying bias or danger.
He predicts machine learning systems will start functioning more like the human brain, using a new kind of computing system that “…[extracts] knowledge quickly using lots of connections.” A UK company called Graphcore is designing a processor that draws weights from a neural net “…in cache on the processor, not in RAM, so they never have to be moved.” Doing so means using less computing energy and increasing the flexibility of the neural net.
Hinton acknowledges the space is not without problems. Hinton is concerned that academic papers, especially those containing potentially revolutionary ideas, are being stymied in a review process dominated by two parties: experienced (but bogged down under paper reviews, or dismissive if they don’t immediately understand an idea) academics, and junior reviewers who lack the understanding to properly engage with certain pieces. He is confident, however, that increased education – already in progress – will correct the “imbalance.”
Hinton is equally assured that AI’s recent ubiquity means fears of a potential ‘AI winter’ – a period where funding slows to a trickle because new milestones are not reached as quickly as anticipated – are unfounded, mentioning that the technology “…drives your cellphone. In the old AI winters, AI wasn't actually part of your everyday life. Now it is.” AI is here to stay – how it improves and is used is being redefined daily.
The Investment and Financial Industry Faces the Same A.I.-Driven Evolution
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