When a single error can cost a life, it’s just not good enough. When the stakes are higher, though, as in radiology or driverless cars, we need to be much more cautious about adopting deep learning. But the stakes are low-if the app makes an occasional error, I am not going to throw away my phone. But automatic, deep-learning-powered photo tagging is also prone to error it may miss some rabbit photos (especially cluttered ones, or ones taken with weird light or unusual angles or with the rabbit partly obscured it occasionally confuses baby photos of my two children. It worked because my rabbit photo was similar enough to other photos in some large database of other rabbit-labeled photos. I asked my iPhone the other day to find a picture of a rabbit that I had taken a few years ago the phone obliged instantly, even though I never labeled the picture. In time we will see that deep learning was only a tiny part of what we need to build if we’re ever going to get trustworthy AI.ĭeep learning, which is fundamentally a technique for recognizing patterns, is at its best when all we need are rough-ready results, where stakes are low and perfect results optional. Google’s latest contribution to language is a system (Lamda) that is so flighty that one of its own authors recently acknowledged it is prone to producing “ bullshit.” 5 Turning the tide, and getting to AI we can really trust, ain’t going to be easy. Sure, Elon Musk recently said that the new humanoid robot he was hoping to build, Optimus, would someday be bigger than the vehicle industry, but as of Tesla’s AI Demo Day 2021, in which the robot was announced, Optimus was nothing more than a human in a costume. In truth, we are still a long way from machines that can genuinely understand human language, and nowhere near the ordinary day-to-day intelligence of Rosey the Robot, a science-fiction housekeeper that could not only interpret a wide variety of human requests but safely act on them in real time. In 2015, shortly after Hinton joined Google, The Guardian reported that the company was on the verge of “developing algorithms with the capacity for logic, natural conversation and even flirtation.” In November 2020, Hinton told MIT Technology Review that “deep learning is going to be able to do everything.” 4 Like AI pioneers before him, Hinton frequently heralds the Great Revolution that is coming. Nautilus Members enjoy an ad-free experience.
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