I'm a bit disappointed in this piece. It doesn't say anything surprising to anyone who read a few words into the DeepMind paper, and it serves to settle some of

I'm a bit disappointed in this piece. It doesn't say anything surprising to anyone who read a few words into the DeepMind paper, and it serves to settle some of Marcus's academic scores: > two people ought to be really pleased by this result: Steven Pinker, and myself. Pinker and I spent the 1990’s lobbying — against enormous hostility from the field — for hybrid systems Told-you-so's are almost always boring, especially when they are part of a larger campaign. Here, Marcus's campaign is that neural nets are not enough, which isn't really news to DeepMind or most other people working with NNs, and doesn't matter much to anyone not working with them. His chief critique is their interpretability. >In 2016 networks have gotten deeper and deeper, but there are still very few provable guarantees about how they work with real-world data. But for most of the world, including the people using whatever Marcus's startup eventually makes, predictive accuracy trumps interpretability. Let's get to 99% accuracy and worry about why later. And that's what researchers have done for many problems with NNs. Of course it's nice to know why, but it's not a fatal flaw if you don't, most of the time. IBM's struggles to market Watson are a bit of a straw man. If you had judged the PC market by IBM's moves a few decades ago, you might have reached the same conclusions, and you would have been dead wrong.