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AI winter is well on its way (2018)

Deep learning has been at the forefront of the so called AI revolution for quite a few years now, and many people had believed that it is the silver bullet that will take us to

We can't just scale up AlexNet and get respectively better results - we have to fiddle with specific architectures, and effectively additional compute does not buy much without order of magnitude more data samples, which are in practice only available in simulated game environments. But because most of these things are not easily verbalizable, they are hard to measure, and consequently we don't optimize our machine learning systems on these aspects at all [see my earlier post for benchmark proposals that would address some of these capabilities]. In fact many top researchers should not be too outraged by my observations, Yann Lecun warned about overexcitement and AI winter for a while, even Geoffrey Hinton - the father of the current outburst of backpropagation - admitted in an interview that this likely is all a dead end, and we need to start over.

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