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In general, any opinions about where we are on the hype curve for machine learning (as a stand-alone concept separate from traditional statistical inference)?

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• visarga 10y ago
  It's not the first hype cycle, so we've already been through all phases, but now I guess we are still raising towards peak hype. The fact is that this time AI has real world applications and benefits, so it's not just a bunch of hot air. It will not have a crush at this level of hype yet because we now have lots of results that were unimaginable even 5 years ago. We're breaking human level accuracy in vision, speech, text and behavior - on specific tasks, not in general yet. In the last 3 years neural nets have become creative - now they can create paintings (neural style transfer), images (GANs), sounds (WaveNet), text (seq2seq, translation, image captioning) and gameplay (Atari, AlphaGo). All these are complex forms of creativity, as opposed to simple classifiers. So we have recent progress, there is no period of lacking results behind us. That's why we're still on the rise.
  • paulddraper 10y ago
    AI has had big-money real world applications for far over a decade, e.g. web search. (Or stock trading, if you believe in that sort of thing.)
  • zwieback 10y ago
    Well said, I would agree that we're just to the left of the peak but that applies more to the areas adjacent to the problems where deep learning has shown some real value. I remember the previous AI peaks in the 80s and 90s as well the neural net and fuzzy logic euphorias. The problem back then was that the results were not that exciting. Now we have some really impressive applications searching vast datasets and recognizing useful features. However, I notice everyone is trying to apply the same algorithms to problems not well suited for that type of approach.