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>I haven’t changed my tune on this particular question since 1992 Well DL certainly hasn't been against a wall since '92. Pulls out phone with highly accurate

• salawat 5y ago
  Yes. In a sense. The sigmoid/neural network was well conceived, the major limiting factor was hardware and training data. ML isn't really that much further I'd wager. We've just finally gotten the computer yo catch up enough we can spit out a handful of reasonable domain specific function simulators. We're no closer to a feasible integration thereof to the point of emergent consciousness.
  • jjcon 5y ago
    >to the point of emergent consciousness But that isn't the goal nor the argument being made (not to rabbit hole in discussing that consciousness isn't even really a scientific term that can meaningfully be applied). We had some mathematical notions yes... and we have made a ton of progress since then. The perceptron doesn't hold a candle to methods today, not even close... though yes it is a building block for the field. I don't know how that could be all that controversial.
• godelski 5y ago
  He's really talking about a path towards AGI, not ML being able to do many tasks like this. There's work towards ML doing causal inference, but CI has been a major challenge for Deep Learning (a specific type of ML) and is likely not possible with it alone (see reference to hybrid models). Of course, if he were saying that ML/DL hasn't improved significantly in these recent decades then yes, he would be being dishonest. There even has been work in explicit density models, symbolic manipulation, and causal inference. All things he (presumably) cares about. But these things don't get nearly the hype nor the research power and thus is a lot slower. In the end there's really two camps. Those trying to do things and those trying to build models that understand. But note that he's using DL as a specific term and not in place of ML nor AI.
• YeGoblynQueenne 5y ago
  Yes, "they had all that" in 1992: speech recognition, image classification, speech synthesis etc. These are problems as old as the field [1]. They didn't work as well as they do now, but computers where not as powerful and datasets where not as large, as they are now. The approaches though, that do all these things today, are still the same old statistical learning approaches, just with more data and more compute. At the same time, we don't know if what statistical approaches can't do, but symbolic approaches excel at, like reasoning, for example, would also benefit from the modern advances in computational power, because there's almost nobody trying. All the large tech corporations are head-over-heels for statistical learning and most people are running behind them, following the current trend. So nobody's trying to scale up, I don't know, classical planning or SAT solvers, to the extent that Google, Facebook et al, have scaled up neural nets. _______________ [1] Specifically, the field of pattern recognition, which is older than machine learning as a field of research. See the Introduction chapter in "Statistical Learning Theory" (the textbook) by Vapnik for a quick run-through of the history of statistical inference, pattern recognition and statisical learning.