...But they have existed for a very long time: since the early 90s for Q1 and since the early 80s for Q2. Now that the DL machinery works, and that so many people are working on both Qs, we have a shot at making real progress.
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Replying to @ylecun @GaryMarcus
I guess the remaining question for your position are: GQ1. Will DL be part of the solution (you said yes) GQ2. Do you agree with "vectors, not symbols; diff functions, not hard logic" GQ3. If not, how do you propose we make reasoning compatible with DL?
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Replying to @ylecun
GQ1. DL is part of the solution GQ2. "vectors, not symbols” is false dichotomy diff functions: yes, in part Operations a la logic, we do need (contra your view) GQ3. Outputs of deep learning may serve as input to reasoning; symbolic techniques needed for some inferences.
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Replying to @GaryMarcus
Most humans don't actually do much that resemble your answer to GQ3, except a small number of humans using pen and paper, and only in the last couple of millennia. Right now, we need to get machines to the level of a house cat. Never mind symbolic mathematics and formal logic.
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Replying to @ylecun
I don’t argue that people can’t use formalisms that involve derivatives just because most people can’t explicitly explain what a derivative is. I think you are confusing formal, conscious use of a certain kind of machinery with what brain does unconsciously.
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Replying to @GaryMarcus
If you see the reasoning engine as *separate* (and qualitatively different) from the deep learning system that provides it with inputs, then we disagree. Unless this reasoning "system" is a pen and paper to do math/logic. And much of human intelligence functions without it.
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Replying to @ylecun
That may distill a second point of disagreement; I see nothing wrong with (for some purposes) having separate systems for (eg) image classification vs reasoning. Certainly it is possible in principle to engineer systems that way; what’s your objection? Efficiencies of training?
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Replying to @GaryMarcus @ylecun
I wonder if we are mixing different methodological questions here. If I need to build a robust and reliable system today, I would mix deep learning, probabilistic programming, and symbolic methods. 1/
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But at the same time, I think it is critically important to see if we can push DL methods to provide a unified solution to perception, reasoning, and action (with robustness and safety). 2/
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I agree with Tom on (1) and think it’s a fine methodology to pursue; re (2), fine — as long as one is intellectually honest about the road blocks faced along the way and intellectually open to alternatives that don’t fit within that framework.
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Not to open this up again, but I'd also add I think on the intellectual honesty front we all agree more than it may seem (just see
@ylecun's comments on Sophia) - certainly@ylecun has spoken at length about limitations of *present day* DL (aka mostly supervised learning).2 replies 0 retweets 2 likes -
My concern in that domain, about misrepresentation of my own views, was explained in my recent Medium post. I opened our NYU debate by saying we agreed that deep learning was not enough, disagreed on solution. (
@ylecun agreed to all via email before the debate.)pic.twitter.com/Sjr1UuxrVh
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