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> The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. --- Dijkstra Better/faster we would not directly compare to humans, but to benchmarks and timed experiments. LeCun is saying to treat "intelligence" the same as "flight" or "swimming". It is a matter of function, not a matter of a specific instantiation on a biological substrate. You don't need to recreate flapping wings to gain "flight", you can strap a combustion engine on a cylinder and beat all birds on earth in regards to speed. You don't say "we don't have flight yet", because an airplane is not able to land on a tree branch. Maybe we don't have yet all the components and aspects of "flight", but this is not a show stopper, and drones have come a long way. Now the more interesting question becomes: What are the laws of aerodynamics for intelligence? Aside: I think it is absolutely insane that a conference workshop with papers yet to go through peer-review, is highlighted as a popsci article on VentureBeat. That's such a narrow workshop, that even researchers in the field may be unaware of it. And now these get to read the paper summaries from a HN-story. "the centre cannot hold". Aside II: Yann LeCun talk from 2019 about this subject (better to debate the source ;)): > Clearly, Deep Learning research would greatly benefit from better theoretical understanding. DL is partly engineering science in which we create new artifacts through theoretical insight, intuition, biological inspiration, and empirical exploration. But understanding DL is a kind of "physical science" in which the general properties of this artifact is to be understood. The history of science and technology is replete with examples where the technological artifact preceded (not followed) the theoretical understanding: the theory of optics followed the invention of the lens, thermodynamics followed the steam engine, aerodynamics largely followed the airplane, information theory followed radio communication, and computer science followed the programmable calculator. My two main points are that (1) empiricism is a perfectly legitimate method of investigation, albeit an inefficient one, and (2) our challenge is to develop the equivalent of thermodynamics for learning and intelligence. While a theoretical underpinning, even if only conceptual, would greatly accelerate progress, one must be conscious of the limited practical implications of general theories. --- https://www.ias.edu/video/DeepLearningConf/2019-0222-YannLeC... |