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I specifically and mindfully added those words because everything is really an open research question. Would you rather I dissembled a false sense of confidence

I specifically and mindfully added those words because everything is really an open research question. Would you rather I dissembled a false sense of confidence? If anything, you're stating your vague case way over-confidently. Turing-completeness is broad and nonspecific. Doing "some computation" is an obvious statement that doesn't add any information. The human brain does not seem to have time limits when it comes to thinking about what to say, and further we don't understand enough about neuroscience to make statements like that. Like I said, these are all active areas of research; the jury is still out on whether any specific approach will be the breakthrough. EDIT (reply to below): in general these statements are either vague and nonspecific, or perfectly correct and non-informative, comments that don't have much to do with my original point.

sapphireblue 10y ago
I agree to your points. Your comment is a quality one, I mostly talked about other ones. >Turing-completeness is quite broad and nonspecific, like I said. It is, but feedforward models (and almost every Bayesian/statistical model) don't possess it even in theory, while RNNs do. >Doing "some computation" is an obvious statement that doesn't add any information. Let me be more specific: currently researchers think that later stages of CNNs do something that is more interpretable as computation than as mere pattern matching. Our world doesn't require 50-level hierarchy, but resnets with 50+ layers do good, looks like because they learn some non-trivial computation. >the jury is still out on whether any of those RNN approaches will be the needed breakthrough. Sure, we'll see. Maybe there won't be need in any breakthrough, just incremental improvement of models. And even current models when scaled up to next-gen hardware (see nervana) can surprise us again with their performance.
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sapphireblue 10y ago

I agree to your points. Your comment is a quality one, I mostly talked about other ones. >Turing-completeness is quite broad and nonspecific, like I said. It is, but feedforward models (and almost every Bayesian/statistical model) don't possess it even in theory, while RNNs do. >Doing "some computation" is an obvious statement that doesn't add any information. Let me be more specific: currently researchers think that later stages of CNNs do something that is more interpretable as computation than as mere pattern matching. Our world doesn't require 50-level hierarchy, but resnets with 50+ layers do good, looks like because they learn some non-trivial computation. >the jury is still out on whether any of those RNN approaches will be the needed breakthrough. Sure, we'll see. Maybe there won't be need in any breakthrough, just incremental improvement of models. And even current models when scaled up to next-gen hardware (see nervana) can surprise us again with their performance.

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