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You can handle arbitrarily complex tasks with large parametric models trained with SGD. The problem is that doing it well requires a *dense sampling* of the input/output space you're learning, because the generalization power of these models is extremely weak. That's expensive. https://t.co/Lsc7zlQBFE
Deep learning is immensely useful, but it does not bring meaningfully closer to understanding intelligence (as for "AGI", that's a sci-fi talking point, as even human intelligence is specialized). Intelligence is 100% about efficient generalization. DL is orthogonal to that.
Schematically, intelligence is skill divided by experience (I = S/E). Deep learning enables arbitrarily high skill levels, but requires insanely high amounts of "experience" (data) to achieve these levels, resulting in an extremely low intelligence factor.
Again, a DL model requires a dense sampling of what it's doing. An intelligent agent (like a human) can do extreme generalization from little data. At this time, no one has any clue how that works. However, it may not necessarily be very complicated. Who knows...
You have strong opinions against AGI as it is commonly understood but I respect your take because you obviously know what you're talking about.
What's commonly understood AGI? Super human performance?
I think a combination of models is where each is specialised at a specific task, but then also part of a 'collective' of models and contributes towards other compute Jobs.
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