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Most people I meet overestimate what deep learning can do (it's curve-fitting, don't expect it to do discrete symbol manipulation, it will solve symbolic tasks via embedding + interpolation) and simultaneously underestimate what you can do with curve-fitting given enough data
Deep learning isn't magic, and it won't work at all on the specific problems you want it to work on, but it is capable of more than you know when it comes to problems where the manifold hypothesis applies (and that's a lot of problems)
This is a bit like watching a bunch of people trying to make steam-powered airplanes and rockets work in the year 1800 (it won't) while largely missing the world-changing potential of applying steam power to trains and large industrial machinery
"Meh, it's just a factory, boring... but look at my cool model rocket!" No, the boring factory is going to change the face of the world, and meanwhile your GPT model rocket won't scale past the toy stage
This is interesting. Do you know any papers for this topic?
Can someone elaborate on manifold hypothesis part?
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