"This is just an outdated view of the state-of-the-art. It's understandable, given that it's outdated by maybe six months, iff you're willing to go with preprints."
That's an understandable, but probably incorrect, view that comes from focusing on claims in state-of-the-art publications too much without the wider context of history & brain function. The problem parent is referring to also includes the general, "common sense" that we build up over time with extreme diversity of experiences that is developed despite tons of curveballs & able to create them ourselves. New knowledge is incorporated into that framework pretty smoothly. An early attempt to duplicate that was Cyc project's database of common sense. There's maybe just five or six total per Minsky with most AI researchers not thinking it's important. Those last words told me to be pessimistic already.
Whereas, the only computer capable of doing what they are trying to do uses a diverse set of subsystems specialized to do their jobs well. A significant amount of it seems dedicated to establishing common sense tying all experiences together. The architecture is capable of long-term planning, reacting to stuff, and even doing nothing when that makes sense. It teaches itself these things based on sensory input. It does it all in real-time with what appears to be a mix of analog and digital-like circuits in tiny amount of space and energy. And despite this, it still takes over a decade of diverse, training data to become effective enough to do stuff like design & publish ANN schemes. :)
There's hardly anything like the brain being done in ANN research that I've seen. The cutting-edge stuff that's made HN is pale imitation with small subset of capabilities trying to make one thing do it all. The pre-print you posted is also rudimentary compared to what I described above. Interestingly, the brain also makes use of feedback designs where most I see shared here (like in the late 90's) was feed-forward as if trying to avoid exploring the most effective technique that already solved the problem. Like the linked paper did.
They just all seem to be going in fundamentally wrong directions. Such directions will lead to nice, local maxima but miss the global maxima by a long shot. Might as well backtrack while they're ahead if they want the real thing.
Sorry - should have quoted the specific thing I was calling out-of-date, which was from a few comments up-thread:
> Brains (read: humans) can learn from very few examples and in very little time. [...] Those are all things that ANNs have proven quite incapable of doing, as has any other technology you might want to think of.
That's an understandable, but probably incorrect, view that comes from focusing on claims in state-of-the-art publications too much without the wider context of history & brain function. The problem parent is referring to also includes the general, "common sense" that we build up over time with extreme diversity of experiences that is developed despite tons of curveballs & able to create them ourselves. New knowledge is incorporated into that framework pretty smoothly. An early attempt to duplicate that was Cyc project's database of common sense. There's maybe just five or six total per Minsky with most AI researchers not thinking it's important. Those last words told me to be pessimistic already.
Whereas, the only computer capable of doing what they are trying to do uses a diverse set of subsystems specialized to do their jobs well. A significant amount of it seems dedicated to establishing common sense tying all experiences together. The architecture is capable of long-term planning, reacting to stuff, and even doing nothing when that makes sense. It teaches itself these things based on sensory input. It does it all in real-time with what appears to be a mix of analog and digital-like circuits in tiny amount of space and energy. And despite this, it still takes over a decade of diverse, training data to become effective enough to do stuff like design & publish ANN schemes. :)
There's hardly anything like the brain being done in ANN research that I've seen. The cutting-edge stuff that's made HN is pale imitation with small subset of capabilities trying to make one thing do it all. The pre-print you posted is also rudimentary compared to what I described above. Interestingly, the brain also makes use of feedback designs where most I see shared here (like in the late 90's) was feed-forward as if trying to avoid exploring the most effective technique that already solved the problem. Like the linked paper did.
They just all seem to be going in fundamentally wrong directions. Such directions will lead to nice, local maxima but miss the global maxima by a long shot. Might as well backtrack while they're ahead if they want the real thing.