Re:Deep learning is hitting a wall?
by YeGoblynQueenne on Friday March 11, 2022 @06:02AM
(#30638503)
No worries about precision, I wasn't trying to tell you off about the use of terminology. I assumed you meant "symbolic AI" so I made sure to clarify my assumption to avoid confusion. I'm also not a native English speaker, my maternal language is Greek. My second language is French though :)
Yes, "GOFAI" overpromised and underdelivered and that was a major reason for the two AI winters that essentially destroyed the field by freezing funding and shrinking research positions and output.
Personally, I'm neither nostalgic of older approaches, nor dismissive of modern approaches. The important thing is to have a clear understanding of the capabilities available, regardless of approach. It's obvious to me that older systems could do things that modern systems can't do (principally, reasoning and knowledge representation) just as modern systems can do things that older systems couldn't do (learning). However, there are approaches that bridge the gap, such as symbolic machine learning], like the approaches I study that learn logic programs from examples using theorem-proving techniques. There is also, of course, continued research in other branches of symbolic AI, like planning and SAT solvers, that seem to have made great progress in the last years. I think the worst that can happen now is to nip such research in the bud by denying it funding just because it's not deep learning.
Gary Marcus' article quotes Emily Bender about how overpromising, this time by the deep learning community, "sucks the oxygen out of the room" for other kinds of research. This is apposite. Research can't become a monoculture, otherwise the ability to innovate will disappear. For innovation, there must be diversity of ideas. The risk I see right now is that such diversity will be lost and that, in the long run, progress in machine learning will stall. Throwing out everything that was learned in the fist 50 years of AI will not help anyone avoid the mistakes of the past, for sure.
Yes, "GOFAI" overpromised and underdelivered and that was a major reason for the two AI winters that essentially destroyed the field by freezing funding and shrinking research positions and output.
Personally, I'm neither nostalgic of older approaches, nor dismissive of modern approaches. The important thing is to have a clear understanding of the capabilities available, regardless of approach. It's obvious to me that older systems could do things that modern systems can't do (principally, reasoning and knowledge representation) just as modern systems can do things that older systems couldn't do (learning). However, there are approaches that bridge the gap, such as symbolic machine learning], like the approaches I study that learn logic programs from examples using theorem-proving techniques. There is also, of course, continued research in other branches of symbolic AI, like planning and SAT solvers, that seem to have made great progress in the last years. I think the worst that can happen now is to nip such research in the bud by denying it funding just because it's not deep learning.
Gary Marcus' article quotes Emily Bender about how overpromising, this time by the deep learning community, "sucks the oxygen out of the room" for other kinds of research. This is apposite. Research can't become a monoculture, otherwise the ability to innovate will disappear. For innovation, there must be diversity of ideas. The risk I see right now is that such diversity will be lost and that, in the long run, progress in machine learning will stall. Throwing out everything that was learned in the fist 50 years of AI will not help anyone avoid the mistakes of the past, for sure.