I agree with essentially all of the criticisms of deep learning in this paper, and I think these are most relevant for AGI:
Deep learning is data hungry, reflecting poor abstraction learning
Deep learning doesn’t transfer well across domains
Deep learning doesn’t handle hierarchical structure
Deep learning has trouble with logical inference
Deep learning doesn’t learn causal structure
Deep learning presumes a stable world
Deep learning requires problem-specific engineering
Together (and individually), these are good reasons to expect “general strategic action in the world on a ~1-month timescale” to be a much harder domain for deep learning to learn how to act in than “play a single game of Go”, hence the problem difficulty factor.
I agree with essentially all of the criticisms of deep learning in this paper, and I think these are most relevant for AGI:
Deep learning is data hungry, reflecting poor abstraction learning
Deep learning doesn’t transfer well across domains
Deep learning doesn’t handle hierarchical structure
Deep learning has trouble with logical inference
Deep learning doesn’t learn causal structure
Deep learning presumes a stable world
Deep learning requires problem-specific engineering
Together (and individually), these are good reasons to expect “general strategic action in the world on a ~1-month timescale” to be a much harder domain for deep learning to learn how to act in than “play a single game of Go”, hence the problem difficulty factor.