[davmre]

AGI has cool ideas, and is in some sense the "right" theoretical framework for AI, but it's not clear that it gives any kind of practical path forward for AI research. The main problem is that its basic idea -- an AI performing Bayesian inference over a hypothesis class of all potential environment-generating computer programs, with a Kolmogorov complexity prior -- is wildly uncomputable, so to make it practical we'd need to find simple, computable approximations that work on real problems. But this is basically what modern ML research is already trying to do -- finding models that are complex enough to capture interesting structure in the world, but still simple enough for efficient inference to be practical.

[ilaksh]

"an AI performing Bayesian inference over a hypothesis class of all potential environment-generating computer programs, with a Kolmogorov complexity prior, -- is wildly uncomputable, so to make it practical we'd need to find simple, computable approximations that work on real problems"

That's not what AGI is trying to do or how they are trying to do it.

[wookietrader]

It's at least one way which has been advocated by leading researcher of the field. If you think differently, you should give references and explain what your AGI definition is.