I think the whole issue of where to start in AGI is hopelessly
contentious, because there is no working AGI other than a brain.  So,
really, the neuroscience approach under that assumption is the most
promising.  But, even though approaches cut corners, it seems, by
introducing a computable analog to the brain.  A lot of people say
"such and such approach won't work" but there is only arguments to
support it -- no proof.

On 7/25/13, Matt Mahoney <[email protected]> wrote:
> 1. AIXI won't solve AGI because AIXI is not computable, and good
> approximations are intractable beyond toy problems.
>
> 2. AIXI won't solve AGI because language, vision, robotics, and art
> are not goal directed optimization processes. Reinforcement learning
> is responsible for a very tiny fraction of what you know because it is
> a low bandwidth signal.
>
> 3. AIXI is not a good model of reinforcement learning in humans and
> other animals. A positive (negative) reinforcement signal makes you
> more (less) likely to repeat behavior that preceded it. That is not
> the same as rationally changing your behavior in a way that increases
> expected reinforcement. If they were the same thing, then your desire
> to use a drug would not depend on whether you have already tried it.
>
> 4. Not all reinforcement signals are the same. Nausea will make you
> less likely to eat something you ate an hour earlier. Electric shock
> would have a different effect.
>
> 5. AIXI apparently can't even play Pac Man very well.
>
> --
> -- Matt Mahoney, [email protected]
>
>
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