The core technical problem of machine intelligence is building an agent that correctly performs inductive inference.
This seems to be highly non-obvious. Even if an AI already had access to a theory of everything, and could engage in near-optimal induction, it isn't at all clear that this helps much for practical purposes.
Not obvious, perhaps, but surely pretty accurate.
Hutter: http://prize.hutter1.net/hfaq.htm#compai
Mahoney: http://cs.fit.edu/~mmahoney/compression/rationale.html
Tyler - Part 2 on: http://matchingpennies.com/machine_forecasting/
FWIW, a theory of everything is not required - induction is performed on sense-data, or preprocessed sense data.
You are overestimating how much we can do just by compression. The key issue is not just the ability to predict accurately but the ability to predict accurately when using limited resources. For example, let A(n) be the Ackermann function and let P(n) be the nth prime number. Then the sequence described by P(A(n)) mod 3 is really compressible. But the time and space resources needed to expand that compressed form is probably massive.
There's a similar concern here. To again use the protein folding example, even if an AI has a really good model for predicti...
Link: johncarlosbaez.wordpress.com/2011/04/24/what-to-do/
His answer, as far as I can tell, seems to be that his Azimuth Project does trump the possibility of working directly on friendly AI or to support it indirectly by making and contributing money.
It seems that he and other people who understand all the arguments in favor of friendly AI and yet decide to ignore it, or disregard it as unfeasible, are rationalizing.
I myself took a different route, I was rather trying to prove to myself that the whole idea of AI going FOOM is somehow flawed rather than trying to come up with justifications for why it would be better to work on something else.
I still have some doubts though. Is it really enough to observe that the arguments in favor of AI going FOOM are logically valid? When should one disregard tiny probabilities of vast utilities and wait for empirical evidence? Yet I think that compared to the alternatives the arguments in favor of friendly AI are water-tight.
The problem why I and other people seem to be reluctant to accept that it is rational to support friendly AI research is that the consequences are unbearable. Robin Hanson recently described the problem:
I believe that people like me feel that to fully accept the importance of friendly AI research would deprive us of the things we value and need.
I feel that I wouldn't be able to justify what I value on the grounds of needing such things. It feels like that I could and should overcome everything that isn't either directly contributing to FAI research or that helps me to earn more money that I could contribute.
Some of us value and need things that consume a lot of time...that's the problem.