“Indeed the problem of sequence prediction could well be considered solved, if it were not for the fact that Solomonoff’s theoretical model is incomputable.”
and:
“Could there exist elegant computable prediction algorithms that are in some sense universal? Unfortunately this is impossible, as pointed out by Dawid.”
and:
“We then prove that some sequences, however, can only be predicted by very complex predictors. This implies that very general prediction algorithms, in particular those that can learn to predict all sequences up to a given Kolmogorov complex[ity], must themselves be complex. This puts an end to our hope of there being an extremely general and yet relatively simple prediction algorithm. We then use this fact to prove that although very powerful prediction algorithms exist, they cannot be mathematically discovered due to Gödel incompleteness. Given how fundamental prediction is to intelligence, this result implies that beyond a moderate level of complexity the development of powerful artificial intelligence algorithms can only be an experimental science.”
While Solomonoff induction is mathematically interesting, the paper itself seems to reject your assessment of it.
But:
“Indeed the problem of sequence prediction could well be considered solved, if it were not for the fact that Solomonoff’s theoretical model is incomputable.”
and:
“Could there exist elegant computable prediction algorithms that are in some sense universal? Unfortunately this is impossible, as pointed out by Dawid.”
and:
“We then prove that some sequences, however, can only be predicted by very complex predictors. This implies that very general prediction algorithms, in particular those that can learn to predict all sequences up to a given Kolmogorov complex[ity], must themselves be complex. This puts an end to our hope of there being an extremely general and yet relatively simple prediction algorithm. We then use this fact to prove that although very powerful prediction algorithms exist, they cannot be mathematically discovered due to Gödel incompleteness. Given how fundamental prediction is to intelligence, this result implies that beyond a moderate level of complexity the development of powerful artificial intelligence algorithms can only be an experimental science.”
While Solomonoff induction is mathematically interesting, the paper itself seems to reject your assessment of it.
Not at all! I have no quarrel whatsoever with any of that (except some minor quibbles about the distinction between “math” and “science”).
I suspect you are not properly weighing the term “elegant” in the second quotation.
The paper is actually arguing that sufficiently comprehensive universal prediction algorithms are necessarily large and complex. Just so.