I wasn’t aware of any actual practical implementations of SI. That link isn’t talking about SI, but it’s similar and really impressive. Something similar to Optimal Ordered Problem Solver Induction would sound like a sensible approach to formalizing induction.
Right now, the most complex problem solved by approximations that I know of is from MC-AIXI http://www.vetta.org/2009/09/monte-carlo-aixi/ where a desktop computer learns to play ’a somewhat reasonable game of Pac-man.′ That was back in 2008-2009, and I don’t know what problems Legg & co. may have solved at their startup.
I’m already aware of that paper, but it seems to me that MC-AIXI is more similar to MC tree search than to SI. I’m quite impressed with the effectiveness of MC tree search for Go.
it seems to me that MC-AIXI is more similar to MC tree search than to SI.
I’m not sure it’s entirely meaningful to say they are very different. A tree of quasi-programs with weightings over them and a random playout for the point at which you have to stop looking deeper. A computable approximation has to be computable, after all, so it’s not too much of a surprise if it reuses computable techniques that have been found to be effective in specific domains.
Ok, they both tree search over a space, whether it is the space of strategies or the space of programs. That does make sense.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy. By comparison, I could imagine some kind of tree search, possibly weighted by heuristics, to be efficient.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy.
It’s crazy, but some universes are crazy. Pity the poor AI who wakes up inside a simulation where the programmer is in fact testing it on every program of length <n!
Have you looked into what they can calculate?
An interesting link from today: https://plus.google.com/u/0/106808239073321070854/posts/ZCyc7TC4Z8U
I wasn’t aware of any actual practical implementations of SI. That link isn’t talking about SI, but it’s similar and really impressive. Something similar to Optimal Ordered Problem Solver Induction would sound like a sensible approach to formalizing induction.
Right now, the most complex problem solved by approximations that I know of is from MC-AIXI http://www.vetta.org/2009/09/monte-carlo-aixi/ where a desktop computer learns to play ’a somewhat reasonable game of Pac-man.′ That was back in 2008-2009, and I don’t know what problems Legg & co. may have solved at their startup.
I’m already aware of that paper, but it seems to me that MC-AIXI is more similar to MC tree search than to SI. I’m quite impressed with the effectiveness of MC tree search for Go.
I’m not sure it’s entirely meaningful to say they are very different. A tree of quasi-programs with weightings over them and a random playout for the point at which you have to stop looking deeper. A computable approximation has to be computable, after all, so it’s not too much of a surprise if it reuses computable techniques that have been found to be effective in specific domains.
Ok, they both tree search over a space, whether it is the space of strategies or the space of programs. That does make sense.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy. By comparison, I could imagine some kind of tree search, possibly weighted by heuristics, to be efficient.
It’s crazy, but some universes are crazy. Pity the poor AI who wakes up inside a simulation where the programmer is in fact testing it on every program of length <n!
We could be in a simulation where the programmer is in fact testing it on every program of length <n! Doesn’t seem so bad.