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> Until we solve this problem, throwing all the machine power in the world at it cannot create an AI that can do everything a person can. I think this is dispr
by nawitus 11y ago
> Until we solve this problem, throwing all the machine power in the world at it cannot create an AI that can do everything a person can.
I think this is disproved by approximating AIXI: https://en.wikipedia.org/wiki/AIXI https://en.wikipedia.org/wiki/AIXI
- keithba 11y agoThis is an interesting assertion. Could you elaborate? AIXI is pareto optimal, but I'm not seeing the line from that to "AI can do everything a person can" - though I want to!
- nawitus 11y agoFrom the conclusions of http://arxiv.org/abs/cs/0004001 http://arxiv.org/abs/cs/0004001 : "All tasks which require intelligence to be solved can naturally be formulated as a maximization of some expected utility in the framework of agents. We gave a functional (2) and an iterative (9) formulation of such a decision theoretic agent, which is general enough to cover all AI problem classes, as has been demonstrated by several examples. The main remaining problem is the unknown prior probability distribution AI of the environment(s). <...> the universal semimeasure, based on ideas from algorithmic information theory, solves the problem of the unknown prior distribution for induction problems. No explicit learning procedure is necessary ... . We unified the theory of universal sequence prediction with the decision theoretic agent by replacing the unknown true prior AI by an appropriately generalized universal semimeasure ξAI. We gave strong arguments that the resulting AIξ model is the most intelligent, parameterless and environmental/application independent model possible. The major drawback of the AIξ model is that it is uncomputable, or more precisely, only asymptotically computable, which makes an implementation impossible. To overcome this problem, we cons tructed a modified model AI, which is still effectively more intelligent than any other time and space bounded algorithm." The copy/paste lost some information, especially symbols, so take a look at the paper for a better read.
- keithba 11y agoThanks for the link - I'm going to read the paper as soon as I can. In the meantime, if you have time, could you elaborate on: "All tasks which require intelligence to be solved can naturally be formulated as a maximization of some expected utility in the framework of agents." Is this proven in the paper, or assumed true outside of it?
- differentView 11y agoHow does that disprove his statement?
- nawitus 11y agoAs stated above, I believe that a sufficiently close approximation of AIXI is an AI that can do everything a person can.
- differentView 11y agoI think you're overestimating "all the machine power in the world".
- nawitus 11y agoI read that as containing even future machines. I would agree that the current supercomputers can't reach a human-level AI using AIXI-approximation.
- davmre 11y agoThrowing all the computing power in the world at an AIXI approximation would get you nowhere near AIXI.
- nawitus 11y agoI believe that a sufficiently close approximation of AIXI is an AI that can do everything a person can.
- coderdude 11y agoThe Monte Carlo implementation randomly searches an infinite search space for possible solutions (right?). I'm not sure if that counts as an approximation but it does make it computationally tractable. Does anyone know if the only room for improvement in computable AIXI is with the method of exploring the solution space?
- davmre 11y agoThat's contestable, but even granting the point, a "sufficiently close approximation" in the sense that we currently know how to run would require more computing power than is or likely ever will be available in this world.
- nawitus 11y agoI can easily grant "than is available", but not "will be available".