What is the ideal computable approximation of perfect Bayesianism?
As explained elsewhere, we want a Friendly AI's model of the world to be as accurate as possible. Thus, we need ideal computable theories of priors and of logical uncertainty, but we also need computable approximations of Bayesian inference. Cooper (1990) showed that inference in unconstrained Bayesian networks is NP-hard, and Dagum & Luby (1993) showed that the corresponding approximation problem is also NP-hard. The most common solution is to use randomized sampling methods, also known as "Monte Carlo" algorithms (Robert & Casella 2010). Another approach is variational approximation (Wainwright & Jordan 2008), which works with a simpler but similar version of the original problem. Another approach is called "belief propagation" — for example, loopy belief propagation (Weiss 2000).
Can we develop a safely confined AI? Can we develop Oracle AI?
One approach to constraining a powerful AI is to give it "good" goals. Another is to externally constrain it, creating a "boxed" AI and thereby "leakproofing the singularity" (Chalmers 2010). A fully leakproof singularity is impossible or pointless: "For an AI system to be useful... to us at all, it must have some effects on us. At a minimum, we must be able to observe it." Still, there may be a way to constrain a superhuman AI such that it is useful but not dangerous. Armstrong et al. (2011) offer a detailed proposal for constraining an AI, but there remain many worries about how safe and sustainable such a solution is. The question remains: Can a superhuman AI be safely confined, and can humans managed to safely confine all superhuman AIs that are created?
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As explained elsewhere, we want a Friendly AI's model of the world to be as accurate as possible. Thus, we need ideal computable theories of priors and of logical uncertainty, but we also need computable approximations of Bayesian inference. Cooper (1990) showed that inference in unconstrained Bayesian networks is NP-hard, and Dagum & Luby (1993) showed that the corresponding approximation problem is also NP-hard. The most common solution is to use randomized sampling methods, also known as "Monte Carlo" algorithms (Robert & Casella 2010). Another approach is variational approximation (Wainwright & Jordan 2008), which works with a simpler but similar version of the original problem. Another approach is called "belief propagation" — for example, loopy belief propagation (Weiss 2000).
Can we develop a safely confined AI? Can we develop Oracle AI?
One approach to constraining a powerful AI is to give it "good" goals. Another is to externally constrain it, creating a "boxed" AI and thereby "leakproofing the singularity" (Chalmers 2010). A fully leakproof singularity is impossible or pointless: "For an AI system to be useful... to us at all, it must have some effects on us. At a minimum, we must be able to observe it." Still, there may be a way to constrain a superhuman AI such that it is useful but not dangerous. Armstrong et al. (2011) offer a detailed proposal for constraining an AI, but there remain many worries about how safe and sustainable such a solution is. The question remains: Can a superhuman AI be safely confined, and can humans managed to safely confine all superhuman AIs that are created?
- More Here
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