This system is only meant to solve problems that are verifiable (e.g. NP problems). Which includes general induction, mathematical proofs, optimization problems, etc. I’m not sure how to extend this system to problems that aren’t efficiently verifiable but it might be possible.
One use of this system would be to write a seed AI once we have a specification for the seed AI. Specifying the seed AI itself is quite difficult, but probably not as difficult as satisfying that specification.
It can prove things about mathematics than can be proven procedurally, but that’s not all that impressive. Lots of real-world problems are either mathematically intractable (really intractable, not just “computers aren’t fast enough yet” intractable) or based in mathematics that aren’t amenable to proofs. So you approximate and estimate and experiment and guess. Then you test the results repeatedly to make sure they don’t induce cancer in 80% of the population, unless the results are so complicated that you can’t figure out what it is you’re supposed to be testing.
Right, this doesn’t solve friendly AI. But lots of problems are verifiable (e.g. hardware design, maybe). And if the hardware design the program creates causes cancer and the humans don’t recognize this until it’s too late, they probably would have invented the cancer-causing hardware anyway. The program has no motive other than to execute an optimization program that does well on a wide variety of problems.
Basically I claim that I’ve solved friendly AI for verifiable problems, which is actually a wide class of problems, including the problems mentioned in the original post (source code optimization etc.)
This system is only meant to solve problems that are verifiable (e.g. NP problems). Which includes general induction, mathematical proofs, optimization problems, etc. I’m not sure how to extend this system to problems that aren’t efficiently verifiable but it might be possible.
One use of this system would be to write a seed AI once we have a specification for the seed AI. Specifying the seed AI itself is quite difficult, but probably not as difficult as satisfying that specification.
It can prove things about mathematics than can be proven procedurally, but that’s not all that impressive. Lots of real-world problems are either mathematically intractable (really intractable, not just “computers aren’t fast enough yet” intractable) or based in mathematics that aren’t amenable to proofs. So you approximate and estimate and experiment and guess. Then you test the results repeatedly to make sure they don’t induce cancer in 80% of the population, unless the results are so complicated that you can’t figure out what it is you’re supposed to be testing.
Right, this doesn’t solve friendly AI. But lots of problems are verifiable (e.g. hardware design, maybe). And if the hardware design the program creates causes cancer and the humans don’t recognize this until it’s too late, they probably would have invented the cancer-causing hardware anyway. The program has no motive other than to execute an optimization program that does well on a wide variety of problems.
Basically I claim that I’ve solved friendly AI for verifiable problems, which is actually a wide class of problems, including the problems mentioned in the original post (source code optimization etc.)