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Self-improvement as perpetual motion seems unlikely. I'm a not-terribly-bright mostly-hairless ape, but I can understand the basics of natural selection. I can

by mrec 12y ago

Self-improvement as perpetual motion seems unlikely. I'm a not-terribly-bright mostly-hairless ape, but I can understand the basics of natural selection. I can imagine setting up a program to breed other hairless apes and ruthlessly select for intelligence. After a few generations, shazam, improvement. The only reason you wouldn't call that process "SELF-improvement" is that I'm not improving myself, but there's no reason for a digital entity to have analog hangups about identity. If it can produce a "new" entity with the same goals but better able to accomplish them, why wouldn't it? Assume this process could be simulated, as GAs have been doing for decades, and it could happen fast. Note that I'm not saying GAs will do this, I'm saying they could, which suggests there's no fundamental law that says they can't, in which case any number of other approaches could work as well.

Retra 12y ago
The problem with this is that you have to determine what the goals are and how to evaluate whether they are met in a meaningful way. A computerized process like this will quickly over-fit to its input and be useless for 'actual' intelligence. The only way past this is to gather good information, which requires a real-world presence. It can't be done in simulation. It's the same reason you can't test in a simulation. Say you wanted to test a lawnmower in a simulation... how hard are the rocks? How deep are the holes? How strong are the blades? How efficient is the battery? If you already know this stuff, then you don't need to test. If you don't know it, then you can't write a meaningful simulation anyway. So that is not an approach that can be automated.
- mrec 12y ago
  That's an interesting argument, but doesn't it assume a small, non-real-world input/goal set? Dumb example off the top of my head: what if the input was the entire StackOverflow corpus with "accepted" information removed, and the goal was to predict as accurately as possible which answer would be accepted for a given question? Yes, it assumes a whole bunch of NLP and domain knowledge, and a "perfect" AI wouldn't get a perfect score because SO posters don't always accept the best answer, but it's big and it's real and it's measurable. A narrower example: did the Watson team test against the full corpus of previous Jeopardy questions? Did they tweak things based on the resulting score? Could that testing/tweaking have been automated by some sort of GA?
  - Retra 12y ago
    The point there is that you can make a computer that's very good at predicting StackOverflow results or Jeopardy, but it won't be able to tie a shoe. If you want computers to be skilled at living in the real world, they have to be trained with real-world experiences. There is just not enough information in StackOverflow or Jeopardy to provide a meaningful representation of the real world. You'll end up overfitting to the data you have. The bottom line is that without sensory input, you can't optimize for real world 'general AI'-like results.

by mrec 12y ago

Retra 12y ago

The problem with this is that you have to determine what the goals are and how to evaluate whether they are met in a meaningful way. A computerized process like this will quickly over-fit to its input and be useless for 'actual' intelligence. The only way past this is to gather good information, which requires a real-world presence. It can't be done in simulation. It's the same reason you can't test in a simulation. Say you wanted to test a lawnmower in a simulation... how hard are the rocks? How deep are the holes? How strong are the blades? How efficient is the battery? If you already know this stuff, then you don't need to test. If you don't know it, then you can't write a meaningful simulation anyway. So that is not an approach that can be automated.

mrec 12y ago
That's an interesting argument, but doesn't it assume a small, non-real-world input/goal set? Dumb example off the top of my head: what if the input was the entire StackOverflow corpus with "accepted" information removed, and the goal was to predict as accurately as possible which answer would be accepted for a given question? Yes, it assumes a whole bunch of NLP and domain knowledge, and a "perfect" AI wouldn't get a perfect score because SO posters don't always accept the best answer, but it's big and it's real and it's measurable. A narrower example: did the Watson team test against the full corpus of previous Jeopardy questions? Did they tweak things based on the resulting score? Could that testing/tweaking have been automated by some sort of GA?
- Retra 12y ago
  The point there is that you can make a computer that's very good at predicting StackOverflow results or Jeopardy, but it won't be able to tie a shoe. If you want computers to be skilled at living in the real world, they have to be trained with real-world experiences. There is just not enough information in StackOverflow or Jeopardy to provide a meaningful representation of the real world. You'll end up overfitting to the data you have. The bottom line is that without sensory input, you can't optimize for real world 'general AI'-like results.