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Once you have a learning machine that can solve simple problems. You can scale it up to solve very complex problems. Its a first step to true AI imho. Al lot of

• chriswarbo 10y ago
  > Once you have a learning machine that can solve simple problems. You can scale it up to solve very complex problems. Nope. It's really easy to solve simple problems; it can sometimes even be done by brute-force. That's what caused the initial optimism around AI, e.g. the 1950s notion that it would be an interesting summer project for a grad student. Insights into computational complexity during the 1960s showed that scaling is actually the difficult part. After all, if brute-force were scalable then there'd be no reason to write any other software (even if a more efficient program were required, the brute-forcer could write it for us). That's why the rapid progress on simple problems, e.g. using Eliza, SHRDLU, General Problem Solver, etc. hasn't been sustained, and why we can't just run those systems on a modern cluster and expect them to tackle realistic problems.
  • mordymoop 10y ago
    The point of this is generality. If you can use the same architecture to do many different types of tasks, and you keep adding to the variety of tasks it can do, eventually you cover all classes of tasks and then you never ever need to switch over to brute-force architectures. This is not nothing.