> 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.
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.