even if you could record and play back “good moves”, the resulting
program would not play chess any better than you do.
If I want to create an AI that plays better chess than I do,
I have to program a search for winning moves. I can’t program
in specific moves because then the chess player really won’t
be any better than I am. [...] If you want [...] better [...],
you necessarily sacrifice your ability to predict the exact answer
in advance—though not necessarily your ability to predict that
the answer will be “good” according to a known criterion of
goodness. “We never run a computer program unless we know
an important fact about the output and we don’t know the
output,” said Marcello Herreshoff.
So the heart of the AI is something that can generate and recognize good answers. In game playing programs, it didn’t take long for the earliest researchers to come up with move and position evaluators that they have been improving on ever since. There have even been some attempts at general move and position evaluators. (See work on Planner, Micro-Planner, and Conniver, which will probably lead you to other similar work.) Move generation has always been simpler in the game worlds than it would be for any general intelligence. The role of creativity hasn’t been explored that much AFAICT, but it’s crucial in realms where the number of options at any point are so much larger than in game worlds.
The next breakthrough will require some different representation of reality and of goals, but Eli seems to be pointing at generation and evaluation of action choices as the heart of intelligent behavior. The heart of it seems to be choosing a representation that makes generation and analysis of possible actions tractable. I’m waiting to see if EY has any new ideas on that front. I can’t see how progress will be made without it, even in the face of all of EY’s other contributions to understanding what the problem is and what it would mean to have a solution.
And EY has clearly said that he’s more interested in behavior (“steering the future”) than recognition or analysis as a characteristic of intelligence.
@Silas
I thought the heart of EY’s post was here:
even if you could record and play back “good moves”, the resulting program would not play chess any better than you do.
If I want to create an AI that plays better chess than I do, I have to program a search for winning moves. I can’t program in specific moves because then the chess player really won’t be any better than I am. [...] If you want [...] better [...], you necessarily sacrifice your ability to predict the exact answer in advance—though not necessarily your ability to predict that the answer will be “good” according to a known criterion of goodness. “We never run a computer program unless we know an important fact about the output and we don’t know the output,” said Marcello Herreshoff.
So the heart of the AI is something that can generate and recognize good answers. In game playing programs, it didn’t take long for the earliest researchers to come up with move and position evaluators that they have been improving on ever since. There have even been some attempts at general move and position evaluators. (See work on Planner, Micro-Planner, and Conniver, which will probably lead you to other similar work.) Move generation has always been simpler in the game worlds than it would be for any general intelligence. The role of creativity hasn’t been explored that much AFAICT, but it’s crucial in realms where the number of options at any point are so much larger than in game worlds.
The next breakthrough will require some different representation of reality and of goals, but Eli seems to be pointing at generation and evaluation of action choices as the heart of intelligent behavior. The heart of it seems to be choosing a representation that makes generation and analysis of possible actions tractable. I’m waiting to see if EY has any new ideas on that front. I can’t see how progress will be made without it, even in the face of all of EY’s other contributions to understanding what the problem is and what it would mean to have a solution.
And EY has clearly said that he’s more interested in behavior (“steering the future”) than recognition or analysis as a characteristic of intelligence.