OK. FWIW, I don’t need examples of existing algorithms approaching theoretical limits.
As I said, the main problem where I think there is important scope for software improvement is induction. By my estimate, the brain spends about 80% of its resources on induction—so performance on induction seems important.
Current performance on the Hutter prize suggests that a perfect compressor could do about 25% better on the target file than the current champion program does.
So, perhaps it depends on how you measure it. If you think a mere 25% is not much improvement, you may not be too impressed. However, there are a few things to bear in mind:
Induction progress works in a similar manner to the rating scale in go: the higher you climb, the more difficult it is to make further progress.
There’s another similarity to go’s rating scale. In go, God is estimated to be 11-dan—not all that much better than a 9-dan human champion. However, this apparent close proximity to perfection is kind-of an illusion. Play go on bigger boards, and larger margins between humans and God are likely to become apparent. Similarly, measure induction using a more challenging corpus, and today’s programs will not appear to be so close to optimal.
The other thing to bear in mind is that intelligent agents are a major part of the environment of other intelligent agents. This means that it is not very realistic to model a fixed set of environmental problems (TSPs, etc), and to measure intelligence with respect to them. Rather there is an intelligence arms race—with many of the problems which intelligent agents face being posed by other agents.
We can see a related effect in mathematics. Many mathematicians work on the unresolved problems at the boundary of what is known in their field. The more progress they make, the harder the unresolved problems become, and the more intelligence is required to deal with them.
OK. FWIW, I don’t need examples of existing algorithms approaching theoretical limits.
As I said, the main problem where I think there is important scope for software improvement is induction. By my estimate, the brain spends about 80% of its resources on induction—so performance on induction seems important.
Current performance on the Hutter prize suggests that a perfect compressor could do about 25% better on the target file than the current champion program does.
So, perhaps it depends on how you measure it. If you think a mere 25% is not much improvement, you may not be too impressed. However, there are a few things to bear in mind:
Induction progress works in a similar manner to the rating scale in go: the higher you climb, the more difficult it is to make further progress.
There’s another similarity to go’s rating scale. In go, God is estimated to be 11-dan—not all that much better than a 9-dan human champion. However, this apparent close proximity to perfection is kind-of an illusion. Play go on bigger boards, and larger margins between humans and God are likely to become apparent. Similarly, measure induction using a more challenging corpus, and today’s programs will not appear to be so close to optimal.
The other thing to bear in mind is that intelligent agents are a major part of the environment of other intelligent agents. This means that it is not very realistic to model a fixed set of environmental problems (TSPs, etc), and to measure intelligence with respect to them. Rather there is an intelligence arms race—with many of the problems which intelligent agents face being posed by other agents.
We can see a related effect in mathematics. Many mathematicians work on the unresolved problems at the boundary of what is known in their field. The more progress they make, the harder the unresolved problems become, and the more intelligence is required to deal with them.