One way you could frame a large disagreement in it is as whether there are likely to be simple insights that can ‘put the last puzzle piece’ in a system (as Bostrom suggest), or massively improve a system in one go, rather than via a lot of smaller insights. This seems like a thing where we should be able to get heaps of evidence from our past experiences with insights and technological progress.
The effectiveness of learning hyper-heuristics for other problems, i.e. how much better algorithmically-produced algorithms perform than human-produced algorithms, and more pertinently, where the performance differential (if any) is heading.
As an example, Effective learning hyper-heuristics for the course timetabling problem says: “The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.”
Similar results can be found for other problems, bin packing, traveling salesman, and vehicle routing being just some off-the-top-of-my-head examples.
What empirical evidence could you look at to better predict the future winner of the Foom Debate? (for those who looked at it above)
One way you could frame a large disagreement in it is as whether there are likely to be simple insights that can ‘put the last puzzle piece’ in a system (as Bostrom suggest), or massively improve a system in one go, rather than via a lot of smaller insights. This seems like a thing where we should be able to get heaps of evidence from our past experiences with insights and technological progress.
The effectiveness of learning hyper-heuristics for other problems, i.e. how much better algorithmically-produced algorithms perform than human-produced algorithms, and more pertinently, where the performance differential (if any) is heading.
As an example, Effective learning hyper-heuristics for the course timetabling problem says: “The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.”
Similar results can be found for other problems, bin packing, traveling salesman, and vehicle routing being just some off-the-top-of-my-head examples.