In the link, he proves that optimal compression is equivalent to AI.
I don't actually think that compression is equivalent to intelligence. However, it is pretty close!
The OP asked what areas of AI study are useful, not which ones would work in principle. You may have evidence that this is the best approach in practice, but you have not presented it yet.
My main presentation in that area is in the first of the links I gave:
http://timtyler.org/machine_forecasting/
Brief summary:
Compression allows an easy way of measuring progress - an area which has been explored by Shane Legg. Also, it successfully breaks a challenging problem down into sub-components - often an important step on the way to solving the problem. Lastly, but perhaps most significantly, developing good quality stream compression engines looks like an easier problem than machine intelligence - and it is one which immediately suggests possible ways to solve it.
I don't know that it is the best approach in practice - just that it looks like a pretty promising one.
It is not clear to me that what we are measuring is progress. We are definitely improving something, but that does not necessarily get us closer to GAI. Different algorithms could have very different compression efficiencies on different types of data. Some of these may require real progress toward AI, but many types of data can be compressed significantly with little intelligence. A program that could compress any type of non-random data could be improved significantly just by focusing on thing that are easy to predict.
I'm stuck wondering on a peculiar question lately - which are the useful areas of AI study? What got me thinking is the opinion occasionally stated (or implied) by Eliezer here that performing general AI research might likely have negative utility, due to indirectly facilitating a chance of unfriendly AI being developed. I've been chewing on the implications of this for quite a while, as acceptance of these arguments would require quite a change in my behavior.
Right now I'm about to start my CompSci PhD studies soon, and had initially planned to focus on unsupervised domain-specific knowledge extraction from the internet, as my current research background is mostly with narrow AI issues in computational linguistics, such as machine-learning, formation of concepts and semantics extraction. However, in the last year my expectations of singularity and existential risks of unfriendly AI have lead me to believe that focusing my efforts on Friendly AI concepts would be a more valuable choice; as a few years of studies in the area would increase the chance of me making some positive contribution later on.
What is your opinion?
Do studies of general AI topics and research in the area carry a positive or negative utility ? What are the research topics that would be of use to Friendly AI, but still are narrow and shallow enough to make some measurable progress by a single individual/tiny team in the course of a few years of PhD thesis preparation? Are there specific research areas that should be better avoided until more progress has been made on Friendliness research ?