Discussion: Pathways for the Aspiring AGI Researcher?

That is interesting. I was aware of the growth of Monte Carlo sampling due to its use in Go AI, and I have an interest in the philosophical aspects of statistical inference though I haven't follwed up on it quite to the extent that I would like. One of the things I'm currently picking at is An Introduction to Kolmogorov Complexity by Li and Vitanyi. I started looking into it after seeing the discussions about complexity and Solomonoff induction on this site.

However, I am still unclear as to why this is more lucrative than other areas related to AGI.

Well, if you've already got the algebraic background needed for arithmetic geometry then you could probably break into the basics fairly painlessly. I found that my familiarity with algebraic structures from the algebraic geometry side of things has made my readings much smoother than they were the first time I tried to tackle it (near the end of my sophomore year I think?), which was very slow going (I was using Maclane's CWM and simply didn't have the appropriate background) and I did not get much out of it. Even if you're already doing your dissertation or something, it hardly seems like too much of a stretch to switch over.

If someone made what they thought was FAI and it wasn't actually FAI, how could you tell until it was too late?

How can you tell that a theorem is correct? By having a convincing proof, or perhaps other arguments to the effect of the theorem being correct, and being trained to accept correct proofs (arguments) and not incorrect ones.

Even though we currently have no idea about how to construct a FAI or how an argument for a particular design being a FAI might look like, we can still refuse to believe that something is a FAI when we are not given sufficient grounds for believing it is one, provided the people who make such decisions are skilled enough to accept correct arguments but not incorrect ones.

The rule to follow is to not actually build or run things that you don't know to be good. By default, they don't even work, and if they do, they are not good, because value is complex and won't be captured by chance. There need to be strong grounds for believing that a particular design will work, and here rationality training is essential, because by default people follow one of the many kinds of crazy reasoning about these things.

If someone made what they thought was FAI and it wasn't actually FAI, how could you tell until it was too late?

See! You've found a problem to work on already! :)

[The downvote on your comment isn't mine btw.]

How do you know that a person is really friendly? You use methods that have worked in the past and look for manipulative techniques that misleadingly friendly people use to make you think they are friendly. We know that someone is friendly via the same methodology that we determine what it means to be friendly, subjective benefits (emotional support etc.) and goal assistance (helping you move when they could simply refuse to do so) without malicious motives that ultimately disservice you.

In the case of FAI we want more surety, and we can presumably get this via simulation and proofs of correctness. I would assume that even after we had a proof of correctness for a meta-ethical system we would want to run it through as many virtual scenarios as possible, since the human brain is simply not capable of the chains of reasoning within the meta-ethics that the machine would be, so we would want to introduce it to scenarios that are as complex as possible in order to determine that it fits our intuition of friendliness.

It seems to me that the bulk of the work is in the arena of identifying the most friendly meta-ethical architecture. The Lokhorst paper lukeprog posted a while ago clarified a few things for me, though I have no access to the cutting edge work on FAI (save for what leaks out into the blog posts), and judging by what Will Newsome has said in the past (cannot find the post) they have compiled a relatively large list of possibly relevant sub-problems that I would be very interested to see (even if many of them are likely to be time drains).

Hmm. I've read most of the sequences and some of his monograph on FAI, but I don't recall him explicitly arguing against dividing up the work into sub-problems. Intuitively it seems that if you trust person X to cautiously do FAI then you should trust them to be able to pick out sub-problems and be able to determine when it is the case that they have been solved satisfactorily.

Could you point me out to the relevant links?

Also, I might be terribly mistaken here, but it seems like not every component of the AGI puzzle need be tied directly to FAI, at least in the development phase. Each one must be fully understood and integrated into an FAI, but I don't see why, say, I need to be as careful when designing a concept model of an ontology engine that can combine and generalize concepts, at least until I want to integrate it into the FAI, at which point the FAI people could review the best attempts at the various components of AGI and figure out whether any of them are acceptable and then how to integrate them into an FAI framework.