Shane Legg's Thesis: Machine Superintelligence, Opinions?

I'm not really in the field, but I am vaguely familiar with the literature and this isn't how it works (though you might get that impression from reading LW).

A vision algorithm might face the following problem: reality picks an underlying physical scene and an image from some joint distribution. The algorithm looks at the image and must infer something about the scene. In this case, you need to integrate over a huge space to calculate likelihoods, which is generally completely intractable and so requires some algorithmic insight. For example, if you want to estimate the probability that there is an apple on the table, you need to integrate over the astronomically many possible scenes in which there is an apple on the table.

I think this paper, entitled "Region competition: Unifying snakes, region growing, and Bayes/MDL for multiband image segmentation" is indicative of the overall mindset. Even though the title explicitly mentions Bayes and MDL, the paper doesn't report any compression results - only segmentation results. Bayes/MDL are viewed as tricks to be used to achieve some other purpose, not as the fundamental principle justifying the research.

I don't think we can resolve this debate, but let me try to clarify the differences in our positions (perhaps confusing to nonspecialists, since we both advocate compression).

Hutter/Legg/Tyler/etc (algorithmic approach) : Compression is the best measure of understanding. Therefore, to achieve general intelligence, we should search for general purpose compressors. It is not interesting to build specialized compressors. To achieve compression in spite of the NFL theorem, one must exploit empirical structure in the data, but the only empirical fact we require is that the world is computable. Because the compressors are general purpose, to demonstrate success it is sufficient to show that they work well on simple benchmark problems. There is no need to study the structure of specific datasets. To achieve good text compression, one simply finds a general purpose compressor and applies it to text. The problem is entirely a problem of mathematics and algorithm design.

Burfoot (empirical approach) : Compression is the best measure of understanding. However, general purpose compressors are far out of reach at this stage. Instead, one should develop specialized compressors that target specific data types (text, images, speech, music, etc). To achieve good compression in spite of the NFL, one must study the empirical structure of the respective data sets, and build that knowledge into the compressors. To compress text well, one should study grammar, parsing, word morphology, and related topics in linguistics. To demonstrate success, it is sufficient to show that a new compressor achieves a better compression rate on a standard benchmark. We should expect good compressor to fail when applied to a data type for which it was not designed. Progress is achieved by obtaining a series of increasingly strong compression results (K-complexity upper bounds) on standard databases, while also adding new databases of greater scope and size.

Again, I don't think this debate can resolved, but I think it's important to clarify the various positions.

OK. Keep going - or take a break. Good luck!

I own a copy of AIMA, though I admittedly haven't read it from cover to cover. I did an independent study learning/coding some basic AI stuff about a year ago, the professor introduced me to AIMA.

not sure if you mentioned before what's your current background

It's a bit difficult to summarize. Is sort of did so here, but I didn't include a lot of detail.

I suppose I could try to hit a few specifics; I was jumping around The Handbook of Brain Theory and Neural Networks for a bit, I picked up the overviews and read a few of the articles, but haven't really come back to it yet; I've read a good number of articles from the MIT Encyclopedia of Cognitive Science; I've read a (small) portion of "Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems" (I ended up ultimately delving too far into molecular biology and organic chem so I abandoned it for the time being, though I would like to look at Comp Neurosci again, maybe using From Neuron to Brain instead, seems more approachable); I read a bit "Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting" partly to get a sense of just how much current computational models of neurons might diverge from actual neuronal behavior but mostly to get an idea of some alternatives.

As I mentioned in my response to timtyler, I tend to cycle through my readings quite a bit. I like to pick up a small cluster of ideas and let them sink in and move on to something else, coming back to the material later if it still seems relevant to my interests. Once it's popped up a few times I make a more concerted effort to learn it. In any event My main goal over the past few months was to try to get a better overview of a large amount of material relevant to FAI.

AIXI is more than just Solomonoff induction. It is Solomonoff induction plus some other stuff.

Right. The other stuff is an account of the most fundamental and elementary kind of reinforcement learning. In my conversations (during meetups to which everyone is invited) with one of the Research Fellows at SIAI, reinforcement learning has come up more than Solomonoff induction.

But yeah, the OP should learn Solomonoff induction first, then decide whether to learn AIXI. That would have happened naturally if he'd started reading Legg's thesis, unless the OP has some wierd habit of always finishing PhD theses that he has started.

Since we've gone back and forth twice, and no one's upvoted my contributions, this will probably be my last comment in this thread.