Is something weird going on with the AGI list here? I just got two emails claiming to be from a month or so ago that where actually sent today...
Anyway in reply to Ben's email,
Ben Goertzel wrote:
But the different trials need not be independent --- we can save the trajectory of each AI's development continuously, and then restart a new branch of "AI x at time y" for any recorded AI x at any recorded time point y.
Also, we can intentionally form composite AI's by taking portions of AI x's mind and portions of AI y's mind and fusing them together into a new AI z...
So we don't need to follow a strict process of evolutionary trial and error, which may accelerate things considerably ---- particularly if, as experimentation progresses, we are able to learn abstract theories about what makes some AI's smarter or stabler or friendlier than others.
I agree totally. Indeed I advocate going further and actually evolving the fundamental structures and dynamics that drive the system --- designing them by hand or trying to prove any useful results about what happens in a complex recurrent network seems to be really difficult. Thus perhaps a combination of artificial evolution, experimentation, and the development of theories to explain what we see is the most likely approach to succeed. At least that's my best guess at the moment based on what I've seen working on various AI/AGI projects in the past.
I sent Ben an email along similar lines a few days back describing my own little (extremely slowly moving and incomplete) set of AGI ideas that I refer to as the vetta project. I've pasted part of what I wrote below for anybody who is interested.
Cheers Shane
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Well it's a mix of things really --- and it changes over time a bit too!
Basically my approach goes something like this:
1) Build a set of precise "IQ" tests for machines. These tests cover everything from bacteria level intelligence to super human intelligence. It's a reasonably complex web of relations: passive predictors, classifiers, simple reactive systems, Markov chains, MDPs, POMDPs and many others..... You can prove a whole bunch of relations between all these mathematically, indeed that's what I did for the first 4 months of my PhD. That's the first step; however it doesn't really capture how difficult a problem is. So for that you need something like like complexity theory (both time and space). Anyway, the point is that you can then measure exactly where in this complex tree of abilities an AGI system is. The most general form of this is what I call "cybernance" and is closely related to the "intelligence order relation" that appears in the AIXI proofs.
2) Define a space of systems that should contain an AGI. This is a bit harder to explain. Again complexity theory comes into it. So things like the fact that I think that the "meta logic" of a system has to be very small and thus the building blocks of the system must be quite simple. Also that the processing of the system must have certain self-organizing properties such as compression of information in space and time, consistency over levels of abstraction and stuff like that. This is the more philosophical part I suppose. The point is that I need to make this space of possible systems as small as I can without making a mistake and excluding a working design for an AGI from the set. Oh, and I should mention that I'm thinking of some kind of information processing network here: some kind of neural network, Hebbian network, HMM, Bayesian network. Basically the space is a super set of all these things and more.
3) Genetic programming. (1) gives us a fine grained multi-objective fitness function and (2) defines a search space. Now I can't just run my GA and expect things to work here! Clearly the space in (2) is going to be pretty large. So at this point it becomes a bit of an experimental science and I have to mix things around a bit. So I'll be restricting the tests to just certain very simple objectives and restricting the space to smaller subspaces to see what works and what doesn't. Then try to cross over solutions to find systems that work for both etc. Hopefully at this stage I can zero in on promising parts of the space of possible designs. Perhaps even design my own attempts at functioning systems and throw them into the evolutionary mix and see if they can breed with other different partial solutions to form new and interesting things.
I guess in a sense it's the natural evolution of intelligence but on steroids: rather than having fitness related to intelligence very indirectly via survival here we measure a kind of computational intelligence very direction and equate it with survival. Also we restrict the space of possible designs as much as we can get away with to speed things up --- this is the theory side of the design I suppose.
So the big question then is: Can I make the theory strong enough to make the search space small enough so that I can make the series of very tiny little steps needed to go from a near zero level of intelligence up to high level intelligence?
Well, at least that's a one page summary of the basic nature of the approach. Hopefully it gives you some idea of what I'm thinking.
As for the name "vetta", in case you ever wondered. In Sanskrit it means, "one who has knowledge". However in Italian it also means "summit" or "peak" which is a reference of course to the the climbing of the GA solutions toward the peak of the fitness function, i.e. cybernance.
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