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Cutting-Edge AI Projects?
Xeth writes "I'm a consultant with DARPA, and I'm working on an initiative to push the boundaries of neuromorphic computing (i.e. artificial intelligence). The project is designed to advance ideas all fronts, including measuring and understanding biological brains, creating AI systems, and investigating the fundamental nature of intelligence. I'm conducting a wide search of these fields, but I wanted to know if any in this community know of neat projects along those lines that I might overlook. Maybe you're working on a project like that and want to talk it up? No promises (seriously), but interesting work will be brought to the attention of the project manager I'm working with. If you want to start up a dialog, send me an email, and we'll see where it goes. I'll also be reading the comments for the story."
numeta
It's mainly a teaching + learning system for a system with input and output. I don't see anything built with it answering any rational questions or coming up with new ideas anytime soon, but if you do AI and don't know about them, you better catch up.
http://www.opencog.org/wiki/Main_Page
http://www.agiri.org/OpenCog_AGI-08.pdf
http://justingibbs.com/how-to-make-singularity-bearable-in-its-infancy
http://www.innergybv.biz/blog/?p=175
http://ieet.org/index.php/IEET/more/goertzel20080620/#When:22:49:00Z
http://xlaurent.blogspot.com/2008/06/opensim-for-opencog.html
There's a number of GSoC projects for OpenCog currently underway also:
http://code.google.com/soc/2008/siai/about.html
So the first release should be very interesting.
How we know is more important than what we know.
A lot of the older AI research is pure theory, but in the last 20 years or so it has been driven by the realization that we don't really have the tools to meet some of the early expectations of the field. If you are interested in the theoretical foundations of AI, though, you might want to look into compression, data representation, and computability, as well as general information theory. Claude Shannon's work would be a good place to start, and is cited frequently enough to give you a guided tour through AI.
Don't take this the wrong way, but I think you're drawing conclusions based on some serious misunderstandings, a large leap of faith, and an unfamiliarity with the fields in question.
As far as the requirement for "free will" in computer systems, you've put the cart before the horse and assumed that free will must exist for a system to simulate the mind, without ever proving that the mind is anything other than a deterministic system of unbelievable complexity. To presume that it is nondeterministic because you cannot adequately predict its behavior is pretty obviously bad logic.
The human brain does not take advantage of any known large-scale quantum effects, and, so far as we know, does not exploit any of them to produce random behavior. Once again, the inability to demonstrate a pattern is not evidence that a pattern does not exist.
Asynchronous computing does not produce or take advantage of quantum uncertainty. The levels of quantum uncertainty involved are swallowed by the impact of the deterministic systems they are filtered through, and drowned out by the impact of chaotic but deterministic variations in process scheduling, resource locking, and timing conflicts. The same goes for parallel computing for the same reasons- network latency is a chaotic, not random, phenomenon.
In terms of the use of quantum uncertainty for intelligent systems, there is no doubt that quantum computing holds tremendous promise, but also that its applications are hugely misunderstood. It is not a cure-all for general computing problems, and it particularly does not solve the problem of being insufficiently able to describe the your problem.
Bottom line is that chaos != randomness, and unpredicted != unpredictable. What you've got is good philosophy, but does not accurately depict the state of AI or what we know about the systems you are describing.