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."

Comments

An obvious one.

[v(*_*)vvvv]

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.

Re:An obvious one.

[QuantumG]

I think the Deep Belief Networks of Hinton et al are way ahead of Numenta.. in that they are real science with measurable results that has been reproduced by multiple implementations. The 2006 paper that started it all and Hinton's presentation on google video:

http://www.gatsby.ucl.ac.uk/~ywteh/research/ebm/nc2006.pdf
http://video.google.com.au/videoplay?docid=228784531481853811

A formal analysis:

http://www.cs.utoronto.ca/~ilya/pubs/2007/inf_deep_net_utml.pdf

Application to natural language processing:

http://www.cs.swarthmore.edu/~meeden/cs81/s08/DahlLaTouche.pdf
http://www.machinelearning.org/proceedings/icml2007/papers/425.pdf

Reproducing Hinton and extension to and evaluation in other domains:

http://www.machinelearning.org/proceedings/icml2007/papers/331.pdf

Use in Computer animation of facial expressions:

http://aclab.ca/users/josh/downloads/pubs/23_Susskind_Hinton_Movellan_Anderson.pdf

Most impressive:

http://www.cs.utoronto.ca/~ilya/pubs/2007/aistats_multilayered.pdf

A C++ implementation (although it has much Python love):

http://plearn.berlios.de/

So yeah, there's some pretty good demonstrations of how powerful DBNs are.. Numenta is lagging behind.

OpenCog

[QuantumG]

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.

Re:Fundamental research?

[debatem1]

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.