i found jacob's lists arbitrary and thus pretty meaningless. (for full disclosure, i'm an hci researcher in a lab that didn't make the lists.) xerox parc and cmu have been the source of a lot of great work (among others), but most of the field is a landscape of intellectual niches, nooks, and crannies spread over many institutions.
to me, ``best'' really depends on the sub-issues you are interested in. once you have identified those issues, you can drill down to the relevant labs and people.
I just finished a transaction on ebay for a craptop (= second, low-end laptop to run linux for email/web/mp3s). i did a fair share of hunting around newsgroups, craigslist, etc. and this was the best deal i found by far. generally, the prices were around $1.50/MHz for working laptops everywhere i looked.
the specs are 90MHz Pentium, 11" screen, 72 MB Ram, sound. no cd-rom, no ethernet, no modem. no idea if the battery works (haven't actually received the laptop yet); not too important for me, tho.
i think the problem is that midrange laptops (166-400) are still quite usable and more efficient than newer ones, so there's still high demand for them. and many folks have the same idea i do for craptops.
the high prices just reflect the laws of economics.... can't beat those!
I am a PhD student at the MIT AI Lab, and I wanted
to thank you for a good stab at a high level
overview of a diverse and dynamic field.
I do reiterate a previous poster's comments that
the best way to divide the field is into "core AI" -- giving computers human capabilities -- and "applications" -- using core AI technologies to improve quality of life.
also, you omitted speech recognition and
reinforcement learning, which are two important
subareas worth mentioning. readers interested
in those areas can go to
i could see this as a way to create a really high-performance application server, but the
cost is the lack of protection the kernel
barrier gives you. i would hate to have to
reboot every time i found a bug in my app!
Slashdot Mirror
to me, ``best'' really depends on the sub-issues you are interested in. once you have identified those issues, you can drill down to the relevant labs and people.
I just finished a transaction on ebay for a craptop (= second, low-end laptop to run linux for email/web/mp3s). i did a fair share of hunting around newsgroups, craigslist, etc. and this was the best deal i found by far. generally, the prices were around $1.50/MHz for working laptops everywhere i looked.
.... can't beat those!
the specs are 90MHz Pentium, 11" screen, 72 MB Ram, sound. no cd-rom, no ethernet, no modem. no idea if the battery works (haven't actually received the laptop yet); not too important for me, tho.
i think the problem is that midrange laptops (166-400) are still quite usable and more efficient than newer ones, so there's still high demand for them. and many folks have the same idea i do for craptops.
the high prices just reflect the laws of economics
I do reiterate a previous poster's comments that the best way to divide the field is into "core AI" -- giving computers human capabilities -- and "applications" -- using core AI technologies to improve quality of life.
also, you omitted speech recognition and reinforcement learning, which are two important subareas worth mentioning. readers interested in those areas can go to
http://sls.lcs.mit.edu/ (mit spoken lang sys)
and
http://www.cse.msu.edu/rlr/ (RL repository)
m.
i could see this as a way to create a really high-performance application server, but the cost is the lack of protection the kernel barrier gives you. i would hate to have to reboot every time i found a bug in my app!