http://www.scottaaronson.com/blog/?p=755#comment-27693.
> Regarding recording, the main concern was that I didn’t want to inhibit open-ended discussion by recording everyone’s umms, uhhs, and half-baked thoughts for posterity. In addition, there didn’t seem like a pressing need for recording, since I’ve already essentially written a “course textbook” in the form of my essay (http://www.scottaaronson.com/papers/philos.pdf). But as I said, we will have student reaction essays for each class session and they will go up on the website.
There is a whole strain of work from the 80s that was partly inspired by Heidegger and phenomenology AND the realization that some classical AI problems were computationally intractable. This work was controversial, to say the least, but influential in its way. I'd suggest that any course on this topic ought to at least touch on this work.
http://mit.dspace.org/bitstream/handle/1721.1/6947/AITR-802....
This really struck a chord with me:
> What the learner acquires through experience is not represented at all but is presented to the learner as more and more finely discriminated situations, and, if the situation does not clearly solicit a single response or if the response does not produce a satisfactory result, the learner is led to further refine his discriminations, which, in turn, solicit more refined responses.
Comparing that to the meaning of "learn" commonly used in ML to mean "finding optimal weights for some features" and you can see the gulf pretty starkly.
