Here is the paper I mentioned in class during our discussion of whether or not computers will ever be able to achieve behavioral complexity comparable to that of humans: When will computer hardware match the human brain, by Hans Moravec in 1997. "This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. The processing power and memory capacity necessary to match general intellectual performance of the human brain are estimated. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s." That soon? It sounds a little bit crazy, but the general idea is pretty solid, in my opinion: that eventually, in the not-too-distant future (maybe not quite so soon as 2022) computers will easily rival or surpass human intelligence. I would be interested to know whether or not Moravec can convince Professor Grobstein.

Although, as Doug pointed out in class, there are important differences between connectionist networks on computers and neurons in the brain, I still think that the name "Neural Networks" is justified by the fundamental similarity between the two. This fundamental similarity, which I think is very important and which also implies many promising possibilities for simulated neural nets--based on phenomena in the brain--that have not yet been explored in AI research, ...this fundamental similarity is the way in which information is manipulated through destroying some info and then copying the result and distributing it to create new info. That is, when the activations of input nodes in a computer network are summed in a node, which node each of those signals came from is lost. But the information that remains--the combined strength of those inputs--is then copied and distributed to nodes in the next layer, where the process is repeated. I think that this method of information manipulation is an incredibly important concept in and of itself; therefore, we should acknowledge connectionist networks' debt to the structure of the brain. Furthermore, I think there is (or will eventually be) a lot more we can do with neural nets based on observations of how the brain works.

Here is my attempt to better articulate my argument that agent-based models are representable as CAs (which we already know because a Turing machine can be built in Conway's life... but this shows that it's not very complicated to do for a simple agent-based model like Langton's Ant): (also, before the argument, the implication I'm going for--that the distinction between CAs and agent-based models is in fact arbitrary, and although this does not mean that the distinction is not a useful concept, we should recognize its subjectivity) I think that any agent-based model can be represented as a CA.