Blogs

I wrote a short essay for a related class about how neural networks might (or might not) be able to help speed up emergent simulations, and I may try and test this idea in my tree simulation project. Here is my essay if you are interested; please let me know what you think!

Throughout this course, I've found one aspect of our discussions and readings to be somewhat troubling. There is a tendency proclaim emergence as a penultimate field, emergent phenomenon as universal and terribly important...in short, emergence as not just a new kind of science but the coming messiah of science. It could just be that the field, in particular the content matter, is inherently of a universal and penetrating sort. So if emergence itself claims to be the end-all, be all of reality, then our conversations and emergent literature should similarly describe it as such. I think this is true to a certain extent. In my eyes, however, there is also a some aggrandizing in our conversations and the literature.

Dr. Ursula Goodenough (Professor of Biology, Washington University, St. Louis, MO, author of The Sacred Depths of Nature): "EMERGENCE: NATURE'S MODE OF CREATIVITY" She'll be speaking as part of a larger forum on Saturday, April 8th from 1PM-5PM in Sharpless Auditorium Ursula Goodenough is currently Professor of Biology at Washington University in St. Louis MO. She was educated at Radcliffe and Barnard Colleges (B.A. Zoology, 1963), Columbia University (M.A. Zoology, 1965) and Harvard University (Ph.D. Biology, 1969), did 2 years of postdoctoral at Harvard, and was Assistant and Associate Professor of Biology at Harvard from 1971-1978 before moving to Washington University.

Are now available (at least some of them).

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.

I found an article on Slashdot today about the decision making patterns used by cockroaches. It echoes many of the same ideas in the book I read for my project (Emergence, by Steven Johnson). It's a very short article and it demonstrates how cockroach/ant/etc colonies can function without a centralized authority dictating the behavior of the group.

The brain is absolutely so complex and interesting. I like how Professor Blank defined an emergent system as being below the level of meaning and rational systems as being at or above the level of meaning. This really clarified much of the confusion I have had about the different systems. It finally has been clear to be the difference between an emergent system and intelligent design; the fact that an emergent system does not have an outcome in mind. I do agree that some emergent systems might not have an outcome in mind, but whose “outcome in mind” is the answer. I do agree with Laura when she said that an emergent system might not have an outcome in mind, but it might have a certain outcome for some other system. I think that something interesting does not always have to be a solution to something, but I think that it can be. I think that emergent systems are emergent until they give a solution for something. It is also possible that anything interesting could always be a solution to something, and maybe we just do not know yet what that something is. I also feel that there can be systems that are emergent and rational. As of now, I think that is how I view the brain. I think that it has components from both emergent and rational systems. I agree that they brain is like a “super computer” made up of many neurons (computers). But I also feel that the brain is not necessarily like an emergent system because it has some goals, such as forming us humans physically the same, but it some sense different, and this is where I think the emergent system somewhat comes in.

A friend of mine sent me an interesting link to an article about a new company's mission to enter the market of 'biotech' pets. GeneDupe consists of a team of biologists and computer scientists who have created a virtual cell that represents the real thing right down to the mitochondria, Golgi bodies, etc. They then 'load' the genome of a particular species, which turns into a fertilized egg and ultimately grows into an adult. To make matters interesting, it seems as though GeneDupe also employs image recognition/processing. The software is able to take a picture of a mythical creature (ie centaur, dragon, griffin, or what have you) and it finds genomes of similar animals, splices the genes together, allows mutation, and then lets evolution take control.

OK, so my title was a bit provocative, but here's what I'm going to do for my project (and hopefully it will work). (When we went around on Wednesday and explained our projects, I said basically this, but now I'd like to elaborate it more.) Training Neural Networks to do ANDs and ORs is all fine and good, but I feel that it misses the point, at least in terms of emergence. Neural Networks show great potential in terms of solving computing and AI problems, but I'd like to go somewhere different. I want to write a simulated world where the creatures are run by neural networks. The inputs to the neural networks will be the "sense": vision, for example, could be represented by two parameters: one for the distance of the nearest object in the line of sight, and another one for the "color", where food would have one color, other creatures another color, and obstacles a third. (The distance and color would have to be normalized so as to be a number between zero and one, of course.) The outputs, then, could be actions: one output could be whether to move forward or not, another to be whether to turn left, right, or not at all, and maybe another could be to change the creature's own color.

I read this article for another class, I thought it was good/applicable to our discussions of connectionism/neural networks. It's a recent fMRI study that looked at memory reinstatement. The researchers used pattern association algorithms to look at distributed representations of the brain during cueing/recall. Their results support a more connectionist model of memory encoding citing, for example, that the fusiform face area does contribute to 'face memory' but if such maxima of activation are removed from the representation other areas are just as indicative of the (face) stimulus.