Con-nect-ion(-ism)

Page 175 caught my eye in chapter 4. The section that was being discussed dealt with "Objectivism and Traditional AI." The bottom of 175 talked about connectionist approaches to AI, and dropped some big names who work a lot with this. Pretty much connectionism are represented with neural networks and how we are able to model and see how the brain works. They went away for a while, but as of the late they have been coming back into fashion with respects to AI.

The part talked about how these types of models are represented are vectors in a multidimensional space, where there positions are not anchored to anything, but can adjust freely due to the environment it is currently in. These models have been used to show how certain genetic algorithms are performed, such as the one proposed by John Henry Holland. He took notions from evolution and applied them to computers, and was able to create a population and reproduce certain traits (especially dominant ones) for many generations. He took evolution and made it so we can see it occur in a matter of seconds, versus many lifetimes.

A person can utilize connectionist models to show associations with other like things. Hebbian Learning can also be represented with using neural networks. Since Hebbian Learning is all about associations, a person could model specific objects with other objects. Hence, machine learning can take place along with semantic workings of the like.

-Bryan