By Margaret A. Boden
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Extra info for Artificial Intelligence and Natural Man
When the illumination or pose changes, when we grow our hair or put on glasses, or when we age, certain parts of the face image change but some parts do not. This is similar to customer behavior in that there are items we buy regularly and also impulse buys. The learning algorithm finds those unchanging discriminatory features and the way they are combined to define a particular person’s face by going over a number of images of that person. What We Talk about When We Talk about Learning In machine learning, the aim is to construct a program that fits the given data.
Machine learning, and prediction, is possible because the world has regularities. Things in the world change smoothly. We are not “beamed” from point A to point B, but we need to pass through a sequence of intermediate locations. Objects occupy a continuous block of space in the world. Nearby points in our visual field belong to the same object and hence mostly have shades of the same color. Sound too, whether in song or speech, changes smoothly. Discontinuities correspond to boundaries, and they are rare.
These disciplines included physics, statistics, psychology, cognitive science, neuroscience, and linguistics, not to mention computer science, electrical engineering, and adaptive control. Perhaps the most important contribution of research on artificial neural networks is this synergy that bridged various disciplines, especially statistics and computer science. The fact that neural network research, which later led to the field of machine learning, started in the 1980s is not accidental. At that time, with advances in VLSI (very-large-scale integration) technology, we gained the capacity to build parallel hardware containing thousands of processors, and artificial neural networks was of interest as a possible theory to distribute computation over a large number of processing units, all running in parallel.
Artificial Intelligence and Natural Man by Margaret A. Boden