Human and Machine Learning

One problem with most work in artificial intelligence (AI) is that much work on trying to model human thinking, e.g. in problem solving, does not make this distinction between different levels of conscious processing. Therefore, in most AI models, in some sense, all the thinking is done at the same level of conscious deliberation, and as a result, is ridiculously slow, and frequently the models do not scale up from small scale toy problems to real world problems. Some people are addressing this difficulty, especially Stuart Russell, who uses the notion of "anytime algorithms" which must deliver a result whenever the conscious deliberation of an agent interrupts the lower level processing which is "doing the thinking".

Humans however, are fairly slow to learn, but fast to decide, and to act. As Alan Newell pointed out many years ago, there just cannot be many synaptic jumps from one neurone to another in the 2 seconds or less that we often make a decision. This is part of the reason for the popularity of connectionism as a philosophy for cognitive science, and neural networks as an approach to understanding the nature of human mental processes.

However, many neural network approaches, especially the most popular, known as "back propagation" take a ridiculous amount of time to learn even the most simple of tasks. These models frequently take thousands of trials to learn to classify some simple examples that people would do with just a handful of examples. Furthermore, most such models just learn to classify examples. But, there is far, far more to human learning than just classifying examples.

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