(8th-June-2020)
• Neural networks are a popular target representation for learning. These networks are inspired by the neurons in the brain but do not actually simulate neurons. Artificial neural networks typically contain many fewer than the approximately 1011 neurons that are in the human brain, and the artificial neurons, called units, are much simpler than their biological counterparts.
• Artificial neural networks are interesting to study for a number of reasons: As part of neuroscience, to understand real neural systems, researchers are simulating the neural systems of simple animals such as worms, which promises to lead to an understanding about which aspects of neural systems are necessary to explain the behavior of these animals.
• Some researchers seek to automate not only the functionality of intelligence (which is what the field of artificial intelligence is about) but also the mechanism of the brain, suitably abstracted. One hypothesis is that the only way to build the functionality of the brain is by using the mechanism of the brain. This hypothesis can be tested by attempting to build intelligence using the mechanism of the brain, as well as without using the mechanism of the brain. Experience with building other machines - such as flying machines, which use the same principles, but not the same mechanism, that birds use to fly - would indicate that this hypothesis may not be true. However, it is interesting to test the hypothesis.
• The brain inspires a new way to think about computation that contrasts with currently available computers. Unlike current computers, which have a few processors and a large but essentially inert memory, the brain consists of a huge number of asynchronous distributed processes, all running concurrently with no master controller. One should not think that the current computers are the only architecture available for computation.
• As far as learning is concerned, neural networks provide a different measure of simplicity as a learning bias than, for example, decision trees. Multilayer neural networks, like decision trees, can represent any function of a set of discrete features. However, the functions that correspond to simple neural networks do not necessarily correspond to simple decision trees. Neural network learning imposes a different bias than decision tree learning. Which is better, in practice, is an empirical question that can be tested on different domains.
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