A simple electrical circuit has learned to recognize flowers based on their petal size. That may seem trivial compared with artificial intelligence systems that recognize faces in a crowd, transcribe spoken words into text, and. However, the tiny circuit outshines conventional machine learning systems in one key way: It teaches itself without any help from a computer—akin to a living brain.
The nodes are arranged in layers, with the first layer taking the inputs and the last layer producing the outputs. For example, the first layer might take as inputs the color of the pixels in black and white photos. The output layer might consist of a single node that yields a 0 if the picture is of a cat and a 1 if it is of a dog.
To train the system with a minimal amount of computing and memory, the researchers actually built two identical networks on top of each other. In the “clamped” network, they fed in the input voltages and fixed the output voltage to the value they wanted. In the “free” network, they fixed just the input voltage and then let all the other voltages float to whatever value they would, which generally gave the wrong voltage at the output.
Realize that 'simple' is a relative word: I'm an electronics engineer, and I've built very many 'simple electrical circuits' in my lifetime (and some complex ones). The only thing that the 'simple electrical circuits' I've ever built have taught themselves, is how to fail..
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