Learning to Learn from Nature

Science has frequently taken pages from the book of Nature when trying to solve its puzzles. The ability to learn is clearly a natural phenomenon. As such it is no surprise that learning algorithms have been inspired by nature.

A well known example are neural networks. These algorithms model, though fairly simply, the operations nerves. Neural networks have been used extensively to perform character recognition, face recognition, to generate gaits, to classify data, and so much more. It has been shown that neural networks approximate the process of Fourier transformations and can approximate any function given the correct basis functions.

Genetic algorithms are another popular example. These algorithms constitute a class of algorithms that model the operations of genetic material. In short, the first generation is created by randomly generating multiple hypotheses. A function, known as the fitness function, is used to measure how accurately a hypothesis estimates the target function. Hypotheses are chosen based on their fitness to be bred. The next generation is then measured and again bred. This process repeats until some desired accuracy is achieved.

Another example, though less well known, is reinforcement learning. Reinforcement learning was inspired by the concepts of pleasure and pain. However Sutton & Barto showed that any algorithm that learns from interaction with its environment is a reinforcement learning algorithm. Reinforcement learning has also been widely employed. It is particularly good  where learning needs to be performed online.

What other learning algorithms have been inspired by nature? What are their origins? How have they grown since then?


  1. why not start a blog on intelligent memory chips ?
    It will be cool enough to do research in chips that are designed to be intelligent.. ain't it???

  2. I'm not sure what you mean exactly but it doesn't sound like my area of expertise.