Algorithms: Transformation

Computation is the heart of an algorithm and involves arithmetic, decision-making, and repetition.

To apply this to getting dressed, you make decisions by doing some math on input quantities. Wearing a jacket might depend on the temperature. To a computer, part of getting dressed algorithm would be "if it is below 50 degrees and raining, then pick the rain jacket and a long-sleeved shirt."

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Computers Evolve a New Path Toward Human Intelligence | Quanta Magazine

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Stanley's realization led to what he calls the steppingstone principle - and, with it, a way of designing algorithms that more fully embraces the endlessly creative potential of biological evolution. Evolutionary algorithms have been around for a long time.

Key Ideas

Neuroevolution

Neuroevolution is a form of artificial intelligence. It is a meta-algorithm, an algorithm for designing algorithms. It adopts the principles of biological evolution in order to design smarter algorithms. Eventually, the algorithms get pretty good at their job.

Evolutionary algorithms

Traditionally, evolutionary algorithms are used to solve specific problems. For instance, the ability to control a two-legged robot. Solutions that perform the best on some metrics are selected to produce offspring.

In spite of successes, these algorithms are more computationally intensive than approaches such as "deep learning."

The steppingstone principle

It goes beyond traditional evolutionary approaches. It explains innovation. Instead of optimizing for a specific goal, it embraces the creative exploration of a diverse population of solutions.

The steppingstone’s potential can be seen by analogy with biological evolution: feathers likely evolved for insulation and only later became handy for flight.

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