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The instructions tell a computer how to transform a set of facts into useful information.
The facts are data. The useful information is knowledge for people, instructions for machines or input for another algorithm. Typical examples are sorting sets of numbers or finding routes through maps.
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."
The last step of an algorithm is output - expressing the answer.
Output to a computer is usually more data. It allows computers to string algorithms together in complex ways to produce more algorithms. Output can also present information, such as putting words on a screen.
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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 algor...
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."
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.