A Primer for Contemporary ‘Machine Learning’ Initiatives
Red Hat’s Ulrich Drepper has a neat write-up at opensource.com on ‘an introduction to machine learning today‘ that both sheds light on the history and past of AI while looking forward to what might come next.
Machine learning and artificial intelligence have changed a lot since the last wave of interest, about 25 years ago.
Machine learning and artificial intelligence (ML/AI) mean different things to different people, but the newest approaches have one thing in common: They are based on the idea that a program’s output should be created mostly automatically from a high-dimensional and possibly huge dataset, with minimal or no intervention or guidance from a human. Open source tools are used in a variety of machine learning and artificial intelligence projects. In this article, I’ll provide an overview of the state of machine learning today.
In the past, AI programs usually were explicitly programmed to perform tasks. In most cases, the machine’s “learning” consisted of adjusting a few parameters, guiding the fixed implementation to add facts to a collection of other facts (a knowledge database), then (efficiently) searching the knowledge database for a solution to a problem, in the form of a path of many small steps from one known solution to the next. In some cases, the database wouldn’t need to or couldn’t be explicitly stored and therefore had to be rebuilt.
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