Science Beyond Reductionism

Reductionism - the idea that difficult problems should be attacked by dividing them into simpler problems - is one the most effective strategies in the hard sciences. The justification "The Whole equals the sum of its Parts" has been used for thousands of years. Physics and related sciences, and the support disciplines of mathematics and computer science are all permeated by this Reductionist stance, and for good reason: It has worked really well. We have found compact explanations for all kinds of phenomena and have solved countless problems using these strategies.

But these strategies don't always work in Life Sciences like Biology, Genomics, Psychology, and Ecology. Often "The Whole is larger than the sum of its Parts" and when taking things apart, emergent phenomena like life, quality, intelligence, and meaning simply disappear. All attempts to capture the essence of life using Reductionist models, equations, and theories of living systems have failed. The Life Sciences have for decades managed to get by using other approaches. They use methods that emphasize Whole Systems and where context is to be exploited rather than discarded as a distracting nuisance. These methods adopt a more "Holistic" stance.

What has gone largely unnoticed is that many of these alternative approaches involve using weaker and weaker models, all the way to what we will call "Model Free Methods" (following Lionel S. Penrose, 1935). We have gathered a zoo of such methods and implemented some of them in computers. Amazingly, they allow discovery of solutions to problems "without understanding the problem" in the Reductionist sense. The advent of computers able to manipulate Big Data has suddenly made these Holistic Methods available as concrete and workable tools - not only in the life sciences but in engineering, economy, and other disciplines previously dominated by Reductionist models .

We finish with the claim that Artificial Intelligence failed in the 20th century because "Intelligence is Holistic" but AI was then mostly practiced by programmers - a profession that has, by its nature, always attracted the most hard-lined Reductionists. In the 21st Century, progress in AI will require that we convert AI into a Life Science and start using Model Free Methods the way other Life Sciences do. Researchers at Syntience Inc. have been pursuing this approach to AI since 2001 using an Algorithm named Artificial Intuition.

For background information, see http://artificial-intuition.com , http://monicasmind.com , or view the videos at http://videos.syntience.com - especially the video featuring Dr. Peter Norvig, Director of Research at Google.