Monica Anderson: Syntience Inc., & Holistic Artificial Intelligence 1

Monica Anderson has started a company - Syntience Inc., which sees the difference between reasoning and understanding.
And here is the rest of it.

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"Animals understand but do not reason; In humans, conscious, logical Reasoning is built on top of subconscious, intuitive Understanding. Both aspects were discussed at some parity until around 1955. With the advent of computers, Cognitive Science became dominated by programmers that concentrated their efforts on logic based, Reductionist theories. Since then, research has been overmuch preoccupied with Reasoning at the expense of Understanding. Without a foundation of Understanding, the Reductionist, logic based efforts at cognition were (and are) building castles in the air, since these systems have nothing to reason about."

Monica Anderson thinks that there has been an inordinate emphasis on the idea that a living things can be reduced to their essential essence.  The idea being that if one can understand the individual components of living things down to their most detailed parts, they will understand how they function and can predict what they will do, think, feel, or whatever information is desired.

The Glories of Reductionism

"I suppose the body to be just a statue or a machine made of earth." Rene Descartes The World And Other Writings, 1637


There is no denying that since the 1650s, with the rise of modern science, thousands of phenomena that have dealt the the structure and causes of things have been uncovered and understood.  This kind of science has been marvelous at answering the short term "why" of things.  By its very nature it ideally suited in breaking down complex structures and understanding their individual components.  From the very beginning of this movement lead by such figures as Isaac Newton and Galileo, mathematics was shown to be the most amazing descriptive language suited to understood a large portion of the visible universe.  Indeed, at the turn of the 20th century it was predicted by many, especially in the are of physics that humanity was on the verge of understanding the entire universe through mathematics and the reductionist approach.  Logical Positivism lead the way as the philosophy that would show the universe to be a massive machine, a massive mechanized entity that would allow man to control, predict and harness it.  Bertrand Russell, a Logical Positivist extraordinaire, capsulized with this bold quote: 

That man is the product of causes which had no prevision of the end they were achieving; that his origin, his growth, his hopes and fears, his loves and his beliefs, are but the outcome of accidental collocations of atoms; that no fire, no heroism, no intensity of thought and feeling can preserve an individual beyond the grave; that all the labors of all the ages, all the devotion, all the aspiration, all the noonday brightness of human genius are destined to extinction in the vast death of the solar system, and that the whole temple of man's achievement must inevitably be buried beneath the debris of a universe in ruins - all these things, if not quite beyond dispute, are yet so nearly certain that no philosophy which rejects them can hope to stand.

The present majority view in science is that with enough detail and examination, all systems will be ultimately understood and predictable to the smallest degree.  Thomas Nagel, not a supporter of reductionism in science, still expressed it well when in a now well known essay, What is it like to be a bat? (1973), stated, "Any reductionist program has to be based on an analysis of what is to be reduced.  If the analysis leaves something out, the problem will be falsely posed."


Ms. Anderson further explains the limits that the reductionist approach encounters.

We are taught that in order to be “Scientific” we must use Reductionist methods and context free models. Computer Science, Mathematics, Physics, and Chemistry favor these approaches since they so often provide excellent results in these domains. But in other disciplines we have discovered that Reductionist methods are insufficient to attack the most important problems we are facing. Biology, Ecology, Psychology, So- ciology, etc., and indeed all sciences that deal with Life, are forced to increasingly use alternative approaches in order to produce useful results. Precise measurements and reliable data are unavailable. Models become too complex and brittle to be useful. Context becomes more important and often dominates the problem statement.

In an early book in this area titled, Cybernetics, by Ashby (1956) the problem is explained in another way.

Science stands today on something of a divide. For two centuries it has been exploring systems that are either intrinsically simple or that are capable of being analyzed into simple components. The fact that such a dogma as 'vary the factors one at a time' could be accepted for a century, shows that scientists were largely concerned in investigating such systems as allowed (by) this method; for this method is fundamentally impossible in the complex systems.

We provide to you an extensive video where Ms. Anderson explains her views on the limits of reductionism given at Future Salon in January of 2010.  If you cannot see the embedded video, here is the link: http://vimeo.com/8937949.

 The Importance of Emergent Systems

Ms. Anderson divides these emergent systems into four major parts which she labels Bizarre Systems.  Ms. Anderson explains that depending on the discipline different nomenclature is used.  Terms like Impredicative Processes, Closed Loops of Causality, or Complex Systems.  The term bizarre systems was first coined by Dr. Kirstie Bellman and popularized by Dr. Stephen Kercel.

1. Chaotic Systems - "long term predictions cannot be made in systems exhibiting "Deep Complexity."
2. Irreducible Systems - reductionist methods do not work in open, inseparable, intractable, or constantly changing systems.  For instance, the laws of Thermodynamics cannot be used in open systems.
3. Ambiguity - Many systems deal with ambiguity, incomplete information, opinions, hypothesis, lies and other misinformation, either internally or as their input.  These cannot use reductionist models since models require complete and correct input data in order to produce useful output.
4. Emergent Effects - Things like quality, meaning, health, intelligence, etc. cannot be modeled.

We will continue with part two of this series in our next installment.