Artificial Intuition – A novel approach to AI

I have come across a web sight presenting a novel concept of Artificial Intuition. Though preliminary, it seems to make a lot of sense. A theory-less predictive method might prove to be a significant as complementing theory and logic based methods. The author is Monica Anderson,

Here are a few highlights from the site:

Artificial Intuition is a new, different, and promising (but so far unproven) way to approach a large subset of the problems we believe require “Artificial Intelligence”.

Here I introduce the approach in general terms, but please note that the theory leads to a very specific, novel, and quite simple algorithm. Exploration of implementations is underway.

The purpose of Intelligence is Prediction. Evolution of the ability to predict agents and phenomena in the environment improved survival rates and created a strong evolutionary pressure to develop better and longer term predictions. This is the reason Intelligence evolved.

I define a “Bizarre Domain” as a problem domain that has all of these four properties: It is Chaotic, it requires a Holistic Stance, it contains Ambiguity, and it exhibits Emergent Properties. I examine sixteen kinds of problems that fall into these four categories. Each problem-kind poses serious problems for logic-based attempts to model the domain and solve problems in it. “Modeling the World”, and “Discovery of Semantics” are problems in Bizarre Domains.

Bizarre Systems

Most humans have not been taught logical thinking, but most humans are still intelligent. Contrary to the majority view, it is implausible that the brain should be based on Logic; I believe intelligence emerges from millions of nested micro-intuitions, and that Artificial Intelligence requires Artificial Intuition. Intuition is surprisingly easy to implement in computers.

Certain situations are complex enough that predictions cannot be made to sufficient precision or sufficiently far ahead to be useful. We call this the region of the absurd; Logical models can not be made, and Intuition will not work either. But “close to the axes” we can use either Logic, Intuition, or either, depending on the problem and the requirements. A diagram is used to illustrate the point.

Complexity Versus Predictability

Intuition, which is what we use to handle our daily problems, can handle significantly more complex problems than science but is incapable of long-term predictions even for simple problems.

Higher up and close to the to the Y axis, Intuition based methods outperform Logic and Science. This is the area where we find Bizarre systems and Bizarre problem domains. Here we find the Discovery of Semantics of all kinds, such as understanding language, analyzing visual data and understand what we see, hearing melodies and sounds, and being able to make sense of the world in general. This is the area in which humans will outperform computers on most tasks. This is the domain of problems requiring Intelligence.

Areas of Competence

We can trade the Seven Values of Logic Based Science for about a dozen Benefits of Intuition Based Methods. The brain needs none of the former and provides all of the latter, which indicates that Artificial Intuition is a Biologically Plausible theory. The system is designed to encourage all of these at low levels. The most important features are expected to be emergent at higher levels.

The author does not mention subjects such as fuzzy logic and Bayesian networks that might be relevant to a theory-less prediction paradigm.  Much more can be found in the web site. A very interesting read.