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Artificial Intuition
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.

Introduction

Most humans have not been taught logical thinking, but most humans are still intelligent. Most of our daily actions such as walking, talking, and understanding the world are based on Intuition, not Logic.

I capitalize (for stylistic reasons) all major named memes such as "Intuition" and "Logic"

Others have used the label "Artificial Intuition" for other ideas.

I will attempt to show that it is implausible that the brain should be based on Logic. I believe Intelligence emerges from millions of nested micro-intuitions, and that true Artificial Intelligence requires Artificial Intuition.

Intuition is surprisingly easy to implement in computers, but requires a lot of memory.

The "N" in the acronym "AN" for Artificial iNtuition is analogous to the Meyers-Briggs use of "N" for iNtuition



"Artificial Intelligence" is not a technology. It is a problem domain that is delineated by the criterion that arriving at solutions would require intelligence. We will recognize Intelligence when we see it; a Turing Test will not be required.



The top of each page has a summary of the page (in italics).



The Syntience video page contains several videos that discuss these matters and also recent work that extends beyond what is discussed in these pages.

I expect Artificial Intuition (AN) to become an important building block in "AI" systems - Those that aspire to solve problems in domains that we think require "Intelligence". But many of these domains can likely be handled using Artificial Intuition alone. I believe AN-based systems will, on their own, be able to provide impressive results in areas like Document Understanding, Speech Recognition, OCR Correction, Entity Extraction, Machine Translation, Web Page Quality Analysis, and Semantic Search; in short, in areas that require discovery of Semantics from lower level representations such as text, DNA sequences, and other streams of spatiotemporal events.

In what follows I will argue that AN approaches are Biologically Plausible; that they rather elegantly sidestep many problems and limitations of Logic-based AI approaches; and that they are likely to be implementable in current or near-future generations of computer hardware.

I will begin by analyzing why certain kinds of problems are thought to "require intelligence"; next I will contrast Intuition- and Logic-based problem-solving mechanisms; and I attempt to make plausible that these difficult and important problems require Intuition rather than Logic.

Be forewarned that acceptance of these ideas will likely require a different stance on the nature of Artificial Intelligence, the nature of Intelligence in general, and on Science itself. Some people, including scientists in disciplines like Ecology and Systems Biology have adopted this stance; if everything on this site sounds to you like "motherhood and apple pie", then you know you already have.