Monica’s Mind

Introduction

Computers are Logic based devices. We used to believe implementing Intelligence in computers would be easy because we thought brains were built on Logic. AI researchers and programmers were especially skilled at logical, step-by-step Reasoning and analysis and when thinking about thinking, all they saw was Logic and Reasoning. But by the end of the 20th century it became clear that there was another component to Intelligence that had been severely neglected – intuitive Understanding.

Reasoning requires Understanding

Reasoning is a conscious, goal-directed and Logic-based step-by-step process that takes seconds to years. In contrast, Understanding is a subconscious, aggregating, Intuition-based and virtually instantaneous recognition of objects, agents, and concepts.
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A million man-years has been spent on Artificial Intelligence. (1)

Since the beginning in the early 1950s we’ve gone through dozens of paradigms and thousands of approaches trying to build software systems and robots that would behave “more intelligently”, more like humans do. This research has given us many advanced programming tricks and a few amazing consumer products, but nothing has come of all this effort that truly deserves the label “intelligence”.

But what if all these failed paradigms and approaches shared a common fatal flaw? Could it be that AI was actually a much easier problem than most people working in the field believe it is?
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It seems like there are hundreds of ideas about how to create an Artificial Intelligence. But if we examine the foundations of AI, and look at the tacit assumptions of historical AI projects, we discover something interesting.

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This blog entry elaborates on previously published material.

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

Have you ever wondered why robots walk with a robotic gait? It is similar to a “zombie” gait, and as we all know, zombies have no brains. A running zombie would look like it was intelligent, wouldn’t it?

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I am the current facilitator of the San Francisco Bay Area AI Meetup. At these meetups I have occasionally been approached by students considering AI as a career path. They want to know what kind of AI research and projects to pursue and what to study.

It would be easiest to study the “Weak AI” technologies used in places like game companies, expert system companies, etc. Weak AI is basically a bagful of clever programming tricks and techniques that allow certain specific problems to be solved using current technology.

But if anyone seriously wants to work on “Strong AI” (true intelligence) and expects to be in it for the long haul I will have to put forward a dilemma that they have to deal with themselves.

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