What Are Intelligence? And Why?

Summary:

Randall Davis's 1996 presidential address was an overview on human intelligence. In order to understand how artificial intelligence might be created, it is important to learn the theories involved with current human and animal reasoning. The five views in reasoning are mathematical logic, psychology, biology, statistics, and economics.

Since the paper was mainly a general overview, I'm more interested in discussing some aspects of the paper as they relate to AI.

Discussion:

This summary is being written post-Davis conference call, so I've had a bit of time to think about our discussion with him, as well as my thoughts from before.

My main conclusion that I've reached after our call was that an artificial intelligence breakthrough shouldn't have been developed by now. From an evolutionary perspective, Davis mentions that intelligence is built over time from individually formed pieces. Each part of an organ is developed over time by incrementally building on previous developments. Even the brain is composed of different sections that are compartmentalized. Sometime in homo sapien's past, these compartments connected to each other in a unique way that intelligence was formed.

Examining the development of AI, I noticed that each subfield of AI is just like another organ or section of the brain. By themselves, the subfields are too focused to offer true intelligence. Tools to recognize language are not built to recognize images, image recognition engines cannot develop plans, and planners cannot understand speech. The only way to increase the intelligence of a system is to find ways to interconnect all of these components to offer reasoning.

Each subfield should also be as developed as possible. Right now, the evolution of AI is in individual specialization. As highly-accurate, full systems begin to be developed, are widely available, and easy-to-use, then diverse systems will be created and the focus on specialization will fade.

My main concern is that research on merging fields will be rather slow. Current graduate students are less likely to work on areas that require expertise in multiple fields, since Ph.D.s focus on high specialization. As the number of fields required to improve the intelligence of systems grows, research on artificial intelligence systems will require more effort from multiple professors and students.