The State of Artificial General Intelligence Research: An Advanced Roomba VS. A Model Brain

There’s almost no end to the methods by which robots — and their “brains” — can be created. But trying to create an AI modeled after the structure of a human brain is an enormous task. There are so many cognitive architecture studies, says writer Andrew Tarantola in a recent Engadget column, that Artificial General Intelligence research is almost too complex to come up with any answers.

“From a historical perspective,” Christian Lebiere, Professor of Psychology at CMU and creator of the ACT-R architecture, told Tarantola, “AI in the old days, was almost synonymous with Strong AI, which is a strong claim about a pretty broad set of human-like capabilities that could be achieved. People, to some extent, over-promised and under-delivered, so that came to a crashing halt in the early ’80s and then, to some extent, the community scaled back to Narrow AI.”

Tarantola cites Paulie’s robot butler from Rocky 4 as an example.

The two most common models

Tarantola explains that this newer field of research often delves into seemingly-different extremes, from models of AI creation which stacks capabilities (like a roomba learning to vacuum, then sweep, then wash dishes, etc.), to something as complicated as (or even more so) the human brain.

There are two cognitive architecture models that the author says are most prominent in the research, but both of which have some “functional overlap.”

ACT-R, a method for creating Artificial General Intelligence which, in simple terms, is like mapping out a human brain, and
Soar, longer, higher-level cognitive capabilities.

“None of these fields or sub-fields are necessarily internally consistent or well-organized,” John E. Laird, Professor of Engineering at the University of Michigan and developer of the Soar cognitive architecture, explained to Engadget. He points out that research in these fields operate as pseudo-confederations, formed mostly by whoever decides to shows up to a specific conference. “It’s very difficult to get necessary and sufficient criteria for AGI or any of the sub fields because it’s really a social contract of the people who decide to work in the area,” he concedes. — from the original Engadget story

The difficulty in choosing a framework from which to work is in the fact that the human brain — seemingly the most obvious inspiration for Artificial General Intelligence construction — is that the brain is not a homogeneous mix of neurons.

Maybe, the article poses, it took so many millions of years for intelligent life to form because it takes so many thousands of generations to create neurological structures complex enough to support it.

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