Challenging the AI Cabal: More Tasteful ART, Please

January 7, 2022

If you follow the often confused fault lines of artificial intelligence (whatever that is), you know that some folks with big IQs have some Antonio Brown-type energy in their logical hearts.

Some of the AI dust ups focus on the messy intersection of management, bias, and cost reduction methods. Others are more esoteric, relying on a happy confluence of high ideals, smart people, and some hand crafted algorithms. Others are just chasing grants, writing research papers for outstanding peer reviewed publications loved by tenure review committees, and giving graduate students something to do before these folks return to their homelands.

Do I sound jaded?

Navigate to “Deep Learning Can’t Be Trusted, Brain Modeling Pioneer Says” and learn about:

Adaptive Resonance Theory (ART).

The article says:

ART can be used with confidence because it is explainable and does not experience catastrophic forgetting, Grossberg says. He adds that ART solves what he has called the stability-plasticity dilemma: How a brain or other learning system can autonomously learn quickly (plasticity) without experiencing catastrophic forgetting (stability).

The method has been around since 1976, and one might assume that decades of investigation and application would allow a better approach to dominate the field of artificial intelligence (whatever that is). The fact that ART is one method suggests that the Darwinian model allows survival, which is good. But the survivor has not stamped out pesky alternatives.

The article adds some color to the ART:

ART’s networks are derived from thought experiments on how people and animals interact with their environment, he adds. “ART circuits emerge as computational solutions of multiple environmental constraints to which humans and other terrestrial animals have successfully adapted….” This fact suggests that ART designs may in some form be embodied in all future autonomous adaptive intelligent devices, whether biological or artificial. “The future of technology and AI will depend increasingly on such self-regulating systems,” Grossberg concludes. “It is already happening with efforts such as designing autonomous cars and airplanes. It’s exciting to think about how much more may be achieved when deeper insights about brain designs are incorporated into highly funded industrial research and applications.”

Will ART paint other methods into a corner? Which AI (whatever that is) can one trust? Perhaps we should ask IBM Watson?

Stephen E Arnold, January 7, 2022

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