The talk was given by Dr. Paul A. Rhodes, the CEO of a company called Evolved Machines, which is focused on studying neural circuitry and finding ways to synthesize it artificially.  This, in essence, is the “new paradigm” mentioned in the title of the talk: the old paradigm being the basis of artificial intelligence on symbolic logic.

Artificial Intelligence, at least in the way that it is defined on Wikipedia, is “founded on the claim that a central property of human beings, intelligence—the sapience of Homo Sapiens—can be so precisely described that it can be simulated by a machine.”  The point of the talk was that the current method of modeling AI systems on symbolic logic does not really simulate the mechanisms that underlie our intelligence – our neural circuitry doesn’t carry out operations in discrete, logical steps, but rather in a probabilistic, chaotic manner.

For this reason, AI researchers have been able to solve certain types of problems extremely well: where the states and set of states are clearly defined (such as playing chess).  However, other types of problems, such as visual recognition of objects in different contexts, remain very difficult.  I would tend to think of the second problem, something that almost human does automatically and without thought, to be much more at the heart of being human than playing chess.

And this is exactly what Dr. Rhodes and Evolved Machines are hoping to accomplish – he showed some great visualizations (which I wish I could have captured for you) of models they have built using simulated neuronal interactions, including one that showed how the neurons which receive signals from retinal nerves might re-wire themselves in response to visual input in order to better, and more quickly, recognize objects.

This is an example of the new paradigm as he described it – artificial neural circuits built of simulated neural components and characteristics, such as:

• Electrically active trees which homeostatically self-regulate
• Dynamic (probabilistic) synapses
• Self-organization of wiring in response to sensory input

To me, it is amazing that computing power has grown to the point where we can start programming computers to model systems that don’t work like computers.  As neuroscience, computer science, and other disciplines continue to collaborate in ways such as these, I am encouraged by the prospect that we will soon see true aspects of human intelligence being synthesized.