Gerald Edelman and AI

Gerald Edelman passed away on May 17, 2014 in La Jolla, California. In 1972 he won the Nobel Prize (together with Rodney Porter) for solving the antibody structure, and explaining how the immune system functions.  His research into antibodies led him to realize the enormous explanatory potential of selective-recognition systems. I had read most of his books, before meeting him in person in Tucson Arizona, during the World Conference on Consciousness in 2004. In his smart suite, this tall, radiantly intelligent and witty man, explained to his audience how his work on the immune system could provide an explanation for consciousness.

Basically, Edelman discovered that we have a great number of structurally different antibody cells in our body. When a bacterium or a virus enters our body these antibody cells  (also called “immunoglobulins”) rush towards them and test how well their structures “match” those of the intruders. This structural variability lies at the heart of antibody-based recognition. Edelman noticed that the adaptive immune response had all the hallmarks of an evolutionary process. The antibody recognition system “evolved” very quickly in order to adapt to the bacterial or viral attack. This was similar to a species adapting to environmental pressure.

Edelman posited that this evolutionary biological mechanism could also explain consciousness. Two significant discoveries strengthened his hypothesis. Firstly, that a fundamental property of cortical neurons is that they are organized in discrete groups of cells. Secondly, that synapses strengthen through use. Edelman theorized that our brain manages to recognize and process information thanks to selection on neuron groups that differ in their connectivity patterns. Several group cells would respond to incoming sensory information; their response would be modified by repetitive recognition that would strengthen, abstract and associate their connectivity. Edelman was in fact describing a cybernetic system with multiple positive feedback loops (he called them “re-entry” loops). Recent research by Stanislas Dehaene on the neural correlates (or “signatures”) of consciousness has shown that this re-entry mechanism is fundamental to how groups of cells respond to sensory information, and how a local recognition event becomes global (i.e. whole brain).

To demonstrate his theory Edelman and his colleagues built a number of “noetic machines” he called “Darwins”, or “brain-based devices” (BBDs). Built around a model of the neural connectivity of a simple brain a Darwin would “discover” the world around it, like an animal would.

Edelman’s robotic research has received very little notice, or appreciation, from the mainstream AI and robot research community. The reason for this is that the mainstream has abandoned long time ago the original goal of AI, which was to build a conscious machine. Since the 1980s AI research (as well as autonomous robots research) has focused on practical applications where pattern recognition and matching is paramount: for instance driveless cars, image and speech recognition, medical diagnosis, etc. This is where the money is nowadays, as the recent acquisition of the company Deep Mind by Google has amply demonstrated.

Edelman’s idea of simulating the brain in the robots is a close kin to neuromorphic computer technologies, still at their infancy. And yet – as I will aim to demonstrate in my forthcoming book “In Our Own Image” (Rider Books, due April 2014) – conscious machines can only evolve with a computer architecture like the one implemented in Edelman’s Darwins. Current AI and robotics will never be able to produce a self-aware machine. The breakthrough for this will come from genetics and a deeper understanding of developmental biology. How a cell divides and evolves into a nervous system? How does a nervous system differentiate across species? How does it develop into a brain? How does this brain communicate with the whole body, processing internal as well as external sensory information? How does the brain cells adapt, and modulate? Answers to these questions will come from biology and neuroscience. When we have the answers, we will have cracked the mechanism that Edelman hypothesized. And then, his curious Darwins may be remembered as the “amoebas”, the protozoa of a new line of evolution on Earth, that of the intelligent machines.