AI that Mimics the Human Brain
The Next Revolution in Artificial Intelligence
The term, Artificial Intelligence was coined in 1956 by John McCarthy at Massachusetts Institute of Technology. This year, computer scientists celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing. Turing set the basis for digital computing in the 1930s to anticipate our current technilogical age. The quest still remains to create a machine as adaptable and intelligent as the human brain.
Computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing’s work to its next logical step by translating her 1993 discovery of “Super-Turing” computation into an adaptable computational system that learns and evolves, using input from the environment in a way much more like our brains do than classic Turing-type computers. She and her post-doctoral research colleague Jeremie Cabessa report on the advance in the current issue of Neural Computation.
“This model is inspired by the brain,” she says. “It is a mathematical formulation of the brain’s neural networks with their adaptive abilities.” The authors show that when the model is installed in an environment offering constant sensory stimuli like the real world, and when all stimulus-response pairs are considered over the machine’s lifetime, the Super Turing model yields an exponentially greater repertoire of behaviors than the classical computer or Turing model. They demonstrate that the Super-Turing model is superior for human-like tasks and learning.
“Each time a Super-Turing machine gets input it literally becomes a different machine. Classical computers work sequentially and can only operate in the very orchestrated, specific environments for which they were programmed. They can look intelligent if they’ve been told what to expect and how to respond, Siegelmann says. But they can’t take in new information or use it to improve problem-solving, provide richer alternatives or perform other higher-intelligence tasks”.
In 1948, Turing himself predicted another kind of computation that would mimic life itself, but died without developing his concept of a machine that could use what he called “adaptive inference.” In 1993, Siegelmann, showed independently in her doctoral thesis that a very different kind of computation, vastly different from the “calculating computer” model and more like Turing’s prediction of life-like intelligence, was possible. She published her findings in Science and in a book shortly after.
Siegelmann says that the new Super-Turing machine will not only be flexible and adaptable but economical. This means that when presented with a visual problem, for example, it will act more like our human brains and choose salient features in the environment on which to focus, rather than using its power to visually sample the entire scene as a camera does. This economy of effort, using only as much attention as needed and is another hallmark of high artificial intelligence.
“If a Turing machine is like a train on a fixed track, a Super-Turing machine is like an airplane. It can haul a heavy load, but also move in endless directions and vary its destination as needed. The Super-Turing framework allows a stimulus to actually change the computer at each computational step, behaving in a way much closer to that of the constantly adapting and evolving brain.”
Siegelmann and two colleagues recently were notified that they will receive a grant to make the first ever Super-Turing computer, based on Analog Recurrent Neural Networks. The device is expected to introduce a level of intelligence not seen before in artificial computation.