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A New Discovery Dubbed Super-Turing
A New Discovery Dubbed Super-Turing
New discovery dubbed "Super-Turing" computation is an adaptable computational system that learns and evolves, using input from the environment like our brains.
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Transcript
Today's modern computers are based on mathematical concepts first laid out in the 1930s by British mathematician Alan Turing. Now, Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing's work to its next logical step.

She is implementing her 1993 discovery of what she has dubbed "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. This breakthrough was described recently in the journal Neural Computation.

The Super-Turing model is a mathematical formulation of the brain's neural networks with their adaptive abilities.

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 a classical computer.

Therefore the Super-Turing model is superior for human-like tasks and learning applications.

Each time a Super-Turing machine gets input, it literally becomes a different machine. You don't want this characteristic in a conventional PC, which is essentially a super-fast calculator. However, if you want a robot to accompany a blind person to the grocery store, you'd like one that can navigate in a dynamic environment.

In any application where a machine needs to interact successfully with a human partner, you want one that can adapt to idiosyncratic speech, recognize facial patterns, and allow interactions between the human and machine partners to evolve just as it would between two humans. That's what a Super-Turing machine offers.

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. But they can't take in new information or use it to improve problem-solving, provide richer alternatives, or perform other higher-intelligence tasks.

Back in 1993, Siegelmann discovered via mathematical analysis that the neural network models had some capabilities that surpassed the Turing model. Each step in Siegelmann's model starts with a new Turing machine that computes once and then adapts based on that computation.

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 raw power to visually sample the entire scene using a camera. This economy of effort, using only as much attention as needed, is another hallmark of high artificial intelligence.

Siegelmann and her colleagues are now in the process of designing and building the first operational Super-Turing computer.

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