Computer scientist Hava Siegelmann takes next step in neural computation
Computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken mathematical genius Alan Turing’s work to its next logical step. Turing set out the basis for digital computing in the 1930s to anticipate the electronic age, and Siegelmann is translating 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. She and her post-doctoral research colleague, Jeremie Cabessa, report on the advance in the current issue of Neural Computation. 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.