George Rebane

Kurzweil reports that computer scientist Dr Hava Siegelmann (Univ of Mass, Amherst) “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."

"This model is inspired by the brain.  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.”  (my italics)

This presumably is the next iteration to brain-like machines like those based temporal hierarchical memory (THM, pdf) modules that were introduced by Jeff Hawkins (here) and now under development.  The key ingredient to both machines is their ability to learn from data and their environment, and then solve problems and/or act on what they have learned.  Both super-Turing and THM computation promise quantum leaps in performance beyond IBM’s impressive Watson genre of super-computers (here)

The introduction and application of such machines is just the next step toward the Singularity event itself.  It will be of great interest to see toward what applications these new AIs will be directed.  The only sure thing is that more educated humans will feel their pressure and presence in the workplace.