Dr. Hava Siegelmann, Associate Professor of Computer Science and Neuroscience
University of Massachusetts Amherst
Super-Turing Computation (STC) is a vastly different human brain analog computational alternative. The real world is infinitely detailed on many levels of abstraction like analog real numbers; to deal with this overwhelming, constantly changing ocean of data, the brain must by necessity operate in a highly efficient and adaptive manner: Efficiency and flexibility characterize Super-Turing (ST) computational processes. ST computation is not Turing computation on steroids; rather, the two methods diverge sharply: Turing computation is easy to program and its widespread use testifies to its utility, but the method suffers from its inability to modify its program. ST computation is exemplified by its flexibility, reliance on interactivity with input, and its foundational ability to learn and to focus on goal-directed data processing.
While I originally saw Super-Turing as a brain-inspired superior computational model, I've come to realize that the same qualities of efficiency, learning, increasing precision by need and continuous dynamics - underlie much of the natural world. Perhaps the world, through billions of years of evolution, arrived at the same efficient Super-Turing method I uncovered in an attempt to construct a more efficient and more capable computational method.
Hava T. SIEGELMANN is an associate professor of Computer Science and Neuroscience at the University of Massachusetts at Amherst, and the director of the BINDS (Biologically Inspired Neural and Dynamical Systems) laboratory. Dr. Siegelmann's research focuses on mathematical modeling of biological systems with particular interest in memory, epigenetics, cellular development and disease evolution. Her research into biologically inspired memory and artificial intelligence has led to machine systems, which are more autonomous: capable of learning, tracking, clustering, associating, and inferring and are more robust and capable of operating in real-world environments. She introduced the highly utile Support Vector Clustering algorithm with Vladimir Vapnik and colleagues. Siegelmann's seminal Turing machine equivalence of recurrent neural networks theorem and the super-Turing theory, which greatly impacted current thinking on computation, have found new utility in her work on machine memory reconsolidation and intelligent cellular function. Her work is often interdisciplinary, and combines methods from the fields of Complexity Science, Networks Theory, Dynamical Systems, Artificial Intelligence and Machine Learning. Dynamical Health is Siegelmann's recent thesis stating that an unbalanced dynamic is the cause of most systemic disorders, that returning the system to balance is extremely beneficial to healing and further, that it is too limiting to focus only on primary causes, when any treatment that returns balance is sufficient for healing. Modeling these systems mathematically provides a means of exploring many possible solutions, which can then be translated to actual treatment. Her recent system biology studies include genetic networks, circadian system, memory reconsolidation, miRNA, cancer, and now addiction. She remains active in supporting young researchers and encouraging minorities and women to enter and advance in the sciences.