Speaker Biography
Prof Abbas Edalat did his PhD in Dynamical Systems at University of Warwick under Christopher Zeeman. He first took up a lectureship in mathematics at Sharif University of technology in Tehran and then joined the Computing Department at Imperial College London where he has been a professor of computer Science and mathematics since 1997.
For the past 25 years, he has developed connections between Domain Theory, a mathematical and logical theory of programming languages, with several areas of mathematical computation including exact computation, computational geometry, measure and integration theory, differential calculus, solution of ODE’s, hybrid systems and optimisation. Edalat has also been a social and political activist and researcher. In early, 2000’s he formulated the Mongol Trauma hypothesis to explain the relative demise of Islamic Societies in the Middle East as a consequence of the enduring trans-generation of trauma caused by the Mongol invasions of the region in the 1200’s. It was partly in response to this hypothesis that in 2010 he embarked on research in Algorithmic Human Development.
Talk Abstract
In the face of the seemingly intractable existential problems we currently encounter on this planet, I argue, we need to systematically develop protocols to enhance emotional and social intelligence and creativity in the human individual. These non-invasive protocols would be based on neuroplasticity and long-term potentiation, and aim at neural retraining for emotion self-regulation and optimal development in the individual regarded as a biological cybernetic system. Algorithmic Human development seeks to aid individual human beings to trade their instinctual or learned traits of destructive aggression for individual and social creativity. Supported by several computational models, including a Hebbian artificial network and the Free Energy Principle, our Self-Attachment protocol, based on the interaction of a rational (Adult) and an emotional (Child) internal agent, has parallels with Machine Learning as it employs the three basic paradigms of “substitution”, “iteration” and “prior updating” in the human individual. I will give the results of a long-term pilot project on the subject and then describe a laughter protocol which directly challenges old and entrenched beliefs of the Bayesian brain about past misfortunes and tragedies. I will finally explain how, during my detention and confinement for eight months last year, I succeeded to extend the domain of this protocol to “existing conditions” and turn a very difficult situation into a highly productive opportunity.