- Dr. Piero Bonissone, GE
- Kevin Kelly, Carnegie Mellon University
- Toby Gibson, European Molecular Biology Laboratory
- Prof. Soo-Young Lee, Brain Science Research Center
Dr. Piero Bonissone- Industrial and Commercial Applications of Computational Intelligence
Dr. Piero Bonissone received an MS in Mechanical Engineering, and an MS and PhD in Electrical Engineering and Computer Science, all from the University of California at Berkeley.
A computer scientist at the GE Global Research since 1979, Dr. Bonissone has been a pioneer in the field of fuzzy logic, AI, soft computing, and approximate reasoning systems applications. Recently he has led a Soft Computing (SC) group in the development of SC application to diagnostics and prognostics of processes and products, including the prediction of remaining life for each locomotive in a fleet, to perform efficient assets selection. His current interests are the development of m ulti-criteria decision making systems applied to PHM issues, and the automation of intelligent systems lifecycle, i.e. the development of processes to create, deploy, and maintain smart SC-based systems that provide customized performance while adapting themselves to avoid obsolescence.
Dr. Bonissone is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), of the Association for the Advancement of Artificial Intelligence (AAAI), of the International Fuzzy Systems Association (IFSA), and a Coolidge Fellow at GE Global Research. He served as Editor in Chief of the International Journal of Approximate Reasoning for 13 years. He is in the editorial board of five technical journals and is Editor-at-Large of the IEEE Computational Intelligence Magazine. He has co-edited six books and has over 150 publications in referred journals, book chapters, and conference proceedings. He has received 34 patents issued from the US Patent Office (plus 39 pending patents). Since 1982, he has been an Adjunct Professor at Rensselaer Polytechnic Institute, in Troy NY. He has co-chaired nine scientific conferences focused on Multi-Criteria Decision-Making, Fuzzy sets, Diagnostics, Prognostics, and Uncertainty Management. Dr. Bonissone is very active in the IEEE, where is currently a member of the Fellow Evaluation Committee. In the past, while serving as President of the IEEE Neural Networks Society (now Computational Intelligence Society) he was also a member of the IEEE Technical Board Activities (TAB). He has been an Executive Committee member of NNS/CIS society for the past 15 years.
Kevin Kelly- A New Explanation of Ockham's Razor
Scientists prefer to believe simpler theories, with Ockham's razor as the putative justification. But how could a fixed bias toward simplicity lead one to unknown, theoretical truths? Bayesian statisticians agree that theories are suitable objects of belief, but merely push the question back a step by tracing our current simplicity bias to a prior such bias. Classical statisticians view theory selection not as a matter of belief, but of reducing the variability or spread of empirical estimates regardless of the truth of the theory used to obtain the estimates. Neither approach begins to explain how a prior bias toward simplicity is better for finding true theories than alternative biases would be. Such an explanation will be presented. According to this alternative approach, Ockham's razor does not reliably indicate or point at the true theory but, rather, follows the most direct route toward the truth, as measured in terms of reversals of opinion prior to convergence to the true theory. The approach is particularly apt when the theory is used to predict the outcomes of untried policies that can alter the probability distribution from which the original data were sampled, as in the case of causal discovery from non-experimental or indirectly experimental data.
Kevin T. Kelly is Professor of Philosophy at Carnegie Mellon University. His research interests include epistemology, philosophy of science, formal learning theory, and computability. He is the author of The Logic of Reliable Inquiry (Oxford University Press) and of numerous articles on such computational and methodological topics as the problem of induction, causal discovery, Ockham's razor as a guide to truth, infinite epistemic regresses, belief revision, and analogies between induction and computability.
Dr. Toby Gibson- European Molecular Biology Laboratory
Dr. Toby Gibson is at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany. He is a computational biologist: i.e. a biologist who finds computers to be useful adjuncts to biological research. He is a co-developer of the widely used multiple sequence alignment software ClustalW. He oversees the development of ELM, the Eukaryotic Linear Motif resource (http://elm.eu.org/) devoted to protein sequence motifs involved in cell signalling and regulation.
Biological concepts such as "evolution through natural selection" or "DNA makes RNA makes protein" are simple to expound but the devil in the detail makes a deep understanding much harder. Biology has a historical - ad hoc - flavour: things evolved the way they did but they could have evolved quite differently: thus a priori predictions from fundamental chemical and physical principles are usually pointless. Biology is also an information-based science: It is hard to remember the names of all ~25,000 human proteins, or the ordering of the 3 billion nucleotides in the DNA. Therefore, in the first instance, biologists use computers simply to store and retrieve data. To keep things interesting, however, bioinformaticists also expend much effort in analysing these data, hoping to gain new biological insight. They are keen to develop new areas such as Computational Systems Biology, to model the components of biological systems.
Toby Gibson is currently fascinated by the developing structure-function paradigm for the massively interacting "hub" proteins such as P53 and IRS-1. These are characterised by large "natively unstructured" protein segments that are repositories of abundant "linear motifs" - short regulatory sites that interact with other proteins. These weakly interacting sites allow for co-operative and combinatorial regulatory interactions in the assemby of large regulatory protein complexes. He considers that an emergent property of the underlying stochastic system is the switch to deterministic signalling effected by these large complexes. A key issue for modelling the cell regulatory systems will therefore be to understand the limits of determinism and define the transition points where stochastic approximations become inappropriate and inefficient.
Prof. Soo-Young Lee- Artificial Brain for Brain-Inspired Human-like Intelligent Systems
Soo-Young Lee received B.S., M.S., and Ph.D. degrees from Seoul National University in 1975, Korea Advanced Institute of Science in 1977, and Polytechnic Institute of New York in 1984, respectively. After working for industries in Korea (1982-1985) and US (1980-1985), he joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, and now is a Full Professor at the Department of Bio & Brain Engineering and also Department of Electrical Engineering and Computer Science.
In 1997 he established Brain Science Research Center, which is the main research organization for the Korean Brain Neuroinformatics Research Program. The research program is one of the Korean Brain Research Promotion Initiatives sponsored by Korean Ministry of Science and Technology from 1998 to 2008, and currently about 35 Ph.D. researchers have joined the research program from many Korean universities.
He is a Past-President of Asia-Pacific Neural Network Assembly. Dr. Lee is the Editor-in-Chief of the online journal, Neural Information Processing-Letters and Reviews, and is on Editorial Board for Neural Processing Letters and Cognitive Neurodynamics. He received Leadership Award and Presidential Award from International Neural Network Society in 1994 and 2001, respectively, and APPNA (Asia-Pacific Neural Network Assembly) Excellent Service Award in 2004.
His research interests have resided in artificial brain, the human-like intelligent Systems based on biological information processing mechanism in our brain. He has worked on the auditory models from the cochlea to the auditory cortex for noisy speech processing, information-theoretic binaural processing models for sound localization and speech enhancement, the unsupervised pro-active developmental models of human knowledge with multi-modal man-machine interactions, and the top-down selective attention models for superimposed pattern recognitions. Especially, he is interested in combining computational neuroscience and information theory, of which example is Independent Component Analysis for blind signal separation and feature extraction. His research scope covers the mathematical models, neuromorphic chips, and real-world applications.