The 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems
10-12th November 2014, Singapore
The field of Artificial Intelligence (AI) has seen stages of stagnation followed by tipping points all over its history. Be it in natural language processing/understanding, neural networks, fuzzy systems, or evolutionary computation, these tipping points marked real advances in AI. Today, AI is getting closer and closer to a new tipping point. However, it is a tipping point of a different kind; one with orders of magnitude more significance than any previous one. The age of the human brain project, the era of big data and models, the advances in computational science, the revolution in hardware and sensors, and the success stories made by computational intelligence researchers are all converging to generate this tipping point.
In this presentation, the audience will get to see some early signs of the next generation AI systems. Red teaming is the science and art of role-playing an opponent. In this talk, the first of its kind Computational Red Teaming (CRT) system that was able to autonomously red team against humans will be demonstrated. The system integrates Encephalographic (EEG) data (brain signals) with performance metrics and the operational environment to red team with humans. The talk will demonstrate that the next generation AI system, where the biological brain is seamlessly integrated with in-Silico brain, is just at our doorsteps!
Biosketch of the Speaker:
Hussein Abbass is a full Professor with the University of New South Wales (UNSW-Australia), Canberra Campus, Australia. In 2014, he is spending his sabbatical visiting the Department of Electrical and Computer Engineering, National University of Singapore. Prof. Abbass is a fellow of the UK Operational Research Society and a fellow of the Australian Computer Society. He is an Associate Editor of six international journals, including the IEEE Transactions on Evolutionary Computation, and the IEEE Computational Intelligence Magazine. He has been serving as the Chair of the Emerging Technologies Technical Committee of the IEEE Computational Intelligence Society (IEEE-CIS) for 2 years and has served on many different committees within IEEE-CIS. Prof. Abbass is currently a College Member of the Australian Research Council (ARC) Engineering, Mathematics, and Information Cluster. He was a member of the Research Evaluation Committee of Excellence of Research Australia in 2010. His was a visiting academic at University of Illinois – Urbana Champaign spending his sabbatical in 2005, and a UNSW John-Yu Fellow at Imperial College London in 2003. He published close to 200 refereed papers. His current research interest is in computational red teaming and integrating human brain data with advanced analytics and automation.
Biosketch of the Speaker:
Nikhil R. Pal is a Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes bioinformatics, brain science, fuzzy logic, pattern analysis, neural networks, and evolutionary computation.
He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, Neural Information Processing—Letters and Reviews, International Journal of Knowledge-Based Intelligent Engineering Systems, International Journal of Neural Systems, Fuzzy Sets and Systems, International Journal of Intelligent Computing in Medical Sciences and Image Processing, Fuzzy Information and Engineering : An International Journal, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Systems Man and Cybernetics—B.
He has given many plenary/keynote speeches in different premier international conferences in the area of computational
intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences.
He was a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS) and was a member of the Administrative
Committee of the IEEE CIS. At present he is the Vice President for Publications of the IEEE CIS.
He is a Fellow of the National Academy of Sciences, India, a Fellow of the Indian National Academy of Engineering, a Fellow of the
Indian National Science Academy, a Fellow of the International Fuzzy Systems Association (IFSA), and a Fellow of the IEEE, USA.
High quality image zooming is an important problem. There are many methods that use multiple low resolution (LR) frames of the same scene with different sub-pixel shifts as input to generate the high resolution (HR) images. Now a days single frame super resolution (SR) methods that use just one LR image to obtain the HR image is gaining popularity. In this talk we shall discuss a novel fuzzy rule based single frame super resolution method. This is a patch based method, where each LR patch is replaced by a HR patch generated by Takagi-Sugeno type fuzzy rules. We shall discuss in details the generations of the training data, the initial generation of the fuzzy rules, their refinement and how to use the rules for generation of SR images. In this context we shall also develop a Gaussian Mixture Regression (GMR) model for the same problem. Comparison of performance of the fuzzy rule-based system with five existing methods as well as with the GMR method in terms of the three quality criteria demonstrates the superior performance of the fuzzy rule-based system.
Previous work on evolving neural networks has focused on single neural networks. However, monolithic neural networks have become too complex to trainand evolve for large and complex problems. It is often better to design acollection of simpler neural networks that work collectively and cooperatively to solve a large and complex problem. The key issue here is how to design such a collection, i.e., an ensemble, automatically so that it has the best generalisation ability. This talk first reviews briefly early work on evolving neural networks. Then a previous idea of designing ensembles, negative correlation learning, is explained. Lastly, several recent studies are introduced, which analyze the impact of diversity on online ensemble learning and that on multi-class class imbalance learning. The ideas behind some new ensemble algorithms for online learning, class imbalance learning, and online class imbalance learning will be presented. Applications of such new ensemble learning algorithms will also be mentioned and future research directions discussed.
Biosketch of the Speaker:
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS).He won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best paper awards.He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the IEEE CIS Evolutionary Computation Pioneer Award in 2013. He was the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation and is an Associate Editor or Editorial Member of more than ten other journals. He has been invited to give 70+ keynote/plenary speeches at international conferences. His major research interests include evolutionary computation and neural network ensembles.