2022 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence

The 13th Annual Meeting of the BICA Society (BICA*AI 2022), Guadalajara, Mexico, hold on September 22-25.

Featured Speakers

Anthony Randal McIntosh
Keynote lecture: Modeling Network Function and Dysfunction with The Viritual Brain.

Description: McIntosh holds a Ph.D. in Psychology and Neuroscience, and is presently the Vice President of Research at Baycrest Health Sciences and Director of the Rotman Research Institute. He is also a Professor of Psychology at the University of Toronto.

McIntosh's work links cognitive and theoretical neuroscience by emphasizing how network operations give rise to human mental processes. His efforts have also produced unique analytic approaches, such as Structural Equation Modeling and Partial Least Squares, that have helped other researchers study the brain from a network perspective.

Yingxu Wang
Keynote lecture: AI vs. NI (Natural Intelligence): How will Brain-Inspired Systems Lead to Autonomous AI and Cognitive Computers?

Description: Wang received a PhD in Computer Science from the Nottingham Trent University, UK, in 1998 and has been a full professor since 1994. He is professor of cognitive systems, brain science, software science, and intelligent mathematics. He is the founding President of International Institute of Cognitive Informatics and Cognitive Computing (I2CICC). He is FIEEE, FBCS, FI2CICC, FAAIA, FWIF, and P.Eng. His basic research has spanned across contemporary scientific disciplines of intelligence, mathematics, knowledge, robotics, computer, information, brain, cognition, software, data, systems, cybernetics, neurology, and linguistics. He has published 600+ peer reviewed papers and 38 books/proceedings.

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  • Armen Pischdotchian
    Keynote lecture: Cross-roads of Machine Learning and Data Science.

    Description: Generative Adversarial Networks and Transformer pipelines are ushering in the very beginning of Artificial General Intelligence, where machines 'experience' and not just learn. Perhaps a better way to think of AI is that of prediction machines that augment human intelligence not supplant it. To gain insights from what it is that you don't know. Watson Studio and AutoAI is designed to take the coding off your hands and enable you to build and deploy models while calculating drift in accuracy in an effort to mitigate bias. Let's explore the nuances of machines that understand, reason, learn and interact -- the very definition of cognitive systems.

    Garrick Orchard
    Keynote lecture: Efficient Bio-inspired Computing with Intel's Loihi 2 Neuromorphic Processor.

    Description: Garrick Orchard received the PhD degree in electrical and computer engineering from Johns Hopkins University, Baltimore, Maryland, in 2012. Thereafter he joined the National University of Singapore where he was awarded the Temasek Research Fellowship in 2015 and held co-appointment between the Temasek Laboratories and Singapore Institute for Neurotechnology. In 2019 he joined the Neuromorphic Computing Laboratory, Intel Labs, Santa Clara, California as a senior research scientist. His research focuses on bio-inspired algorithms and architectures for visual sensing and computation.

    Emmanuel Ortiz López
    Keynote lecture: Interpretability.

    Description: Emmanuel Ortiz López obtained his B. SC. degree in communication and electronic engineering in 2009 and his M. Eng. degree in Electric in 2011. Currently, he is a Ph.D. candidate at Universidad de Guanajuato in the Electronics Engineering Department from the Universidad of Guanajuato, Salamanca, Mexico. As part of Continental since 2018, he has been working in applied research of inference optimization and interpretability of Deep Learning models (for computer vision applications). He is interested in machine learning applications for computer vision on edge.



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