31 mars et 1er avril – Séminaires DIC-ISC-CRIA

Mercredi 31 mars 2021 EXCEPTIONNELLEMENT à 19h

Exploring robotic minds using predictive coding and active inference frameworks

Par Jun Tani

Le lien zoom pour y assister est (veuillez indiquer votre nom complet dès votre entrée pour nous faciliter la tâche de vous admettre au séminaire) : ​https://uqam.zoom.us/j/84473395235

Résumé :
The focus of my research has been to investigate how cognitive agents can acquire structural representation via iterative interaction with the world, exercising agency and learning from resultant perceptual experience.  For this purpose, my group has investigated various models analogous to predictive coding and active inference frameworks. For the past two decades, we have applied these frameworks to develop cognitive constructs for robots. My talk attempts to clarify underlying cognitive and mind mechanisms for compositionality, social cognition, and consciousness from analysis of emergent phenomena observed in these robotics experiments.

References:
(1) Tani, J. (2016). “Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.”, Oxford University Press. link
(2) Tani, J., & White, J. (2020). Cognitive neurorobotics and self in the shared world, a focused review of ongoing research. Adaptive Behavior, 1059712320962158.

Bio:
Jun Tani received the D.Eng. degree from Sophia University, Tokyo in 1995. He started his research career with Sony Computer Science Lab. in 1993. He became a Team Leader of the Laboratory for Behavior and Dynamic Cognition, RIKEN Brain Science Institute, Saitama, Japan in 2001. He became a Full Professor with the Electrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, South Korea in 2012. He is currently a Full Professor with the Okinawa Institute of Science and Technology, Okinawa, Japan. His current research interests include cognitive neuroscience, developmental psychology, phenomenology, complex adaptive systems, and robotics.


Jeudi 1er avril 2021 à 10h30
 
The challenge of modeling the acquisition of mathematical concepts.

Par Alberto Testolin

Le lien zoom pour y assister est (veuillez indiquer votre nom complet dès votre entrée pour nous faciliter la tâche de vous admettre au séminaire) : ​https://uqam.zoom.us/j/84473395235
 
Résumé :
Mathematics is one of the most impressive achievements of human cultural evolution. Despite we perceive it as being overly abstract, it is widely believed that mathematical skills are rooted into a phylogenetically ancient “number sense”, which allows us to approximately represent quantities. However, the relationship between number sense and the subsequent acquisition of symbolic mathematical concepts remains controversial. In this seminar I will discuss how recent advances in AI and deep learning research might allow to investigate how the acquisition of numerical concepts could be grounded into sensorimotor experiences. Success in this challenging enterprise would have immediate implications for cognitive science, but also far-reaching impact for educational practice and for the creation of the next generation of intelligent machines.

References:
1) Zorzi, M., & Testolin, A. (2018). An emergentist perspective on the origin of number sense. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1740), 20170043.
https://royalsocietypublishing.org/doi/full/10.1098/rstb.2017.0043
2) Overmann, K. A. (2018). Constructing a concept of number. Journal of Numerical Cognition. 4, 464–493.
https://jnc.psychopen.eu/article/view/161/html

Bio:
Dr. Alberto Testolin received the M.Sc. degree in Computer Science and the Ph.D. degree in Psychological Sciences from the University of Padova, Italy, in 2011 and 2015, respectively. In 2019 he was Visiting Scholar at the Department of Psychology at Stanford University. He is currently Assistant Professor at the University of Padova, with a joint appointment at the Department of Information Engineering and the Department of General Psychology. He is broadly interested in artificial intelligence, machine learning and cognitive neuroscience. His main research interests are statistical learning theory, predictive coding, sensory perception, cognitive modeling and applications of deep learning to signal processing and optimization. He is an active member of the IEEE Task Force on Deep Learning.

Institut des sciences cognitives

Fondé en 2003, l'Institut des Sciences Cognitives de l'UQAM vise à favoriser la recherche et le développement de compétences dans le domaine des sciences cognitives, à en partager les connaissances, à faciliter les échanges interdisciplinaires et à animer la communauté locale.

Coordonnées

Institut des sciences cognitives
Local DS-4202
320, rue Sainte-Catherine Est
Montréal (Québec) H2X 1L7