coggroup September 2022

coggroup@mx1.psych.mcgill.ca

2 participants
2 discussions

[Coggroup] ANIMAL COGNITION AND AI by Murray Shanahan (Imperial College & DeepMind): Sept 29, 10:30am
by Stevan R. Harnad, Dr. 28 Sep '22

28 Sep '22

Animal Cognition and AI Murray Shanahan Cognitive Robotics, Imperial College & DeepMind UQÀM ISC DIC CRIA Cognitive Informatics Seminar September 29, 2022 Thursday, 10:30 am Zoom: https://uqam.zoom.us/j/88481835073 Abstract: Common sense in humans is founded on a set of basic capacities that are possessed by many other animals, capacities pertaining to the understanding of objects, space, and causality. The field of animal cognition has developed numerous experimental protocols for studying these capacities and, thanks to progress in deep reinforcement learning (RL), it is now possible to apply these methods directly to evaluate RL agents in 3D environments. The Animal-AI Environment aims to apply the ability-oriented testing used in comparative psychology to AI systems. Besides evaluation, the animal cognition literature offers a rich source of behavioural data, which can serve as inspiration for RL tasks and curricula. Bio: Murray Shanahan is Professor of Cognitive Robotics at Imperial College London and Senior Research Scientist at DeepMind. His publications span artificial intelligence, robotics, logic, dynamical systems, computational neuroscience, and philosophy of mind. His work up to 2000 was in the tradition of classical, symbolic AI. He then turned his attention to the brain and its embodiment. His current interests include neurodynamics, consciousness, machine learning, and the impacts of artificial intelligence. References: Shanahan, M., Crosby, M., Beyret, B., & Cheke, L. (2020). Artificial intelligence and the common sense of animals<https://www.sciencedirect.com/science/article/pii/S1364661320302163>. Trends in cognitive sciences, 24(11), 862-872. Voudouris, K., Crosby, M., Beyret, B., Hernández-Orallo, J., Shanahan, M., Halina, M., & Cheke, L. G. (2022). Direct Human-AI Comparison in the Animal-AI Environment<https://www.frontiersin.org/articles/10.3389/fpsyg.2022.711821/full?&utm_so…>. Frontiers in Psychology, 1884. Shanahan, M., & Mitchell, M. (2022). Abstraction for Deep Reinforcement Learning<https://arxiv.org/pdf/2202.05839.pdf>. UCAI 2022 arXiv preprint arXiv:2202.05839. Shanahan, M., Embodiment and the Inner Life: Cognition and Consciousness in the Space of Possible Minds<https://www.doc.ic.ac.uk/~mpsha/EIL.html>, Oxford University Press (2010). Full text<https://www.doc.ic.ac.uk/~mpsha/ShanahanBook2010.pdf>

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[Coggroup] REMINDER: 10:30 am Thurs Sept 15: Music creation with deep learning techniques
by Harnad, Stevan 12 Sep '22

12 Sep '22

Music creation with deep learning techniques Jean-Pierre Briot Université Sorbonne, Paris COGNITIVE INFORMATICS SEMINAR UQÀM ISC DIC CRIA Thursday, 10:30 am September 15, 2022 Zoom: https://uqam.zoom.us/j/88481835073 Abstract: A growing application area for the current wave of deep learning (the return of artificial neural networks on steroids) is the generation of creative content, notably the case of music (and also images and text). The motivation is in using machine learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This talk will survey some recent achievements in deep-learning-based music generation, using recent and dedicated generative architectures such as VAE, GAN and Transformer, analyzing principles, successes as well as challenges, including the limits of automated generation versus providing assistance to human musicians. Jean-Pierre Briot is a senior researcher (research director) in computer science at LIP6, joint computer science research lab of CNRS (Centre National de la Recherche Scientifique) and Sorbonne Université in Paris, France. He is also permanent visiting professor at PUC-Rio in Rio de Janeiro, Brazil. His general research interests are the design of intelligent adaptive and cooperative software, at the crossroads of artificial intelligence, distributed systems and software engineering, with various applications in the internet of things, decision support systems and computer music. His current interest is the use of AI techniques (notably deep learning-based) within music creation processes. He is the principal author of a recent reference book on deep learning techniques for music generation Briot, J. P., Hadjeres, G., & Pachet, F. D. (2020). Deep learning techniques for music generation (Vol. 1). Heidelberg: Springer. https://link.springer.com/book/10.1007/978-3-319-70163-9<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Flink.spri…> For more details (including access to publications): http://webia.lip6.fr/~briot/cv/<https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwebia.lip6…> Briot, J. P. (2021). From artificial neural networks to deep learning for music generation: history, concepts and trends. Neural Computing and Applications, 33(1), 39-65. https://hal.sorbonne-universite.fr/hal-02539189v3/file/nn4music-hal-v3.pdf Briot, J. P. (2019). Apprentissage profond et génération de musique, Hors série Intelligence artificielle, Tangente - L'aventure mathématique, (68):30-37, September 2019. https://webia.lip6.fr/~briot/cv/apgm-2019<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwebia.lip…>

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