News
On September 17, 2020, participants gathered to watch and participate in the AI Meetup from the comfort of their homes. The event featured:
Amii researcher at the xAI Lab
Artificial intelligence systems are widely regarded as having a magical ability to predict or classify when provided with input data. However, what is unclear to users is why these systems give specific outcomes. The field of eXplainable AI (XAI) attempts to answer this question. In Mohammad’s talk, he covers the definitions and fundamentals of XAI, walks the audience through industry use cases and provides examples to illustrate it. He then briefly explains one of the main works in XAI (LIME) and the xAI Lab’s extension to it called Black-box Association Rule-Based Explanations.
Amii Fellow & Associate Professor at UAlberta
Reinforcement learning (RL) is a machine learning technique that has received increasing interest from the academic and industrial research communities. In this talk, Matthew aims to explain why RL is awesome, highlight how RL is different from other machine learning methods, give examples of where RL can be used and presents a selection of open RL research problems. He also provides suggestions for how those interested could best learn more about RL (outside of the classroom).
Thanks to all who were able to join us! If you missed our September Meetup, watch it now:
May 31st 2023
News
A new paper presented this week by Amii scientists at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) details how they could use machine learning to significantly increase the efficiency of Attabotic's robotic warehouses and the promise the technology holds to transform supply chain logistics.
May 31st 2023
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On April 28, Jiamin He —MSc student at the University of Alberta — presented “Consistent Emphatic Temporal-Difference Learning" at the AI Seminar.
May 11th 2023
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Russ Greiner — Amii Fellow, Canada CIFAR AI Chair and one of the founding members of Amii — is being recognized for his outstanding contribution to furthering computing science with a Lifetime Achievement Award in Computing Science from CS-Can | Info-Can.
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