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Amii’s Virtual AI Meetup Recap – Sept 17, 2020

On September 17, 2020, participants gathered to watch and participate in the AI Meetup from the comfort of their homes. The event featured:

Mohammad Motallebi

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.

Matthew E. Taylor

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:


Join us on October 15 for our next Virtual AI Meetup! Register here.

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