Amii’s Virtual AI Meetup Recap – Oct 15, 2020

On October 15, 2020, participants gathered to watch and participate in the AI Meetup from the comfort of their homes. The event featured:

Patrick Pilarski

Amii Fellow & Associate Professor and Canada Research Chair – Faculty of Medicine & Dentistry

Time is fundamental to reinforcement learning. The literature to date has described many ways that animals and machines use aspects of the flow of time and temporal patterns to make predictions, inform decisions, process past experiences, and plan for the future. In this talk, Patrick begins with a survey of how and why agents perceive and represent time, as selected from the animal learning and neuroscience literature. He then suggests what he thinks is a desirable set of time-related abilities for machine agents to acquire, demonstrate, and master as they continually interact with the environment around them.

Luke Kumar

Machine Learning Scientist at Amii

Luke’s talk – Predicting 30-Day Risk of Adverse Outcomes at the Time of an Opioid Dispense – stems from Amii’s recent engagement with health analytics company OKAKI, wherein Amii helped OKAKI plan, launch and run their first ML project. Luke shares the results of the project, which uses ML to predict opioid dispense risk based on health data from Alberta. Read the OKAKI Case Study for more information.

Thanks to all who were able to join us! If you missed our October Meetup, watch it now:

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