Doina Precup's guest appearances

Doina Precup

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Women in AI

Sara Hooker

Rana El Kaliouby

Inmar Givoni

Rob Walker

Sanmi Koyejo

Chelsea Finn

Dario Amodei

Jianxiong Xiao

Kate Darling

Jason Holmberg

Lotte Bransen

Christopher Berner

David Spiegelhalter

Kristen Grauman

Stephen Merity

OwlTail

Today we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward specification for RL agents, where she hypothesizes that a reward signal in a complex environment could lead an agent to develop attributes of intuitive intelligence. We also dig into quite a few of her papers, including On the Expressivity of Markov Reward, which won a NeruIPS 2021 outstanding paper award. Finally, we discuss the analogy between hierarchical RL and CNNs, her work in continual RL, and her thoughts on the evolution of RL in the recent past and present, and the biggest challenges facing the field going forward.The complete show notes for this episode can be found at twimlai.com/go/567

Today we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward specification for RL agents, where she hypothesizes that a reward signal in a complex environment could lead an agent to develop attributes of intuitive intelligence. We also dig into quite a few of her papers, including <a href="https://openreview.net/forum?id=9DlCh34E1bN" rel="nofollow" target="_blank">On the Expressivity of Markov Reward</a>, which won a NeruIPS 2021 outstanding paper award. Finally, we discuss the analogy between hierarchical RL and CNNs, her work in continual RL, and her thoughts on the evolution of RL in the recent past and present, and the biggest challenges facing the field going forward. The complete show notes for this episode can be found at <a href="twimlai.com/go/567" rel="nofollow" target="_blank">twimlai.com/go/567</a>

Hierarchical and Continual RL with Doina Precup - #567

Women in AI is a biweekly podcast from RE•WORK, meeting with leading female minds in AI, Deep Learning and Machine Learning. We will speak to CEOs, CTOs, Data Scientists, Engineers, Researchers and Industry Professionals to learn about their cutting edge work and technological advancements, as well as their impact on AI for social good and diversity in the workplace.

Women in AI is a biweekly podcast from RE•WORK, meeting with leading female minds in AI, Deep Learning and Machine Learning. We will speak to CEOs, CTOs, Data Scientists, Engineers, Researchers and Industry Professionals to learn about their cutting edge work and technological advancements, as well as their impact on AI for social good and diversity in the workplace.

Episode 05: Sci-fi to AI - Interview with Doina Precup, McGill University