Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560

Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560!

Avsnitt(779)

CTIBench: Evaluating LLMs in Cyber Threat Intelligence with Nidhi Rastogi - #729

CTIBench: Evaluating LLMs in Cyber Threat Intelligence with Nidhi Rastogi - #729

Today, we're joined by Nidhi Rastogi, assistant professor at Rochester Institute of Technology to discuss Cyber Threat Intelligence (CTI), focusing on her recent project CTIBench—a benchmark for evalu...

30 Apr 202556min

Generative Benchmarking with Kelly Hong - #728

Generative Benchmarking with Kelly Hong - #728

In this episode, Kelly Hong, a researcher at Chroma, joins us to discuss "Generative Benchmarking," a novel approach to evaluating retrieval systems, like RAG applications, using synthetic data. Kelly...

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Exploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen - #727

Exploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen - #727

In this episode, Emmanuel Ameisen, a research engineer at Anthropic, returns to discuss two recent papers: "Circuit Tracing: Revealing Language Model Computational Graphs" and "On the Biology of a Lar...

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Teaching LLMs to Self-Reflect with Reinforcement Learning with Maohao Shen - #726

Today, we're joined by Maohao Shen, PhD student at MIT to discuss his paper, “Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search.” We dig into...

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Waymo's Foundation Model for Autonomous Driving with Drago Anguelov - #725

Waymo's Foundation Model for Autonomous Driving with Drago Anguelov - #725

Today, we're joined by Drago Anguelov, head of AI foundations at Waymo, for a deep dive into the role of foundation models in autonomous driving. Drago shares how Waymo is leveraging large-scale machi...

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Dynamic Token Merging for Efficient Byte-level Language Models with Julie Kallini - #724

Dynamic Token Merging for Efficient Byte-level Language Models with Julie Kallini - #724

Today, we're joined by Julie Kallini, PhD student at Stanford University to discuss her recent papers, “MrT5: Dynamic Token Merging for Efficient Byte-level Language Models” and “Mission: Impossible L...

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Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

Today, we're joined by Jonas Geiping, research group leader at Ellis Institute and the Max Planck Institute for Intelligent Systems to discuss his recent paper, “Scaling up Test-Time Compute with Late...

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Imagine while Reasoning in Space: Multimodal Visualization-of-Thought with Chengzu Li - #722

Imagine while Reasoning in Space: Multimodal Visualization-of-Thought with Chengzu Li - #722

Today, we're joined by Chengzu Li, PhD student at the University of Cambridge to discuss his recent paper, “Imagine while Reasoning in Space: Multimodal Visualization-of-Thought.” We explore the motiv...

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