Today we’re joined by Robert Osazuwa Ness, a senior researcher at Microsoft Research, to break down the latest trends in the world of causal modeling. In our conversation with Robert, we explore advances in areas like causal discovery, causal representation learning, and causal judgements. We also discuss the impact causality could have on large language models, especially in some of the recent use cases we’ve seen like Bing Search and ChatGPT. Finally, we discuss the benchmarks for causal modeling, the top causality use cases, and the most exciting opportunities in the field.
The complete show notes for this episode can be found at twimlai.com/go/616.
Ensuring LLM Safety for Production Applications with Shreya Rajpal - #647
What’s Next in LLM Reasoning? with Roland Memisevic - #646
Is ChatGPT Getting Worse? with James Zou - #645
Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - #644
Inverse Reinforcement Learning Without RL with Gokul Swamy - #643
Explainable AI for Biology and Medicine with Su-In Lee - #642
Transformers On Large-Scale Graphs with Bayan Bruss - #641
The Enterprise LLM Landscape with Atul Deo - #640
BloombergGPT - an LLM for Finance with David Rosenberg - #639
Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - #638
Privacy vs Fairness in Computer Vision with Alice Xiang - #637
Unifying Vision and Language Models with Mohit Bansal - #636
Data Augmentation and Optimized Architectures for Computer Vision with Fatih Porikli - #635
Mojo: A Supercharged Python for AI with Chris Lattner - #634
Stable Diffusion and LLMs at the Edge with Jilei Hou - #633
Modeling Human Behavior with Generative Agents with Joon Sung Park - #632
Towards Improved Transfer Learning with Hugo Larochelle - #631
Language Modeling With State Space Models with Dan Fu - #630
Building Maps and Spatial Awareness in Blind AI Agents with Dhruv Batra - #629
AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - #628
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