Causal Models in Practice at Lyft with Sean Taylor - #486

Causal Models in Practice at Lyft with Sean Taylor - #486

Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. The complete show notes for this episode can be found at twimlai.com/go/486.

Avsnitt(779)

Stable Diffusion and LLMs at the Edge with Jilei Hou - #633

Stable Diffusion and LLMs at the Edge with Jilei Hou - #633

Today we’re joined by Jilei Hou, a VP of Engineering at Qualcomm Technologies. In our conversation with Jilei, we focus on the emergence of generative AI, and how they've worked towards providing thes...

12 Juni 202340min

Modeling Human Behavior with Generative Agents with Joon Sung Park - #632

Modeling Human Behavior with Generative Agents with Joon Sung Park - #632

Today we’re joined by Joon Sung Park, a PhD Student at Stanford University. Joon shares his passion for creating AI systems that can solve human problems and his work on the recent paper Generative Ag...

5 Juni 202346min

Towards Improved Transfer Learning with Hugo Larochelle - #631

Towards Improved Transfer Learning with Hugo Larochelle - #631

Today we’re joined by Hugo Larochelle, a research scientist at Google Deepmind. In our conversation with Hugo, we discuss his work on transfer learning, understanding the capabilities of deep learning...

29 Maj 202338min

Language Modeling With State Space Models with Dan Fu - #630

Language Modeling With State Space Models with Dan Fu - #630

Today we’re joined by Dan Fu, a PhD student at Stanford University. In our conversation with Dan, we discuss the limitations of state space models in language modeling and the search for alternative b...

22 Maj 202328min

Building Maps and Spatial Awareness in Blind AI Agents with Dhruv Batra - #629

Building Maps and Spatial Awareness in Blind AI Agents with Dhruv Batra - #629

Today we continue our coverage of ICLR 2023 joined by Dhruv Batra, an associate professor at Georgia Tech and research director of the Fundamental AI Research (FAIR) team at META. In our conversation,...

15 Maj 202343min

AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - #628

AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - #628

Today we’re joined by Jerry Liu, co-founder and CEO of Llama Index. In our conversation with Jerry, we explore the creation of Llama Index, a centralized interface to connect your external data with t...

8 Maj 202341min

Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos - #627

Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos - #627

Today we kick off our coverage of the 2023 ICLR conference joined by Christos Louizos, an ML researcher at Qualcomm Technologies. In our conversation with Christos, we explore his paper Hyperparameter...

1 Maj 202333min

Are LLMs Overhyped or Underappreciated? with Marti Hearst - #626

Are LLMs Overhyped or Underappreciated? with Marti Hearst - #626

Today we’re joined by Marti Hearst, Professor at UC Berkeley. In our conversation with Marti, we explore the intricacies of AI language models and their usefulness in improving efficiency but also the...

24 Apr 202337min

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