Today we continue our AI Trends 2024 series with a conversation with Thomas Dietterich, distinguished professor emeritus at Oregon State University. As you might expect, Large Language Models figured prominently in our conversation, and we covered a vast array of papers and use cases exploring current research into topics such as monolithic vs. modular architectures, hallucinations, the application of uncertainty quantification (UQ), and using RAG as a sort of memory module for LLMs. Lastly, don’t miss Tom’s predictions on what he foresees happening this year as well as his words of encouragement for those new to the field.
The complete show notes for this episode can be found at twimlai.com/go/666.
Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
Managing Data Labeling Ops for Success with Audrey Smith - #583
Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
On The Path Towards Robot Vision with Aljosa Osep - #581
More Language, Less Labeling with Kate Saenko - #580
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
Data Governance for Data Science with Adam Wood - #578
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
The Fallacy of "Ground Truth" with Shayan Mohanty - #576
Principle-centric AI with Adrien Gaidon - #575
Data Debt in Machine Learning with D. Sculley - #574
AI for Enterprise Decisioning at Scale with Rob Walker - #573
Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell - #572
Studying Machine Intelligence with Been Kim - #571
Advances in Neural Compression with Auke Wiggers - #570
Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello - #569
Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
Hierarchical and Continual RL with Doina Precup - #567
Open-Source Drug Discovery with DeepChem with Bharath Ramsundar - #566
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