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.
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Fairness and Robustness in Federated Learning with Virginia Smith -#504
Scaling AI at H&M Group with Errol Koolmeister - #503
Evolving AI Systems Gracefully with Stefano Soatto - #502
ML Innovation in Healthcare with Suchi Saria - #501
Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500
The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499
Vector Quantization for NN Compression with Julieta Martinez - #498
Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497
Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496
Advancing NLP with Project Debater w/ Noam Slonim - #495
Bringing AI Up to Speed with Autonomous Racing w/ Madhur Behl - #494
AI and Society: Past, Present and Future with Eric Horvitz - #493
Agile Applied AI Research with Parvez Ahammad - #492
Haptic Intelligence with Katherine J. Kuchenbecker - #491
Data Science on AWS with Chris Fregly and Antje Barth - #490
Accelerating Distributed AI Applications at Qualcomm with Ziad Asghar - #489
Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488
Applied AI Research at AWS with Alex Smola - #487
Causal Models in Practice at Lyft with Sean Taylor - #486
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