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.
Applied AI Research at AWS with Alex Smola - #487
Causal Models in Practice at Lyft with Sean Taylor - #486
Using AI to Map the Human Immune System w/ Jabran Zahid - #485
Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch - #484
What the Human Brain Can Tell Us About NLP Models with Allyson Ettinger - #483
Probabilistic Numeric CNNs with Roberto Bondesan - #482
Building a Unified NLP Framework at LinkedIn with Huiji Gao - #481
Dask + Data Science Careers with Jacqueline Nolis - #480
Machine Learning for Equitable Healthcare Outcomes with Irene Chen - #479
AI Storytelling Systems with Mark Riedl - #478
Creating Robust Language Representations with Jamie Macbeth - #477
Reinforcement Learning for Industrial AI with Pieter Abbeel - #476
AutoML for Natural Language Processing with Abhishek Thakur - #475
Inclusive Design for Seeing AI with Saqib Shaikh - #474
Theory of Computation with Jelani Nelson - #473
Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472
Operationalizing AI at Dataiku with Conor Jensen - #471
ML Lifecycle Management at Algorithmia with Diego Oppenheimer - #470
End to End ML at Cloudera with Santiago Giraldo - #469 [TWIMLcon Sponsor Series]
ML Platforms for Global Scale at Prosus with Paul van der Boor - #468 [TWIMLcon Sponsor Series]
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