AI Trends 2024: Machine Learning & Deep Learning with Thomas Dietterich - 666

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. 🔔 Subscribe to our channel for more great content just like this: https://youtube.com/twimlai?sub_confirmation=1 🗣️ CONNECT WITH US! =============================== Subscribe to the TWIML AI Podcast: https://twimlai.com/podcast/twimlai/ Join our Slack Community: https://twimlai.com/community/ Subscribe to our newsletter: https://twimlai.com/newsletter/ Want to get in touch? Send us a message: https://twimlai.com/contact/ 📖 CHAPTERS =============================== 00:00 - Introduction 02:46 - Sparks of artificial general intelligence 04:51 - Embers of autoregression 10:03 - Influence of LLMs in the field 12:05 - Dissociating language and thought in large language models 16:04 - Future of LLMs: monolithic vs. modular architecture 18:56 - Uncertainty quantification 21:57 - Sources of uncertainty in machine learning - A statisticians’ view 25:29 - A gentle introduction to conformal prediction and distribution-free uncertainty quantification 27:37 - What uncertainties do we need in Bayesian deep learning for computer vision? 30:46 - DEUP: Direct Epistemic Uncertainty Prediction 34:21 - Uncertainty quantification as one solution in tackling hallucinations 37:57 - Survey of hallucination in natural language generation 38:36 - Cognitive mirage 39:26 - A stitch in time saves nine 41:00 - How to quantify uncertainty in LLMs 42:05 - Language model