#212 Thomas Dietterich: The Future of Machine Learning, Deep Learning and Computer Vision

This episode is sponsored by Speechmatics. Check it out at www.speechmatics.com/realtime   Today, we're joined by Dr. Thomas G. Dietterich, a pioneer in machine learning who recently was honored with the Award for Research Excellence from the International Joint Conference on Artificial Intelligence, one of the top awards for AI researchers.   Dietterich traces the field's progression from early rule-based systems to modern machine learning paradigms and delves into his work on novel category detection and open set problems. He also discusses the evolution of ensemble methods in the context of large language models (LLMs), highlighting the shift from combining many cheap models to more selective approaches with expensive models.   He advocates for a foundation model approach to capture the variability of the world.   Join us for a deep dive into the future of AI, where Thomas explains why the development of novel materials and drugs may have the most transformative impact on our economy. Plus, hear about his latest work on multi-instance learning, weak supervision, and the role of reinforcement learning in real-world applications like wildfire management.   Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest trends and insights in AI and machine learning!     Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI     (00:00) Introduction to Thomas Dietterich's Machine Learning Journey (02:34) The Early Days of Machine Learning and AI Systems (04:29) Tackling the Multiple Instance Problem in Drug Design (05:41) AI in Sustainability (07:17) The Challenge of Novelty Detection in AI Systems (08:00) Addressing the Open Set Problem in Cybersecurity and Computer Vision (09:11) The Evolution of Deep Learning in Computer Vision (11:21) How Deep Learning Handles Novel Representations (12:01) Foundation Models and Self-Supervised Learning (14:11) Vision Transformers vs. Convolutional Neural Networks (16:05) The Role of Multi-Instance Learning in Weakly Labeled Data (18:36) Ensemble Learning and Deep Networks in Machine Learning (20:33) The Future of AI: Large Language Models and Their Applications (23:51) Symbolic Regression and AI's Role in Scientific Discovery (34:44) AI in Wildfire Management: Using Reinforcement Learning (39:32) AI-Driven Problem Formulation and Optimization in Industry (41:30) The Future of AI Reasoning Systems and Problem Solving (45:03) The Limits of Large Language Models in Scientific Research (50:12) Closing Thoughts: Open Challenges and Opportunities in AI  

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