#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     In this episode of the Eye on AI podcast, host Craig Smith sits down with Thomas G. Dietterich, a pioneer in the field of machine learning, to explore the evolving landscape of AI and its application in real-world problems.   Thomas shares his journey from the early days of AI, where rule-based systems dominated, to the breakthroughs in deep learning that have revolutionized computer vision. He delves into the challenges of detecting novelty in AI, emphasizing the importance of teaching machines to recognize "unknown unknowns."   The conversation highlights the growing field of computational sustainability, where AI is used to solve pressing environmental problems, from designing new materials to optimizing wildfire management. Thomas also provides insights into the role of transformers and generative AI, discussing their power and limitations, particularly in tasks like object recognition and problem formulation.   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