AI Frontiers with James Zou: The Future of Multi-Modal AI in Medicine
Feb 21, 2024
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Expert in AI research, James Zou, discusses the future of multi-modal AI in medicine. Topics include using social media for medical data, ethical implications of AI, and challenges of model drift. Explore the convergence of genomics, machine learning, and medicine, as well as the unique use of Twitter for medical image discussions. Learn about training AI algorithms in medicine and the behavior changes of Chat GPT-4 over time. Dive into personal interests and AI art perception in the field of medicine.
Social media like Twitter can be a valuable resource for gathering medical data and fostering informative communities in healthcare.
Continuous monitoring and evaluation of AI language models like GPT-4 is crucial due to behavior changes over time that can impact safety and reasoning abilities.
Reinforcement Learning from Human Feedback (RLHF) is essential for aligning AI models with human preferences, but it can lead to behavior drift and unintended consequences.
Deep dives
Twitter as Diverse Community Platform for Medical Professionals
Twitter is not just a noisy space, but a collection of diverse communities, including medical professionals who use it to build informative communities and engage in educational dialogues. The platform curates a dataset of high-quality Twitter discussions, which includes pathology images and corresponding conversations among professionals.
James Zoh's Contributions to AI and Biomedicine
James Zoh, a professor at Stanford University, has made significant contributions to various topics in artificial intelligence and biomedicine. His research includes creating a foundation model for pathology using medical Twitter images and exploring the behavior changes of large language models over time.
Behavior Changes in AI Language Models
The study examines how behavior of AI language models, such as GPT-4, can change over time. The research shows substantial differences in behavior between different versions of GPT-4, highlighting the importance of continually monitoring and evaluating these models. The changes in behavior can impact safety, reasoning abilities, and response to various types of questions.
The Role of Human Feedback in AI Model Training
Reinforcement Learning from Human Feedback (RLHF) is one of the ways AI models like GPT-4 are trained. Human feedback helps align the model's behavior with human preferences, enhancing safety and usability. However, the study reveals that continuous training and human feedback can lead to behavior drift and unintended consequences.
Implications and Challenges of AI in Healthcare
AI has the potential to revolutionize healthcare by improving diagnostics, translating medical texts, and enhancing patient communication. While some areas of medicine readily embrace AI, integrating it into later stages, such as clinical trials and healthcare workflows, presents challenges related to incentives, regulation, and risk mitigation.
In this episode of the AI Grand Rounds podcast, Dr. James Zou shares his personal journey to discovering machine learning during his graduate studies at Harvard. Fascinated by the potential of AI and its application to genomics and medicine, Dr. Zou embarked on a journey that took him from journalism to the forefront of AI research. He has been instrumental at Stanford in translating machine learning advancements into clinical settings, particularly through genomics. The discussion also delves into the unique use of social media for gathering medical data, showcasing an innovative approach to AI model training with real-world medical discussions. Dr. Zou touches on the ethical implications of AI, the importance of responsible AI development, and the potential of language models like GPT-4 in medicine, despite the challenges of model drift and alignment with human preferences.