Podcasts

MIT CSAIL Associate Professor Jacob Andreas walks listeners through how LLMs like ChatGPT evolved from academic curiosities to industry-disrupting technologies. 

Reflecting on the transformations he has observed in AI research, Professor Andreas discusses how some ideas—like linking NLP research to cognitive science—have taken a backseat, while others—like the importance of large-scale training data—remain central.

MIT Economics Professor David Autor says that AI is “not like a calculator where you just punch in the numbers and get the right answer. It's much harder to figure out how to be effective with it.” Offering unique insights into the future of work in an AI-powered world, Professor Autor explains his biggest worries, the greatest upside scenarios, and how he believes we should be approaching AI as a tool.

Have we achieved Artificial General Intelligence? MIT CSAIL Professor Manolis Kellis argues yes. Computers can do nearly every intellectual task that humans are capable of and are rapidly tackling the physical tasks. What does this mean for the future of AI integration, regulation, and development? Hear Professor Kellis’ ideas about how businesses can incorporate LLMs (large language models) to minimize silos, why we shouldn’t put up too many guardrails on AI technology, and how human-AI collaboration can lead to broader societal benefit, including healthcare.

Associate Professor Marzyeh Ghassemi discusses why, despite its tremendous potential for good, AI must be approached with caution, especially in high risk areas like healthcare. Biases in training data can perpetuate real-world inequalities, providers might rely too much on potentially faulty AI, and tools created for one demographic might not necessarily translate to a different population. However, considering the incredible potential of AI to tackle challenges in transformative ways, it’s worth approaching these considerations with rigorous academic insight. 

This month’s podcast is a double feature. First up, Associate Professor and Chief Health AI Officer at the University of California San Diego Karandeep Singh explains the reality of using artificial intelligence for medicine. Professor Singh extrapolates on what works, what doesn’t, and how some challenges are social rather than technical.