The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In this discussion, Joel Hestness, a principal research scientist and lead of the core machine learning team at Cerebras, dives into the groundbreaking Wafer Scale Engine 3. He explains how this custom silicon surpasses traditional AI hardware, focusing on its unique architecture and memory capabilities. Joel also covers advancements in large language model training, innovative optimization techniques, and the integration of open-source frameworks like PyTorch. Additionally, he shares exciting research on weight-sparse training and novel optimizers that leverage higher-order statistics.

Laurent Boinot, Power and Utilities Lead for the Americas at Microsoft, dives into the fascinating intersection of AI and energy infrastructure. He discusses the challenges of North America's power systems and how AI is enhancing demand forecasting and grid optimization. Highlights include AI's role in securing utility systems and improving customer interactions. Laurent also explores the future of nuclear power and the pivotal role of electric vehicles in American energy management. It's a glimpse into a smarter, more efficient energy future!

Aza Jalalvand, a research scholar at Princeton University, dives into the fascinating realm of using deep reinforcement learning to stabilize plasma in nuclear fusion reactors. He discusses the development of a model to combat the tearing mode instability while collecting complex data from fusion experiments. Aza highlights the critical role of machine learning in enhancing plasma understanding, the challenges of real-time data management, and the promising future of AI in clean energy production. Tune in for insights on the electrifying intersection of AI and fusion technology!

In this discussion, Kirk Marple, CEO of Graphlit, dives into the innovative world of GraphRAG, which combines knowledge graphs with retrieval-augmented generation. He shares insights on building knowledge graphs and the challenges of entity extraction. The conversation covers dynamic prompting techniques that enhance AI model responses and the integration of multiple storage types for effective data management. Kirk also explores the future of AI agents and their applications, showcasing the evolution of cloud services in graph-based technologies.

In this engaging discussion, Alex Havrilla, a PhD student at Georgia Tech, dives into his research on enhancing reasoning in large language models using reinforcement learning. He explains the importance of creativity and exploration in AI problem-solving. Alex also highlights his findings on the effects of noise during training, revealing how resilient models can be. The conversation touches on the potential of combining language models with traditional methods to bolster AI reasoning, offering a glimpse into the exciting future of reinforcement learning.

Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab, dives into the fascinating world of large language models. He discusses the vital role of interpretability in AI, exploring how knowledge is stored and accessed. The conversation shifts to model editing, emphasizing the challenges of deleting sensitive information while maintaining data integrity. Hase also highlights the risks of easy-to-hard generalization in releasing open-source models and the impact of instructional prompts on model performance. This insightful dialogue unravels complexities in AI decision-making.

Jonas Geiping, a research group leader at the ELLIS Institute and Max Planck Institute, sheds light on his groundbreaking work on coercing large language models (LLMs). He discusses the alarming potential for LLMs to engage in harmful actions when misused. The conversation dives into the evolving landscape of AI security, exploring adversarial attacks and the significance of open models for research. They also touch on the complexities of input optimization and the balance between safeguarding models while maintaining their functionality.

Join Mido Assran, a research scientist at Meta's FAIR, as he delves into the groundbreaking V-JEPA model, which aims to bridge human and machine intelligence. He explains how V-JEPA's self-supervised training enables efficient learning from unlabeled video data without the distraction of pixel details. Mido also tackles innovations in visual prediction, the use of advanced techniques for video processing, and the complexities of temporal prediction. This insightful conversation highlights the future of AI reasoning beyond generative models.

Sherry Yang, a Senior Research Scientist at Google DeepMind and a PhD candidate at UC Berkeley, discusses her groundbreaking work on video as a universal interface for AI reasoning. She draws parallels between video generation models and language models, highlighting their potential in real-world decision-making tasks. The conversation covers the integration of video in robotics, the challenges of effective labeling, and the exciting applications of interactive simulators. Sherry also unveils UniSim, showcasing the future of engaging with AI-generated environments.

Sayash Kapoor, a Ph.D. student at Princeton University, discusses his research on the societal impact of open foundation models. He highlights the controversies surrounding AI safety and the potential risks of releasing model weights. The conversation delves into critical issues, such as biosecurity concerns linked to language models and the challenges of non-consensual imagery in AI. Kapoor advocates for a unified framework to evaluate these risks, emphasizing the need for transparency and legal protections in AI development.