698 episodes

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.

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

    • Technology
    • 4.7 • 397 Ratings

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.

    Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla

    Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla

    Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and exploration in problem solving and explores the opportunities presented by applying reinforcement learning algorithms to the challenge of improving reasoning in large language models. Alex also shares his research on the effect of noise on language model training, highlighting the robustness of LLM architecture. Finally, we delve into the future of RL, and the potential of combining language models with traditional methods to achieve more robust AI reasoning.

    The complete show notes for this episode can be found at twimlai.com/go/680.

    • 46 min
    Localizing and Editing Knowledge in LLMs with Peter Hase

    Localizing and Editing Knowledge in LLMs with Peter Hase

    Today we're joined by Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab. We discuss "scalable oversight", and the importance of developing a deeper understanding of how large neural networks make decisions. We learn how matrices are probed by interpretability researchers, and explore the two schools of thought regarding how LLMs store knowledge. Finally, we discuss the importance of deleting sensitive information from model weights, and how "easy-to-hard generalization" could increase the risk of releasing open-source foundation models.

    The complete show notes for this episode can be found at twimlai.com/go/679.

    • 49 min
    Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping

    Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping

    Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be exploited, highlighting the risk of deploying LLM agents that interact with the real world. We discuss the role of open models in enabling security research, the challenges of optimizing over certain constraints, and the ongoing difficulties in achieving robustness in neural networks. Finally, we delve into the future of AI security, and the need for a better approach to mitigate the risks posed by optimized adversarial attacks.

    The complete show notes for this episode can be found at twimlai.com/go/678.

    • 48 min
    V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran

    V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran

    Today we’re joined by Mido Assran, a research scientist at Meta’s Fundamental AI Research (FAIR). In this conversation, we discuss V-JEPA, a new model being billed as “the next step in Yann LeCun's vision” for true artificial reasoning. V-JEPA, the video version of Meta’s Joint Embedding Predictive Architecture, aims to bridge the gap between human and machine intelligence by training models to learn abstract concepts in a more efficient predictive manner than generative models. V-JEPA uses a novel self-supervised training approach that allows it to learn from unlabeled video data without being distracted by pixel-level detail. Mido walks us through the process of developing the architecture and explains why it has the potential to revolutionize AI.

    The complete show notes for this episode can be found at twimlai.com/go/677.

    • 47 min
    Video as a Universal Interface for AI Reasoning with Sherry Yang

    Video as a Universal Interface for AI Reasoning with Sherry Yang

    Today we’re joined by Sherry Yang, senior research scientist at Google DeepMind and a PhD student at UC Berkeley. In this interview, we discuss her new paper, "Video as the New Language for Real-World Decision Making,” which explores how generative video models can play a role similar to language models as a way to solve tasks in the real world. Sherry draws the analogy between natural language as a unified representation of information and text prediction as a common task interface and demonstrates how video as a medium and generative video as a task exhibit similar properties. This formulation enables video generation models to play a variety of real-world roles as planners, agents, compute engines, and environment simulators. Finally, we explore UniSim, an interactive demo of Sherry's work and a preview of her vision for interacting with AI-generated environments.

    The complete show notes for this episode can be found at twimlai.com/go/676.

    • 49 min
    Assessing the Risks of Open AI Models with Sayash Kapoor

    Assessing the Risks of Open AI Models with Sayash Kapoor

    Today we’re joined by Sayash Kapoor, a Ph.D. student in the Department of Computer Science at Princeton University. Sayash walks us through his paper: "On the Societal Impact of Open Foundation Models.” We dig into the controversy around AI safety, the risks and benefits of releasing open model weights, and how we can establish common ground for assessing the threats posed by AI. We discuss the application of the framework presented in the paper to specific risks, such as the biosecurity risk of open LLMs, as well as the growing problem of "Non Consensual Intimate Imagery" using open diffusion models.

    The complete show notes for this episode can be found at twimlai.com/go/675.

    • 40 min

Customer Reviews

4.7 out of 5
397 Ratings

397 Ratings

Ashish US ,

The best aiml podcast

I am following this podcast since 2017, my AI learning direction comes this podcast. Thankyou Sam !

mfnyai14 ,

Best pod for ai/ml practioners

Consistently great content - this is the podcast that actual ai/ml practitioners

atxfitgal ,

Clear and educational

I’m a total noob to these topics but an otherwise smart person, and this podcast strikes a perfect balance between technical and accessible. Looking forward to binging and following. Well done!

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