Today we’re joined by Rishabh Agarwal, a research scientist at Google Brain in Montreal. In our conversation with Rishabh, we discuss his recent paper Deep Reinforcement Learning at the Edge of the Statistical Precipice, which won an outstanding paper award at the most recent NeurIPS conference. In this paper, Rishabh and his coauthors call for a change in how deep RL performance is reported on benchmarks when using only a few runs, acknowledging that typically, DeepRL algorithms are evaluated by the performance on a large suite of tasks. Using the Atari 100k benchmark, they found substantial disparities in the conclusions from point estimates alone versus statistical analysis. We explore the reception of this paper from the research community, some of the more surprising results, what incentives researchers have to implement these types of changes in self-reporting when publishing, and much more.

The complete show notes for this episode can be found at twimlai.com/go/559

Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559

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.

Technology

Tech News

Podcasting

Gadgets

Software How-To

News

Science

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.

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

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.

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

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.

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

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.

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

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.

Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559

Download our free app to listen on your phone