Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang

Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang

In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119

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AI Orchestration for Smart Cities and the Enterprise with Robin Braun and Luke Norris - #755

AI Orchestration for Smart Cities and the Enterprise with Robin Braun and Luke Norris - #755

Today, we're joined by Robin Braun, VP of AI business development for hybrid cloud at HPE, and Luke Norris, co-founder and CEO of Kamiwaza, to discuss how AI systems can be used to automate complex wo...

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Building an AI Mathematician with Carina Hong - #754

Building an AI Mathematician with Carina Hong - #754

In this episode, Carina Hong, founder and CEO of Axiom, joins us to discuss her work building an "AI Mathematician." Carina explains why this is a pivotal moment for AI in mathematics, citing a conver...

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High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

High-Efficiency Diffusion Models for On-Device Image Generation and Editing with Hung Bui - #753

In this episode, Hung Bui, Technology Vice President at Qualcomm, joins us to explore the latest high-efficiency techniques for running generative AI, particularly diffusion models, on-device. We dive...

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Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Vibe Coding's Uncanny Valley with Alexandre Pesant - #752

Today, we're joined by Alexandre Pesant, AI lead at Lovable, who joins us to discuss the evolution and practice of vibe coding. Alex shares his take on how AI is enabling a shift in software developme...

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Dataflow Computing for AI Inference with Kunle Olukotun - #751

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Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Recurrence and Attention for Long-Context Transformers with Jacob Buckman - #750

Today, we're joined by Jacob Buckman, co-founder and CEO of Manifest AI to discuss achieving long context in transformers. We discuss the bottlenecks of scaling context length and recent techniques to...

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The Decentralized Future of Private AI with Illia Polosukhin - #749

In this episode, Illia Polosukhin, a co-author of the seminal "Attention Is All You Need" paper and co-founder of Near AI, joins us to discuss his vision for building private, decentralized, and user-...

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Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capab...

23 Sep 20251h 3min

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