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.
Subscribe: iTunes / SoundCloud / Google Play / Stitcher / RSS
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!
TWiML Online Meetup Update
I’d like to send a huge shout out to everyone who participated in the TWiML Online Meetup earlier this week. In our community segment we had a very fun and wide ranging discussion about freezing your brain (and if you missed that startup’s announcement you probably have no idea what I’m talking about), ML and AI in the healthcare space, and more. Community member Nicholas Teague, who goes by @_NicT_ on twitter, also briefly spoke about his essay “A Toddler Learns to Speak”, where he explores connections between different modalities in machine learning. Finally, a hearty thank you to Sean Devlin, who presented a deep dive on Deep Reinforcement Learning and Google DeepMind’s seminal paper in the space. Be on the lookout for the video recording and details on next month’s meetup at twimlai.com/meetup.
You all know I travel to a ton of events each year, and event season is just getting underway for me. One of the events I’m most excited about is my very own AI Summit, the successor to the awesome Future of Data Summit event I produced last year. This year’s event takes place April 30th and May 1st, and is once again being held in Las Vegas, in conjunction with the Interop ITX conference.
This year’s event is much more AI focused, and is targeting enterprise line-of-business and IT managers and leaders who want to get smart on AI very quickly. Think of it as a two-day, no-fluff, Technical MBA in machine learning & AI. I’ll be presenting an ML & AI bootcamp, and I’ll have experts coming in to present mini workshops on computer vision, natural language processing and conversational applications, ML and AI for IoT and industrial applications, data management for AI, building an AI-first culture in your organization, and operationalizing ML and AI. For more information on the program visit twimlai.com/aisummit-interop-2018/.
Mentioned in the Interview
- Paper – Adversarial Attacks on Neural Network Policies
- Paper – Delving into transferable examples
- Paper – BADnet
- NPM Library story
- Reward Hacking
- IMPALA – DeepMind
- Paper – Hierarchical Reward – Deepmind
- Paper – Robust Adversarial Reinforcement Learning
- Goodhart’s Law
- Register for the Artificial Intelligence Conference here!
- Check out @ShirinGlander’s Great TWiML Sketches!
- TWiML Presents: Series page
- TWiML Events Page
- TWiML Meetup
- TWiML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0