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.
Brain-Inspired Hardware and Algorithm Co-Design with Melika Payvand - #585
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
Managing Data Labeling Ops for Success with Audrey Smith - #583
Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
On The Path Towards Robot Vision with Aljosa Osep - #581
More Language, Less Labeling with Kate Saenko - #580
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
Data Governance for Data Science with Adam Wood - #578
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
The Fallacy of "Ground Truth" with Shayan Mohanty - #576
Principle-centric AI with Adrien Gaidon - #575
Data Debt in Machine Learning with D. Sculley - #574
AI for Enterprise Decisioning at Scale with Rob Walker - #573
Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell - #572
Studying Machine Intelligence with Been Kim - #571
Advances in Neural Compression with Auke Wiggers - #570
Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello - #569
Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
Hierarchical and Continual RL with Doina Precup - #567
Open-Source Drug Discovery with DeepChem with Bharath Ramsundar - #566
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