Scott Emmons | When Your AIs Deceive You: Challenges of Partial Observability in Reinforcement Learning from Human Feedback
![](https://foresight.org/wp-content/uploads/2023/10/Scott-Emmons.jpg)
Presenter DC, Worldcoin Foundation DC is a research engineer at the Worldcoin Foundation where he focuses on advancing the state of the art of privacy preserving digital identity, programmable cryptography and blockchain scalability. Summary: An introduction to Zero Knowledge Machine Learning. In the age of AI we need to have ways of verifying data provenance… Continue reading DC | An Overview of Zero Knowledge Machine Learning
Presenter Christian Schroeder de Witt Christian is a researcher in foundational AI, information security, and AI safety, with a current focus on the limits of undetectability. Lately, he has been busy pioneering the field of Multi-Agent Security (masec.ai), which aims to overcome the safety and security issues inherent in contemporary approaches to multi-agent AI. His… Continue reading C. Schroeder de Witt | Secret Collusion Among Generative AI Agents: Toward Multi-Agent Security
Presenter David Bloomin David Bloomin, with over 20 years in software engineering, helped shape large-scale infrastructure and AI projects at early-stage Google, Facebook, and Asana. Now, he delves into multi-agent reinforcement learning and collective intelligence-inspired AI, also co-founding Plurality Institute to advance collective intelligence research. His work blends practical engineering with a quest to explore… Continue reading David Bloomin | Metta Learning – Love Is All You Need
Presenter Jules Hedges Jules is a mathematician and computer scientist who was a pioneer of the recently developed field of applied category theory. His main scientific interests are in microeconomics and machine learning. He is a co-founder of the Institute for Categorical Cybernetics (https://cybercat.institute), a nonprofit organization for research and open source software development, and… Continue reading Jules Hedges | Compositional Game Theory – Towards Incentives Modelling at Scale