Machine Learning Street Talk (MLST)

#94 - ALAN CHAN - AI Alignment and Governance #NEURIPS

Dec 26, 2022

In this talk, Alan Chan, a PhD student at Mila with a focus on AI alignment and governance, shares insights from his research on aligning AI with human values. He discusses the skepticism around AI alignment and the complexities of defining intelligence in AI systems. The conversation touches on the moral implications of AI decision-making, the risks of oversimplifying reward mechanisms in reinforcement learning, and the urgent need for collaborative safety measures in AI development. Chan's enthusiasm for the ethical aspects of AI governance is truly infectious.

13:34

Creator website

Episode guests

Alan Chan

AI Chapters

Episode notes

Intro

4min

Bottlenecks and Definitions of Intelligence in AI

2min

Exploring Agency and Morality in Machine Learning Systems

3min

Navigating Consequentialism in AI and Human Reasoning

2min

Navigating the Risks of AI: Alignment, Governance, and Ethical Considerations

3min

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Alan Chan is a PhD student at Mila, the Montreal Institute for Learning Algorithms, supervised by Nicolas Le Roux. Before joining Mila, Alan was a Masters student at the Alberta Machine Intelligence Institute and the University of Alberta, where he worked with Martha White. Alan's expertise and research interests encompass value alignment and AI governance. He is currently exploring the measurement of harms from language models and the incentives that agents have to impact the world. Alan's research focuses on understanding and controlling the values expressed by machine learning models. His projects have examined the regulation of explainability in algorithmic systems, scoring rules for performative binary prediction, the effects of global exclusion in AI development, and the role of a graduate student in approaching ethical impacts in AI research. In addition, Alan has conducted research into inverse policy evaluation for value-based sequential decision-making, and the concept of "normal accidents" and AI systems. Alan's research is motivated by the need to align AI systems with human values, and his passion for scientific and governance work in this field. Alan's energy and enthusiasm for his field is infectious.

This was a discussion at NeurIPS. It was in quite a loud environment so the audio quality could have been better.

References:

The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future [Tim Chivers]

https://www.amazon.co.uk/Does-Not-Hate-You-Superintelligence/dp/1474608795

The implausibility of intelligence explosion [Chollet]

https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec

Superintelligence: Paths, Dangers, Strategies [Bostrom]

https://www.amazon.co.uk/Superintelligence-Dangers-Strategies-Nick-Bostrom/dp/0199678111

A Theory of Universal Artificial Intelligence based on Algorithmic Complexity [Hutter]

https://arxiv.org/abs/cs/0004001

YT version: https://youtu.be/XBMnOsv9_pk

MLST Discord: https://discord.gg/aNPkGUQtc5

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