Hear This Idea

In this bonus episode we are sharing an episode by another podcast: How I Learned To Love Shrimp. It is co-hosted by Amy Odene and James Ozden, who together are "showcasing innovative and impactful ways to help animals". In this interview they speak to David Coman-Hidy, who is the former President of The Humane –League, one of the largest farm animal advocacy organisations in the world. He now works as a Partner at Sharpen Strategy working to coach animal advocacy organisations.

Dr Richard Bruns is a Senior Scholar at the Johns Hopkins Center for Health Security, and before that was a Senior Economist at the US Food and Drug Administration (the FDA). In this episode we talk about the importance of indoor air quality (IAQ), and how to improve it. Including: Estimating the DALY cost of unclean indoor air from pathogens and particulate matter How much pandemic risk could be reduced from improving IAQ? How economists convert health losses into dollar figures — and how not to put a price on life Key interventions to improve IAQ Air filtration Germicidal UV light (especially Far-UVC light) Barriers to adoption, including UV smog and empirical studies needed most National and state-level policy changes to get these interventions adopted widely You can get in touch through our website or on Twitter. Consider leaving us an honest review wherever you're listening to this — it's the best free way to support the show. Thanks for listening!

Liv Boeree is a former poker champion turned science communicator and podcaster, with a background in astrophysics. In 2014, she founded the nonprofit Raising for Effective Giving, which has raised more than $14 million for effective charities. Before retiring from professional poker in 2019, Liv was the Female Player of the Year for three years running. Currently she hosts the Win-Win podcast (you’ll enjoy it if you enjoy this podcast). You can see more links and a full transcript at hearthisidea.com/episodes/boeree. In this episode we talk about: Is the ‘poker mindset’ valuable? Is it learnable? How and why to bet on your beliefs — and whether there are outcomes you shouldn’t make bets on Would cities be better without public advertisements? What is Moloch, and why is it a useful abstraction? How do we escape multipolar traps? Why might advanced AI (not) act like profit-seeking companies? What’s so important about complexity? What is complexity, for that matter? You can get in touch through our website or on Twitter. Consider leaving us an honest review wherever you're listening to this — it's the best free way to support the show. Thanks for listening!

Steven Teles s is a Professor of Political Science at Johns Hopkins University and a Senior Fellow at the Niskanen Center. His work focuses on American politics and he written several books on topics such as elite politics, the judiciary, and mass incarceration. You can see more links and a full transcript at hearthisidea.com/teles In this episode we talk about: The rise of the conservative legal movement; How ideas can come to be entrenched in American politics; Challenges in building a new academic field like "law and economics"; The limitations of doing quantitative evaluations of advocacy groups. If you have any feedback, you can get a free book for filling out our new feedback form. You can also get in touch through our website or on Twitter. Consider leaving us a review wherever you're listening to this — it's the best free way to support the show. Thanks for listening! Key links:

Michael Cohen is is a DPhil student at the University of Oxford with Mike Osborne. He will be starting a postdoc with Professor Stuart Russell at UC Berkeley, with the Center for Human-Compatible AI. His research considers the expected behaviour of generally intelligent artificial agents, with a view to designing agents that we can expect to behave safely. You can see more links and a full transcript at www.hearthisidea.com/episodes/cohen. We discuss: What is reinforcement learning, and how is it different from supervised and unsupervised learning? Michael's recently co-authored paper titled 'Advanced artificial agents intervene in the provision of reward' Why might it be hard to convey what we really want to RL learners — even when we know exactly what we want? Why might advanced RL systems might tamper with their sources of input, and why could this be very bad? What assumptions need to hold for this "input tampering" outcome? Is reward really the optimisation target? Do models "get reward"? What's wrong with the analogy between RL systems and evolution? Key links: Michael's personal website 'Advanced artificial agents intervene in the provision of reward' by Michael K. Cohen, Marcus Hutter, and Michael A. Osborne 'Pessimism About Unknown Unknowns Inspires Conservatism' by Michael Cohen and Marcus Hutter 'Intelligence and Unambitiousness Using Algorithmic Information Theory' by Michael Cohen, Badri Vallambi, and Marcus Hutter 'Quantilizers: A Safer Alternative to Maximizers for Limited Optimization' by Jessica Taylor 'RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning' by Marc Rigter, Bruno Lacerda, and Nick Hawes 'Quantilizers: A Safer Alternative to Maximizers for Limited Optimization' by Jessica Taylor Season 40 of Survivor

Michael Aird is a senior research manager at Rethink Priorities, where he co-leads the Artificial Intelligence Governance and Strategy team alongside Amanda El-Dakhakhni. Before that, he conducted nuclear risk research for Rethink Priorities and longtermist macrostrategy research for Convergence Analysis, the Center on Long-Term Risk, and the Future of Humanity Institute, which is where we know each other from. Before that, he was a teacher and a stand up comedian. He previously spoke to us about impact-driven research on Episode 52. In this episode, we talk about: The basic case for working on existential risk from AI How to begin figuring out what to do to reduce the risks Threat models for the risks of advanced AI 'Theories of victory' for how the world mitigates the risks 'Intermediate goals' in AI governance What useful (and less useful) research looks like for reducing AI x-risk Practical advice for usefully contributing to efforts to reduce existential risk from AI Resources for getting started and finding job openings Key links: Apply to be a Compute Governance Researcher or Research Assistant at Rethink Priorities (applications open until June 12, 2023) Rethink Priority's survey on intermediate goals in AI governance The Rethink Priorities newsletter The Rethink Priorities tab on the Effective Altruism Forum Some AI Governance Research Ideas compiled by Markus Anderljung & Alexis Carlier Strategic Perspectives on Long-term AI Governance by Matthijs Maas Michael's posts on the Effective Altruism Forum (under the username "MichaelA") The 80,000 Hours job board

Anders Sandberg is a researcher, futurist, transhumanist and author. He holds a PhD in computational neuroscience from Stockholm University, and is currently a Senior Research Fellow at the Future of Humanity Institute at the University of Oxford. His research covers human enhancement, exploratory engineering, and 'grand futures' for humanity. This episode is a recording of a live interview at EAGx Cambridge (2023). You can find upcoming effective altruism conferences here: www.effectivealtruism.org/ea-global We talk about: What is exploratory engineering and what is it good for? Progress on whole brain emulation Are we near the end of humanity's tech tree? Is diversity intrinsically valuable in grand futures? How Anders does research Virtue ethics for civilisations Anders' takes on AI risk and whether LLMs are close to general intelligence And much more! Further reading and a transcript is available on our website: hearthisidea.com/episodes/sandberg-live If you have any feedback, you can get a free book for filling out our new feedback form. You can also get in touch through our website or on Twitter. Consider leaving us a review wherever you're listening to this — it's the best free way to support the show. Thanks for listening!