Future of Life Institute Podcast

It’s not just about the natural world. The side effects of climate change remain relatively unknown, but we can expect a warming world to impact every facet of our lives. In fact, as recent research shows, global warming is already affecting our mental and physical well-being, and this impact will only increase. Climate change could decrease the efficacy of our public safety institutions. It could damage our economies. It could even impact the way that we vote, potentially altering our democracies themselves. Yet even as these effects begin to appear, we’re already growing numb to the changing climate patterns behind them, and we’re failing to act. In honor of Earth Day, this month’s podcast focuses on these side effects and what we can do about them. Ariel spoke with Dr. Nick Obradovich, a research scientist at the MIT Media Lab, and Dr. Fran Moore, an assistant professor in the Department of Environmental Science and Policy at the University of California, Davis. They study the social and economic impacts of climate change, and they shared some of their most remarkable findings. Topics discussed in this episode include: - How getting used to climate change may make it harder for us to address the issue - The social cost of carbon - The effect of temperature on mood, exercise, and sleep - The effect of temperature on public safety and democratic processes - Why it’s hard to get people to act - What we can all do to make a difference - Why we should still be hopeful

The space of AI alignment research is highly dynamic, and it's often difficult to get a bird's eye view of the landscape. This podcast is the second of two parts attempting to partially remedy this by providing an overview of technical AI alignment efforts. In particular, this episode seeks to continue the discussion from Part 1 by going in more depth with regards to the specific approaches to AI alignment. In this podcast, Lucas spoke with Rohin Shah. Rohin is a 5th year PhD student at UC Berkeley with the Center for Human-Compatible AI, working with Anca Dragan, Pieter Abbeel and Stuart Russell. Every week, he collects and summarizes recent progress relevant to AI alignment in the Alignment Newsletter.  Topics discussed in this episode include: -Embedded agency -The field of "getting AI systems to do what we want" -Ambitious value learning -Corrigibility, including iterated amplification, debate, and factored cognition -AI boxing and impact measures -Robustness through verification, adverserial ML, and adverserial examples -Interpretability research -Comprehensive AI Services -Rohin's relative optimism about the state of AI alignment You can take a short (3 minute) survey to share your feedback about the podcast here: https://www.surveymonkey.com/r/YWHDFV7

The space of AI alignment research is highly dynamic, and it's often difficult to get a bird's eye view of the landscape. This podcast is the first of two parts attempting to partially remedy this by providing an overview of the organizations participating in technical AI research, their specific research directions, and how these approaches all come together to make up the state of technical AI alignment efforts. In this first part, Rohin moves sequentially through the technical research organizations in this space and carves through the field by its varying research philosophies. We also dive into the specifics of many different approaches to AI safety, explore where they disagree, discuss what properties varying approaches attempt to develop/preserve, and hear Rohin's take on these different approaches. You can take a short (3 minute) survey to share your feedback about the podcast here: https://www.surveymonkey.com/r/YWHDFV7 In this podcast, Lucas spoke with Rohin Shah. Rohin is a 5th year PhD student at UC Berkeley with the Center for Human-Compatible AI, working with Anca Dragan, Pieter Abbeel and Stuart Russell. Every week, he collects and summarizes recent progress relevant to AI alignment in the Alignment Newsletter. Topics discussed in this episode include: - The perspectives of CHAI, MIRI, OpenAI, DeepMind, FHI, and others - Where and why they disagree on technical alignment - The kinds of properties and features we are trying to ensure in our AI systems - What Rohin is excited and optimistic about - Rohin's recommended reading and advice for improving at AI alignment research

Why are we so concerned about lethal autonomous weapons? Ariel spoke to four experts –– one physician, one lawyer, and two human rights specialists –– all of whom offered their most powerful arguments on why the world needs to ensure that algorithms are never allowed to make the decision to take a life. It was even recorded from the United Nations Convention on Conventional Weapons, where a ban on lethal autonomous weapons was under discussion. We've compiled their arguments, along with many of our own, and now, we want to turn the discussion over to you. We’ve set up a comments section on the FLI podcast page (www.futureoflife.org/whyban), and we want to know: Which argument(s) do you find most compelling? Why?

See full article here: https://futureoflife.org/2019/03/06/ai-alignment-through-debate-with-geoffrey-irving/ "To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences. One approach to specifying complex goals asks humans to judge during training which agent behaviors are safe and useful, but this approach can fail if the task is too complicated for a human to directly judge. To help address this concern, we propose training agents via self play on a zero sum debate game. Given a question or proposed action, two agents take turns making short statements up to a limit, then a human judges which of the agents gave the most true, useful information...  In practice, whether debate works involves empirical questions about humans and the tasks we want AIs to perform, plus theoretical questions about the meaning of AI alignment. " AI safety via debate (https://arxiv.org/pdf/1805.00899.pdf) Debate is something that we are all familiar with. Usually it involves two or more persons giving arguments and counter arguments over some question in order to prove a conclusion. At OpenAI, debate is being explored as an AI alignment methodology for reward learning (learning what humans want) and is a part of their scalability efforts (how to train/evolve systems to solve questions of increasing complexity). Debate might sometimes seem like a fruitless process, but when optimized and framed as a two-player zero-sum perfect-information game, we can see properties of debate and synergies with machine learning that may make it a powerful truth seeking process on the path to beneficial AGI. On today's episode, we are joined by Geoffrey Irving. Geoffrey is a member of the AI safety team at OpenAI. He has a PhD in computer science from Stanford University, and has worked at Google Brain on neural network theorem proving, cofounded Eddy Systems to autocorrect code as you type, and has worked on computational physics and geometry at Otherlab, D. E. Shaw Research, Pixar, and Weta Digital. He has screen credits on Tintin, Wall-E, Up, and Ratatouille.  Topics discussed in this episode include: -What debate is and how it works -Experiments on debate in both machine learning and social science -Optimism and pessimism about debate -What amplification is and how it fits in -How Geoffrey took inspiration from amplification and AlphaGo -The importance of interpretability in debate -How debate works for normative questions -Why AI safety needs social scientists

In this special two-part podcast Ariel Conn is joined by Max Tegmark for a conversation with Dr. Matthew Meselson, biologist and Thomas Dudley Cabot Professor of the Natural Sciences at Harvard University. Part Two focuses on three major incidents in the history of biological weapons: the 1979 anthrax outbreak in Russia, the use of Agent Orange and other herbicides in Vietnam, and the Yellow Rain controversy in the early 80s. Dr. Meselson led the investigations into all three and solved some perplexing scientific mysteries along the way.

In this special two-part podcast Ariel Conn is joined by Max Tegmark for a conversation with Dr. Matthew Meselson, biologist and Thomas Dudley Cabot Professor of the Natural Sciences at Harvard University. Dr. Meselson began his career with an experiment that helped prove Watson and Crick’s hypothesis on the structure and replication of DNA. He then got involved in disarmament, working with the US government to halt the use of Agent Orange in Vietnam and developing the Biological Weapons Convention. From the cellular level to that of international policy, Dr. Meselson has made significant contributions not only to the field of biology, but also towards the mitigation of existential threats. In Part One, Dr. Meselson describes how he designed the experiment that helped prove Watson and Crick’s hypothesis, and he explains why this type of research is uniquely valuable to the scientific community. He also recounts his introduction to biological weapons, his reasons for opposing them, and the efforts he undertook to get them banned. Dr. Meselson was a key force behind the U.S. ratification of the Geneva Protocol, a 1925 treaty banning biological warfare, as well as the conception and implementation of the Biological Weapons Convention, the international treaty that bans biological and toxin weapons.

See the full article here: https://futureoflife.org/2019/02/21/human-cognition-and-the-nature-of-intelligence-with-joshua-greene/ "How do we combine concepts to form thoughts? How can the same thought be represented in terms of words versus things that you can see or hear in your mind's eyes and ears? How does your brain distinguish what it's thinking about from what it actually believes? If I tell you a made up story, yesterday I played basketball with LeBron James, maybe you'd believe me, and then I say, oh I was just kidding, didn't really happen. You still have the idea in your head, but in one case you're representing it as something true, in another case you're representing it as something false, or maybe you're representing it as something that might be true and you're not sure. For most animals, the ideas that get into its head come in through perception, and the default is just that they are beliefs. But humans have the ability to entertain all kinds of ideas without believing them. You can believe that they're false or you could just be agnostic, and that's essential not just for idle speculation, but it's essential for planning. You have to be able to imagine possibilities that aren't yet actual. So these are all things we're trying to understand. And then I think the project of understanding how humans do it is really quite parallel to the project of trying to build artificial general intelligence." -Joshua Greene Josh Greene is a Professor of Psychology at Harvard, who focuses on moral judgment and decision making. His recent work focuses on cognition, and his broader interests include philosophy, psychology and neuroscience. He is the author of Moral Tribes: Emotion, Reason, and the Gap Bewtween Us and Them.  Joshua Greene's research focuses on further understanding key aspects of both individual and collective intelligence. Deepening our knowledge of these subjects allows us to understand the key features which constitute human general intelligence, and how human cognition aggregates and plays out through group choice and social decision making. By better understanding the one general intelligence we know of, namely humans, we can gain insights into the kinds of features that are essential to general intelligence and thereby better understand what it means to create beneficial AGI. This particular episode was recorded at the Beneficial AGI 2019 conference in Puerto Rico. We hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, iTunes, Google Play, Stitcher, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here. Topics discussed in this episode include: -The multi-modal and combinatorial nature of human intelligence -The symbol grounding problem -Grounded cognition -Modern brain imaging -Josh's psychology research using John Rawls’ veil of ignorance -Utilitarianism reframed as 'deep pragmatism'

Three generals are voting on whether to attack or retreat from their siege of a castle. One of the generals is corrupt and two of them are not. What happens when the corrupted general sends different answers to the other two generals? A Byzantine fault is "a condition of a computer system, particularly distributed computing systems, where components may fail and there is imperfect information on whether a component has failed. The term takes its name from an allegory, the "Byzantine Generals' Problem", developed to describe this condition, where actors must agree on a concerted strategy to avoid catastrophic system failure, but some of the actors are unreliable." The Byzantine Generals' Problem and associated issues in maintaining reliable distributed computing networks is illuminating for both AI alignment and modern networks we interact with like Youtube, Facebook, or Google. By exploring this space, we are shown the limits of reliable distributed computing, the safety concerns and threats in this space, and the tradeoffs we will have to make for varying degrees of efficiency or safety. The Byzantine Generals' Problem, Poisoning, and Distributed Machine Learning with El Mahdi El Mahmdi is the ninth podcast in the AI Alignment Podcast series, hosted by Lucas Perry. El Mahdi pioneered Byzantine resilient machine learning devising a series of provably safe algorithms he recently presented at NeurIPS and ICML. Interested in theoretical biology, his work also includes the analysis of error propagation and networks applied to both neural and biomolecular networks. This particular episode was recorded at the Beneficial AGI 2019 conference in Puerto Rico. We hope that you will join in the conversations by following us or subscribing to our podcasts on Youtube, SoundCloud, iTunes, Google Play, Stitcher, or your preferred podcast site/application. You can find all the AI Alignment Podcasts here. If you're interested in exploring the interdisciplinary nature of AI alignment, we suggest you take a look here at a preliminary landscape which begins to map this space. Topics discussed in this episode include: The Byzantine Generals' Problem What this has to do with artificial intelligence and machine learning Everyday situations where this is important How systems and models are to update in the context of asynchrony Why it's hard to do Byzantine resilient distributed ML. Why this is important for long-term AI alignment An overview of Adversarial Machine Learning and where Byzantine-resilient Machine Learning stands on the map is available in this (9min) video . A specific focus on Byzantine Fault Tolerant Machine Learning is available here (~7min) In particular, El Mahdi argues in the first interview (and in the podcast) that technical AI safety is not only relevant for long term concerns, but is crucial in current pressing issues such as social media poisoning of public debates and misinformation propagation, both of which fall into Poisoning-resilience. Another example he likes to use is social media addiction, that could be seen as a case of (non) Safely Interruptible learning. This value misalignment is already an issue with the primitive forms of AIs that optimize our world today as they maximize our watch-time all over the internet. The latter (Safe Interruptibility) is another technical AI safety question El Mahdi works on, in the context of Reinforcement Learning. This line of research was initially dismissed as "science fiction", in this interview (5min), El Mahdi explains why it is a realistic question that arises naturally in reinforcement learning El Mahdi's work on Byzantine-resilient Machine Learning and other relevant topics is available on his Google scholar profile.

Every January, we like to look back over the past 12 months at the progress that’s been made in the world of artificial intelligence. Welcome to our annual “AI breakthroughs” podcast, 2018 edition. Ariel was joined for this retrospective by researchers Roman Yampolskiy and David Krueger. Roman is an AI Safety researcher and professor at the University of Louisville. He also recently published the book, Artificial Intelligence Safety & Security. David is a PhD candidate in the Mila lab at the University of Montreal, where he works on deep learning and AI safety. He's also worked with safety teams at the Future of Humanity Institute and DeepMind and has volunteered with 80,000 hours. Roman and David shared their lists of 2018’s most promising AI advances, as well as their thoughts on some major ethical questions and safety concerns. They also discussed media coverage of AI research, why talking about “breakthroughs” can be misleading, and why there may have been more progress in the past year than it seems.