Read the full transcript here. What is progress? How do we (and should we) measure progress? What are the most important questions to ask in progress studies? What are the factors that lead to progress? Why has large-scale progress taken so long (i.e., why did we not see much progress until the Industrial Revolution)? Why did industrial revolution, scientific revolution, and democratic revolution all seem to start within a relatively short period of time of each other? How can we prevent progress from slowing down, stopping, or even reversing? What factors have contributed to the slowing of progress in the last 50 years? What's the state of progress in nuclear energy? What is the history of attitudes towards progress? And why is it important for people to believe that progress is good?Jason Crawford is the founder of The Roots of Progress, where he writes and speaks about the history of technology and the philosophy of progress. Previously, he spent 18 years as a software engineer, engineering manager, and startup founder. Follow him on Twitter at @jasoncrawford. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. What are "forward-chaining" and "backward-chaining", and how do they connect with theory of change? What sorts of mental habits and heuristics prevent you from brainstorming ideas effectively? How can you harness feedback effectively to sharpen your ideas? From whom should you solicit feedback? How can you view your own products with fresh eyes? What are some common struggles people encounter when starting or changing careers, and how can they be overcome? Why are small experiments so under-used? How can we construct a sustainable work life? What are the best ways to rest and recover from overwork and burnout?Lynette Bye is a productivity coach who works with effective altruists. Before that, she studied the psychology of self-control at Harvard University and the University of Pennsylvania. You can find out more and read her blog at lynettebye.com. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. Researchers in the Effective Altruism movement often view their work through a utilitarian lens, so why haven't they traditionally paid much attention to the psychological research into subjective wellbeing (i.e., people's self-reported levels of happiness, life satisfaction, feelings of purpose and meaning in life, etc.)? Are such subjective measures reliable and accurate? Or rather, which such measures are the most reliable and accurate? What are the pros and cons of using QALYs and DALYs to quantify wellbeing? Why is there sometimes a disconnect between the projected level of subjective wellbeing of a health condition and its actual level (e.g., some people can learn to manage and cope with "major" diseases, but some people with "minor" conditions like depression or anxiety might be in a constant state of agony)? What are some new and promising approaches to quantifying wellbeing? The EA movement typically uses the criteria of scale, neglectedness, and tractability for prioritizing cause areas; is that framework still relevant and useful? How do those criteria apply on a personal level? And how do those criteria taken together differ conceptually from cost-effectiveness? How effective are psychological interventions at improving subjective wellbeing? How well do such interventions work in different cultures? How can subjective wellbeing measures be improved? How can philosophers help us do good better?Michael Plant is the Founder and Director of the Happier Lives Institute, a non-profit research institute that searches for the most cost-effective ways to increase global well-being. Michael is also a Research Fellow at the Wellbeing Research Centre, Oxford. He has a PhD in Philosophy from Oxford, and his thesis, entitled Doing Good Badly? Philosophical Issues Related to Effective Altruism, was supervised by Peter Singer and Hilary Greaves. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. Should pleasure and pain be measured on logarithmic scales? How might such a scale affect utilitarian calculations? How do harmonic energy waves in the brain correspond to states of (or intensities of) consciousness? What sorts of conclusions can we draw about brain states given the resolutions and sampling rates of tools like fMRI, EEG, and MEG? What is the symmetry theory of homeostatic regulation, and how does it connect to pleasure and pain? Are uncomfortable or confused mental states computationally useful to us? To what extent can the concepts of musical consonance and dissonance map onto energy states in the brain?Andrés Gomez Emilsson has a Master's Degree in Psychology with an emphasis in computational models from Stanford and a professional background in graph theory, statistics, and affective science. Andrés was also the co-founder of the Stanford Transhumanist Association and first place winner of the Norway Math Olympiad. His work at QRI ranges from algorithm design, to psychedelic theory, to neurotechnology development, to mapping and studying the computational properties of consciousness. Andrés blogs at qualiacomputing.com. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. What is positive psychology? What is the PERMA model? From a creativity standpoint, is there a connection between music and writing? In various artistic fields, how hard is it to be creative without first achieving some level of technical mastery? How can one hone the skill of creativity? How useful is optimism for achieving happiness? What are the different sources from which humans derive pleasure? To what extent is western culture conscious of ageism? What does positive psychology have to say about interpersonal relationships? What is the value and purpose of extended education generally and degrees specifically? What is wisdom?Georgia Shreve is a composer, fiction writer, playwright, and poet. She holds degrees from Stanford, Brown, Columbia, and PENN. Her poetry and fiction have been published in magazines such as the New Yorker, New Republic, and New Criterion, and her short story, "The Countess of M-", won the Stanford Magazine Fiction award. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. What is machine learning? What are neural networks? How can humans interpret the meaning or functionality of the various layers of a neural network? What is a transformer, and how does it build on the idea of a neural network? Does a transformer have a conceptual advantage over neural nets, or is a transformer basically the equivalent of neural nets plus a lot of compute power? Why have we started hearing so much about neural nets in just the last few years even though they've existed conceptually for many decades? What kind of ML model is GPT-3? What learning sub-tasks are encapsulated in the process of learning how to autocomplete text? What is "few-shot" learning? What is the difference between GPT-2 and GPT-3? How big of a deal is GPT-3? Right now, GPT-3's responses are not guaranteed to contain true statements; is there a way to train future GPT or similar models to say only true things (or to indicate levels of confidence in the truthfulness of its statements)? Should people whose jobs revolve around writing or summarizing text be worried about being replaced by GPT-3? What are the relevant copyright issues related to text generation models? A website's "robots.txt" file or a "noindex" HTML attribute in its pages' meta tags tells web crawlers which content they can and cannot access; could a similar solution exist for writers, programmers, and others who want to limit or prevent their text from being used as training data for models like GPT-3? What are some of the scarier features of text generation models? What does the creation of models like GPT-3 tell us (if anything) about how and when we might create artificial general intelligence?Learn more about GPT-3 here. And learn more about Jeremy Nixon and listen to his episode here.Further reading:"Kanye West, Donald Trump And Jim Brown: The Full Transcript"lsusr's website"The Humans Are Dead" by Flight of the Conchords StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. How can we apply the theory of measurement accuracy to human judgments? How can cognitive biases affect both the bias term and the noise term in measurement error? How much noise should we expect in judgments of various kinds? Is there reason to think that machines will eventually make better decisions than humans in all domains? How does machine decision-making differ (if at all) from human decision-making? In what domains should we work to reduce variance in decision-making? If machines learn use human decisions as training data, then to what extent will human biases become "baked into" machine decisions? And can such biases be compensated for? Are there any domains where human judgment will always be preferable to machine judgment? What does the "fragile families" study tell us about the limits of predicting life outcomes? What does good decision "hygiene" look like? Why do people focus more on bias than noise when trying to reduce error? To what extent can people improve their decision-making abilities? How can we recognize good ideas when we have them? Humans aren't fully rational, but are they irrational?Daniel Kahneman is Professor of Psychology and Public Affairs Emeritus at the Princeton School of Public and International Affairs, the Eugene Higgins Professor of Psychology Emeritus at Princeton University, and a fellow of the Center for Rationality at the Hebrew University in Jerusalem. Dr. Kahneman has held the position of professor of psychology at the Hebrew University in Jerusalem (1970-1978), the University of British Columbia (1978-1986), and the University of California, Berkeley (1986-1994). He is a member of the National Academy of Science, the Philosophical Society, the American Academy of Arts and Sciences, and is a fellow of the American Psychological Association, the American Psychological Society, the Society of Experimental Psychologists, and the Econometric Society. He has been the recipient of many awards, among them the Distinguished Scientific Contribution Award of the American Psychological Association (1982) and the Grawemeyer Prize (2002), both jointly with Amos Tversky, the Warren Medal of the Society of Experimental Psychologists (1995), the Hilgard Award for Career Contributions to General Psychology (1995), the Nobel Prize in Economic Sciences (2002), the Lifetime Contribution Award of the American Psychological Association (2007), and the Presidential Medal of Freedom (2013). He holds honorary degrees from numerous universities. Find out more about him here.Here's the link to the Thought Saver deck that accompanies this episode: https://app.thoughtsaver.com/embed/JGXcbe19e1?start=1&end=17 StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Read the full transcript here. What is 80,000 Hours, and why is it so important? Does doing the most good in the world require being completely selfless and altruistic? What are the career factors that contribute to impactfulness? How should people choose among the various problem areas on which they could work? What sorts of long-term AI outcomes are possible (besides merely apocalyptic scenarios), and why is it so important to get AI right? How much should we value future generations? How much should we be worried about catastrophic and/or existential risks? Has the 80,000 Hours organizing shifted its emphasis over time to longer-term causes? How many resources should we devote to meta-research into discovering and rating the relative importance of various problems? How important is personal fit in considering a career?Ben Todd is the CEO and cofounder of 80,000 Hours, a non-profit that has reached millions of people and helped 1000+ people find careers tackling the world's most pressing problems. He helped to start the effective altruism movement in Oxford in 2011. He's the author of the 80,000 Hours Career Guide and Key Ideas series. Find out more about Ben at benjamintodd.org. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]

Scott Miller, psychotherapy effectiveness expert, discusses making psychotherapy more effective, the influence of confirmation bias, core skills and factors in therapy, measuring effectiveness and identifying intrinsic values, the theory of change in psychotherapy, creating a feedback-friendly culture, knowing when to switch therapists, and embracing cultural perspectives in therapy.

Read the full transcript here. What makes studying humans harder than studying other parts of the universe? Is social science currently improving its rigor, relevance, and self-reflection? Is it improving its predictive power over time? Why have sample sizes historically been so small in social science studies? Is social science actually able to accumulate knowledge? Have social scientists been able to move the "needle" on real-world problems like vaccine adoption? Is social science becoming more diverse? Specifically, does social science have a political bias? Are universities in crisis? Do the incentive structures in universities make them difficult or even impossible to reform?Matt Grossmann is Director of the Institute for Public Policy and Social Research and Professor of Political Science at Michigan State University. He is also Senior Fellow at the Niskanen Center and a Contributor at FiveThirtyEight. He has published analysis in The New York Times, The Washington Post, and Politico, and hosts the Science of Politics podcast. He is the author or co-author of How Social Science Got Better, Asymmetric Politics, Red State Blues, The Not-So-Special Interests, Artists of the Possible, and Campaigns & Elections, as well as dozens of journal articles. You can find more about him on his website. StaffSpencer Greenberg — Host / DirectorJosh Castle — ProducerRyan Kessler — Audio EngineerUri Bram — FactotumJanaisa Baril — TranscriptionistMusicBroke for FreeJosh WoodwardLee RosevereQuiet Music for Tiny Robotswowamusiczapsplat.comAffiliatesClearer ThinkingGuidedTrackMind EasePositlyUpLift[Read more]