80,000 Hours Podcast

Since it was founded, 80,000 Hours has done one-on-one calls to supplement our online content and offer more personalised advice. We try to help people get clear on their most plausible paths, the key uncertainties they face in choosing between them, and provide resources, pointers, and introductions to help them in those paths. I (Michelle Hutchinson) joined the team a couple of years ago after working at Oxford's Global Priorities Institute, and these days I'm 80,000 Hours' Head of Advising. Since then, chatting to hundreds of people about their career plans has given me some idea of the kinds of things it’s useful for people to hear about when thinking through their careers. So we thought it would be useful to discuss some on the show for everyone to hear. • Links to learn more, summary and full transcript. • See over 500 vacancies on our job board. • Apply for one-on-one career advising. Among other common topics, we cover: • Why traditional careers advice involves thinking through what types of roles you enjoy followed by which of those are impactful, while we recommend going the other way: ranking roles on impact, and then going down the list to find the one you think you’d most flourish in. • That if you’re pitching your job search at the right level of role, you’ll need to apply to a large number of different jobs. So it's wise to broaden your options, by applying for both stretch and backup roles, and not over-emphasising a small number of organisations. • Our suggested process for writing a longer term career plan: 1. shortlist your best medium to long-term career options, then 2. figure out the key uncertainties in choosing between them, and 3. map out concrete next steps to resolve those uncertainties. • Why many listeners aren't spending enough time finding out about what the day-to-day work is like in paths they're considering, or reaching out to people for advice or opportunities. • The difficulty of maintaining the ambition to increase your social impact, while also being proud of and motivated by what you're already accomplishing. I also thought it might be useful to give people a sense of what I do and don’t do in advising calls, to help them figure out if they should sign up for it. If you’re wondering whether you’ll benefit from advising, bear in mind that it tends to be more useful to people: 1. With similar views to 80,000 Hours on what the world’s most pressing problems are, because we’ve done most research on the problems we think it’s most important to address. 2. Who don’t yet have close connections with people working at effective altruist organisations. 3. Who aren’t strongly locationally constrained. If you’re unsure, it doesn’t take long to apply, and a lot of people say they find the application form itself helps them reflect on their plans. We’re particularly keen to hear from people from under-represented backgrounds. Also in this episode: • I describe mistakes I’ve made in advising, and career changes made by people I’ve spoken with. • Rob and I argue about what risks to take with your career, like when it’s sensible to take a study break, or start from the bottom in a new career path. • I try to forecast how I’ll change after I have a baby, Rob speculates wildly on what motherhood is like, and Arden and I mercilessly mock Rob. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:50)The process of advising (00:09:34)We’re not just excited about our priority paths (00:14:37)Common things Michelle says during advising (00:18:13)Interpersonal comparisons (00:31:18)Thinking about current impact (00:40:31)Applying to different kinds of orgs (00:42:29)Difference in impact between jobs / causes (00:49:04)Common mistakes (00:55:40)Career change stories (01:11:44)When is advising really useful for people? (01:24:28)Managing risk in careers (01:55:29)Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

Our lives currently revolve around the global emergency of COVID-19; you’re probably reading this while confined to your house, as the death toll from the worst pandemic since 1918 continues to rise. The question of how to tackle COVID-19 has been foremost in the minds of many, including here at 80,000 Hours. Today's guest, Dr Gregory Lewis, acting head of the Biosecurity Research Group at Oxford University's Future of Humanity Institute, puts the crisis in context, explaining how COVID-19 compares to other diseases, pandemics of the past, and possible worse crises in the future. COVID-19 is a vivid reminder that we are unprepared to contain or respond to new pathogens. How would we cope with a virus that was even more contagious and even more deadly? Greg's work focuses on these risks -- of outbreaks that threaten our entire future through an unrecoverable collapse of civilisation, or even the extinction of humanity. Links to learn more, summary and full transcript. If such a catastrophe were to occur, Greg believes it’s more likely to be caused by accidental or deliberate misuse of biotechnology than by a pathogen developed by nature. There are a few direct causes for concern: humans now have the ability to produce some of the most dangerous diseases in history in the lab; technological progress may enable the creation of pathogens which are nastier than anything we see in nature; and most biotechnology has yet to even be conceived, so we can’t assume all the dangers will be familiar. This is grim stuff, but it needn’t be paralysing. In the years following COVID-19, humanity may be inspired to better prepare for the existential risks of the next century: improving our science, updating our policy options, and enhancing our social cohesion. COVID-19 is a tragedy of stunning proportions, and its immediate threat is undoubtedly worthy of significant resources. But we will get through it; if a future biological catastrophe poses an existential risk, we may not get a second chance. It is therefore vital to learn every lesson we can from this pandemic, and provide our descendants with the security we wish for ourselves. Today’s episode is the hosting debut of our Strategy Advisor, Howie Lempel. 80,000 Hours has focused on COVID-19 for the last few weeks and published over ten pieces about it, and a substantial benefit of this interview was to help inform our own views. As such, at times this episode may feel like eavesdropping on a private conversation, and it is likely to be of most interest to people primarily focused on making the long-term future go as well as possible. In this episode, Howie and Greg cover: • Reflections on the first few months of the pandemic • Common confusions around COVID-19 • How COVID-19 compares to other diseases • What types of interventions have been available to policymakers • Arguments for and against working on global catastrophic biological risks (GCBRs) • How to know if you’re a good fit to work on GCBRs • The response of the effective altruism community, as well as 80,000 Hours in particular, to COVID-19 • And much more. Chapters:Rob’s intro (00:00:00)The interview begins (00:03:15)What is COVID-19? (00:16:05)If you end up infected, how severe is it likely to be? (00:19:21)How does COVID-19 compare to other diseases? (00:25:42)Common confusions around COVID-19 (00:32:02)What types of interventions were available to policymakers? (00:46:20)Nonpharmaceutical Interventions (01:04:18)What can you do personally? (01:18:25)Reflections on the first few months of the pandemic (01:23:46)Global catastrophic biological risks (GCBRs) (01:26:17)Counterarguments to working on GCBRs (01:45:56)How do GCBRs compare to other problems? (01:49:05)Careers (01:59:50)The response of the effective altruism community to COVID-19 (02:11:42)The response of 80,000 Hours to COVID-19 (02:28:12)Get this episode by subscribing: type '80,000 Hours' into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

In a few days we'll be putting out a conversation with Dr Greg Lewis, who studies how to prevent global catastrophic biological risks at Oxford's Future of Humanity Institute. Greg also wrote a new problem profile on that topic for our website, and reading that is a good lead-in to our interview with him. So in a bit of an experiment we decided to make this audio version of that article, narrated by the producer of the 80,000 Hours Podcast, Keiran Harris. We’re thinking about having audio versions of other important articles we write, so it’d be great if you could let us know if you’d like more of these. You can email us your view at [email protected]. If you want to check out all of Greg’s graphs and footnotes that we didn’t include, and get links to learn more about GCBRs - you can find those here. And if you want to read more about COVID-19, the 80,000 Hours team has produced a fantastic package of 10 pieces about how to stop the pandemic. You can find those here.

From home isolation Rob and Howie just recorded an episode on: 1. How many could die in the crisis, and the risk to your health personally. 2. What individuals might be able to do help tackle the coronavirus crisis. 3. What we suspect governments should do in response to the coronavirus crisis. 4. The importance of personally not spreading the virus, the properties of the SARS-CoV-2 virus, and how you can personally avoid it. 5. The many places society screwed up, how we can avoid this happening again, and why be optimistic. We have rushed this episode out to share information as quickly as possible in a fast-moving situation. If you would prefer to read you can find the transcript here. We list a wide range of valuable resources and links in the blog post attached to the show (over 60, including links to projects you can join). See our 'problem profile' on global catastrophic biological risks for information on these grave threats and how you can contribute to preventing them. We have also just added a COVID-19 landing page on our site. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran Harris.

To do good, most of us look to use our time and money to affect the world around us today. But perhaps that's all wrong. If you took $1,000 you were going to donate and instead put it in the stock market — where it grew on average 5% a year — in 100 years you'd have $125,000 to give away instead. And in 200 years you'd have $17 million. This astonishing fact has driven today's guest, economics researcher Philip Trammell at Oxford's Global Priorities Institute, to investigate the case for and against so-called 'patient philanthropy' in depth. If the case for patient philanthropy is as strong as Phil believes, many of us should be trying to improve the world in a very different way than we are now. He points out that on top of being able to dispense vastly more, whenever your trustees decide to use your gift to improve the world, they'll also be able to rely on the much broader knowledge available to future generations. A donor two hundred years ago couldn't have known distributing anti-malarial bed nets was a good idea. Not only did bed nets not exist — we didn't even know about germs, and almost nothing in medicine was justified by science. ADDED: Does the COVID-19 emergency mean we should actually use resources right now? See Phil's first thoughts on this question here. • Links to learn more, summary and full transcript. What similar leaps will our descendants have made in 200 years, allowing your now vast foundation to benefit more people in even greater ways? And there's a third reason to wait as well. What are the odds that we today live at the most critical point in history, when resources happen to have the greatest ability to do good? It's possible. But the future may be very long, so there has to be a good chance that some moment in the future will be both more pivotal and more malleable than our own. Of course, there are many objections to this proposal. If you start a foundation you hope will wait around for centuries, might it not be destroyed in a war, revolution, or financial collapse? Or might it not drift from its original goals, eventually just serving the interest of its distant future trustees, rather than the noble pursuits you originally intended? Or perhaps it could fail for the reverse reason, by staying true to your original vision — if that vision turns out to be as deeply morally mistaken as the Rhodes' Scholarships initial charter, which limited it to 'white Christian men'. Alternatively, maybe the world will change in the meantime, making your gift useless. At one end, humanity might destroy itself before your trust tries to do anything with the money. Or perhaps everyone in the future will be so fabulously wealthy, or the problems of the world already so overcome, that your philanthropy will no longer be able to do much good. Are these concerns, all of them legitimate, enough to overcome the case in favour of patient philanthropy? In today's conversation with researcher Phil Trammell and my 80,000 Hours colleague Howie Lempel, we try to answer that, and also discuss: • Real attempts at patient philanthropy in history and how they worked out • Should we have a mixed strategy, where some altruists are patient and others impatient? • Which causes most need money now, and which later? • What is the research frontier here? • What does this all mean for what listeners should do differently? Chapters:Rob’s intro (00:00:00)The interview begins (00:02:23)Consequences for getting this question wrong (00:06:03)What have people had to say about this question in the past? (00:07:22)The case for saving (00:11:51)Hundred year leases (00:29:28)Should we be concerned about one group taking control of the world? (00:34:51)Finding better interventions in the future (00:37:20)The hinge of history (00:43:46)Does uncertainty lead us to wanting to wait? (01:01:52)Counterarguments (01:11:36)What about groups who have a particular sense of urgency? (01:40:46)How much should we actually save? (02:01:35)Implications for career choices (02:19:49) Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

This week Oxford academic and 80,000 Hours trustee Dr Toby Ord released his new book The Precipice: Existential Risk and the Future of Humanity. It's about how our long-term future could be better than almost anyone believes, but also how humanity's recklessness is putting that future at grave risk — in Toby's reckoning, a 1 in 6 chance of being extinguished this century. I loved the book and learned a great deal from it (buy it here, US and audiobook release March 24). While preparing for this interview I copied out 87 facts that were surprising, shocking or important. Here's a sample of 16: 1. The probability of a supervolcano causing a civilisation-threatening catastrophe in the next century is estimated to be 100x that of asteroids and comets combined. 2. The Biological Weapons Convention — a global agreement to protect humanity — has just four employees, and a smaller budget than an average McDonald’s. 3. In 2008 a 'gamma ray burst' reached Earth from another galaxy, 10 billion light years away. It was still bright enough to be visible to the naked eye. We aren't sure what generates gamma ray bursts but one cause may be two neutron stars colliding. 4. Before detonating the first nuclear weapon, scientists in the Manhattan Project feared that the high temperatures in the core, unprecedented for Earth, might be able to ignite the hydrogen in water. This would set off a self-sustaining reaction that would burn off the Earth’s oceans, killing all life above ground. They thought this was unlikely, but many atomic scientists feared their calculations could be missing something. As far as we know, the US President was never informed of this possibility, but similar risks were one reason Hitler stopped… N.B. I've had to cut off this list as we only get 4,000 characters in these show notes, so: Click here to read the whole list, see a full transcript, and find related links. And if you like the list, you can get a free copy of the introduction and first chapter by joining our mailing list. While I've been studying these topics for years and known Toby for the last eight, a remarkable amount of what's in The Precipice was new to me. Of course the book isn't a series of isolated amusing facts, but rather a systematic review of the many ways humanity's future could go better or worse, how we might know about them, and what might be done to improve the odds. And that's how we approach this conversation, first talking about each of the main threats, then how we can learn about things that have never happened before, then finishing with what a great future for humanity might look like and how it might be achieved. Toby is a famously good explainer of complex issues — a bit of a modern Carl Sagan character — so as expected this was a great interview, and one which Arden Koehler and I barely even had to work for. Some topics Arden and I ask about include: • What Toby changed his mind about while writing the book • Are people exaggerating when they say that climate change could actually end civilization? • What can we learn from historical pandemics? • Toby’s estimate of unaligned AI causing human extinction in the next century • Is this century the most important time in human history, or is that a narcissistic delusion? • Competing vision for humanity's ideal future • And more. Get this episode by subscribing: type '80,000 Hours' into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

The 80,000 Hours Podcast is about “the world’s most pressing problems and how you can use your career to solve them”, and in this episode we tackle that question in the most direct way possible. Last year we published a summary of all our key ideas, which links to many of our other articles, and which we are aiming to keep updated as our opinions shift. All of us added something to it, but the single biggest contributor was our CEO and today's guest, Ben Todd, who founded 80,000 Hours along with Will MacAskill back in 2012. This key ideas page is the most read on the site. By itself it can teach you a large fraction of the most important things we've discovered since we started investigating high impact careers. • Links to learn more, summary and full transcript. But it's perhaps more accurate to think of it as a mini-book, as it weighs in at over 20,000 words. Fortunately it's designed to be highly modular and it's easy to work through it over multiple sessions, scanning over the articles it links to on each topic. Perhaps though, you'd prefer to absorb our most essential ideas in conversation form, in which case this episode is for you. If you want to have a big impact with your career, and you say you're only going to read one article from us, we recommend you read our key ideas page. And likewise, if you're only going to listen to one of our podcast episodes, it should be this one. We have fun and set a strong pace, running through: • Common misunderstandings of our advice • A high level overview of what 80,000 Hours generally recommends • Our key moral positions • What are the most pressing problems to work on and why? • Which careers effectively contribute to solving those problems? • Central aspects of career strategy like how to weigh up career capital, personal fit, and exploration • As well as plenty more. One benefit of this podcast over the article is that we can more easily communicate uncertainty, and dive into the things we're least sure about, or didn’t yet cover within the article. Note though that our what’s in the article is more precisely stated, our advice is going to keep shifting, and we're aiming to keep the key ideas page current as our thinking evolves over time. This episode was recorded in November 2019, so if you notice a conflict between the page and this episode in the future, go with the page! Get the episode by subscribing: type 80,000 Hours into your podcasting app. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

Today's bonus episode of the podcast is a quick conversation between me and my fellow 80,000 Hours researcher Arden Koehler about a few topics, including the demandingness of morality, work-life balance, and emotional reactions to injustice. Arden is about to graduate with a philosophy PhD from New York University, so naturally we dive right into some challenging implications of utilitarian philosophy and how it might be applied to real life. Issues we talk about include: • If you’re not going to be completely moral, should you try being a bit more ethical, or give up? • Should you feel angry if you see an injustice, and if so, why? • How much should we ask people to live frugally? So far the feedback on the post-episode chats that we've done have been positive, so we thought we'd go ahead and try out this freestanding one. But fair warning: it's among the more difficult episodes to follow, and probably not the best one to listen to first, as you'll benefit from having more context! If you'd like to listen to more of Arden you can find her in episode 67, David Chalmers on the nature and ethics of consciousness, or episode 66, Peter Singer on being provocative, EA, and how his moral views have changed. Here's more information on some of the issues we touch on: • Consequentialism on Wikipedia • Appropriate dispositions on the Stanford Encyclopaedia of Philosophy • Demandingness objection on Wikipedia • And a paper on epistemic normativity. ——— I mention the call for papers of the Academic Workshop on Global Priorities in the introduction — you can learn more here. And finally, Toby Ord — one of our founding Trustees and a Senior Research Fellow in Philosophy at Oxford University — has his new book The Precipice: Existential Risk and the Future of Humanity coming out next week. I've read it and very much enjoyed it. Find out where you can pre-order it here. We'll have an interview with him coming up soon.

nCoV is alarming governments and citizens around the world. It has killed more than 1,000 people, brought the Chinese economy to a standstill, and continues to show up in more and more places. But bad though it is, it's much closer to a warning shot than a worst case scenario. The next emerging infectious disease could easily be more contagious, more fatal, or both. Despite improvements in the last few decades, humanity is still not nearly prepared enough to contain new diseases. We identify them too slowly. We can't do enough to reduce their spread. And we lack vaccines or drugs treatments for at least a year, if they ever arrive at all. • Links to learn more, summary and full transcript. This is a precarious situation, especially with advances in biotechnology increasing our ability to modify viruses and bacteria as we like. In today's episode, Cassidy Nelson, a medical doctor and research scholar at Oxford University's Future of Humanity Institute, explains 12 things her research group think urgently need to happen if we're to keep the risk at acceptable levels. The ideas are: Science 1. Roll out genetic sequencing tests that lets you test someone for all known and unknown pathogens in one go. 2. Fund research into faster ‘platform’ methods for going from pathogen to vaccine, perhaps using innovation prizes. 3. Fund R&D into broad-spectrum drugs, especially antivirals, similar to how we have generic antibiotics against multiple types of bacteria. Response 4. Develop a national plan for responding to a severe pandemic, regardless of the cause. Have a backup plan for when things are so bad the normal processes have stopped working entirely. 5. Rigorously evaluate in what situations travel bans are warranted. (They're more often counterproductive.) 6. Coax countries into more rapidly sharing their medical data, so that during an outbreak the disease can be understood and countermeasures deployed as quickly as possible. 7. Set up genetic surveillance in hospitals, public transport and elsewhere, to detect new pathogens before an outbreak — or even before patients develop symptoms. 8. Run regular tabletop exercises within governments to simulate how a pandemic response would play out. Oversight 9. Mandate disclosure of accidents in the biosafety labs which handle the most dangerous pathogens. 10. Figure out how to govern DNA synthesis businesses, to make it harder to mail order the DNA of a dangerous pathogen. 11. Require full cost-benefit analysis of 'dual-use' research projects that can generate global risks. 12. And finally, to maintain momentum, it's necessary to clearly assign responsibility for the above to particular individuals and organisations. These advances can be pursued by politicians and public servants, as well as academics, entrepreneurs and doctors, opening the door for many listeners to pitch in to help solve this incredibly pressing problem. In the episode Rob and Cassidy also talk about: • How Cassidy went from clinical medicine to a PhD studying novel pathogens with pandemic potential. • The pros, and significant cons, of travel restrictions. • Whether the same policies work for natural and anthropogenic pandemics. • Ways listeners can pursue a career in biosecurity. • Where we stand with nCoV as of today.Chapters:Rob’s intro (00:00:00)The interview begins (00:03:27)Where we stand with nCov today (00:07:24)Policy idea 1: A drastic change to diagnostic testing (00:34:58)Policy idea 2: Vaccine platforms (00:47:08)Policy idea 3: Broad-spectrum therapeutics (00:54:48)Policy idea 4: Develop a national plan for responding to a severe pandemic, regardless of the cause (01:02:15)Policy idea 5: A different approach to travel bans (01:15:59)Policy idea 6: Data sharing (01:16:48)Policy idea 7: Prevention (01:24:45)Policy idea 8: transparency around lab accidents (01:33:58)Policy idea 9: DNA synthesis screening (01:39:22)Policy idea 10: Dual Use Research oversight (01:48:47)Policy idea 11: Pandemic tabletop exercises (02:00:00)Policy idea 12: Coordination (02:12:20) Get this episode by subscribing: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Transcriptions: Zakee Ulhaq.

The State Council of China's 2017 AI plan was the starting point of China’s AI planning; China’s approach to AI is defined by its top-down and monolithic nature; China is winning the AI arms race; and there is little to no discussion of issues of AI ethics and safety in China. How many of these ideas have you heard? In his paper Deciphering China's AI Dream, today's guest, PhD student Jeff Ding, outlines why he believes none of these claims are true. • Links to learn more, summary and full transcript. • What’s the best charity to donate to? He first places China’s new AI strategy in the context of its past science and technology plans, as well as other countries’ AI plans. What is China actually doing in the space of AI development? Jeff emphasises that China's AI strategy did not appear out of nowhere with the 2017 state council AI development plan, which attracted a lot of overseas attention. Rather that was just another step forward in a long trajectory of increasing focus on science and technology. It's connected with a plan to develop an 'Internet of Things', and linked to a history of strategic planning for technology in areas like aerospace and biotechnology. And it was not just the central government that was moving in this space; companies were already pushing forward in AI development, and local level governments already had their own AI plans. You could argue that the central government was following their lead in AI more than the reverse. What are the different levers that China is pulling to try to spur AI development? Here, Jeff wanted to challenge the myth that China's AI development plan is based on a monolithic central plan requiring people to develop AI. In fact, bureaucratic agencies, companies, academic labs, and local governments each set up their own strategies, which sometimes conflict with the central government. Are China's AI capabilities especially impressive? In the paper Jeff develops a new index to measure and compare the US and China's progress in AI. Jeff’s AI Potential Index — which incorporates trends and capabilities in data, hardware, research and talent, and the commercial AI ecosystem — indicates China’s AI capabilities are about half those of America. His measure, though imperfect, dispels the notion that China's AI capabilities have surpassed the US or make it the world's leading AI power. Following that 2017 plan, a lot of Western observers thought that to have a good national AI strategy we'd need to figure out how to play catch-up with China. Yet Chinese strategic thinkers and writers at the time actually thought that they were behind — because the Obama administration had issued a series of three white papers in 2016. Finally, Jeff turns to the potential consequences of China’s AI dream for issues of national security, economic development, AI safety and social governance. He claims that, despite the widespread belief to the contrary, substantive discussions about AI safety and ethics are indeed emerging in China. For instance, a new book from Tencent’s Research Institute is proactive in calling for stronger awareness of AI safety issues. In today’s episode, Rob and Jeff go through this widely-discussed report, and also cover: • The best analogies for thinking about the growing influence of AI • How do prominent Chinese figures think about AI? • Coordination with China • China’s social credit system • Suggestions for people who want to become professional China specialists • And more. Chapters:Rob’s intro (00:00:00)The interview begins (00:01:02)Deciphering China’s AI Dream (00:04:17)Analogies for thinking about AI (00:12:30)How do prominent Chinese figures think about AI? (00:16:15)Cultural cliches in the West and China (00:18:59)Coordination with China on AI (00:24:03)Private companies vs. government research (00:28:55)Compute (00:31:58)China’s social credit system (00:41:26)Relationship between China and other countries beyond AI (00:43:51)Careers advice (00:54:40)Jeffrey’s talk at EAG (01:16:01)Rob’s outro (01:37:12) Producer: Keiran Harris.Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

Two 80,000 Hours researchers, Robert Wiblin and Howie Lempel, record an experimental bonus episode about the new 2019-nCoV virus.See this list of resources, including many discussed in the episode, to learn more.In the 1h15m conversation we cover:• What is it? • How many people have it? • How contagious is it? • What fraction of people who contract it die?• How likely is it to spread out of control?• What's the range of plausible fatalities worldwide?• How does it compare to other epidemics?• What don't we know and why? • What actions should listeners take, if any?• How should the complexities of the above be communicated by public health professionals?Here's a link to the hygiene advice from Laurie Garrett mentioned in the episode.Recorded 2 Feb 2020.The 80,000 Hours Podcast is produced by Keiran Harris.

You’re given a box with a set of dice in it. If you roll an even number, a person's life is saved. If you roll an odd number, someone else will die. Each time you shake the box you get $10. Should you do it? A committed consequentialist might say, "Sure! Free money!" But most will think it obvious that you should say no. You've only gotten a tiny benefit, in exchange for moral responsibility over whether other people live or die. And yet, according to today’s return guest, philosophy Prof Will MacAskill, in a real sense we’re shaking this box every time we leave the house, and those who think shaking the box is wrong should probably also be shutting themselves indoors and minimising their interactions with others. • Links to learn more, summary and full transcript. • Job opportunities at the Global Priorities Institute. To see this, imagine you’re deciding whether to redeem a coupon for a free movie. If you go, you’ll need to drive to the cinema. By affecting traffic throughout the city, you’ll have slightly impacted the schedules of thousands or tens of thousands of people. The average life is about 30,000 days, and over the course of a life the average person will have about two children. So — if you’ve impacted at least 7,500 days — then, statistically speaking, you've probably influenced the exact timing of a conception event. With 200 million sperm in the running each time, changing the moment of copulation, even by a fraction of a second, will almost certainly mean you've changed the identity of a future person. That different child will now impact all sorts of things as they go about their life, including future conception events. And then those new people will impact further future conceptions events, and so on. After 100 or maybe 200 years, basically everybody alive will be a different person because you went to the movies. As a result, you’ll have changed when many people die. Take car crashes as one example: about 1.3% of people die in car crashes. Over that century, as the identities of everyone change as a result of your action, many of the 'new' people will cause car crashes that wouldn't have occurred in their absence, including crashes that prematurely kill people alive today. Of course, in expectation, exactly the same number of people will have been saved from car crashes, and will die later than they would have otherwise. So, if you go for this drive, you’ll save hundreds of people from premature death, and cause the early death of an equal number of others. But you’ll get to see a free movie, worth $10. Should you do it? This setup forms the basis of ‘the paralysis argument’, explored in one of Will’s recent papers. Because most 'non-consequentialists' endorse an act/omission distinction… post truncated due to character limit, finish reading the full explanation here. So what's the best way to fix this strange conclusion? We discuss a few options, but the most promising might bring people a lot closer to full consequentialism than is immediately apparent. In this episode Will and I also cover: • Are, or are we not, living in the most influential time in history? • The culture of the effective altruism community • Will's new lower estimate of the risk of human extinction • Why Will is now less focused on AI • The differences between Americans and Brits • Why feeling guilty about characteristics you were born with is crazy • And plenty more. Chapters:Rob’s intro (00:00:00)The interview begins (00:04:03)The paralysis argument (00:15:42)The case for strong longtermism (00:55:21)Longtermism for risk-averse altruists (00:58:01)Are we living in the most influential time in history? (01:14:37)The risk of human extinction in the next hundred years (02:15:20)Implications for the effective altruism community (02:50:03)Culture of the effective altruism community (03:06:28)Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq.

Rebroadcast: this episode was originally released in October 2018. Paul Christiano is one of the smartest people I know. After our first session produced such great material, we decided to do a second recording, resulting in our longest interview so far. While challenging at times I can strongly recommend listening — Paul works on AI himself and has a very unusually thought through view of how it will change the world. This is now the top resource I'm going to refer people to if they're interested in positively shaping the development of AI, and want to understand the problem better. Even though I'm familiar with Paul's writing I felt I was learning a great deal and am now in a better position to make a difference to the world. A few of the topics we cover are:• Why Paul expects AI to transform the world gradually rather than explosively and what that would look like • Several concrete methods OpenAI is trying to develop to ensure AI systems do what we want even if they become more competent than us • Why AI systems will probably be granted legal and property rights • How an advanced AI that doesn't share human goals could still have moral value • Why machine learning might take over science research from humans before it can do most other tasks • Which decade we should expect human labour to become obsolete, and how this should affect your savings plan. • Links to learn more, summary and full transcript. • Rohin Shah's AI alignment newsletter. Here's a situation we all regularly confront: you want to answer a difficult question, but aren't quite smart or informed enough to figure it out for yourself. The good news is you have access to experts who *are* smart enough to figure it out. The bad news is that they disagree. If given plenty of time — and enough arguments, counterarguments and counter-counter-arguments between all the experts — should you eventually be able to figure out which is correct? What if one expert were deliberately trying to mislead you? And should the expert with the correct view just tell the whole truth, or will competition force them to throw in persuasive lies in order to have a chance of winning you over? In other words: does 'debate', in principle, lead to truth? According to Paul Christiano — researcher at the machine learning research lab OpenAI and legendary thinker in the effective altruism and rationality communities — this question is of more than mere philosophical interest. That's because 'debate' is a promising method of keeping artificial intelligence aligned with human goals, even if it becomes much more intelligent and sophisticated than we are. It's a method OpenAI is actively trying to develop, because in the long-term it wants to train AI systems to make decisions that are too complex for any human to grasp, but without the risks that arise from a complete loss of human oversight. If AI-1 is free to choose any line of argument in order to attack the ideas of AI-2, and AI-2 always seems to successfully defend them, it suggests that every possible line of argument would have been unsuccessful. But does that mean that the ideas of AI-2 were actually right? It would be nice if the optimal strategy in debate were to be completely honest, provide good arguments, and respond to counterarguments in a valid way. But we don't know that's the case. The 80,000 Hours Podcast is produced by Keiran Harris.

Rebroadcast: this episode was originally released in May 2018. Joseph Stalin had a life-extension program dedicated to making himself immortal. What if he had succeeded? According to Bryan Caplan in episode #32, there’s an 80% chance that Stalin would still be ruling Russia today. Today’s guest disagrees. Like Stalin he has eyes for his own immortality - including an insurance plan that will cover the cost of cryogenically freezing himself after he dies - and thinks the technology to achieve it might be around the corner. Fortunately for humanity though, that guest is probably one of the nicest people on the planet: Dr Anders Sandberg of Oxford University. Full transcript of the conversation, summary, and links to learn more. The potential availability of technology to delay or even stop ageing means this disagreement matters, so he has been trying to model what would really happen if both the very best and the very worst people in the world could live forever - among many other questions. Anders, who studies low-probability high-stakes risks and the impact of technological change at the Future of Humanity Institute, is the first guest to appear twice on the 80,000 Hours Podcast and might just be the most interesting academic at Oxford. His research interests include more or less everything, and bucking the academic trend towards intense specialization has earned him a devoted fan base. Last time we asked him why we don’t see aliens, and how to most efficiently colonise the universe. In today’s episode we ask about Anders’ other recent papers, including: • Is it worth the money to freeze your body after death in the hope of future revival, like Anders has done? • How much is our perception of the risk of nuclear war biased by the fact that we wouldn’t be alive to think about it had one happened? • If biomedical research lets us slow down ageing would culture stagnate under the crushing weight of centenarians? • What long-shot drugs can people take in their 70s to stave off death? • Can science extend human (waking) life by cutting our need to sleep? • How bad would it be if a solar flare took down the electricity grid? Could it happen? • If you’re a scientist and you discover something exciting but dangerous, when should you keep it a secret and when should you share it? • Will lifelike robots make us more inclined to dehumanise one another? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Rebroadcast: this episode was originally released in January 2018. Immanuel Kant is a profoundly influential figure in modern philosophy, and was one of the earliest proponents for universal democracy and international cooperation. He also thought that women have no place in civil society, that it was okay to kill illegitimate children, and that there was a ranking in the moral worth of different races. Throughout history we’ve consistently believed, as common sense, truly horrifying things by today’s standards. According to University of Oxford Professor Will MacAskill, it’s extremely likely that we’re in the same boat today. If we accept that we’re probably making major moral errors, how should we proceed?• Full transcript, key points & links to articles discussed in the show. If our morality is tied to common sense intuitions, we’re probably just preserving these biases and moral errors. Instead we need to develop a moral view that criticises common sense intuitions, and gives us a chance to move beyond them. And if humanity is going to spread to the stars it could be worth dedicating hundreds or thousands of years to moral reflection, lest we spread our errors far and wide. Will is an Associate Professor in Philosophy at Oxford University, author of Doing Good Better, and one of the co-founders of the effective altruism (EA) community. In this interview we discuss a wide range of topics: • How would we go about a ‘long reflection’ to fix our moral errors? • Will’s forthcoming book on how one should reason and act if you don't know which moral theory is correct. What are the practical implications of so-called ‘moral uncertainty’? • If we basically solve existential risks, what does humanity do next? • What are some of Will’s most unusual philosophical positions? • What are the best arguments for and against utilitarianism? • Given disagreements among philosophers, how much should we believe the findings of philosophy as a field? • What are some the biases we should be aware of within academia? • What are some of the downsides of becoming a professor? • What are the merits of becoming a philosopher? • How does the media image of EA differ to the actual goals of the community? • What kinds of things would you like to see the EA community do differently? • How much should we explore potentially controversial ideas? • How focused should we be on diversity? • What are the best arguments against effective altruism? Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

What is it like to be you right now? You're seeing this text on the screen, smelling the coffee next to you, and feeling the warmth of the cup. There’s a lot going on in your head — your conscious experience. Now imagine beings that are identical to humans, but for one thing: they lack this conscious experience. If you spill your coffee on them, they’ll jump like anyone else, but inside they'll feel no pain and have no thoughts: the lights are off. The concept of these so-called 'philosophical zombies' was popularised by today’s guest — celebrated philosophy professor David Chalmers — in order to explore the nature of consciousness. In a forthcoming book he poses a classic 'trolley problem': "Suppose you have a conscious human on one train track, and five non-conscious humanoid zombies on another. If you do nothing, a trolley will hit and kill the conscious human. If you flip a switch to redirect the trolley, you can save the conscious human, but in so doing kill the five non-conscious humanoid zombies. What should you do?" Many people think you should divert the trolley, precisely because the lack of conscious experience means the moral status of the zombies is much reduced or absent entirely. So, which features of consciousness qualify someone for moral consideration? One view is that the only conscious states that matter are those that have a positive or negative quality, like pleasure and suffering. But Dave’s intuitions are quite different. • Links to learn more, summary and full transcript. • Advice on how to read our advice. • Anonymous answers on: bad habits, risk and failure. Instead of zombies he asks us to consider 'Vulcans', who can see and hear and reflect on the world around them, but are incapable of experiencing pleasure or pain. Now imagine a further trolley problem: suppose you have a normal human on one track, and five Vulcans on the other. Should you divert the trolley to kill the five Vulcans in order to save the human? Dave firmly believes the answer is no, and if he's right, pleasure and suffering can’t be the only things required for moral status. The fact that Vulcans are conscious in other ways must matter in itself. Dave is one of the world's top experts on the philosophy of consciousness. He helped return the question 'what is consciousness?' to the centre stage of philosophy with his 1996 book 'The Conscious Mind', which argued against then-dominant materialist theories of consciousness. This comprehensive interview, at over four hours long, outlines each contemporary theory of consciousness, what they have going for them, and their likely ethical implications. Those theories span the full range from illusionism, the idea that consciousness is in some sense an 'illusion', to panpsychism, according to which it's a fundamental physical property present in all matter. These questions are absolutely central for anyone who wants to build a positive future. If insects were conscious our treatment of them could already be an atrocity. If computer simulations of people will one day be conscious, how will we know, and how should we treat them? And what is it about consciousness that matters, if anything? Dave Chalmers is probably the best person on the planet to ask these questions, and Rob & Arden cover this and much more over the course of what is both our longest ever episode, and our personal favourite so far. Chapters:Rob's intro (00:00:00)The interview begins (00:02:11)Philosopher’s survey (00:06:37)Free will (00:13:37)Survey correlations (00:20:06)Progress in philosophy (00:35:01)Simulations (00:51:30)The problem of consciousness (01:13:01)Dualism and panpsychism (01:26:52)Is consciousness an illusion? (01:34:52)Idealism (01:43:13)Integrated information theory (01:51:08)Moral status and consciousness (02:06:10)Higher order views of consciousness (02:11:46)The views of philosophers on eating meat (02:20:23)Artificial consciousness (02:34:25)The zombie and vulcan trolley problems (02:38:43)Illusionism and moral status (02:56:12)Panpsychism and moral status (03:06:19)Mind uploading (03:15:58)Personal identity (03:22:51)Virtual reality and the experience machine (03:28:56)Singularity (03:42:44)AI alignment (04:07:39)Careers in academia (04:23:37)Having fun disagreements (04:32:54)Rob’s outro (04:42:14) Producer: Keiran Harris.

In 1989, the professor of moral philosophy Peter Singer was all over the news for his inflammatory opinions about abortion. But the controversy stemmed from Practical Ethics — a book he’d actually released way back in 1979. It took a German translation ten years on for protests to kick off. According to Singer, he honestly didn’t expect this view to be as provocative as it became, and he certainly wasn’t aiming to stir up trouble and get attention. But after the protests and the increasing coverage of his work in German media, the previously flat sales of Practical Ethics shot up. And the negative attention he received ultimately led him to a weekly opinion column in The New York Times. • Singer's book The Life You Can Save has just been re-released as a 10th anniversary edition, available as a free e-book and audiobook, read by a range of celebrities. Get it here. • Links to learn more, summary and full transcript. Singer points out that as a result of this increased attention, many more people also read the rest of the book — which includes chapters with a real ability to do good, covering global poverty, animal ethics, and other important topics. So should people actively try to court controversy with one view, in order to gain attention for another more important one? Perhaps sometimes, but controversy can also just have bad consequences. His critics may view him as someone who says whatever he thinks, hang the consequences, but Singer says that he gives public relations considerations plenty of thought. One example is that Singer opposes efforts to advocate for open borders. Not because he thinks a world with freedom of movement is a bad idea per se, but rather because it may help elect leaders like Mr Trump. Another is the focus of the effective altruism community. Singer certainly respects those who are focused on improving the long-term future of humanity, and thinks this is important work that should continue. But he’s troubled by the possibility of extinction risks becoming the public face of the movement. He suspects there's a much narrower group of people who are likely to respond to that kind of appeal, compared to those who are drawn to work on global poverty or preventing animal suffering. And that to really transform philanthropy and culture more generally, the effective altruism community needs to focus on smaller donors with more conventional concerns. Rob is joined in this interview by Arden Koehler, the newest addition to the 80,000 Hours team, both for the interview and a post-episode discussion. They only had an hour with Peter, but also cover: • What does he think is the most plausible alternatives to consequentialism? • Is it more humane to eat wild caught animals than farmed animals? • The re-release of The Life You Can Save • His most and least strategic career decisions • Population ethics, and other arguments for and against prioritising the long-term future • What led to his changing his mind on significant questions in moral philosophy? • And more. In the post-episode discussion, Rob and Arden continue talking about: • The pros and cons of keeping EA as one big movement • Singer’s thoughts on immigration • And consequentialism with side constraints. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcriptions: Zakee Ulhaq. Illustration of Singer: Matthias Seifarth.

"…it started when the Soviet Union fell apart and there was a real desire to ensure security of nuclear materials and pathogens, and that scientists with [WMD-related] knowledge could get paid so that they wouldn't go to countries and sell that knowledge." Ambassador Bonnie Jenkins has had an incredible career in diplomacy and global security. Today she’s a nonresident senior fellow at the Brookings Institution and president of Global Connections Empowering Global Change, where she works on global health, infectious disease and defence innovation. In 2017 she founded her own nonprofit, the Women of Color Advancing Peace, Security and Conflict Transformation (WCAPS). But in this interview we focus on her time as Ambassador at the U.S. Department of State under the Obama administration, where she worked for eight years as Coordinator for Threat Reduction Programs in the Bureau of International Security and Nonproliferation. In that role, Bonnie coordinated the Department of State’s work to prevent weapons of mass destruction (WMD) terrorism with programmes funded by other U.S. departments and agencies, and as well as other countries. • Links to learn more, summary and full transcript. • Talks from over 100 other speakers at EA Global. • Having trouble with podcast 'chapters' on this episode? Please report any problems to keiran at 80000hours dot org. What was it like to be an ambassador focusing on an issue, rather than an ambassador of a country? Bonnie says the travel was exhausting. She could find herself in Africa one week, and Indonesia the next. She’d meet with folks going to New York for meetings at the UN one day, then hold her own meetings at the White House the next. Each event would have a distinct purpose. For one, she’d travel to Germany as a US Representative, talking about why the two countries should extend their partnership. For another, she could visit the Food and Agriculture Organization to talk about why they need to think more about biosecurity issues. No day was like the previous one. Bonnie was also a leading U.S. official in the launch and implementation of the Global Health Security Agenda discussed at length in episode 27. Before returning to government in 2009, Bonnie served as program officer for U.S. Foreign and Security Policy at the Ford Foundation. She also served as counsel on the 9/11 Commission. Bonnie was the lead staff member conducting research, interviews, and preparing commission reports on counterterrorism policies in the Office of the Secretary of Defense and on U.S. military plans targeting al-Qaeda before 9/11. And as if that all weren't curious enough four years ago Bonnie decided to go vegan. We talk about her work so far as well as: • How listeners can start a career like hers • Mistakes made by Mr Obama and Mr Trump • Networking, the value of attention, and being a vegan in DC • And 2020 Presidential candidates.Chapters:Rob’s intro (00:00:00)The interview begins (00:01:54)What is Bonnie working on at the moment? (00:02:45)Bonnie’s time at the Department of State (00:04:08)The history of Cooperative Threat Reduction work (00:08:48)Biggest uncontrolled nuclear material threats today (00:11:36)Biggest security issues in the world today (00:13:57)The Biological Weapons Convention (00:17:52)Projects Bonnie worked on that she’s particularly proud of (00:20:55)The day to day life of an Ambassador on an issue (00:23:03)Biggest misunderstandings of the field (00:25:41)How do we get more done in this area? (00:29:48)The Global Health Security Agenda (00:32:52)The implications for countries who give up WMDs (00:34:55)The fallout from a change in government (00:38:40)Listener submitted questions (00:39:39)How might listeners be able to contribute to solving these problems with their own careers? (00:54:55)Is Bonnie glad she went into the military early in her career? (01:06:25)Networking in DC (01:12:27)What are the downsides to pursuing a career like Bonnie’s? (01:15:27)Being a vegan in DC (01:16:47)Women of Color Advancing Peace, Security and Conflict Transformation (01:19:15)The value of attention in DC (01:28:25)Any ways WCAPS could accidentally make things worse? (01:30:08)Message for women of colour in the audience (01:33:05)TV shows relevant to Bonnie’s work (01:35:19)Candidates for 2020 (01:36:57) The 80,000 Hours Podcast is produced by Keiran Harris.

November 3 2020, 10:32PM: CNN, NBC, and FOX report that Donald Trump has narrowly won Florida, and with it, re-election. November 3 2020, 11:46PM: The NY Times and Wall Street Journal report that some group has successfully hacked electronic voting systems across the country, including Florida. The malware has spread to tens of thousands of machines and deletes any record of its activity, so the returning officer of Florida concedes they actually have no idea who won the state — and don't see how they can figure it out. What on Earth happens next? Today’s guest — world-renowned computer security expert Bruce Schneier — thinks this scenario is plausible, and the ensuing chaos would sow so much distrust that half the country would never accept the election result. Unfortunately the US has no recovery system for a situation like this, unlike parliamentary democracies, which can just rerun the election a few weeks later.Links to learn more, summary and full transcript.Motivating article: Information security careers for global catastrophic risk reduction by Zabel and MuehlhauserThe Constitution says the state legislature decides, and they can do so however they like; one tied local election in Texas was settled by playing a hand of poker. Elections serve two purposes. The first is the obvious one: to pick a winner. The second, but equally important, is to convince the loser to go along with it — which is why hacks often focus on convincing the losing side that the election wasn't fair. Schneier thinks there's a need to agree how this situation should be handled before something like it happens, and America falls into severe infighting as everyone tries to turn the situation to their political advantage. And to fix our voting systems, we urgently need two things: a voter-verifiable paper ballot and risk-limiting audits. According to Schneier, computer security experts look at current electronic voting machines and can barely believe their eyes. But voting machine designers never understand the security weakness of what they're designing, because they have a bureaucrat's rather than a hacker's mindset. The ideal computer security expert walks into a shop and thinks, "You know, here's how I would shoplift." They automatically see where the cameras are, whether there are alarms, and where the security guards aren't watching. In this episode we discuss this hacker mindset, and how to use a career in security to protect democracy and guard dangerous secrets from people who shouldn't get access to them.We also cover: • How can we have surveillance of dangerous actors, without falling back into authoritarianism? • When if ever should information about weaknesses in society's security be kept secret? • How secure are nuclear weapons systems around the world? • How worried should we be about deep-fakes? • Schneier’s critiques of blockchain technology • How technologists should be vital in shaping policy • What are the most consequential computer security problems today? • Could a career in information security be very useful for reducing global catastrophic risks? • And more.Chapters:Rob’s intro (00:00:00)Bruce’s Codex talk (00:02:23)The interview begins (00:15:42)What is Bruce working on at the moment? (00:16:35)How technologists could be vital in shaping policy (00:18:52)Most consequential computer security problems today (00:24:12)How secure are nuclear weapons systems around the world? (00:34:41)Stuxnet and NotPetya (00:42:29)Messing with democracy (00:44:44)How worried should we be about deepfakes? (00:50:02)The similarities between hacking computers and potentially hacking biology in the future (00:55:08)Bruce’s critiques of crypto (01:00:05)What are some of the most kind of widely-held but incorrect beliefs among computer security people? (01:03:04)The hacking mindset (01:05:35)Voting machines (01:09:22)How secretive should people be about potentially harmful information? (01:16:48)Could a career in information security be very useful for reducing global catastrophic risks? (01:21:46)How to develop the skills needed in computer security (01:33:44)Ubiquitous surveillance (01:52:46)Why is Bruce optimistic? (02:05:28)Rob’s outro (02:06:43)The 80,000 Hours Podcast is produced by Keiran Harris.

Today's episode is a compilation of interviews I recently recorded for two other shows, Love Your Work and The Neoliberal Podcast. If you've listened to absolutely everything on this podcast feed, you'll have heard four interviews with me already, but fortunately I don't think these two include much repetition, and I've gotten a decent amount of positive feedback on both. First up, I speak with David Kadavy on his show, Love Your Work. This is a particularly personal and relaxed interview. We talk about all sorts of things, including nicotine gum, plastic straw bans, whether recycling is important, how many lives a doctor saves, why interviews should go for at least 2 hours, how athletes doping could be good for the world, and many other fun topics. • Our annual impact survey is about to close — I'd really appreciate if you could take 3–10 minutes to fill it out now. • The blog post about this episode. At some points we even actually discuss effective altruism and 80,000 Hours, but you can easily skip through those bits if they feel too familiar. The second interview is with Jeremiah Johnson on the Neoliberal Podcast. It starts 2 hours and 15 minutes into this recording. Neoliberalism in the sense used by this show is not the free market fundamentalism you might associate with the term. Rather it's a centrist or even centre-left view that supports things like social liberalism, multilateral international institutions, trade, high rates of migration, racial justice, inclusive institutions, financial redistribution, prioritising the global poor, market urbanism, and environmental sustainability. This is the more demanding of the two conversations, as listeners to that show have already heard of effective altruism, so we were able to get the best arguments Jeremiah could offer against focusing on improving the long term future of the world. Jeremiah is more of a fan of donating to evidence-backed global health charities recommended by GiveWell, and does so himself. I appreciate him having done his homework and forcing me to do my best to explain how well my views can stand up to counterarguments. It was a challenge for me to paint the whole picture in the half an hour we spent on longterm and I expect there's answers in there which will be fresh even for regular listeners. I hope you enjoy both conversations! Feel free to email me with any feedback. The 80,000 Hours Podcast is produced by Keiran Harris.

1. Fill out our annual impact survey here. 2. Find a great vacancy on our job board. 3. Learn about our key ideas, and get links to our top articles. 4. Join our newsletter for an email about what's new, every 2 weeks or so. 5. Or follow our pages on Facebook and Twitter. —— Once a year 80,000 Hours runs a survey to find out whether we've helped our users have a larger social impact with their life and career. We and our donors need to know whether our services, like this podcast, are helping people enough to continue them or scale them up, and it's only by hearing from you that we can make these decisions in a sensible way. So, if 80,000 Hours' podcast, job board, articles, headhunting, advising or other projects have somehow contributed to your life or career plans, please take 3–10 minutes to let us know how. You can also let us know where we've fallen short, which helps us fix problems with what we're doing. We've refreshed the survey this year, hopefully making it easier to fill out than in the past. We'll keep this appeal up for about two weeks, but if you fill it out now that means you definitely won't forget! Thanks so much, and talk to you again in a normal episode soon. — RobTag for internal use: this RSS feed is originating in BackTracks.

Historically, progress in the field of cryptography has had major consequences. It has changed the course of major wars, made it possible to do business on the internet, and enabled private communication between both law-abiding citizens and dangerous criminals. Could it have similarly significant consequences in future? Today's guest — Vitalik Buterin — is world-famous as the lead developer of Ethereum, a successor to the cryptographic-currency Bitcoin, which added the capacity for smart contracts and decentralised organisations. Buterin first proposed Ethereum at the age of 20, and by the age of 23 its success had likely made him a billionaire. At the same time, far from indulging hype about these so-called 'blockchain' technologies, he has been candid about the limited good accomplished by Bitcoin and other currencies developed using cryptographic tools — and the breakthroughs that will be needed before they can have a meaningful social impact. In his own words, *"blockchains as they currently exist are in many ways a joke, right?"* But Buterin is not just a realist. He's also an idealist, who has been helping to advance big ideas for new social institutions that might help people better coordinate to pursue their shared goals. Links to learn more, summary and full transcript. By combining theories in economics and mechanism design with advances in cryptography, he has been pioneering the new interdiscriplinary field of 'cryptoeconomics'. Economist Tyler Cowen hasobserved that, "at 25, Vitalik appears to repeatedly rediscover important economics results from famous papers, without knowing about the papers at all." Along with previous guest Glen Weyl, Buterin has helped develop a model for so-called 'quadratic funding', which in principle could transform the provision of 'public goods'. That is, goods that people benefit from whether they help pay for them or not. Examples of goods that are fully or partially 'public goods' include sound decision-making in government, international peace, scientific advances, disease control, the existence of smart journalism, preventing climate change, deflecting asteroids headed to Earth, and the elimination of suffering. Their underprovision in part reflects the difficulty of getting people to pay for anything when they can instead free-ride on the efforts of others. Anything that could reduce this failure of coordination might transform the world. But these and other related proposals face major hurdles. They're vulnerable to collusion, might be used to fund scams, and remain untested at a small scale — not to mention that anything with a square root sign in it is going to struggle to achieve societal legitimacy. Is the prize large enough to justify efforts to overcome these challenges? In today's extensive three-hour interview, Buterin and I cover: • What the blockchain has accomplished so far, and what it might achieve in the next decade; • Why many social problems can be viewed as a coordination failure to provide a public good; • Whether any of the ideas for decentralised social systems emerging from the blockchain community could really work; • His view of 'effective altruism' and 'long-termism'; • Why he is optimistic about 'quadratic funding', but pessimistic about replacing existing voting with 'quadratic voting'; • Why humanity might have to abandon living in cities; • And much more. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Imagine that – one day – humanity dies out. At some point, many millions of years later, intelligent life might well evolve again. Is there any message we could leave that would reliably help them out? In his second appearance on the 80,000 Hours Podcast, machine learning researcher and polymath Paul Christiano suggests we try to answer this question with a related thought experiment: are there any messages we might want to send back to our ancestors in the year 1700 that would have made history likely to go in a better direction than it did? It seems there probably are. • Links to learn more, summary, and full transcript. • Paul's first appearance on the show in episode 44. • An out-take on decision theory. We could tell them hard-won lessons from history; mention some research questions we wish we'd started addressing earlier; hand over all the social science we have that fosters peace and cooperation; and at the same time steer clear of engineering hints that would speed up the development of dangerous weapons. But, as Christiano points out, even if we could satisfactorily figure out what we'd like to be able to tell our ancestors, that's just the first challenge. We'd need to leave the message somewhere that they could identify and dig up. While there are some promising options, this turns out to be remarkably hard to do, as anything we put on the Earth's surface quickly gets buried far underground. But even if we figure out a satisfactory message, and a ways to ensure it's found, a civilization this far in the future won't speak any language like our own. And being another species, they presumably won't share as many fundamental concepts with us as humans from 1700. If we knew a way to leave them thousands of books and pictures in a material that wouldn't break down, would they be able to decipher what we meant to tell them, or would it simply remain a mystery? That's just one of many playful questions discussed in today's episode with Christiano — a frequent writer who's willing to brave questions that others find too strange or hard to grapple with. We also talk about why divesting a little bit from harmful companies might be more useful than I'd been thinking. Or whether creatine might make us a bit smarter, and carbon dioxide filled conference rooms make us a lot stupider. Finally, we get a big update on progress in machine learning and efforts to make sure it's reliably aligned with our goals, which is Paul's main research project. He responds to the views that DeepMind's Pushmeet Kohli espoused in a previous episode, and we discuss whether we'd be better off if AI progress turned out to be most limited by algorithmic insights, or by our ability to manufacture enough computer processors. Some other issues that come up along the way include: • Are there any supplements people can take that make them think better? • What implications do our views on meta-ethics have for aligning AI with our goals? • Is there much of a risk that the future will contain anything optimised for causing harm? • An out-take about the implications of decision theory, which we decided was too confusing and confused to stay in the main recording. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours Podcast is produced by Keiran Harris.

From 1870 to 1950, the introduction of electricity transformed life in the US and UK, as people gained access to lighting, radio and a wide range of household appliances for the first time. Electricity turned out to be a general purpose technology that could help with almost everything people did. Some think this is the best historical analogy we have for how machine learning could alter life in the 21st century. In addition to massively changing everyday life, past general purpose technologies have also changed the nature of war. For example, when electricity was introduced to the battlefield, commanders gained the ability to communicate quickly with units in the field over great distances. How might international security be altered if the impact of machine learning reaches a similar scope to that of electricity? Today's guest — Helen Toner — recently helped found the Center for Security and Emerging Technology at Georgetown University to help policymakers prepare for such disruptive technical changes that might threaten international peace. • Links to learn more, summary and full transcript • Philosophy is one of the hardest grad programs. Is it worth it, if you want to use ideas to change the world? by Arden Koehler and Will MacAskill • The case for building expertise to work on US AI policy, and how to do it by Niel Bowerman • AI strategy and governance roles on the job board Their first focus is machine learning (ML), a technology which allows computers to recognise patterns, learn from them, and develop 'intuitions' that inform their judgement about future cases. This is something humans do constantly, whether we're playing tennis, reading someone's face, diagnosing a patient, or figuring out which business ideas are likely to succeed. Sometimes these ML algorithms can seem uncannily insightful, and they're only getting better over time. Ultimately a wide range of different ML algorithms could end up helping us with all kinds of decisions, just as electricity wakes us up, makes us coffee, and brushes our teeth -- all in the first five minutes of our day. Rapid advances in ML, and the many prospective military applications, have people worrying about an 'AI arms race' between the US and China. Henry Kissinger and the past CEO of Google Eric Schmidt recently wrote that AI could "destabilize everything from nuclear détente to human friendships." Some politicians talk of classifying and restricting access to ML algorithms, lest they fall into the wrong hands. But if electricity is the best analogy, you could reasonably ask — was there an arms race in electricity in the 19th century? Would that have made any sense? And could someone have changed the course of history by changing who first got electricity and how they used it, or is that a fantasy? In today's episode we discuss the research frontier in the emerging field of AI policy and governance, how to have a career shaping US government policy, and Helen's experience living and studying in China. We cover: • Why immigration is the main policy area that should be affected by AI advances today. • Why talking about an 'arms race' in AI is premature. • How Bobby Kennedy may have positively affected the Cuban Missile Crisis. • Whether it's possible to become a China expert and still get a security clearance. • Can access to ML algorithms be restricted, or is that just not practical? • Whether AI could help stabilise authoritarian regimes. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Have you ever been infuriated by a doctor's unwillingness to give you an honest, probabilistic estimate about what to expect? Or a lawyer who won't tell you the chances you'll win your case? Their behaviour is so frustrating because accurately predicting the future is central to every action we take. If we can't assess the likelihood of different outcomes we're in a complete bind, whether the decision concerns war and peace, work and study, or Black Mirror and RuPaul's Drag Race. Which is why the research of Professor Philip Tetlock is relevant for all of us each and every day. He has spent 40 years as a meticulous social scientist, collecting millions of predictions from tens of thousands of people, in order to figure out how good humans really are at foreseeing the future, and what habits of thought allow us to do better. Along with other psychologists, he identified that many ordinary people are attracted to a 'folk probability' that draws just three distinctions — 'impossible', 'possible' and 'certain' — and which leads to major systemic mistakes. But with the right mindset and training we can become capable of accurately discriminating between differences as fine as 56% as against 57% likely. • Links to learn more, summary and full transcript • The calibration training app • Sign up for the Civ-5 counterfactual forecasting tournament • A review of the evidence on good forecasting practices • Learn more about Effective Altruism Global In the aftermath of Iraq and WMDs the US intelligence community hired him to prevent the same ever happening again, and his guide — Superforecasting: The Art and Science of Prediction — became a bestseller back in 2014. That was five years ago. In today's interview, Tetlock explains how his research agenda continues to advance, today using the game Civilization 5 to see how well we can predict what would have happened in elusive counterfactual worlds we never get to see, and discovering how simple algorithms can complement or substitute for human judgement. We discuss how his work can be applied to your personal life to answer high-stakes questions, like how likely you are to thrive in a given career path, or whether your business idea will be a billion-dollar unicorn — or fall apart catastrophically. (To help you get better at figuring those things out, our site now has a training app developed by the Open Philanthropy Project and Clearer Thinking that teaches you to distinguish your '70 percents' from your '80 percents'.) We also bring some tough methodological questions raised by the author of a recent review of the forecasting literature. And we find out what jobs people can take to make improving the reasonableness of decision-making in major institutions that shape the world their profession, as it has been for Tetlock over many decades. We view Tetlock's work as so core to living well that we've brought him back for a second and longer appearance on the show — his first was back in episode 15. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

It can often feel hopeless to be an activist seeking social change on an obscure issue where most people seem opposed or at best indifferent to you. But according to a new book by Professor Cass Sunstein, they shouldn't despair. Large social changes are often abrupt and unexpected, arising in an environment of seeming public opposition.The Communist Revolution in Russia spread so swiftly it confounded even Lenin. Seventy years later the Soviet Union collapsed just as quickly and unpredictably.In the modern era we have gay marriage, #metoo and the Arab Spring, as well as nativism, Euroskepticism and Hindu nationalism.How can a society that so recently seemed to support the status quo bring about change in years, months, or even weeks?Sunstein — coauthor of Nudge, Obama White House official, and by far the most cited legal scholar of the late 2000s — aims to unravel the mystery and figure out the implications in his new book How Change Happens. He pulls together three phenomena which social scientists have studied in recent decades: preference falsification, variable thresholds for action, and group polarisation. If Sunstein is to be believed, together these are a cocktail for social shifts that are chaotic and fundamentally unpredictable. • Links to learn more, summary and full transcript. • 80,000 Hours Annual Review 2018. • How to donate to 80,000 Hours. In brief, people constantly misrepresent their true views, even to close friends and family. They themselves aren't quite sure how socially acceptable their feelings would have to become, before they revealed them, or joined a campaign for social change. And a chance meeting between a few strangers can be the spark that radicalises a handful of people, who then find a message that can spread their views to millions. According to Sunstein, it's "much, much easier" to create social change when large numbers of people secretly or latently agree with you. But 'preference falsification' is so pervasive that it's no simple matter to figure out when that's the case. In today's interview, we debate with Sunstein whether this model of cultural change is accurate, and if so, what lessons it has for those who would like to shift the world in a more humane direction. We discuss: • How much people misrepresent their views in democratic countries. • Whether the finding that groups with an existing view tend towards a more extreme position would stand up in the replication crisis. • When is it justified to encourage your own group to polarise? • Sunstein's difficult experiences as a pioneer of animal rights law. • Whether activists can do better by spending half their resources on public opinion surveys. • Should people be more or less outspoken about their true views? • What might be the next social revolution to take off? • How can we learn about social movements that failed and disappeared? • How to find out what people really think. Chapters:• Rob’s intro (00:00:00)• Cass's Harvard lecture on How Change Happens (00:02:59)• Rob & Cass's conversation about the book (00:41:43) The 80,000 Hours Podcast is produced by Keiran Harris.

When you're building a bridge, responsibility for making sure it won't fall over isn't handed over to a few 'bridge not falling down engineers'. Making sure a bridge is safe to use and remains standing in a storm is completely central to the design, and indeed the entire project.When it comes to artificial intelligence, commentators often distinguish between enhancing the capabilities of machine learning systems and enhancing their safety. But to Pushmeet Kohli, principal scientist and research team leader at DeepMind, research to make AI robust and reliable is no more a side-project in AI design than keeping a bridge standing is a side-project in bridge design.Far from being an overhead on the 'real' work, it’s an essential part of making AI systems work at all. We don’t want AI systems to be out of alignment with our intentions, and that consideration must arise throughout their development.Professor Stuart Russell — co-author of the most popular AI textbook — has gone as far as to suggest that if this view is right, it may be time to retire the term ‘AI safety research’ altogether. • Want to be notified about high-impact opportunities to help ensure AI remains safe and beneficial? Tell us a bit about yourself and we’ll get in touch if an opportunity matches your background and interests. • Links to learn more, summary and full transcript. • And a few added thoughts on non-research roles. With the goal of designing systems that are reliably consistent with desired specifications, DeepMind have recently published work on important technical challenges for the machine learning community. For instance, Pushmeet is looking for efficient ways to test whether a system conforms to the desired specifications, even in peculiar situations, by creating an 'adversary' that proactively seeks out the worst failures possible. If the adversary can efficiently identify the worst-case input for a given model, DeepMind can catch rare failure cases before deploying a model in the real world. In the future single mistakes by autonomous systems may have very large consequences, which will make even small failure probabilities unacceptable. He's also looking into 'training specification-consistent models' and formal verification', while other researchers at DeepMind working on their AI safety agenda are figuring out how to understand agent incentives, avoid side-effects, and model AI rewards. In today’s interview, we focus on the convergence between broader AI research and robustness, as well as: • DeepMind’s work on the protein folding problem • Parallels between ML problems and past challenges in software development and computer security • How can you analyse the thinking of a neural network? • Unique challenges faced by DeepMind’s technical AGI safety team • How do you communicate with a non-human intelligence? • What are the biggest misunderstandings about AI safety and reliability? • Are there actually a lot of disagreements within the field? • The difficulty of forecasting AI development Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours Podcast is produced by Keiran Harris.

This is a cross-post of some interviews Rob did recently on two other podcasts — Mission Daily (from 2m) and The Good Life (from 1h13m). Some of the content will be familiar to regular listeners — but if you’re at all interested in Rob’s personal thoughts, there should be quite a lot of new material to make listening worthwhile. The first interview is with Chad Grills. They focused largely on new technologies and existential risks, but also discuss topics like: • Why Rob is wary of fiction • Egalitarianism in the evolution of hunter gatherers • How to stop social media screwing up politics • Careers in government versus business The second interview is with Prof Andrew Leigh - the Shadow Assistant Treasurer in Australia. This one gets into more personal topics than we usually cover on the show, like: • What advice would Rob give to his teenage self? • Which person has most shaped Rob’s view of living an ethical life? • Rob’s approach to giving to the homeless • What does Rob do to maximise his own happiness? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

You’re 29 years old, and you’ve just been given a job in the White House. How do you quickly figure out how the US Executive Branch behemoth actually works, so that you can have as much impact as possible - before you quit or get kicked out?That was the challenge put in front of Tom Kalil in 1993.He had enough success to last a full 16 years inside the Clinton and Obama administrations, working to foster the development of the internet, then nanotechnology, and then cutting-edge brain modelling, among other things.But not everyone figures out how to move the needle. In today's interview, Tom shares his experience with how to increase your chances of getting an influential role in government, and how to make the most of the opportunity if you get in.Links to learn more, summary and full transcript. Interested in US AI policy careers? Apply for one-on-one career advice here.Vacancies at the Center for Security and Emerging Technology.Our high-impact job board, which features other related opportunities. He believes that Congressional gridlock leads people to greatly underestimate how much the Executive Branch can and does do on its own every day. Decisions by individuals change how billions of dollars are spent; regulations are enforced, and then suddenly they aren't; and a single sentence in the State of the Union can get civil servants to pay attention to a topic that would otherwise go ignored. Over years at the White House Office of Science and Technology Policy, 'Team Kalil' built up a white board of principles. For example, 'the schedule is your friend': setting a meeting date with the President can force people to finish something, where they otherwise might procrastinate. Or 'talk to who owns the paper'. People would wonder how Tom could get so many lines into the President's speeches. The answer was "figure out who's writing the speech, find them with the document, and tell them to add the line." Obvious, but not something most were doing. Not everything is a precise operation though. Tom also tells us the story of NetDay, a project that was put together at the last minute because the President incorrectly believed it was already organised – and decided he was going to announce it in person. In today's episode we get down to nuts & bolts, and discuss: • How did Tom spin work on a primary campaign into a job in the next White House? • Why does Tom think hiring is the most important work he did, and how did he decide who to bring onto the team? • How do you get people to do things when you don't have formal power over them? • What roles in the US government are most likely to help with the long-term future, or reducing existential risks? • Is it possible, or even desirable, to get the general public interested in abstract, long-term policy ideas? • What are 'policy entrepreneurs' and why do they matter? • What is the role for prizes in promoting science and technology? What are other promising policy ideas? • Why you can get more done by not taking credit. • What can the White House do if an agency isn't doing what it wants? • How can the effective altruism community improve the maturity of our policy recommendations? • How much can talented individuals accomplish during a short-term stay in government? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Elephants in chains at travelling circuses; pregnant pigs trapped in coffin sized crates at factory farms; deers living in the wild. We should welcome the last as a pleasant break from the horror, right? Maybe, but maybe not. While we tend to have a romanticised view of nature, life in the wild includes a range of extremely negative experiences. Many animals are hunted by predators, and constantly have to remain vigilant about the risk of being killed, and perhaps experiencing the horror of being eaten alive. Resource competition often leads to chronic hunger or starvation. Their diseases and injuries are never treated. In winter animals freeze to death; in droughts they die of heat or thirst. There are fewer than 20 people in the world dedicating their lives to researching these problems. But according to Persis Eskander, researcher at the Open Philanthropy Project, if we sum up the negative experiences of all wild animals, their sheer number could make the scale of the problem larger than most other near-term concerns. Links to learn more, summary and full transcript. Persis urges us to recognise that nature isn’t inherently good or bad, but rather the result of an amoral evolutionary process. For those that can't survive the brutal indifference of their environment, life is often a series of bad experiences, followed by an even worse death. But should we actually intervene? How do we know what animals are sentient? How often do animals feel hunger, cold, fear, happiness, satisfaction, boredom, and intense agony? Are there long-term technologies that could eventually allow us to massively improve wild animal welfare? For most of these big questions, the answer is: we don’t know. And Persis thinks we're far away from knowing enough to start interfering with ecosystems. But that's all the more reason to start looking at these questions. There are some concrete steps we could take today, like improving the way wild caught fish are slaughtered. Fish might lack the charisma of a lion or the intelligence of a pig, but if they have the capacity to suffer — and evidence suggests that they do — we should be thinking of ways to kill them painlessly rather than allowing them to suffocate to death over hours. In today’s interview we explore wild animal welfare as a new field of research, and discuss: • Do we have a moral duty towards wild animals or not? • How should we measure the number of wild animals? • What are some key activities that generate a lot of suffering or pleasure for wild animals that people might not fully appreciate? • Is there a danger in imagining how we as humans would feel if we were put into their situation? • Should we eliminate parasites and predators? • How important are insects? • How strongly should we focus on just avoiding humans going in and making things worse? • How does this compare to work on farmed animal suffering? • The most compelling arguments for humanity not dedicating resources to wild animal welfare • Is there much of a case for the idea that this work could improve the very long-term future of humanity? Rob is then joined by two of his colleagues — Niel Bowerman and Michelle Hutchinson — to quickly discuss: • The importance of figuring out your values • Chemistry, psychology, and other different paths towards working on wild animal welfare • How to break into new fields Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Governance matters. Policy change quickly took China from famine to fortune; Singapore from swamps to skyscrapers; and Hong Kong from fishing village to financial centre. Unfortunately, many governments are hard to reform and — to put it mildly — it's not easy to found a new country. This has prompted poverty-fighters and political dreamers to look for creative ways to get new and better 'pseudo-countries' off the ground. The poor could then voluntary migrate to in search of security and prosperity. And innovators would be free to experiment with new political and legal systems without having to impose their ideas on existing jurisdictions. The 'seasteading movement' imagined founding new self-governing cities on the sea, but obvious challenges have kept that one on the drawing board. Nobel Prize winner and World Bank President Paul Romer suggested 'charter cities', where a host country would volunteer for another country with better legal institutions to effectively govern some of its territory. But that idea too ran aground for political, practical and personal reasons. Now Mark Lutter and Tamara Winter, of The Center for Innovative Governance Research (CIGR), are reviving the idea of 'charter cities', with some modifications. Gone is the idea of transferring sovereignty. Instead these cities would look more like the 'special economic zones' that worked miracles for Taiwan and China among others. But rather than keep the rest of the country's rules with a few pieces removed, they hope to start from scratch, opting in to the laws they want to keep, in order to leap forward to "best practices in commercial law." Links to learn more, summary and full transcript. Rob on The Good Life: Andrew Leigh in Conversation — on 'making the most of your 80,000 hours'. The project has quickly gotten attention, with Mark and Tamara receiving funding from Tyler Cowen's Emergent Ventures (discussed in episode 45) and winning a Pioneer tournament. Starting afresh with a new city makes it possible to clear away thousands of harmful rules without having to fight each of the thousands of interest groups that will viciously defend their privileges. Initially the city can fund infrastructure and public services by gradually selling off its land, which appreciates as the city flourishes. And with 40 million people relocating to cities every year, there are plenty of prospective migrants. CIGR is fleshing out how these arrangements would work, advocating for them, and developing supporting services that make it easier for any jurisdiction to implement. They're currently in the process of influencing a new prospective satellite city in Zambia. Of course, one can raise many criticisms of this idea: Is it likely to be taken up? Is CIGR really doing the right things to make it happen? Will it really reduce poverty if it is? We discuss those questions, as well as: • How did Mark get a new organisation off the ground, with fundraising and other staff? • What made China's 'special economic zones' so successful? • What are the biggest challenges in getting new cities off the ground? • How did Mark find and hire Tamara? How did he know this was a good idea? • Should people care about this idea if they aren't focussed on tackling poverty? • Why aren't people already doing this? • Why does Tamara support more people starting families? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

OpenAI’s Dactyl is an AI system that can manipulate objects with a human-like robot hand. OpenAI Five is an AI system that can defeat humans at the video game Dota 2. The strange thing is they were both developed using the same general-purpose reinforcement learning algorithm. How is this possible and what does it show? In today's interview Jack Clark, Policy Director at OpenAI, explains that from a computational perspective using a hand and playing Dota 2 are remarkably similar problems. A robot hand needs to hold an object, move its fingers, and rotate it to the desired position. In Dota 2 you control a team of several different people, moving them around a map to attack an enemy. Your hand has 20 or 30 different joints to move. The number of main actions in Dota 2 is 10 to 20, as you move your characters around a map. When you’re rotating an objecting in your hand, you sense its friction, but you don’t directly perceive the entire shape of the object. In Dota 2, you're unable to see the entire map and perceive what's there by moving around – metaphorically 'touching' the space. Read our new in-depth article on becoming an AI policy specialist: The case for building expertise to work on US AI policy, and how to do it Links to learn more, summary and full transcript This is true of many apparently distinct problems in life. Compressing different sensory inputs down to a fundamental computational problem which we know how to solve only requires the right general-purpose software. The creation of such increasingly 'broad-spectrum' learning algorithms like has been a key story of the last few years, and this development like have unpredictable consequences, heightening the huge challenges that already exist in AI policy. Today’s interview is a mega-AI-policy-quad episode; Jack is joined by his colleagues Amanda Askell and Miles Brundage, on the day they released their fascinating and controversial large general language model GPT-2. We discuss: • What are the most significant changes in the AI policy world over the last year or two? • What capabilities are likely to develop over the next five, 10, 15, 20 years? • How much should we focus on the next couple of years, versus the next couple of decades? • How should we approach possible malicious uses of AI? • What are some of the potential ways OpenAI could make things worse, and how can they be avoided? • Publication norms for AI research • Where do we stand in terms of arms races between countries or different AI labs? • The case for creating newsletters • Should the AI community have a closer relationship to the military? • Working at OpenAI vs. working in the US government • How valuable is Twitter in the AI policy world? Rob is then joined by two of his colleagues – Niel Bowerman & Michelle Hutchinson – to quickly discuss: • The reaction to OpenAI's release of GPT-2 • Jack’s critique of our US AI policy article • How valuable are roles in government? • Where do you start if you want to write content for a specific audience? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours Podcast is produced by Keiran Harris.

“Politics. Business. Opinion. Science. Sports. Animal welfare. Existential risk.” Is this a plausible future lineup for major news outlets? Funded by the Rockefeller Foundation and given very little editorial direction, Vox's Future Perfect aspires to be more or less that. Competition in the news business creates pressure to write quick pieces on topical political issues that can drive lots of clicks with just a few hours' work. But according to Kelsey Piper, staff writer for this new section of Vox's website focused on effective altruist themes, Future Perfect's goal is to run in the opposite direction and make room for more substantive coverage that's not tied to the news cycle. They hope that in the long-term talented writers from other outlets across the political spectrum can also be attracted to tackle these topics. Links to learn more, summary and full transcript. Links to Kelsey's top articles. Some skeptics of the project have questioned whether this general coverage of global catastrophic risks actually helps reduce them. Kelsey responds: if you decide to dedicate your life to AI safety research, what’s the likely reaction from your family and friends? Do they think of you as someone about to join "that weird Silicon Valley apocalypse thing"? Or do they, having read about the issues widely, simply think “Oh, yeah. That seems important. I'm glad you're working on it.” Kelsey believes that really matters, and is determined by broader coverage of these kinds of topics. If that's right, is journalism a plausible pathway for doing the most good with your career, or did Kelsey just get particularly lucky? After all, journalism is a shrinking industry without an obvious revenue model to fund many writers looking into the world's most pressing problems. Kelsey points out that one needn't take the risk of committing to journalism at an early age. Instead listeners can specialise in an important topic, while leaving open the option of switching into specialist journalism later on, should a great opportunity happen to present itself. In today’s episode we discuss that path, as well as: • What’s the day to day life of a Vox journalist like? • How can good journalism get funded? • Are there meaningful tradeoffs between doing what's in the interest of Vox and doing what’s good? • How concerned should we be about the risk of effective altruism being perceived as partisan? • How well can short articles effectively communicate complicated ideas? • Are there alternative business models that could fund high quality journalism on a larger scale? • How do you approach the case for taking AI seriously to a broader audience? • How valuable might it be for media outlets to do Tetlock-style forecasting? • Is it really a good idea to heavily tax billionaires? • How do you avoid the pressure to get clicks? • How possible is it to predict which articles are going to be popular? • How did Kelsey build the skills necessary to work at Vox? • General lessons for people dealing with very difficult life circumstances Rob is then joined by two of his colleagues – Keiran Harris & Michelle Hutchinson – to quickly discuss: • The risk political polarisation poses to long-termist causes • How should specialists keep journalism available as a career option? • Should we create a news aggregator that aims to make someone as well informed as possible in big-picture terms? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

This is a cross-post of an interview Rob did with Julia Galef on her podcast Rationally Speaking. Rob and Julia discuss how the career advice 80,000 Hours gives has changed over the years, and the biggest misconceptions about our views. The topics will be familiar to the most fervent fans of this show — but we think that if you’ve listened to less than about half of the episodes we've released so far, you’ll find something new to enjoy here. Julia may be familiar to you as the guest on episode 7 of the show, way back in September 2017. The conversation also covers topics like: • How many people should try to get a job in finance and donate their income? • The case for working to reduce global catastrophic risks in targeted ways, and historical precedents for this kind of work • Why reducing risk is a better way to help the future than increasing economic growth • What percentage of the world should ideally follow 80,000 Hours advice? Links to learn more, summary and full transcript. If you’re interested in the cooling and expansion of the universe, which comes up on the show, you should definitely check out our 29th episode with Dr Anders Sandberg. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into any podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Pro-market economists love to wax rhapsodic about the capacity of markets to pull together the valuable local information spread across all of society about what people want and how to make it. But when it comes to politics and voting - which also aim to aggregate the preferences and knowledge found in millions of individuals - the enthusiasm for finding clever institutional designs often turns to skepticism. Today's guest, freewheeling economist Glen Weyl, won't have it, and is on a warpath to reform liberal democratic institutions in order to save them. Just last year he wrote Radical Markets: Uprooting Capitalism and Democracy for a Just Society with Eric Posner, but has already moved on, saying "in the 6 months since the book came out I've made more intellectual progress than in the whole 10 years before that." Weyl believes we desperately need more efficient, equitable and decentralised ways to organise society, that take advantage of what each person knows, and his research agenda has already been making breakthroughs. Links to learn more, summary and full transcript Our high impact job board Join our newsletter Despite a history in the best economics departments in the world - Harvard, Princeton, Yale and the University of Chicago - he is too worried for the future to sit in his office writing papers. Instead he has left the academy to try to inspire a social movement, RadicalxChange, with a vision of social reform as expansive as his own. You can sign up for their conference in Detroit in March here Economist Alex Tabarrok called his latest proposal, known as 'liberal radicalism', "a quantum leap in public-goods mechanism-design" - we explain how it works in the show. But the proposal, however good in theory, might struggle in the real world because it requires large subsidies, and compensates for people's selfishness so effectively that it might even be an overcorrection. An earlier mechanism - 'quadratic voting' (QV) - would allow people to express the relative strength of their preferences in the democratic process. No longer would 51 people who support a proposal, but barely care about the issue, outvote 49 incredibly passionate opponents, predictably making society worse in the process. We explain exactly how in the episode. Weyl points to studies showing that people are more likely to vote strongly not only about issues they *care* more about, but issues they *know* more about. He expects that allowing people to specialise and indicate when they know what they're talking about will create a democracy that does more to aggregate careful judgement, rather than just passionate ignorance. But these and indeed all of Weyl's ideas have faced criticism. Some say the risk of unintended consequences is too great, or that they solve the wrong problem. Others see these proposals as unproven, impractical, or just another example of an intellectual engaged in grand social planning. I raise these concerns to see how he responds. As big a topic as all of that is, this extended conversation also goes into the blockchain, problems with the effective altruism community and how auctions could replace private property. Don't miss it. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Politics in rich countries seems to be going nuts. What's the explanation? Rising inequality? The decline of manufacturing jobs? Excessive immigration? Martin Gurri spent decades as a CIA analyst and in his 2014 book The Revolt of The Public and Crisis of Authority in the New Millennium, predicted political turbulence for an entirely different reason: new communication technologies were flipping the balance of power between the public and traditional authorities. In 1959 the President could control the narrative by leaning on his friends at four TV stations, who felt it was proper to present the nation's leader in a positive light, no matter their flaws. Today, it's impossible to prevent someone from broadcasting any grievance online, whether it's a contrarian insight or an insane conspiracy theory. Links to learn more, summary and full transcript. According to Gurri, trust in society's institutions - police, journalists, scientists and more - has been undermined by constant criticism from outsiders, and exposed to a cacophony of conflicting opinions on every issue, the public takes fewer truths for granted. We are now free to see our leaders as the flawed human beings they always have been, and are not amused. Suspicious they are being betrayed by elites, the public can also use technology to coordinate spontaneously and express its anger. Keen to 'throw the bastards out' protesters take to the streets, united by what they don't like, but without a shared agenda or the institutional infrastructure to figure out how to fix things. Some popular movements have come to view any attempt to exercise power over others as suspect. If Gurri is to be believed, protest movements in Egypt, Spain, Greece and Israel in 2011 followed this script, while Brexit, Trump and the French yellow vests movement subsequently vindicated his theory. In this model, politics won't return to its old equilibrium any time soon. The leaders of tomorrow will need a new message and style if they hope to maintain any legitimacy in this less hierarchical world. Otherwise, we're in for decades of grinding conflict between traditional centres of authority and the general public, who doubt both their loyalty and competence. But how much should we believe this theory? Why do Canada and Australia remain pools of calm in the storm? Aren't some malcontents quite concrete in their demands? And are protest movements actually more common (or more nihilistic) than they were decades ago? In today's episode we ask these questions and add an hour-long discussion with two of Rob's colleagues - Keiran Harris and Michelle Hutchinson - to further explore the ideas in the book. The conversation covers: * How do we know that the internet is driving this rather than some other phenomenon? * How do technological changes enable social and political change? * The historical role of television * Are people also more disillusioned now with sports heroes and actors? * Which countries are finding good ways to make politics work in this new era? * What are the implications for the threat of totalitarianism? * What is this is going to do to international relations? Will it make it harder for countries to cooperate and avoid conflict? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

If an asteroid impact or nuclear winter blocked the sun for years, our inability to grow food would result in billions dying of starvation, right? According to Dr David Denkenberger, co-author of Feeding Everyone No Matter What: no. If he's to be believed, nobody need starve at all. Even without the sun, David sees the Earth as a bountiful food source. Mushrooms farmed on decaying wood. Bacteria fed with natural gas. Fish and mussels supported by sudden upwelling of ocean nutrients - and more. Dr Denkenberger is an Assistant Professor at the University of Alaska Fairbanks, and he's out to spread the word that while a nuclear winter might be horrible, experts have been mistaken to assume that mass starvation is an inevitability. In fact, the only thing that would prevent us from feeding the world is insufficient preparation. ∙ Links to learn more, summary and full transcript Not content to just write a book pointing this out, David has gone on to found a growing non-profit - the Alliance to Feed the Earth in Disasters (ALLFED) - to prepare the world to feed everyone come what may. He expects that today 10% of people would find enough food to survive a massive disaster. In principle, if we did everything right, nobody need go hungry. But being more realistic about how much we're likely to invest, David thinks a plan to inform people ahead of time could save 30%, and a decent research and development scheme 80%. ∙ 80,000 Hours' updated article on How to find the best charity to give to ∙ A potential donor evaluates ALLFED According to David's published cost-benefit analyses, work on this problem may be able to save lives, in expectation, for under $100 each, making it an incredible investment. These preparations could also help make humanity more resilient to global catastrophic risks, by forestalling an ‘everyone for themselves' mentality, which then causes trade and civilization to unravel. But some worry that David's cost-effectiveness estimates are exaggerations, so I challenge him on the practicality of his approach, and how much his non-profit's work would actually matter in a post-apocalyptic world. In our extensive conversation, we cover: * How could the sun end up getting blocked, or agriculture otherwise be decimated? * What are all the ways we could we eat nonetheless? What kind of life would this be? * Can these methods be scaled up fast? * What is his organisation, ALLFED, actually working on? * How does he estimate the cost-effectiveness of this work, and what are the biggest weaknesses of the approach? * How would more food affect the post-apocalyptic world? Won't people figure it out at that point anyway? * Why not just leave guidebooks with this information in every city? * Would these preparations make nuclear war more likely? * What kind of people is ALLFED trying to hire? * What would ALLFED do with more money? * How he ended up doing this work. And his other engineering proposals for improving the world, including ideas to prevent a supervolcano explosion. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

If I told you it's possible to deliver an extra year of ideal primary-level education for under $1, would you believe me? Hopefully not - the claim is absurd on its face. But it may be true nonetheless. The very best education interventions are phenomenally cost-effective, and they're not the kinds of things you'd expect, says Dr Rachel Glennerster. She's Chief Economist at the UK's foreign aid agency DFID, and used to run J-PAL, the world-famous anti-poverty research centre based in MIT's Economics Department, where she studied the impact of a wide range of approaches to improving education, health, and governing institutions. According to Dr Glennerster: "...when we looked at the cost effectiveness of education programs, there were a ton of zeros, and there were a ton of zeros on the things that we spend most of our money on. So more teachers, more books, more inputs, like smaller class sizes - at least in the developing world - seem to have no impact, and that's where most government money gets spent." "But measurements for the top ones - the most cost effective programs - say they deliver 460 LAYS per £100 spent ($US130). LAYS are Learning-Adjusted Years of Schooling. Each one is the equivalent of the best possible year of education you can have - Singapore-level." Links to learn more, summary and full transcript. "...the two programs that come out as spectacularly effective... well, the first is just rearranging kids in a class." "You have to test the kids, so that you can put the kids who are performing at grade two level in the grade two class, and the kids who are performing at grade four level in the grade four class, even if they're different ages - and they learn so much better. So that's why it's so phenomenally cost effective because, it really doesn't cost anything." "The other one is providing information. So sending information over the phone [for example about how much more people earn if they do well in school and graduate]. So these really small nudges. Now none of those nudges will individually transform any kid's life, but they are so cheap that you get these fantastic returns on investment - and we do very little of that kind of thing." In this episode, Dr Glennerster shares her decades of accumulated wisdom on which anti-poverty programs are overrated, which are neglected opportunities, and how we can know the difference, across a range of fields including health, empowering women and macroeconomic policy. Regular listeners will be wondering - have we forgotten all about the lessons from episode 30 of the show with Dr Eva Vivalt? She threw several buckets of cold water on the hope that we could accurately measure the effectiveness of social programs at all. According to Vivalt, her dataset of hundreds of randomised controlled trials indicates that social science findings don’t generalize well at all. The results of a trial at a school in Namibia tell us remarkably little about how a similar program will perform if delivered at another school in Namibia - let alone if it's attempted in India instead. Rachel offers a different and more optimistic interpretation of Eva's findings. To learn more and figure out who you sympathise with more, you'll just have to listen to the episode. Regardless, Vivalt and Glennerster agree that we should continue to run these kinds of studies, and today’s episode delves into the latest ideas in global health and development. Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Please let us know if we've helped you: Fill out our annual impact survey Ever felt that you were so busy you spent all your time paralysed trying to figure out where to start, and couldn't get much done? Computer scientists have a term for this - thrashing - and it's a common reason our computers freeze up. The solution, for people as well as laptops, is to 'work dumber': pick something at random and finish it, without wasting time thinking about the bigger picture. Bestselling author Brian Christian studied computer science, and in the book Algorithms to Live By he's out to find the lessons it can offer for a better life. He investigates into when to quit your job, when to marry, the best way to sell your house, how long to spend on a difficult decision, and how much randomness to inject into your life. In each case computer science gives us a theoretically optimal solution, and in this episode we think hard about whether its models match our reality. Links to learn more, summary and full transcript. One genre of problems Brian explores in his book are 'optimal stopping problems', the canonical example of which is ‘the secretary problem’. Imagine you're hiring a secretary, you receive *n* applicants, they show up in a random order, and you interview them one after another. You either have to hire that person on the spot and dismiss everybody else, or send them away and lose the option to hire them in future. It turns out most of life can be viewed this way - a series of unique opportunities you pass by that will never be available in exactly the same way again. So how do you attempt to hire the very best candidate in the pool? There's a risk that you stop before finding the best, and a risk that you set your standards too high and let the best candidate pass you by. Mathematicians of the mid-twentieth century produced an elegant optimal approach: spend exactly one over *e*, or approximately 37% of your search, just establishing a baseline without hiring anyone, no matter how promising they seem. Then immediately hire the next person who's better than anyone you've seen so far. It turns out that your odds of success in this scenario are also 37%. And the optimal strategy and the odds of success are identical regardless of the size of the pool. So as *n* goes to infinity you still want to follow this 37% rule, and you still have a 37% chance of success. Even if you interview a million people. But if you have the option to go back, say by apologising to the first applicant and begging them to come work with you, and you have a 50% chance of your apology being accepted, then the optimal explore percentage rises all the way to 61%. Today’s episode focuses on Brian’s book-length exploration of how insights from computer algorithms can and can't be applied to our everyday lives. We cover: * Computational kindness, and the best way to schedule meetings * How can we characterize a computational model of what people are actually doing, and is there a rigorous way to analyse just how good their instincts actually are? * What’s it like being a human confederate in the Turing test competition? * Is trying to detect fake social media accounts a losing battle? * The canonical explore/exploit problem in computer science: the multi-armed bandit * What’s the optimal way to buy or sell a house? * Why is information economics so important? * What kind of decisions should people randomize more in life? * How much time should we spend on prioritisation? Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

After dropping out of a machine learning PhD at Stanford, Daniel Ziegler needed to decide what to do next. He’d always enjoyed building stuff and wanted to shape the development of AI, so he thought a research engineering position at an org dedicated to aligning AI with human interests could be his best option. He decided to apply to OpenAI, and spent about 6 weeks preparing for the interview before landing the job. His PhD, by contrast, might have taken 6 years. Daniel thinks this highly accelerated career path may be possible for many others. On today’s episode Daniel is joined by Catherine Olsson, who has also worked at OpenAI, and left her computational neuroscience PhD to become a research engineer at Google Brain. She and Daniel share this piece of advice for those curious about this career path: just dive in. If you're trying to get good at something, just start doing that thing, and figure out that way what's necessary to be able to do it well. Catherine has even created a simple step-by-step guide for 80,000 Hours, to make it as easy as possible for others to copy her and Daniel's success. Please let us know how we've helped you: fill out our 2018 annual impact survey so that 80,000 Hours can continue to operate and grow. Blog post with links to learn more, a summary & full transcript. Daniel thinks the key for him was nailing the job interview. OpenAI needed him to be able to demonstrate the ability to do the kind of stuff he'd be working on day-to-day. So his approach was to take a list of 50 key deep reinforcement learning papers, read one or two a day, and pick a handful to actually reproduce. He spent a bunch of time coding in Python and TensorFlow, sometimes 12 hours a day, trying to debug and tune things until they were actually working. Daniel emphasizes that the most important thing was to practice *exactly* those things that he knew he needed to be able to do. His dedicated preparation also led to an offer from the Machine Intelligence Research Institute, and so he had the opportunity to decide between two organisations focused on the global problem that most concerns him. Daniel’s path might seem unusual, but both he and Catherine expect it can be replicated by others. If they're right, it could greatly increase our ability to get new people into important ML roles in which they can make a difference, as quickly as possible. Catherine says that her move from OpenAI to an ML research team at Google now allows her to bring a different set of skills to the table. Technical AI safety is a multifaceted area of research, and the many sub-questions in areas such as reward learning, robustness, and interpretability all need to be answered to maximize the probability that AI development goes well for humanity. Today’s episode combines the expertise of two pioneers and is a key resource for anyone wanting to follow in their footsteps. We cover: * What are OpenAI and Google Brain doing? * Why work on AI? * Do you learn more on the job, or while doing a PhD? * Controversial issues within ML * Is replicating papers a good way of determining suitability? * What % of software developers could make similar transitions? * How in-demand are research engineers? * The development of Dota 2 bots * Do research scientists have more influence on the vision of an org? * Has learning more made you more or less worried about the future? Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

The barista gives you your coffee and change, and you walk away from the busy line. But you suddenly realise she gave you $1 less than she should have. Do you brush your way past the people now waiting, or just accept this as a dollar you’re never getting back? According to philosophy Professor Hilary Greaves - Director of Oxford University's Global Priorities Institute, which is hiring - this simple decision will completely change the long-term future by altering the identities of almost all future generations. How? Because by rushing back to the counter, you slightly change the timing of everything else people in line do during that day - including changing the timing of the interactions they have with everyone else. Eventually these causal links will reach someone who was going to conceive a child. By causing a child to be conceived a few fractions of a second earlier or later, you change the sperm that fertilizes their egg, resulting in a totally different person. So asking for that $1 has now made the difference between all the things that this actual child will do in their life, and all the things that the merely possible child - who didn't exist because of what you did - would have done if you decided not to worry about it. As that child's actions ripple out to everyone else who conceives down the generations, ultimately the entire human population will become different, all for the sake of your dollar. Will your choice cause a future Hitler to be born, or not to be born? Probably both! Links to learn more, summary and full transcript. Some find this concerning. The actual long term effects of your decisions are so unpredictable, it looks like you’re totally clueless about what's going to lead to the best outcomes. It might lead to decision paralysis - you won’t be able to take any action at all. Prof Greaves doesn’t share this concern for most real life decisions. If there’s no reasonable way to assign probabilities to far-future outcomes, then the possibility that you might make things better in completely unpredictable ways is more or less canceled out by equally likely opposite possibility. But, if instead we’re talking about a decision that involves highly-structured, systematic reasons for thinking there might be a general tendency of your action to make things better or worse -- for example if we increase economic growth -- Prof Greaves says that we don’t get to just ignore the unforeseeable effects. When there are complex arguments on both sides, it's unclear what probabilities you should assign to this or that claim. Yet, given its importance, whether you should take the action in question actually does depend on figuring out these numbers. So, what do we do? Today’s episode blends philosophy with an exploration of the mission and research agenda of the Global Priorities Institute: to develop the effective altruism movement within academia. We cover: * How controversial is the multiverse interpretation of quantum physics? * Given moral uncertainty, how should population ethics affect our real life decisions? * How should we think about archetypal decision theory problems? * What are the consequences of cluelessness for those who based their donation advice on GiveWell style recommendations? * How could reducing extinction risk be a good cause for risk-averse people? Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

I've probably spent more time reading Tyler Cowen - Professor of Economics at George Mason University - than any other author. Indeed it's his incredibly popular blog Marginal Revolution that prompted me to study economics in the first place. Having spent thousands of hours absorbing Tyler's work, it was a pleasure to be able to question him about his latest book and personal manifesto: Stubborn Attachments: A Vision for a Society of Free, Prosperous, and Responsible Individuals. Tyler makes the case that, despite what you may have heard, we *can* make rational judgments about what is best for society as a whole. He argues: 1. Our top moral priority should be preserving and improving humanity's long-term future 2. The way to do that is to maximise the rate of sustainable economic growth 3. We should respect human rights and follow general principles while doing so. We discuss why Tyler believes all these things, and I push back where I disagree. In particular: is higher economic growth actually an effective way to safeguard humanity's future, or should our focus really be elsewhere? In the process we touch on many of moral philosophy's most pressing questions: Should we discount the future? How should we aggregate welfare across people? Should we follow rules or evaluate every situation individually? How should we deal with the massive uncertainty about the effects of our actions? And should we trust common sense morality or follow structured theories? Links to learn more, summary and full transcript. After covering the book, the conversation ranges far and wide. Will we leave the galaxy, and is it a tragedy if we don't? Is a multi-polar world less stable? Will humanity ever help wild animals? Why do we both agree that Kant and Rawls are overrated? Today's interview is released on both the 80,000 Hours Podcast and Tyler's own show: Conversation with Tyler. Tyler may have had more influence on me than any other writer but this conversation is richer for our remaining disagreements. If the above isn't enough to tempt you to listen, we also look at: * Why couldn’t future technology make human life a hundred or a thousand times better than it is for people today? * Why focus on increasing the rate of economic growth rather than making sure that it doesn’t go to zero? * Why shouldn’t we dedicate substantial time to the successful introduction of genetic engineering? * Why should we completely abstain from alcohol and make it a social norm? * Why is Tyler so pessimistic about space? Is it likely that humans will go extinct before we manage to escape the galaxy? * Is improving coordination and international cooperation a major priority? * Why does Tyler think institutions are keeping up with technology? * Given that our actions seem to have very large and morally significant effects in the long run, are our moral obligations very onerous? * Can art be intrinsically valuable? * What does Tyler think Derek Parfit was most wrong about, and what was he was most right about that’s unappreciated today? Get this episode by subscribing: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Paul Christiano is one of the smartest people I know. After our first session produced such great material, we decided to do a second recording, resulting in our longest interview so far. While challenging at times I can strongly recommend listening - Paul works on AI himself and has a very unusually thought through view of how it will change the world. This is now the top resource I'm going to refer people to if they're interested in positively shaping the development of AI, and want to understand the problem better. Even though I'm familiar with Paul's writing I felt I was learning a great deal and am now in a better position to make a difference to the world. A few of the topics we cover are: * Why Paul expects AI to transform the world gradually rather than explosively and what that would look like * Several concrete methods OpenAI is trying to develop to ensure AI systems do what we want even if they become more competent than us * Why AI systems will probably be granted legal and property rights * How an advanced AI that doesn't share human goals could still have moral value * Why machine learning might take over science research from humans before it can do most other tasks * Which decade we should expect human labour to become obsolete, and how this should affect your savings plan. Links to learn more, summary and full transcript. Important new article: These are the world’s highest impact career paths according to our research Here's a situation we all regularly confront: you want to answer a difficult question, but aren't quite smart or informed enough to figure it out for yourself. The good news is you have access to experts who *are* smart enough to figure it out. The bad news is that they disagree. If given plenty of time - and enough arguments, counterarguments and counter-counter-arguments between all the experts - should you eventually be able to figure out which is correct? What if one expert were deliberately trying to mislead you? And should the expert with the correct view just tell the whole truth, or will competition force them to throw in persuasive lies in order to have a chance of winning you over? In other words: does 'debate', in principle, lead to truth? According to Paul Christiano - researcher at the machine learning research lab OpenAI and legendary thinker in the effective altruism and rationality communities - this question is of more than mere philosophical interest. That's because 'debate' is a promising method of keeping artificial intelligence aligned with human goals, even if it becomes much more intelligent and sophisticated than we are. It's a method OpenAI is actively trying to develop, because in the long-term it wants to train AI systems to make decisions that are too complex for any human to grasp, but without the risks that arise from a complete loss of human oversight. If AI-1 is free to choose any line of argument in order to attack the ideas of AI-2, and AI-2 always seems to successfully defend them, it suggests that every possible line of argument would have been unsuccessful. But does that mean that the ideas of AI-2 were actually right? It would be nice if the optimal strategy in debate were to be completely honest, provide good arguments, and respond to counterarguments in a valid way. But we don't know that's the case. Get this episode by subscribing: type '80,000 Hours' into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

In Stanley Kubrick’s iconic film Dr. Strangelove, the American president is informed that the Soviet Union has created a secret deterrence system which will automatically wipe out humanity upon detection of a single nuclear explosion in Russia. With US bombs heading towards the USSR and unable to be recalled, Dr Strangelove points out that “the whole point of this Doomsday Machine is lost if you keep it a secret – why didn’t you tell the world, eh?” The Soviet ambassador replies that it was to be announced at the Party Congress the following Monday: “The Premier loves surprises”. Daniel Ellsberg - leaker of the Pentagon Papers which helped end the Vietnam War and Nixon presidency - claims in his new book The Doomsday Machine: Confessions of a Nuclear War Planner that Dr. Strangelove might as well be a documentary. After attending the film in Washington DC in 1964, he and a colleague wondered how so many details of their nuclear planning had leaked. Links to learn more, summary and full transcript. The USSR did in fact develop a doomsday machine, Dead Hand, which probably remains active today. If the system can’t contact military leaders, it checks for signs of a nuclear strike, and if it detects them, automatically launches all remaining Soviet weapons at targets across the northern hemisphere. As in the film, the Soviet Union long kept Dead Hand completely secret, eliminating any strategic benefit, and rendering it a pointless menace to humanity. You might think the United States would have a more sensible nuclear launch policy. You’d be wrong. As Ellsberg explains, based on first-hand experience as a nuclear war planner in the 50s, that the notion that only the president is able to authorize the use of US nuclear weapons is a carefully cultivated myth. The authority to launch nuclear weapons is delegated alarmingly far down the chain of command – significantly raising the chance that a lone wolf or communication breakdown could trigger a nuclear catastrophe. The whole justification for this is to defend against a ‘decapitating attack’, where a first strike on Washington disables the ability of the US hierarchy to retaliate. In a moment of crisis, the Russians might view this as their best hope of survival. Ostensibly, this delegation removes Russia’s temptation to attempt a decapitating attack – the US can retaliate even if its leadership is destroyed. This strategy only works, though, if the tell the enemy you’ve done it. Instead, since the 50s this delegation has been one of the United States most closely guarded secrets, eliminating its strategic benefit, and rendering it another pointless menace to humanity. Strategically, the setup is stupid. Ethically, it is monstrous. So – how was such a system built? Why does it remain to this day? And how might we shrink our nuclear arsenals to the point they don’t risk the destruction of civilization? Daniel explores these questions eloquently and urgently in his book. Today we cover: * Why full disarmament today would be a mistake and the optimal number of nuclear weapons to hold * How well are secrets kept in the government? * What was the risk of the first atomic bomb test? * The effect of Trump on nuclear security * Do we have a reliable estimate of the magnitude of a ‘nuclear winter’? * Why Gorbachev allowed Russia’s covert biological warfare program to continue Get this episode by subscribing: type 80,000 Hours into your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Consider two familiar moments at a family reunion. Our host, Uncle Bill, takes pride in his barbecuing skills. But his niece Becky says that she now refuses to eat meat. A groan goes round the table; the family mostly think of this as an annoying picky preference. But if seriously considered as a moral position, as they might if instead Becky were avoiding meat on religious grounds, it would usually receive a very different reaction. An hour later Bill expresses a strong objection to abortion. Again, a groan goes round the table; the family mostly think that he has no business in trying to foist his regressive preference on anyone. But if considered not as a matter of personal taste, but rather as a moral position - that Bill genuinely believes he’s opposing mass-murder - his comment might start a serious conversation. Amanda Askell, who recently completed a PhD in philosophy at NYU focused on the ethics of infinity, thinks that we often betray a complete lack of moral empathy. All sides of the political spectrum struggle to get inside the mind of people we disagree with and see issues from their point of view. Links to learn more, summary and full transcript. This often happens because of confusion between preferences and moral positions. Assuming good faith on the part of the person you disagree with, and actually engaging with the beliefs they claim to hold, is perhaps the best remedy for our inability to make progress on controversial issues. One potential path for progress surrounds contraception; a lot of people who are anti-abortion are also anti-contraception. But they’ll usually think that abortion is much worse than contraception, so why can’t we compromise and agree to have much more contraception available? According to Amanda, a charitable explanation for this is that people who are anti-abortion and anti-contraception engage in moral reasoning and advocacy based on what, in their minds, is the best of all possible worlds: one where people neither use contraception nor get abortions. So instead of arguing about abortion and contraception, we could discuss the underlying principle that one should advocate for the best possible world, rather than the best probable world. Successfully break down such ethical beliefs, absent political toxicity, and it might be possible to actually converge on a key point of agreement. Today’s episode blends such everyday topics with in-depth philosophy, including: * What is 'moral cluelessness' and how can we work around it? * Amanda's biggest criticisms of social justice activists, and of critics of social justice activists * Is there an ethical difference between prison and corporal punishment? * How to resolve 'infinitarian paralysis' - the inability to make decisions when infinities are involved. * What’s effective altruism doing wrong? * How should we think about jargon? Are a lot of people who don’t communicate clearly just scamming us? * How can people be more successful within the cocoon of school and university? * How did Amanda find doing a philosophy PhD, and how will she decide what to do now? Links: * Career review: Congressional staffer * Randomised experiment on quitting * Psychology replication quiz * Should you focus on your comparative advantage. Get this episode by subscribing: type 80,000 Hours into your podcasting app. The 80,000 Hours podcast is produced by Keiran Harris.

With 698 inmates per 100,000 citizens, the U.S. is by far the leader among large wealthy nations in incarceration. But what effect does imprisonment actually have on crime? According to David Roodman, Senior Advisor to the Open Philanthropy Project, the marginal effect is zero. * 80,000 HOURS IMPACT SURVEY - Let me know how this show has helped you with your career. * ROB'S AUDIOBOOK RECOMMENDATIONS This stunning rebuke to the American criminal justice system comes from the man Holden Karnofsky’s called "the gold standard for in-depth quantitative research", whose other investigations include the risk of geomagnetic storms, whether deworming improves health and test scores, and the development impacts of microfinance. Links to learn more, summary and full transcript. The effects of crime can be split into three categories; before, during, and after. Does having tougher sentences deter people from committing crime? After reviewing studies on gun laws and ‘three strikes’ in California, David concluded that the effect of deterrence is zero. Does imprisoning more people reduce crime by incapacitating potential offenders? Here he says yes, noting that crimes like motor vehicle theft have gone up in a way that seems pretty clearly connected with recent Californian criminal justice reforms (though the effect on violent crime is far lower). Finally, do the after-effects of prison make you more or less likely to commit future crimes? This one is more complicated. Concerned that he was biased towards a comfortable position against incarceration, David did a cost-benefit analysis using both his favored reading of the evidence and the devil's advocate view; that there is deterrence and that the after-effects are beneficial. For the devil’s advocate position David used the highest assessment of the harm caused by crime, which suggests a year of prison prevents about $92,000 in crime. But weighed against a lost year of liberty, valued at $50,000, plus the cost of operating prisons, the numbers came out exactly the same. So even using the least-favorable cost-benefit valuation of the least favorable reading of the evidence -- it just breaks even. The argument for incarceration melts further when you consider the significant crime that occurs within prisons, de-emphasised because of a lack of data and a perceived lack of compassion for inmates. In today’s episode we discuss how to conduct such impactful research, and how to proceed having reached strong conclusions. We also cover: * How do you become a world class researcher? What kinds of character traits are important? * Are academics aware of following perverse incentives? * What’s involved in data replication? How often do papers replicate? * The politics of large orgs vs. small orgs * Geomagnetic storms as a potential cause area * How much does David rely on interviews with experts? * The effects of deworming on child health and test scores * Should we have more ‘data vigilantes’? * What are David’s critiques of effective altruism? * What are the pros and cons of starting your career in the think tank world? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours Podcast is produced by Keiran Harris.

Experts believe that artificial intelligence will be better than humans at driving trucks by 2027, working in retail by 2031, writing bestselling books by 2049, and working as surgeons by 2053. But how seriously should we take these predictions? Katja Grace, lead author of ‘When Will AI Exceed Human Performance?’, thinks we should treat such guesses as only weak evidence. But she also says there might be much better ways to forecast transformative technology, and that anticipating such advances could be one of our most important projects. Note: Katja's organisation AI Impacts is currently hiring part- and full-time researchers. There’s often pessimism around making accurate predictions in general, and some areas of artificial intelligence might be particularly difficult to forecast. But there are also many things we’re able to predict confidently today -- like the climate of Oxford in five years -- that we no longer give ourselves much credit for. Some aspects of transformative technologies could fall into this category. And these easier predictions could give us some structure on which to base the more complicated ones. Links to learn more, summary and full transcript. One controversial debate surrounds the idea of an intelligence explosion; how likely is it that there will be a sudden jump in AI capability? And one way to tackle this is to investigate a more concrete question: what’s the base rate of any technology having a big discontinuity? A significant historical example was the development of nuclear weapons. Over thousands of years, the efficacy of explosives didn’t increase by much. Then within a few years, it got thousands of times better. Discovering what leads to such anomalies may allow us to better predict the possibility of a similar jump in AI capabilities. In today’s interview we also discuss: * Why is AI impacts one of the most important projects in the world? * How do you structure important surveys? Why do you get such different answers when asking what seem to be very similar questions? * How does writing an academic paper differ from posting a summary online? * When will unguided machines be able to produce better and cheaper work than humans for every possible task? * What’s one of the most likely jobs to be automated soon? * Are people always just predicting the same timelines for new technologies? * How do AGI researchers different from other AI researchers in their predictions? * What are attitudes to safety research like within ML? Are there regional differences? * How much should we believe experts generally? * How does the human brain compare to our best supercomputers? How many human brains are worth all the hardware in the world? * How quickly has the processing capacity for machine learning problems been increasing? * What can we learn from the development of previous technologies in figuring out how fast transformative AI will arrive? * What should we expect from a post AI dominated economy? * How much influence can people ever have on things that will happen in 20 years? Are there any examples of people really trying to do this? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours podcast is produced by Keiran Harris.

Will Trump be re-elected? Will North Korea give up their nuclear weapons? Will your friend turn up to dinner? Spencer Greenberg, founder of ClearerThinking.org has a process for working out such real life problems. Let’s work through one here: how likely is it that you’ll enjoy listening to this episode? The first step is to figure out your ‘prior probability’; what’s your estimate of how likely you are to enjoy the interview before getting any further evidence? Other than applying common sense, one way to figure this out is called reference class forecasting: looking at similar cases and seeing how often something is true, on average. Spencer is our first ever return guest. So one reference class might be, how many Spencer Greenberg episodes of the 80,000 Hours Podcast have you enjoyed so far? Being this specific limits bias in your answer, but with a sample size of at most 1 - you’d probably want to add more data points to reduce variability. Zooming out, how many episodes of the 80,000 Hours Podcast have you enjoyed? Let’s say you’ve listened to 10, and enjoyed 8 of them. If so 8 out of 10 might be your prior probability. But maybe the two you didn’t enjoy had something in common. If you’ve liked similar episodes in the past, you’d update in favour of expecting to enjoy it, and if you’ve disliked similar episodes in the past, you’d update negatively. You can zoom out further; what fraction of long-form interview podcasts have you ever enjoyed? Then you’d look to update whenever new information became available. Do the topics seem interesting? Did Spencer make a great point in the first 5 minutes? Was this description unbearably self-referential? Speaking of the Question of Evidence: in a world where Spencer was not worth listening to, how likely is it that we’d invite him back for a second episode? Links to learn more, summary and full transcript. We’ll run through several diverse examples, and how to actually work out the changing probabilities as you update. But that’s only a fraction of the conversation. We also discuss: * How could we generate 20-30 new happy thoughts a day? What would that do to our welfare? * What do people actually value? How do EAs differ from non EAs? * Why should we care about the distinction between intrinsic and instrumental values? * Would hedonic utilitarians really want to hook themselves up to happiness machines? * What types of activities are people generally under-confident about? Why? * When should you give a lot of weight to your prior belief? * When should we trust common sense? * Does power posing have any effect? * Are resumes worthless? * Did Trump explicitly collude with Russia? What are the odds of him getting re-elected? * What’s the probability that China and the US go to War in the 21st century? * How should we treat claims of expertise on diets? * Why were Spencer’s friends suspicious of Theranos for years? * How should we think about the placebo effect? * Does a shift towards rationality typically cause alienation from family and friends? How do you deal with that? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below. The 80,000 Hours podcast is produced by Keiran Harris.

Will people who think carefully about how to maximize welfare eventually converge on the same views? The effective altruism community has spent a lot of time over the past 10 years debating how best to increase happiness and reduce suffering, and gradually narrowed in on the world’s poorest people, all animals capable of suffering, and future generations. Yew-Kwang Ng, Professor of Economics at Nanyang Technological University in Singapore, was independently working on this exact question since the 70s. Many of his conclusions have ended up foreshadowing what is now conventional wisdom within effective altruism - though other views he holds remain controversial or little-known. For instance, he thinks we ought to explore increasing pleasure via direct brain stimulation, and that genetic engineering may be an important tool for increasing happiness in the future. His work has suggested that the welfare of most wild animals is on balance negative and he thinks that in the future this is a problem humanity might work to solve. Yet he thinks that greatly improved conditions for farm animals could eventually justify eating meat. He has spent most of his life advocating for the view that happiness, broadly construed, is the only intrinsically valuable thing. If it’s true that careful researchers will converge as Prof Ng believes, these ideas may prove as prescient as his other, now widely accepted, opinions. Link to our summary and appreciation of Kwang’s top publications and insights throughout a lifetime of research. Kwang has led an exceptional life. While in high school he was drawn to physics, mathematics, and philosophy, yet he chose to study economics because of his dream: to establish communism in an independent Malaya. But events in the Soviet Union and China, in addition to his burgeoning knowledge and academic appreciation of economics, would change his views about the practicability of communism. He would soon complete his journey from young revolutionary to academic economist, and eventually become a columnist writing in support of Deng Xiaoping’s Chinese economic reforms in the 80s. He got his PhD at Sydney University in 1971, and has since published over 250 refereed papers - covering economics, biology, politics, mathematics, philosophy, psychology, and sociology. He's most well-known for his work in welfare economics, and proposed ‘welfare biology’ as a new field of study. In 2007, he was made a Distinguished Fellow of the Economic Society of Australia, the highest award that the society bestows. Links to learn more, summary and full transcript. In this episode we discuss how he developed some of his most unusual ideas and his fascinating life story, including: * Why Kwang believes that *’Happiness Is Absolute, Universal, Ultimate, Unidimensional, Cardinally Measurable and Interpersonally Comparable’* * What are the most pressing questions in economics? * Did Kwang have to worry about censorship from the Chinese government when promoting market economics, or concern for animal welfare? * Welfare economics and where Kwang thinks it went wrong Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

What’s the value of preventing the death of a 5-year-old child, compared to a 20-year-old, or an 80-year-old? The global health community has generally regarded the value as proportional to the number of health-adjusted life-years the person has remaining - but GiveWell, one of the world’s foremost charity evaluators, no longer uses that approach. They found that contrary to the years-remaining’ method, many of their staff actually value preventing the death of an adult more than preventing the death of a young child. However there’s plenty of disagreement: the team’s estimates of the relative value span a four-fold range. As James Snowden - a research consultant at GiveWell - explains in this episode, there’s no way around making these controversial judgement calls based on limited information. If you try to ignore a question like this, you just implicitly take an unreflective stand on it instead. And for each charity they look into there’s 1 or 2 dozen of these highly uncertain parameters they need to estimate. GiveWell has been trying to find better ways to make these decisions since its inception in 2007. Lives hang in the balance, so they want their staff to say what they really believe and bring their private knowledge to the table, rather than just defer to a imaginary consensus. Their strategy is a massive spreadsheet that lists dozens of things they need to estimate, and asking every staff member to give a figure and justification. Then once a year, the GiveWell team get together and try to identify what they really disagree about and think through what evidence it would take to change their minds. Full transcript, summary of the conversation and links to learn more. Often the people who have the greatest familiarity with a particular intervention are the ones who drive the decision, as others defer to them. But the group can also end up with very different figures, based on different prior beliefs about moral issues and how the world works. In that case then use the median of everyone’s best guess to make their key decisions. In making his estimate of the relative badness of dying at different ages, James specifically considered two factors: how many years of life do you lose, and how much interest do you have in those future years? Currently, James believes that the worst time for a person to die is around 8 years of age. We discuss his experiences with such calculations, as well as a range of other topics: * Why GiveWell’s recommendations have changed more than it looks. * What are the biggest research priorities for GiveWell at the moment? * How do you take into account the long-term knock-on effects from interventions? * If GiveWell's advice were going to end up being very different in a couple years' time, how might that happen? * Are there any charities that James thinks are really cost-effective which GiveWell hasn't funded yet? * How does domestic government spending in the developing world compare to effective charities? * What are the main challenges with policy related interventions? * How much time do you spend discovering new interventions? Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.