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.

Almost nobody is able to do groundbreaking physics research themselves, and by the time his brilliance was appreciated, Einstein was hardly limited by funding. But what if you could find a way to unlock the secrets of the universe like Einstein nonetheless? Today’s guest, Tanya Singh, sees herself as doing something like that every day. She’s Executive Assistant to one of her intellectual heroes who she believes is making a huge contribution to improving the world: Professor Bostrom at Oxford University's Future of Humanity Institute (FHI). She couldn’t get more work out of Bostrom with extra donations, as his salary is already easily covered. But with her superior abilities as an Executive Assistant, Tanya frees up hours of his time every week, essentially ‘buying’ more Bostrom in a way nobody else can. She also help manage FHI more generally, in so doing freeing up more than an hour of other staff time for each hour she works. This gives her the leverage to do more good than other people or other positions. In our previous episode, Tara Mac Aulay objected to viewing operations work as predominately a way of freeing up other people's time: “A good ops person doesn’t just allow you to scale linearly, but also can help figure out bottlenecks and solve problems such that the organization is able to do qualitatively different work, rather than just increase the total quantity”, Tara said. Full transcript, summary and links to learn more. Tara’s right that buying time for people at the top of their field is just one path to impact, though it’s one Tanya says she finds highly motivating. Other paths include enabling complex projects that would otherwise be impossible, allowing you to hire and grow much faster, and preventing disasters that could bring down a whole organisation - all things that Tanya does at FHI as well. In today’s episode we discuss all of those approaches, as we dive deeper into the broad class of roles we refer to as ‘operations management’. We cover the arguments we made in ‘Why operations management is one of the biggest bottlenecks in effective altruism’, as well as: * Does one really need to hire people aligned with an org’s mission to work in ops? * The most notable operations successes in the 20th Century. * What’s it like being the only operations person in an org? * The role of a COO as compared to a CEO, and the options for career progression. * How do good operation teams allow orgs to scale quickly? * How much do operations staff get to set their org’s strategy? * Which personal weaknesses aren’t a huge problem in operations? * How do you automate processes? Why don’t most people do this? * Cultural differences between Britain and India where Tanya grew up. 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.

"You don't need permission. You don't need to be allowed to do something that's not in your job description. If you think that it's gonna make your company or your organization more successful and more efficient, you can often just go and do it." How broken is the world? How inefficient is a typical organisation? Looking at Tara Mac Aulay’s life, the answer seems to be ‘very’. At 15 she took her first job - an entry-level position at a chain restaurant. Rather than accept her place, Tara took it on herself to massively improve the store’s shambolic staff scheduling and inventory management. After cutting staff costs 30% she was quickly promoted, and at 16 sent in to overhaul dozens of failing stores in a final effort to save them from closure. That’s just the first in a startling series of personal stories that take us to a hospital drug dispensary where pharmacists are wasting a third of their time, a chemotherapy ward in Bhutan that’s killing its patients rather than saving lives, and eventually the Centre for Effective Altruism, where Tara becomes CEO and leads it through start-up accelerator Y Combinator. In this episode Tara shows how the ability to do practical things, avoid major screw-ups, and design systems that scale, is both rare and precious. Full transcript, key quotes and links to learn more. People with an operations mindset spot failures others can't see and fix them before they bring an organisation down. This kind of resourcefulness can transform the world by making possible critical projects that would otherwise fall flat on their face. But as Tara's experience shows they need to figure out what actually motivates the authorities who often try to block their reforms. We explore how people with this skillset can do as much good as possible, what 80,000 Hours got wrong in our article 'Why operations management is one of the biggest bottlenecks in effective altruism’, as well as: * Tara’s biggest mistakes and how to deal with the delicate politics of organizational reform. * How a student can save a hospital millions with a simple spreadsheet model. * The sociology of Bhutan and how medicine in the developing world often makes things worse rather than better. * What most people misunderstand about operations, and how to tell if you have what it takes. * And finally, operations jobs people should consider applying for, such as those open now at the Centre for Effective Altruism. 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.

Today's episode is a cross-post of an interview I did with The Jolly Swagmen Podcast which came out this week. I recommend regular listeners skip to 24 minutes in to avoid hearing things they already know. Later in the episode I talk about my contrarian views, utilitarianism, how 80,000 Hours has changed and will change in the future, where I think EA is performing worst, how to use social media most effectively, and whether or not effective altruism is any sacrifice. Subscribe and get the episode by searching for '80,000 Hours' in your podcasting app. Blog post of the episode to share, including a list of topics and links to learn more. "Most people want to help others with their career, but what’s the best way to do that? Become a doctor? A politician? Work at a non-profit? How can any of us figure out the best way to use our skills to improve the world? Rob Wiblin is the Director of Research at 80,000 Hours, an organisation founded in Oxford in 2011, which aims to answer just this question and help talented people find their highest-impact career path. He hosts a popular podcast on ‘the world’s most pressing problems and how you can use your career to solve them’. After seven years of research, the 80,000 Hours team recommends against becoming a teacher, or a doctor, or working at most non-profits. And they claim their research shows some common careers do 10 or 100x as much good as others. 80,000 Hours was one of the organisations that kicked off the effective altruism movement, was a Y Combinator-backed non-profit, and has already shifted over 80 million career hours through its advice. Joe caught up with Rob in Berkeley, California, to discuss how 80,000 Hours assesses which of the world’s problems are most pressing, how you can build career capital and succeed in any role, and why you could easily save more lives than a doctor - if you think carefully about your impact." 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.

In 1991 Edwin Edwards won the Louisiana gubernatorial election. In 2001, he was found guilty of racketeering and received a 10 year invitation to Federal prison. The strange thing about that election? By 1991 Edwards was already notorious for his corruption. Actually, that’s not it. The truly strange thing is that Edwards was clearly the good guy in the race. How is that possible? His opponent was former Ku Klux Klan Grand Wizard David Duke. How could Louisiana end up having to choose between a criminal and a Nazi sympathiser? It’s not like they lacked other options: the state’s moderate incumbent governor Buddy Roemer ran for re-election. Polling showed that Roemer was massively preferred to both the career criminal and the career bigot, and would easily win a head-to-head election against either. Unfortunately, in Louisiana every candidate from every party competes in the first round, and the top two then go on to a second - a so-called ‘jungle primary’. Vote splitting squeezed out the middle, and meant that Roemer was eliminated in the first round. Louisiana voters were left with only terrible options, in a run-off election mostly remembered for the proliferation of bumper stickers reading “Vote for the Crook. It’s Important.” We could look at this as a cultural problem, exposing widespread enthusiasm for bribery and racism that will take generations to overcome. But according to Aaron Hamlin, Executive Director of The Center for Election Science (CES), there’s a simple way to make sure we never have to elect someone hated by more than half the electorate: change how we vote. He advocates an alternative voting method called approval voting, in which you can vote for as many candidates as you want, not just one. That means that you can always support your honest favorite candidate, even when an election seems like a choice between the lesser of two evils. Full transcript, links to learn more, and summary of key points. If you'd like to meet Aaron he's doing events for CES in San Francisco, DC, Philadelphia, New York and Brooklyn over the next two weeks - RSVP here. While it might not seem sexy, this single change could transform politics. Approval voting is adored by voting researchers, who regard it as the best simple voting system available. Which do they regard as unquestionably the worst? First-past-the-post - precisely the disastrous system used and exported around the world by the US and UK. Aaron has a practical plan to spread approval voting across the US using ballot initiatives - and it just might be our best shot at making politics a bit less unreasonable. The Center for Election Science is a U.S. non-profit which aims to fix broken government by helping the world adopt smarter election systems. They recently received a $600,000 grant from the Open Philanthropy Project to scale up their efforts. 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.

Joseph Stalin had a life-extension program dedicated to making himself immortal. What if he had succeeded?  According to our last guest, Bryan Caplan, 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. ***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.*** 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.

Bryan Caplan’s claim in *The Case Against Education* is striking: education doesn’t teach people much, we use little of what we learn, and college is mostly about trying to seem smarter than other people - so the government should slash education funding. It’s a dismaying - almost profane - idea, and one people are inclined to dismiss out of hand. But having read the book, I have to admit that Bryan can point to a surprising amount of evidence in his favour. After all, imagine this dilemma: you can have either a Princeton education without a diploma, or a Princeton diploma without an education. Which is the bigger benefit of college - learning or convincing people you’re smart? It’s not so easy to say. For this interview, I searched for the best counterarguments I could find and challenged Bryan on what seem like his weakest or most controversial claims. Wouldn’t defunding education be especially bad for capable but low income students? If you reduced funding for education, wouldn’t that just lower prices, and not actually change the number of years people study? Is it really true that students who drop out in their final year of college earn about the same as people who never go to college at all? What about studies that show that extra years of education boost IQ scores? And surely the early years of primary school, when you learn reading and arithmetic, *are* useful even if college isn’t. I then get his advice on who should study, what they should study, and where they should study, if he’s right that college is mostly about separating yourself from the pack. Full transcript, links to learn more, and summary of key points. We then venture into some of Bryan’s other unorthodox views - like that immigration restrictions are a human rights violation, or that we should worry about the risk of global totalitarianism. Bryan is a Professor of Economics at George Mason University, and a blogger at *EconLog*. He is also the author of *Selfish Reasons to Have More Kids: Why Being a Great Parent is Less Work and More Fun Than You Think*, and *The Myth of the Rational Voter: Why Democracies Choose Bad Policies*. 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. In this lengthy interview, Rob and Bryan cover: * How worried should we be about China’s new citizen ranking system as a means of authoritarian rule? * How will advances in surveillance technology impact a government’s ability to rule absolutely? * Does more global coordination make us safer, or more at risk? * Should the push for open borders be a major cause area for effective altruism? * Are immigration restrictions a human rights violation? * Why aren’t libertarian-minded people more focused on modern slavery? * Should altruists work on criminal justice reform or reducing land use regulations? * What’s the greatest art form: opera, or Nicki Minaj? * What are the main implications of Bryan’s thesis for society? * Is elementary school more valuable than university? * What does Bryan think are the best arguments against his view? * Do years of education affect political affiliation? * How do people really improve themselves and their circumstances? * Who should and who shouldn’t do a masters or PhD? * The value of teaching foreign languages in school * Are there some skills people can develop that have wide applicability? Get this episode by subscribing: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

The debate around the impacts of artificial intelligence often centres on ‘superintelligence’ - a general intellect that is much smarter than the best humans, in practically every field. But according to Allan Dafoe - Assistant Professor of Political Science at Yale University - even if we stopped at today's AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including: * Mass labor displacement, unemployment, and inequality; * The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order; * Imagery intelligence and other mechanisms for revealing most of the ballistic missile-carrying submarines that countries rely on to be able to respond to nuclear attack; * Ubiquitous sensors and algorithms that can identify individuals through face recognition, leading to universal surveillance; * Autonomous weapons with an independent chain of command, making it easier for authoritarian regimes to violently suppress their citizens. Allan is Co-Director of the Governance of AI Program, at the Future of Humanity Institute within Oxford University. His goals have been to understand the causes of world peace and stability, which in the past has meant studying why war has declined, the role of reputation and honor as drivers of war, and the motivations behind provocation in crisis escalation. Full transcript, links to learn more, and summary of key points. His current focus is helping humanity safely navigate the invention of advanced artificial intelligence. I ask Allan: * What are the distinctive characteristics of artificial intelligence from a political or international governance point of view? * Is Allan’s work just a continuation of previous research on transformative technologies, like nuclear weapons? * How can AI be well-governed? * How should we think about the idea of arms races between companies or countries? * What would you say to people skeptical about the importance of this topic? * How urgently do we need to figure out solutions to these problems? When can we expect artificial intelligence to be dramatically better than today? * What’s the most urgent questions to deal with in this field? * What can people do if they want to get into the field? * Is there anything unusual that people can look for in themselves to tell if they're a good fit to do this kind of research? 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.

If we have a study on the impact of a social program in a particular place and time, how confident can we be that we’ll get a similar result if we study the same program again somewhere else? Dr Eva Vivalt is a lecturer in the Research School of Economics at the Australian National University. She compiled a huge database of impact evaluations in global development - including 15,024 estimates from 635 papers across 20 types of intervention - to help answer this question. Her finding: not confident at all. The typical study result differs from the average effect found in similar studies so far by almost 100%. That is to say, if all existing studies of a particular education program find that it improves test scores by 10 points - the next result is as likely to be negative or greater than 20 points, as it is to be between 0-20 points. She also observed that results from smaller studies done with an NGO - often pilot studies - were more likely to look promising. But when governments tried to implement scaled-up versions of those programs, their performance would drop considerably.  For researchers hoping to figure out what works and then take those programs global, these failures of generalizability and ‘external validity’ should be disconcerting. Is ‘evidence-based development’ writing a cheque its methodology can’t cash? Should this make us invest less in empirical research, or more to get actually reliable results? Or as some critics say, is interest in impact evaluation distracting us from more important issues, like national or macroeconomic reforms that can’t be easily trialled? We discuss this as well as Eva’s other research, including Y Combinator’s basic income study where she is a principal investigator. Full transcript, links to related papers, and highlights from the conversation. Links mentioned at the start of the show: * 80,000 Hours Job Board * 2018 Effective Altruism Survey **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.** Questions include: * What is the YC basic income study looking at, and what motivates it? * How do we get people to accept clean meat? * How much can we generalize from impact evaluations? * How much can we generalize from studies in development economics? * Should we be running more or fewer studies? * Do most social programs work or not? * The academic incentives around data aggregation * How much can impact evaluations inform policy decisions? * How often do people change their minds? * Do policy makers update too much or too little in the real world? * How good or bad are the predictions of experts? How does that change when looking at individuals versus the average of a group? * How often should we believe positive results? * What’s the state of development economics? * Eva’s thoughts on our article on social interventions * How much can we really learn from being empirical? * How much should we really value RCTs? * Is an Economics PhD overrated or underrated? Get this episode by subscribing to our podcast: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

Part 2 out now: #33 - Dr Anders Sandberg on what if we ended ageing, solar flares & the annual risk of nuclear war The universe is so vast, yet we don’t see any alien civilizations. If they exist, where are they? Oxford University’s Anders Sandberg has an original answer: they’re ‘sleeping’, and for a very compelling reason. Because of the thermodynamics of computation, the colder it gets, the more computations you can do. The universe is getting exponentially colder as it expands, and as the universe cools, one Joule of energy gets worth more and more. If they wait long enough this can become a 10,000,000,000,000,000,000,000,000,000,000x gain. So, if a civilization wanted to maximize its ability to perform computations – its best option might be to lie in wait for trillions of years. Why would a civilization want to maximise the number of computations they can do? Because conscious minds are probably generated by computation, so doing twice as many computations is like living twice as long, in subjective time. Waiting will allow them to generate vastly more science, art, pleasure, or almost anything else they are likely to care about. Full transcript, related links, and key quotes. But there’s no point waking up to find another civilization has taken over and used up the universe’s energy. So they’ll need some sort of monitoring to protect their resources from potential competitors like us. It’s plausible that this civilization would want to keep the universe’s matter concentrated, so that each part would be in reach of the other parts, even after the universe’s expansion. But that would mean changing the trajectory of galaxies during this dormant period. That we don’t see anything like that makes it more likely that these aliens have local outposts throughout the universe, and we wouldn’t notice them until we broke their rules. But breaking their rules might be our last action as a species. This ‘aestivation hypothesis’ is the invention of Dr Sandberg, a Senior Research Fellow at the Future of Humanity Institute at Oxford University, where he looks at low-probability, high-impact risks, predicting the capabilities of future technologies and very long-range futures for humanity. In this incredibly fun conversation we cover this and other possible explanations to the Fermi paradox, as well as questions like: * Should we want optimists or pessimists working on our most important problems? * How should we reason about low probability, high impact risks? * Would a galactic civilization want to stop the stars from burning? * What would be the best strategy for exploring and colonising the universe? * How can you stay coordinated when you’re spread across different galaxies? * What should humanity decide to do with its future? 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.

A researcher is working on creating a new virus – one more dangerous than any that exist naturally. They believe they’re being as careful as possible. After all, if things go wrong, their own life and that of their colleagues will be in danger. But if an accident is capable of triggering a global pandemic – hundreds of millions of lives might be at risk. How much additional care will the researcher actually take in the face of such a staggering death toll? In a new paper Dr Owen Cotton-Barratt, a Research Fellow at Oxford University’s Future of Humanity Institute, argues it’s impossible to expect them to make the correct adjustments. If they have an accident that kills 5 people – they’ll feel extremely bad. If they have an accident that kills 500 million people, they’ll feel even worse – but there’s no way for them to feel 100 million times worse. The brain simply doesn’t work that way. So, rather than relying on individual judgement, we could create a system that would lead to better outcomes: research liability insurance.  Links to learn more, summary and full transcript. Once an insurer assesses how much damage a particular project is expected to cause and with what likelihood – in order to proceed, the researcher would need to take out insurance against the predicted risk. In return, the insurer promises that they’ll pay out – potentially tens of billions of dollars – if things go really badly. This would force researchers think very carefully about the cost and benefits of their work – and incentivize the insurer to demand safety standards on a level that individual researchers can’t be expected to impose themselves. ***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.*** Owen is currently hiring for a selective, two-year research scholars programme at Oxford. In this wide-ranging conversation Owen and I also discuss: * Are academics wrong to value personal interest in a topic over its importance? * What fraction of research has very large potential negative consequences? * Why do we have such different reactions to situations where the risks are known and unknown? * The downsides of waiting for tenure to do the work you think is most important. * What are the benefits of specifying a vague problem like ‘make AI safe’ more clearly? * How should people balance the trade-offs between having a successful career and doing the most important work? * Are there any blind alleys we’ve gone down when thinking about AI safety? * Why did Owen give to an organisation whose research agenda he is skeptical of? 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.

How about this for a movie idea: a main character has to prevent a new contagious strain of Ebola spreading around the world. She’s the best of the best. So good in fact, that her work on early detection systems contains the strain at its source. Ten minutes into the movie, we see the results of her work – nothing happens. Life goes on as usual. She continues to be amazingly competent, and nothing continues to go wrong. Fade to black. Roll credits. If your job is to prevent catastrophes, success is when nobody has to pay attention to you. But without regular disasters to remind authorities why they hired you in the first place, they can’t tell if you’re actually achieving anything. And when budgets come under pressure you may find that success condemns you to the chopping block. Dr Tom Inglesby, Director of the Center for Health Security at the Johns Hopkins Bloomberg School of Public Health, worries this may be about to happen to the scientists working on the ‘Global Health Security Agenda’. In 2014 Ebola showed the world why we have to detect and contain new diseases before they spread, and that when it comes to contagious diseases the nations of the world sink or swim together. Fifty countries decided to work together to make sure all their health systems were up to the challenge. Back then Congress provided 5 years’ funding to help some of the world’s poorest countries build the basic health security infrastructure necessary to control pathogens before they could reach the US. Links to learn more, job opportunities, and full transcript. But with Ebola fading from public memory and no recent tragedies to terrify us, Congress may not renew that funding and the project could fall apart. (Learn more about how you can help: http://www.nti.org/analysis/articles/protect-us-investments-global-health-security/ ) But there are positive signs as well - the center Inglesby leads recently received a $16 million grant from the Open Philanthropy Project to further their work preventing global catastrophes. It also runs the [Emerging Leaders in Biosecurity Fellowship](http://www.centerforhealthsecurity.org/our-work/emergingbioleaders/) to train the next generation of biosecurity experts for the US government. And Inglesby regularly testifies to Congress on the threats we all face and how to address them. In this in-depth interview we try to provide concrete guidance for listeners who want to to pursue a career in health security. Some of the topics we cover include: * Should more people in medicine work on security? * What are the top jobs for people who want to improve health security and how do they work towards getting them? * What people can do to protect funding for the Global Health Security Agenda. * Should we be more concerned about natural or human caused pandemics? Which is more neglected? * Should we be allocating more attention and resources to global catastrophic risk scenarios? * Why are senior figures reluctant to prioritize one project or area at the expense of another? * What does Tom think about the idea that in the medium term, human-caused pandemics will pose a far greater risk than natural pandemics, and so we should focus on specific counter-measures? * Are the main risks and solutions understood, and it’s just a matter of implementation? Or is the principal task to identify and understand them? * How is the current US government performing in these areas? * Which agencies are empowered to think about low probability high magnitude events? And more... Get this episode by subscribing: search for '80,000 Hours' in your podcasting app. The 80,000 Hours Podcast is produced by Keiran Harris.

First, decide on the type of animal. Next, pick the cell type. Then take a small, painless biopsy, and put the cells in a solution that makes them feel like they’re still in the body. Once the cells are in this comfortable state, they'll proliferate. One cell becomes two, two becomes four, four becomes eight, and so on. Continue until you have enough cells to make a burger, a nugget, a sausage, or a piece of bacon, then concentrate them until they bind into solid meat. It's all surprisingly straightforward in principle according to Marie Gibbons​, a research fellow with The Good Food Institute, who has been researching how to improve this process at Harvard Medical School. We might even see clean meat sold commercially within a year. The real technical challenge is developing large bioreactors and cheap solutions so that we can make huge volumes and drive down costs. This interview covers the science and technology involved at each stage of clean meat production, the challenges and opportunities that face cutting-edge researchers like Marie, and how you could become one of them. Full transcript, key points, and links to learn more. Marie’s research focuses on turkey cells. But as she explains, with clean meat the possibilities extend well beyond those of traditional meat. Chicken, cow, pig, but also panda - and even dinosaurs could be on the menus of the future. Today’s episode is hosted by Natalie Cargill, a barrister in London with a background in animal advocacy. Natalie and Marie also discuss: * Why Marie switched from being a vet to developing clean meat * For people who want to dedicate themselves to animal welfare, how does working in clean meat fare compared to other career options? How can people get jobs in the area? * How did this become an established field? * How important is the choice of animal species and cell type in this process? * What are the biggest problems with current production methods? * Is this kind of research best done in an academic setting, a commercial setting, or a balance between the two? * How easy will it be to get consumer acceptance? * How valuable would extra funding be for cellular agriculture? * Can we use genetic modification to speed up the process? * Is it reasonable to be sceptical of the possibility of clean meat becoming financially competitive with traditional meat any time in the near future? 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.

On February 2, 1685, England’s King Charles II was struck by a sudden illness. Fortunately his physicians were the best of the best. To reassure the public they kept them abreast of the King’s treatment regimen. King Charles was made to swallow a toxic metal; had blistering agents applied to his scalp; had pigeon droppings attached to his feet; was prodded with a red-hot poker; given forty drops of ooze from “the skull of a man that was never buried”; and, finally, had crushed stones from the intestines of an East Indian goat forced down his throat. Sadly, despite these heroic efforts, he passed away the following week.  Why did the doctors go this far? Prof, Robin Hanson, Associate Professor of Economics at George Mason University suspects that on top of any medical beliefs they also had a hidden motive: it needed to be clear, to the king and the public, that the physicians cared enormously about saving His Royal Majesty. Only by going ‘all out’ would they be protected against accusations of negligence should the King die.  Full transcript, summary, and links to articles discussed in the show. If you believe Hanson, the same desire to be seen to care about our family and friends explains much of what’s perverse about our medical system today. And not just medicine - Robin thinks we’re mostly kidding ourselves when we say our charities exist to help others, our schools exist to educate students and our politics are about choosing wise policies.  So important are hidden motives for navigating our social world that we have to deny them to ourselves, lest we accidentally reveal them to others. Robin is a polymath economist, who has come up with surprising and novel insight in a range of fields including psychology, politics and futurology. In this extensive episode we discuss his latest book with Kevin Simler, *The Elephant in the Brain: Hidden Motives in Everyday Life*, but also: * What was it like being part of a competitor group to the ‘World Wide Web’, and being beaten to the post? * If people aren’t going to school to learn, what’s education all about? * What split brain patients tell us about our ability to justify anything * The hidden motivations that shape religions * Why we choose the friends we do * Why is our attitude to medicine mysterious? * What would it look like if people were focused on doing as much good as possible?  * Are we better off donating now, when we’re older, or even wait until well after our deaths? * How much of the behavior of ‘effective altruists’ can we assume is genuinely motivated by wanting to do as much good as possible? * What does Robin mean when he refers to effective altruism as a youth movement? Is that a good or bad thing? * And much more...

How honest should we be? How helpful? How friendly? If our society claims to value honesty, for instance, but in reality accepts an awful lot of lying – should we go along with those lax standards? Or, should we attempt to set a new norm for ourselves? Dr Stefan Schubert, a researcher at the Social Behaviour and Ethics Lab at Oxford University, has been modelling this in the context of the effective altruism community. He thinks people trying to improve the world should hold themselves to very high standards of integrity, because their minor sins can impose major costs on the thousands of others who share their goals. Summary, related links and full transcript. In addition, when a norm is uniquely important to our situation, we should be willing to question society and come up with something different and hopefully better. But in other cases, we can be better off sticking with whatever our culture expects, both to save time, avoid making mistakes, and ensure others can predict our behaviour. In this interview Stefan offers a range of views on the projects and culture that make up ‘effective altruism’ - including where it’s going right and where it’s going wrong. Stefan did his PhD in formal epistemology, before moving on to a postdoc in political rationality at the London School of Economics, while working on advocacy projects to improve truthfulness among politicians. At the time the interview was recorded Stefan was a researcher at the Centre for Effective Altruism in Oxford. We discuss: * Should we trust our own judgement more than others’? * How hard is it to improve political discourse? * What should we make of well-respected academics writing articles that seem to be completely misinformed? * How is effective altruism (EA) changing? What might it be doing wrong? * How has Stefan’s view of EA changed? * Should EA get more involved in politics, or steer clear of it? Would it be a bad idea for a talented graduate to get involved in party politics? * How much should we cooperate with those with whom we have disagreements? * What good reasons are there to be inconsiderate? * Should effective altruism potentially focused on a more narrow range of problems? *The 80,000 Hours podcast is produced by Keiran Harris.* **If you subscribe to our podcast, you can listen at leisure on your phone, speed up the conversation if you like, and get notified about future episodes. You can do so by searching ‘80,000 Hours’ wherever you get your podcasts.**

Want to help steer the 21st century’s most transformative technology? First complete an undergrad degree in computer science and mathematics. Prioritize harder courses over easier ones. Publish at least one paper before you apply for a PhD. Find a supervisor who’ll have a lot of time for you. Go to the top conferences and meet your future colleagues. And finally, get yourself hired. That’s Dr Jan Leike’s advice on how to join him as a Research Scientist at DeepMind, the world’s leading AI team. Jan is also a Research Associate at the Future of Humanity Institute at the University of Oxford, and his research aims to make machine learning robustly beneficial. His current focus is getting AI systems to learn good ‘objective functions’ in cases where we can’t easily specify the outcome we actually want. Full transcript, summary and links to learn more. How might you know you’re a good fit for research? Jan says to check whether you get obsessed with puzzles and problems, and find yourself mulling over questions that nobody knows the answer to. To do research in a team you also have to be good at clearly and concisely explaining your new ideas to other people. We also discuss: * Where Jan's views differ from those expressed by Dario Amodei in episode 3 * Why is AGI safety one of the world’s most pressing problems? * Common misconceptions about AI * What are some of the specific things DeepMind is researching? * The ways in which today’s AI systems can fail * What are the best techniques available today for teaching an AI the right objective function? * What’s it like to have some of the world’s greatest minds as coworkers? * Who should do empirical research and who should do theoretical research * What’s the DeepMind application process like? * The importance of researchers being comfortable with the unknown. *The 80,000 Hours Podcast is produced by Keiran Harris.*

How people kill themselves varies enormously depending on which means are most easily available. In the United States, suicide by firearm stands out. In Hong Kong, where most people live in high rise buildings, jumping from a height is more common. And in some countries in Asia and Africa with many poor agricultural communities, the leading means is drinking pesticide. There’s a good chance you’ve never heard of this issue before. And yet, of the 800,000 people who kill themselves globally each year 20% die from pesticide self-poisoning. Full transcript, summary and links to articles discussed in today's show. Research suggests most people who try to kill themselves with pesticides reflect on the decision for less than 30 minutes, and that less than 10% of those who don't die the first time around will try again. Unfortunately, the fatality rate from pesticide ingestion is 40% to 70%. Having such dangerous chemicals near people's homes is therefore an enormous public health issue not only for the direct victims, but also the partners and children they leave behind. Fortunately researchers like Dr Leah Utyasheva have figured out a very cheap way to massively reduce pesticide suicide rates. In this episode, Leah and I discuss: * How do you prevent pesticide suicide and what’s the evidence it works? * How do you know that most people attempting suicide don’t want to die? * What types of events are causing people to have the crises that lead to attempted suicide? * How much money does it cost to save a life in this way? * How do you estimate the probability of getting law reform passed in a particular country? * Have you generally found politicians to be sympathetic to the idea of banning these pesticides? What are their greatest reservations? * The comparison of getting policy change rather than helping person-by-person * The importance of working with locals in places like India and Nepal, rather than coming in exclusively as outsiders * What are the benefits of starting your own non-profit versus joining an existing org and persuading them of the merits of the cause? * Would Leah in general recommend starting a new charity? Is it more exciting than it is scary? * Is it important to have an academic leading this kind of work? * How did The Centre for Pesticide Suicide Prevention get seed funding? * How does the value of saving a life from suicide compare to savings someone from malaria * Leah’s political campaigning for the rights of vulnerable groups in Eastern Europe  * What are the biggest downsides of human rights work?

The Green Revolution averted mass famine during the 20th century. The contraceptive pill gave women unprecedented freedom in planning their own lives. Both are widely recognised as scientific breakthroughs that transformed the world. But few know that those breakthroughs only happened when they did because of a philanthropist willing to take a risky bet on a new idea. Today’s guest, Holden Karnofsky, has been looking for philanthropy’s biggest success stories because he’s Executive Director of the Open Philanthropy Project, which gives away over $100 million per year - and he’s hungry for big wins. Full transcript, related links, job opportunities and summary of the interview. In the 1940s, poverty reduction overseas was not a big priority for many. But the Rockefeller Foundation decided to fund agricultural scientists to breed much better crops for the developing world - thereby massively increasing their food production. In the 1950s, society was a long way from demanding effective birth control. Activist Margaret Sanger had the idea for the pill, and endocrinologist Gregory Pincus the research team – but they couldn’t proceed without a $40,000 research check from biologist and women’s rights activist Katherine McCormick. In both cases, it was philanthropists rather than governments that led the way. The reason, according to Holden, is that while governments have enormous resources, they’re constrained by only being able to fund reasonably sure bets. Philanthropists can transform the world by filling the gaps government leaves - but to seize that opportunity they have to hire outstanding researchers, think long-term and be willing to fail most of the time. Holden knows more about this type of giving than almost anyone. As founder of GiveWell and then the Open Philanthropy Project, he has been working feverishly since 2007 to find outstanding giving opportunities. This practical experience has made him one of the most influential figures in the development of the school of thought that has come to be known as effective altruism. We’ve recorded this episode now because [the Open Philanthropy Project is hiring](https://www.openphilanthropy.org/get-involved/jobs) for a large number of positions, which we think would allow the right person to have a very large positive influence on the world. They’re looking for a large number of entry lever researchers to train up, 3 specialist researchers into potential risks from advanced artificial intelligence, as well as a Director of Operations, Operations Associate and General Counsel. But the conversation goes well beyond specifics about these jobs. We also discuss: * How did they pick the problems they focus on, and how will they change over time? * What would Holden do differently if he were starting Open Phil again today? * What can we learn from the history of philanthropy? * What makes a good Program Officer. * The importance of not letting hype get ahead of the science in an emerging field. * The importance of honest feedback for philanthropists, and the difficulty getting it. * How do they decide what’s above the bar to fund, and when it’s better to hold onto the money? * How philanthropic funding can most influence politics. * What Holden would say to a new billionaire who wanted to give away most of their wealth. * Why Open Phil is building a research field around the safe development of artificial intelligence * Why they invested in OpenAI. * Academia’s faulty approach to answering practical questions. * What potential utopias do people most want, according to opinion polls? Keiran Harris helped produce today’s episode.

Before the US Civil War, it was easier for the North to morally oppose slavery. Why? Because unlike the South they weren’t profiting much from its existence. The fight for abolition was partly won because many no longer saw themselves as having a selfish stake in its continuation. Bruce Friedrich, executive director of The Good Food Institute (GFI), thinks the same may be true in the fight against speciesism. 98% of people currently eat meat. But if eating meat stops being part of most people’s daily lives -- it should be a lot easier to convince them that farming practices are just as cruel as they look, and that the suffering of these animals really matters. Full transcript, related links, job opportunities and summary of the interview. That’s why GFI is “working with scientists, investors, and entrepreneurs” to create plant-based meat, dairy and eggs as well as clean meat alternatives to animal products. In 2016, Animal Charity Evaluators named GFI one of its recommended charities. In this interview I’m joined by my colleague Natalie Cargill, and we ask Bruce about: * What’s the best meat replacement product out there right now? * How effective is meat substitute research for people who want to reduce animal suffering as much as possible? * When will we get our hands on clean meat? And why does Bruce call it clean meat, rather than in vitro meat or cultured meat? * What are the challenges of producing something structurally identical to meat? * Can clean meat be healthier than conventional meat? * Do plant-based alternatives have a better shot at success than clean meat? * Is there a concern that, even if the product is perfect, people still won’t eat it? Why might that happen? * What’s it like being a vegan in a family made up largely of hunters and meat-eaters? * What kind of pushback should be expected from the meat industry? Keiran Harris helped produce today’s episode.

Rogue elements within a state’s security forces enrich dozens of kilograms of uranium. It’s then assembled into a crude nuclear bomb. The bomb is transported on a civilian aircraft to Washington D.C, and loaded onto a delivery truck. The truck is driven by an American citizen midway between the White House and the Capitol Building. The driver casually steps out of the vehicle, and detonates the weapon. There are more than 80,000 instant deaths. There are also at least 100,000 seriously wounded, with nowhere left to treat them. Full blog post about this episode, including a transcript, summary and links to resources mentioned in the show It’s likely that one of those immediately killed would be Samantha Pitts-Kiefer, who works only one block away from the White House. Samantha serves as Senior Director of The Global Nuclear Policy Program at the Nuclear Threat Initiative, and warns that the chances of a nuclear terrorist attack are alarmingly high. Terrorist groups have expressed a desire for nuclear weapons, and the material required to build those weapons is scattered throughout the world at a diverse range of sites – some of which lack the necessary security. When you combine the massive death toll with the accompanying social panic and economic disruption – the consequences of a nuclear 9/11 would be a disasterare almost unthinkable. And yet, Samantha reminds us – we must confront the possibility. Clearly, this is far from the only nuclear nightmare. We also discuss: * In the case of nuclear war, what fraction of the world's population would die? * What is the biggest nuclear threat? * How concerned should we be about North Korea? * How often has the world experienced nuclear near misses? * How might a conflict between India and Pakistan escalate to the nuclear level? * How quickly must a president make a decision in the result of a suspected first strike? * Are global sources of nuclear material safely secured? * What role does cyber security have in preventing nuclear disasters? * How can we improve relations between nuclear armed states? * What do you think about the campaign for complete nuclear disarmament? * If you could tell the US government to do three things, what are the key priorities today? * Is it practical to get members of congress to pay attention to nuclear risks? * Could modernisation of nuclear weapons actually make the world safer?

Ofir Reich started out doing math in the military, before spending 8 years in tech startups - but then made a sharp turn to become a data scientist focussed on helping the global poor. At UC Berkeley’s Center for Effective Global Action he helps prevent tax evasion by identifying fake companies in India, enable Afghanistan to pay its teachers electronically, and raise yields for Ethiopian farmers by messaging them when local conditions make it ideal to apply fertiliser. Or at least that’s the hope - he’s also working on ways to test whether those interventions actually work. Full post about this episode, including a transcript and relevant links to learn more. Why dedicate his life to helping the global poor? Ofir sees little moral difference between harming people and failing to help them. After all, if you had to press a button to keep all of your money from going to charity, and you pressed that button, would that be an action, or an inaction? Is there even an answer? After reflecting on cases like this, he decided that to not engage with a problem is an active choice, one whose consequences he is just as morally responsible for as if he were directly involved. On top of his life philosophy we also discuss: * The benefits of working in a top academic environment * How best to start a career in global development * Are RCTs worth the money? Should we focus on big picture policy change instead? Or more economic theory? * How the delivery standards of nonprofits compare to top universities * Why he doesn’t enjoy living in the San Francisco bay area * How can we fix the problem of most published research being false? * How good a career path is data science? * How important is experience in development versus technical skills? * How he learned much of what he needed to know in the army * How concerned should effective altruists be about burnout? Keiran Harris helped produce today’s episode.

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 and links to articles and career guides 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 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 free, one-on-one career advice We’ve helped hundreds of people compare their options, get introductions, and find high impact jobs. If you want to work on global priorities research or other important questions in academia, find out if our coaching can help you.

In the 40s and 50s neoliberalism was a fringe movement within economics. But by the 80s it had become a dominant school of thought in public policy, and achieved major policy changes across the English speaking world. How did this happen? In part because its leaders invested heavily in training academics to study and develop their ideas. Whether you think neoliberalism was good or bad, its history demonstrates the impact building a strong intellectual base within universities can have. Michelle Hutchinson is working to get a different set of ideas a hearing in academia by setting up the Global Priorities Institute (GPI) at Oxford University. The Institute, which is currently hiring for three roles, aims to bring together outstanding philosophers and economists to research how to most improve the world. The hope is that it will spark widespread academic engagement with effective altruist thinking, which will hone the ideas and help them gradually percolate into society more broadly. Link to the full blog post about this episode including transcript and links to learn more Its research agenda includes questions like: * How do we compare the good done by focussing on really different types of causes? * How does saving lives actually affect the world relative to other things we could do? * What are the biggest wins governments should be focussed on getting? Before moving to GPI, Michelle was the Executive Director of Giving What We Can and a founding figure of the effective altruism movement. She has a PhD in Applied Ethics from Oxford on prioritization and global health. We discuss: * What is global priorities research and why does it matter? * How is effective altruism seen in academia? Is it important to convince academics of the value of your work, or is it OK to ignore them? * Operating inside a university is quite expensive, so is it even worth doing? Who can pay for this kind of thing? * How hard is it to do something innovative inside a university? How serious are the administrative and other barriers? * Is it harder to fundraise for a new institute, or hire the right people? * Have other social movements benefitted from having a prominent academic arm? * How can people prepare themselves to get research roles at a place like GPI? * Many people want to have roles doing this kind of research. How many are actually cut out for it? What should those who aren’t do instead? * What are the odds of the Institute’s work having an effect on the real world? Get free, one-on-one career advice We’ve helped hundreds of people compare their options, get introductions, and find high impact jobs. If you want to work on global priorities research or other important questions in academia, find out if our coaching can help you.

Prof Philip Tetlock is a social science legend. Over forty years he has researched whose predictions we can trust, whose we can’t and why - and developed methods that allow all of us to be better at predicting the future. After the Iraq WMDs fiasco, the US intelligence services hired him to figure out how to ensure they’d never screw up that badly again. The result of that work – Superforecasting – was a media sensation in 2015. Full transcript, brief summary, apply for coaching and links to learn more. It described Tetlock’s Good Judgement Project, which found forecasting methods so accurate they beat everyone else in open competition, including thousands of people in the intelligence services with access to classified information. Today he’s working to develop the best forecasting process ever, by combining top human and machine intelligence in the Hybrid Forecasting Competition, which you can sign up and participate in. We start by describing his key findings, and then push to the edge of what is known about how to foresee the unforeseeable: * Should people who want to be right just adopt the views of experts rather than apply their own judgement? * Why are Berkeley undergrads worse forecasters than dart-throwing chimps? * Should I keep my political views secret, so it will be easier to change them later? * How can listeners contribute to his latest cutting-edge research? * What do we know about our accuracy at predicting low-probability high-impact disasters? * Does his research provide an intellectual basis for populist political movements? * Was the Iraq War caused by bad politics, or bad intelligence methods? * What can we learn about forecasting from the 2016 election? * Can experience help people avoid overconfidence and underconfidence? * When does an AI easily beat human judgement? * Could more accurate forecasting methods make the world more dangerous? * How much does demographic diversity line up with cognitive diversity? * What are the odds we’ll go to war with China? * Should we let prediction tournaments run most of the government? Listen to it. Get free, one-on-one career advice. Want to work on important social science research like Tetlock? We’ve helped hundreds of people compare their options and get introductions. Find out if our coaching can help you.

What if you knew that ducks were being killed with pitchforks? Rabbits dumped alive into containers? Or pigs being strangled with forklifts? Would you be willing to go undercover to expose the crime? That’s a real question that confronts volunteers at Animal Equality (AE). In this episode we speak to Sharon Nunez and Jose Valle, who founded AE in 2006 and then grew it into a multi-million dollar international animal rights organisation. They’ve been chosen as one of the most effective animal protection orgs in the world by Animal Charity Evaluators for the last 3 consecutive years. Blog post about the episode, including links and full transcript. A related previous episode, strongly recommended: Lewis Bollard on how to end factory farming as soon as possible. In addition to undercover investigations AE has also designed a 3D virtual-reality farm experience called iAnimal360. People get to experience being trapped in a cage – in a room designed to kill then - and can’t just look away. How big an impact is this having on users? Sharon Nuñez and Jose Valle also tackle: * How do they track their goals and metrics week to week? * How much does an undercover investigation cost? * Why don’t people donate more to factory farmed animals, given that they’re the vast majority of animals harmed directly by humans? * How risky is it to attempt to build a career in animal advocacy? * What led to a change in their focus from bullfighting in Spain to animal farming? * How does working with governments or corporate campaigns compare with early strategies like creating new vegans/vegetarians? * Has their very rapid growth been difficult to handle? * What should our listeners study or do if they want to work in this area? * How can we get across the message that horrific cases are a feature - not a bug - of factory farming? * Do the owners or workers of factory farms ever express shame at what they do?

In both rich and poor countries, government policy is often based on no evidence at all and many programs don’t work. This has particularly harsh effects on the global poor - in some countries governments only spend $100 on each citizen a year so they can’t afford to waste a single dollar. Enter MIT’s Poverty Action Lab (J-PAL). Since 2003 they’ve conducted experiments to figure out what policies actually help recipients, and then tried to get them implemented by governments and non-profits. Claire Walsh leads J-PAL’s Government Partnership Initiative, which works to evaluate policies and programs in collaboration with developing world governments, scale policies that have been shown to work, and generally promote a culture of evidence-based policymaking. Summary, links to career opportunities and topics discussed in the show. We discussed (her views only, not J-PAL’s): * How can they get evidence backed policies adopted? Do politicians in the developing world even care whether their programs actually work? Is the norm evidence-based policy, or policy-based evidence? * Is evidence-based policy an evidence-based strategy itself? * Which policies does she think would have a particularly large impact on human welfare relative to their cost? * How did she come to lead one of J-PAL’s departments at 29? * How do you evaluate the effectiveness of energy and environment programs (Walsh’s area of expertise), and what are the standout approaches in that area? * 80,000 Hours has warned people about the downsides of starting your career in a non-profit. Walsh started her career in a non-profit and has thrived, so are we making a mistake? * Other than J-PAL, what are the best places to work in development? What are the best subjects to study? Where can you go network to break into the sector? * Is living in poverty as bad as we think? And plenty of other things besides. We haven’t run an RCT to test whether this episode will actually help your career, but I suggest you listen anyway. Trust my intuition on this one.