#213 – Will MacAskill on AI causing a “century in a decade” – and how we're completely unprepared

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.

17 Feb 201956min

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.

8 Feb 20192h 44min

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.

29 Jan 20192h 31min

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.

27 Des 20182h 57min

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.

20 Des 20181h 35min

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.

22 Nov 20183h 15min

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.

2 Nov 20182h 4min

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.

23 Okt 20182h 49min