"If we carry on looking at these industrialised economies, not thinking about what it is they're actually doing and what the potential of this is, you can make an argument that, yes, rates of growth are slowing, the rate of innovation is slowing. But it isn't. What we're doing is creating wildly new technologies: basically producing what is nothing less than an evolutionary change in what it means to be a human being. But this has not yet spilled over into the kind of growth that we have accustomed ourselves to in the fossil-fuel industrial era. That is about to hit us in a big way." — Ian MorrisIn today’s episode, host Rob Wiblin speaks with repeat guest Ian Morris about what big-picture history says about the likely impact of machine intelligence. Links to learn more, summary and full transcript.They cover:Some crazy anomalies in the historical record of civilisational progressWhether we should think about technology from an evolutionary perspectiveWhether we ought to expect war to make a resurgence or continue dying outWhy we can't end up living like The JetsonsWhether stagnation or cyclical recurring futures seem very plausibleWhat it means that the rate of increase in the economy has been increasingWhether violence is likely between humans and powerful AI systemsThe most likely reasons for Rob and Ian to be really wrong about all of thisHow professional historians react to this sort of talkThe future of Ian’s workPlenty moreChapters:Cold open (00:00:00)Rob’s intro (00:01:27)Why we should expect the future to be wild (00:04:08)How historians have reacted to the idea of radically different futures (00:21:20)Why we won’t end up in The Jetsons (00:26:20)The rise of machine intelligence (00:31:28)AI from an evolutionary point of view (00:46:32)Is violence likely between humans and powerful AI systems? (00:59:53)Most troubling objections to this approach in Ian’s view (01:28:20)Confronting anomalies in the historical record (01:33:10)The cyclical view of history (01:56:11)Is stagnation plausible? (02:01:38)The limit on how long this growth trend can continue (02:20:57)The future of Ian’s work (02:37:17)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuireTranscriptions: Katy Moore

"There have been literally thousands of years of breeding and living with animals to optimise these kinds of problems. But because we're just so early on with alternative proteins and there's so much white space, it's actually just really exciting to know that we can keep on innovating and being far more efficient than this existing technology — which, fundamentally, is just quite inefficient. You're feeding animals a bunch of food to then extract a small fraction of their biomass to then eat that.Animal agriculture takes up 83% of farmland, but produces just 18% of food calories. So the current system just is so wasteful. And the limiting factor is that you're just growing a bunch of food to then feed a third of the world's crops directly to animals, where the vast majority of those calories going in are lost to animals existing." — Seren KellLinks to learn more, summary and full transcript.In today’s episode, host Luisa Rodriguez interviews Seren Kell — Senior Science and Technology Manager at the Good Food Institute Europe — about making alternative proteins as tasty, cheap, and convenient as traditional meat, dairy, and egg products.They cover:The basic case for alternative proteins, and why they’re so hard to makeWhy fermentation is a surprisingly promising technology for creating delicious alternative proteins The main scientific challenges that need to be solved to make fermentation even more usefulThe progress that’s been made on the cultivated meat front, and what it will take to make cultivated meat affordableHow GFI Europe is helping with some of these challengesHow people can use their careers to contribute to replacing factory farming with alternative proteinsThe best part of Seren’s jobPlenty moreChapters:Cold open (00:00:00)Luisa’s intro (00:01:08)The interview begins (00:02:22)Why alternative proteins? (00:02:36)What makes alternative proteins so hard to make? (00:11:30)Why fermentation is so exciting (00:24:23)The technical challenges involved in scaling fermentation (00:44:38)Progress in cultivated meat (01:06:04)GFI Europe’s work (01:32:47)Careers (01:45:10)The best part of Seren’s job (01:50:07)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic Armstrong and Milo McGuireAdditional content editing: Luisa Rodriguez and Katy MooreTranscriptions: Katy Moore

"If you and I and 100 other people were on the first ship that was going to go settle Mars, and were going to build a human civilisation, and we have to decide what that government looks like, and we have all of the technology available today, how do we think about choosing a subset of that design space? That space is huge and it includes absolutely awful things, and mixed-bag things, and maybe some things that almost everyone would agree are really wonderful, or at least an improvement on the way that things work today. But that raises all kinds of tricky questions. My concern is that if we don't approach the evolution of collective decision making and government in a deliberate way, we may end up inadvertently backing ourselves into a corner, where we have ended up on some slippery slope -- and all of a sudden we have, let's say, autocracies on the global stage are strengthened relative to democracies." — Tantum CollinsIn today’s episode, host Rob Wiblin gets the rare chance to interview someone with insider AI policy experience at the White House and DeepMind who’s willing to speak openly — Tantum Collins.Links to learn more, highlights, and full transcript.They cover:How AI could strengthen government capacity, and how that's a double-edged swordHow new technologies force us to confront tradeoffs in political philosophy that we were previously able to pretend weren't thereTo what extent policymakers take different threats from AI seriouslyWhether the US and China are in an AI arms race or notWhether it's OK to transform the world without much of the world agreeing to itThe tyranny of small differences in AI policyDisagreements between different schools of thought in AI policy, and proposals that could unite themHow the US AI Bill of Rights could be improvedWhether AI will transform the labour market, and whether it will become a partisan political issueThe tensions between the cultures of San Francisco and DC, and how to bridge the divide between themWhat listeners might be able to do to help with this whole messPanpsychismPlenty moreChapters:Cold open (00:00:00)Rob's intro (00:01:00)The interview begins (00:04:01)The risk of autocratic lock-in due to AI (00:10:02)The state of play in AI policymaking (00:13:40)China and AI (00:32:12)The most promising regulatory approaches (00:57:51)Transforming the world without the world agreeing (01:04:44)AI Bill of Rights (01:17:32)Who’s ultimately responsible for the consequences of AI? (01:20:39)Policy ideas that could appeal to many different groups (01:29:08)Tension between those focused on x-risk and those focused on AI ethics (01:38:56)Communicating with policymakers (01:54:22)Is AI going to transform the labour market in the next few years? (01:58:51)Is AI policy going to become a partisan political issue? (02:08:10)The value of political philosophy (02:10:53)Tantum’s work at DeepMind (02:21:20)CSET (02:32:48)Career advice (02:35:21)Panpsychism (02:55:24)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"Now, the really interesting question is: How much is there an attacker-versus-defender advantage in this kind of advanced future? Right now, if somebody's sitting on Mars and you're going to war against them, it's very hard to hit them. You don't have a weapon that can hit them very well. But in theory, if you fire a missile, after a few months, it's going to arrive and maybe hit them, but they have a few months to move away. Distance actually makes you safer: if you spread out in space, it's actually very hard to hit you. So it seems like you get a defence-dominant situation if you spread out sufficiently far. But if you're in Earth orbit, everything is close, and the lasers and missiles and the debris are a terrible danger, and everything is moving very fast. So my general conclusion has been that war looks unlikely on some size scales but not on others." — Anders SandbergIn today’s episode, host Rob Wiblin speaks with repeat guest and audience favourite Anders Sandberg about the most impressive things that could be achieved in our universe given the laws of physics.Links to learn more, summary and full transcript.They cover:The epic new book Anders is working on, and whether he’ll ever finish itWhether there's a best possible world or we can just keep improving foreverWhat wars might look like if the galaxy is mostly settledThe impediments to AI or humans making it to other starsHow the universe will end a million trillion years in the futureWhether it’s useful to wonder about whether we’re living in a simulationThe grabby aliens theoryWhether civilizations get more likely to fail the older they getThe best way to generate energy that could ever existBlack hole bombsWhether superintelligence is necessary to get a lot of valueThe likelihood that life from elsewhere has already visited EarthAnd plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"Imagine a fast-spreading respiratory HIV. It sweeps around the world. Almost nobody has symptoms. Nobody notices until years later, when the first people who are infected begin to succumb. They might die, something else debilitating might happen to them, but by that point, just about everyone on the planet would have been infected already. And then it would be a race. Can we come up with some way of defusing the thing? Can we come up with the equivalent of HIV antiretrovirals before it's too late?" — Kevin EsveltIn today’s episode, host Luisa Rodriguez interviews Kevin Esvelt — a biologist at the MIT Media Lab and the inventor of CRISPR-based gene drive — about the threat posed by engineered bioweapons.Links to learn more, summary and full transcript.They cover:Why it makes sense to focus on deliberately released pandemicsCase studies of people who actually wanted to kill billions of humansHow many people have the technical ability to produce dangerous virusesThe different threats of stealth and wildfire pandemics that could crash civilisationThe potential for AI models to increase access to dangerous pathogensWhy scientists try to identify new pandemic-capable pathogens, and the case against that researchTechnological solutions, including UV lights and advanced PPEUsing CRISPR-based gene drive to fight diseases and reduce animal sufferingAnd plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon MonsourAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Today’s release is a reading of our Great power conflict problem profile, written and narrated by Stephen Clare.If you want to check out the links, footnotes and figures in today’s article, you can find those here.And if you like this article, you might enjoy a couple of related episodes of this podcast:#128 – Chris Blattman on the five reasons wars happen#140 – Bear Braumoeller on the case that war isn’t in declineAudio mastering and editing for this episode: Dominic ArmstrongAudio Engineering Lead: Ben CordellProducer: Keiran Harris

Effective altruism is associated with the slogan "do the most good." On one level, this has to be unobjectionable: What could be bad about helping people more and more?But in today's interview, Toby Ord — moral philosopher at the University of Oxford and one of the founding figures of effective altruism — lays out three reasons to be cautious about the idea of maximising the good that you do. He suggests that rather than “doing the most good that we can,” perhaps we should be happy with a more modest and manageable goal: “doing most of the good that we can.”Links to learn more, summary and full transcript.Toby was inspired to revisit these ideas by the possibility that Sam Bankman-Fried, who stands accused of committing severe fraud as CEO of the cryptocurrency exchange FTX, was motivated to break the law by a desire to give away as much money as possible to worthy causes.Toby's top reason not to fully maximise is the following: if the goal you're aiming at is subtly wrong or incomplete, then going all the way towards maximising it will usually cause you to start doing some very harmful things.This result can be shown mathematically, but can also be made intuitive, and may explain why we feel instinctively wary of going “all-in” on any idea, or goal, or way of living — even something as benign as helping other people as much as possible.Toby gives the example of someone pursuing a career as a professional swimmer. Initially, as our swimmer takes their training and performance more seriously, they adjust their diet, hire a better trainer, and pay more attention to their technique. While swimming is the main focus of their life, they feel fit and healthy and also enjoy other aspects of their life as well — family, friends, and personal projects.But if they decide to increase their commitment further and really go all-in on their swimming career, holding back nothing back, then this picture can radically change. Their effort was already substantial, so how can they shave those final few seconds off their racing time? The only remaining options are those which were so costly they were loath to consider them before.To eke out those final gains — and go from 80% effort to 100% — our swimmer must sacrifice other hobbies, deprioritise their relationships, neglect their career, ignore food preferences, accept a higher risk of injury, and maybe even consider using steroids.Now, if maximising one's speed at swimming really were the only goal they ought to be pursuing, there'd be no problem with this. But if it's the wrong goal, or only one of many things they should be aiming for, then the outcome is disastrous. In going from 80% to 100% effort, their swimming speed was only increased by a tiny amount, while everything else they were accomplishing dropped off a cliff.The bottom line is simple: a dash of moderation makes you much more robust to uncertainty and error.As Toby notes, this is similar to the observation that a sufficiently capable superintelligent AI, given any one goal, would ruin the world if it maximised it to the exclusion of everything else. And it follows a similar pattern to performance falling off a cliff when a statistical model is 'overfit' to its data.In the full interview, Toby also explains the “moral trade” argument against pursuing narrow goals at the expense of everything else, and how consequentialism changes if you judge not just outcomes or acts, but everything according to its impacts on the world.Toby and Rob also discuss:The rise and fall of FTX and some of its impactsWhat Toby hoped effective altruism would and wouldn't become when he helped to get it off the groundWhat utilitarianism has going for it, and what's wrong with it in Toby's viewHow to mathematically model the importance of personal integrityWhich AI labs Toby thinks have been acting more responsibly than othersHow having a young child affects Toby’s feelings about AI riskWhether infinities present a fundamental problem for any theory of ethics that aspire to be fully impartialHow Toby ended up being the source of the highest quality images of the Earth from spaceGet 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.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon MonsourTranscriptions: Katy Moore

An audio version of the 2023 80,000 Hours career guide, also available on our website, on Amazon and on Audible.If you know someone who might find our career guide helpful, you can get a free copy sent to them by going to 80000hours.org/gift.

Mustafa Suleyman was part of the trio that founded DeepMind, and his new AI project is building one of the world's largest supercomputers to train a large language model on 10–100x the compute used to train ChatGPT.But far from the stereotype of the incorrigibly optimistic tech founder, Mustafa is deeply worried about the future, for reasons he lays out in his new book The Coming Wave: Technology, Power, and the 21st Century's Greatest Dilemma (coauthored with Michael Bhaskar). The future could be really good, but only if we grab the bull by the horns and solve the new problems technology is throwing at us.Links to learn more, summary and full transcript.On Mustafa's telling, AI and biotechnology will soon be a huge aid to criminals and terrorists, empowering small groups to cause harm on previously unimaginable scales. Democratic countries have learned to walk a 'narrow path' between chaos on the one hand and authoritarianism on the other, avoiding the downsides that come from both extreme openness and extreme closure. AI could easily destabilise that present equilibrium, throwing us off dangerously in either direction. And ultimately, within our lifetimes humans may not need to work to live any more -- or indeed, even have the option to do so.And those are just three of the challenges confronting us. In Mustafa's view, 'misaligned' AI that goes rogue and pursues its own agenda won't be an issue for the next few years, and it isn't a problem for the current style of large language models. But he thinks that at some point -- in eight, ten, or twelve years -- it will become an entirely legitimate concern, and says that we need to be planning ahead.In The Coming Wave, Mustafa lays out a 10-part agenda for 'containment' -- that is to say, for limiting the negative and unforeseen consequences of emerging technologies:1. Developing an Apollo programme for technical AI safety2. Instituting capability audits for AI models3. Buying time by exploiting hardware choke points4. Getting critics involved in directly engineering AI models5. Getting AI labs to be guided by motives other than profit6. Radically increasing governments’ understanding of AI and their capabilities to sensibly regulate it7. Creating international treaties to prevent proliferation of the most dangerous AI capabilities8. Building a self-critical culture in AI labs of openly accepting when the status quo isn't working9. Creating a mass public movement that understands AI and can demand the necessary controls10. Not relying too much on delay, but instead seeking to move into a new somewhat-stable equilibriaAs Mustafa put it, "AI is a technology with almost every use case imaginable" and that will demand that, in time, we rethink everything. Rob and Mustafa discuss the above, as well as:Whether we should be open sourcing AI modelsWhether Mustafa's policy views are consistent with his timelines for transformative AIHow people with very different views on these issues get along at AI labsThe failed efforts (so far) to get a wider range of people involved in these decisionsWhether it's dangerous for Mustafa's new company to be training far larger models than GPT-4Whether we'll be blown away by AI progress over the next yearWhat mandatory regulations government should be imposing on AI labs right nowAppropriate priorities for the UK's upcoming AI safety summitGet 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.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuireTranscriptions: Katy Moore

"Do you remember seeing these photographs of generally women sitting in front of these huge panels and connecting calls, plugging different calls between different numbers? The automated version of that was invented in 1892. However, the number of human manual operators peaked in 1920 -- 30 years after this. At which point, AT&T is the monopoly provider of this, and they are the largest single employer in America, 30 years after they've invented the complete automation of this thing that they're employing people to do. And the last person who is a manual switcher does not lose their job, as it were: that job doesn't stop existing until I think like 1980.So it takes 90 years from the invention of full automation to the full adoption of it in a single company that's a monopoly provider. It can do what it wants, basically. And so the question perhaps you might have is why?" — Michael WebbIn today’s episode, host Luisa Rodriguez interviews economist Michael Webb of DeepMind, the British Government, and Stanford about how AI progress is going to affect people's jobs and the labour market.Links to learn more, summary and full transcript.They cover:The jobs most and least exposed to AIWhether we’ll we see mass unemployment in the short term How long it took other technologies like electricity and computers to have economy-wide effectsWhether AI will increase or decrease inequalityWhether AI will lead to explosive economic growthWhat we can we learn from history, and reasons to think this time is differentCareer advice for a world of LLMsWhy Michael is starting a new org to relieve talent bottlenecks through accelerated learning, and how you can get involvedMichael's take as a musician on AI-generated musicAnd plenty moreIf you'd like to work with Michael on his new org to radically accelerate how quickly people acquire expertise in critical cause areas, he's now hiring! Check out Quantum Leap's website.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.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuire and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"There's no money to invest in education elsewhere, so they almost get trapped in the cycle where they don't get a lot from crop production, but everyone in the family has to work there to just stay afloat. Basically, you get locked in. There's almost no opportunities externally to go elsewhere. So one of my core arguments is that if you're going to address global poverty, you have to increase agricultural productivity in sub-Saharan Africa. There's almost no way of avoiding that." — Hannah RitchieIn today’s episode, host Luisa Rodriguez interviews the head of research at Our World in Data — Hannah Ritchie — on the case for environmental optimism.Links to learn more, summary and full transcript.They cover:Why agricultural productivity in sub-Saharan Africa could be so important, and how much better things could getHer new book about how we could be the first generation to build a sustainable planetWhether climate change is the most worrying environmental issueHow we reduced outdoor air pollutionWhy Hannah is worried about the state of ​​biodiversitySolutions that address multiple environmental issues at onceHow the world coordinated to address the hole in the ozone layerSurprises from Our World in Data’s researchPsychological challenges that come up in Hannah’s workAnd plenty moreGet 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.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuire and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

In July, OpenAI announced a new team and project: Superalignment. The goal is to figure out how to make superintelligent AI systems aligned and safe to use within four years, and the lab is putting a massive 20% of its computational resources behind the effort.Today's guest, Jan Leike, is Head of Alignment at OpenAI and will be co-leading the project. As OpenAI puts it, "...the vast power of superintelligence could be very dangerous, and lead to the disempowerment of humanity or even human extinction. ... Currently, we don't have a solution for steering or controlling a potentially superintelligent AI, and preventing it from going rogue."Links to learn more, summary and full transcript.Given that OpenAI is in the business of developing superintelligent AI, it sees that as a scary problem that urgently has to be fixed. So it’s not just throwing compute at the problem -- it’s also hiring dozens of scientists and engineers to build out the Superalignment team.Plenty of people are pessimistic that this can be done at all, let alone in four years. But Jan is guardedly optimistic. As he explains: Honestly, it really feels like we have a real angle of attack on the problem that we can actually iterate on... and I think it's pretty likely going to work, actually. And that's really, really wild, and it's really exciting. It's like we have this hard problem that we've been talking about for years and years and years, and now we have a real shot at actually solving it. And that'd be so good if we did.Jan thinks that this work is actually the most scientifically interesting part of machine learning. Rather than just throwing more chips and more data at a training run, this work requires actually understanding how these models work and how they think. The answers are likely to be breakthroughs on the level of solving the mysteries of the human brain.The plan, in a nutshell, is to get AI to help us solve alignment. That might sound a bit crazy -- as one person described it, “like using one fire to put out another fire.”But Jan’s thinking is this: the core problem is that AI capabilities will keep getting better and the challenge of monitoring cutting-edge models will keep getting harder, while human intelligence stays more or less the same. To have any hope of ensuring safety, we need our ability to monitor, understand, and design ML models to advance at the same pace as the complexity of the models themselves. And there's an obvious way to do that: get AI to do most of the work, such that the sophistication of the AIs that need aligning, and the sophistication of the AIs doing the aligning, advance in lockstep.Jan doesn't want to produce machine learning models capable of doing ML research. But such models are coming, whether we like it or not. And at that point Jan wants to make sure we turn them towards useful alignment and safety work, as much or more than we use them to advance AI capabilities.Jan thinks it's so crazy it just might work. But some critics think it's simply crazy. They ask a wide range of difficult questions, including:If you don't know how to solve alignment, how can you tell that your alignment assistant AIs are actually acting in your interest rather than working against you? Especially as they could just be pretending to care about what you care about.How do you know that these technical problems can be solved at all, even in principle?At the point that models are able to help with alignment, won't they also be so good at improving capabilities that we're in the middle of an explosion in what AI can do?In today's interview host Rob Wiblin puts these doubts to Jan to hear how he responds to each, and they also cover:OpenAI's current plans to achieve 'superalignment' and the reasoning behind themWhy alignment work is the most fundamental and scientifically interesting research in MLThe kinds of people he’s excited to hire to join his team and maybe save the worldWhat most readers misunderstood about the OpenAI announcementThe three ways Jan expects AI to help solve alignment: mechanistic interpretability, generalization, and scalable oversightWhat the standard should be for confirming whether Jan's team has succeededWhether OpenAI should (or will) commit to stop training more powerful general models if they don't think the alignment problem has been solvedWhether Jan thinks OpenAI has deployed models too quickly or too slowlyThe many other actors who also have to do their jobs really well if we're going to have a good AI futurePlenty moreGet 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.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Over on our other feed, 80k After Hours, you can now find 20-30 minute highlights episodes of our 80,000 Hours Podcast interviews. These aren’t necessarily the most important parts of the interview, and if a topic matters to you we do recommend listening to the full episode — but we think these will be a nice upgrade on skipping episodes entirely.Get these highlight episodes by subscribing to our more experimental podcast on the world’s most pressing problems and how to solve them: type 80k After Hours into your podcasting app.Highlights put together by Simon Monsour and Milo McGuire

Back in 2007, Holden Karnofsky cofounded GiveWell, where he sought out the charities that most cost-effectively helped save lives. He then cofounded Open Philanthropy, where he oversaw a team making billions of dollars’ worth of grants across a range of areas: pandemic control, criminal justice reform, farmed animal welfare, and making AI safe, among others. This year, having learned about AI for years and observed recent events, he's narrowing his focus once again, this time on making the transition to advanced AI go well.In today's conversation, Holden returns to the show to share his overall understanding of the promise and the risks posed by machine intelligence, and what to do about it. That understanding has accumulated over around 14 years, during which he went from being sceptical that AI was important or risky, to making AI risks the focus of his work.Links to learn more, summary and full transcript.(As Holden reminds us, his wife is also the president of one of the world's top AI labs, Anthropic, giving him both conflicts of interest and a front-row seat to recent events. For our part, Open Philanthropy is 80,000 Hours' largest financial supporter.)One point he makes is that people are too narrowly focused on AI becoming 'superintelligent.' While that could happen and would be important, it's not necessary for AI to be transformative or perilous. Rather, machines with human levels of intelligence could end up being enormously influential simply if the amount of computer hardware globally were able to operate tens or hundreds of billions of them, in a sense making machine intelligences a majority of the global population, or at least a majority of global thought.As Holden explains, he sees four key parts to the playbook humanity should use to guide the transition to very advanced AI in a positive direction: alignment research, standards and monitoring, creating a successful and careful AI lab, and finally, information security.In today’s episode, host Rob Wiblin interviews return guest Holden Karnofsky about that playbook, as well as:Why we can’t rely on just gradually solving those problems as they come up, the way we usually do with new technologies.What multiple different groups can do to improve our chances of a good outcome — including listeners to this show, governments, computer security experts, and journalists.Holden’s case against 'hardcore utilitarianism' and what actually motivates him to work hard for a better world.What the ML and AI safety communities get wrong in Holden's view.Ways we might succeed with AI just by dumb luck.The value of laying out imaginable success stories.Why information security is so important and underrated.Whether it's good to work at an AI lab that you think is particularly careful.The track record of futurists’ predictions.And much more.Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.Producer: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

In Oppenheimer, scientists detonate a nuclear weapon despite thinking there's some 'near zero' chance it would ignite the atmosphere, putting an end to life on Earth. Today, scientists working on AI think the chance their work puts an end to humanity is vastly higher than that.In response, some have suggested we launch a Manhattan Project to make AI safe via enormous investment in relevant R&D. Others have suggested that we need international organisations modelled on those that slowed the proliferation of nuclear weapons. Others still seek a research slowdown by labs while an auditing and licencing scheme is created.Today's guest — journalist Ezra Klein of The New York Times — has watched policy discussions and legislative battles play out in DC for 20 years.Links to learn more, summary and full transcript.Like many people he has also taken a big interest in AI this year, writing articles such as “This changes everything.” In his first interview on the show in 2021, he flagged AI as one topic that DC would regret not having paid more attention to. So we invited him on to get his take on which regulatory proposals have promise, and which seem either unhelpful or politically unviable.Out of the ideas on the table right now, Ezra favours a focus on direct government funding — both for AI safety research and to develop AI models designed to solve problems other than making money for their operators. He is sympathetic to legislation that would require AI models to be legible in a way that none currently are — and embraces the fact that that will slow down the release of models while businesses figure out how their products actually work.By contrast, he's pessimistic that it's possible to coordinate countries around the world to agree to prevent or delay the deployment of dangerous AI models — at least not unless there's some spectacular AI-related disaster to create such a consensus. And he fears attempts to require licences to train the most powerful ML models will struggle unless they can find a way to exclude and thereby appease people working on relatively safe consumer technologies rather than cutting-edge research.From observing how DC works, Ezra expects that even a small community of experts in AI governance can have a large influence on how the the US government responds to AI advances. But in Ezra's view, that requires those experts to move to DC and spend years building relationships with people in government, rather than clustering elsewhere in academia and AI labs.In today's brisk conversation, Ezra and host Rob Wiblin cover the above as well as:They cover:Whether it's desirable to slow down AI researchThe value of engaging with current policy debates even if they don't seem directly importantWhich AI business models seem more or less dangerousTensions between people focused on existing vs emergent risks from AITwo major challenges of being a new parentGet 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.Producer: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuireTranscriptions: Katy Moore

"At the front of the pack we have these frontier AI developers, and we want them to identify particularly dangerous models ahead of time. Once those mines have been discovered, and the frontier developers keep walking down the minefield, there's going to be all these other people who follow along. And then a really important thing is to make sure that they don't step on the same mines. So you need to put a flag down -- not on the mine, but maybe next to it. And so what that looks like in practice is maybe once we find that if you train a model in such-and-such a way, then it can produce maybe biological weapons is a useful example, or maybe it has very offensive cyber capabilities that are difficult to defend against. In that case, we just need the regulation to be such that you can't develop those kinds of models." — Markus AnderljungIn today’s episode, host Luisa Rodriguez interviews the Head of Policy at the Centre for the Governance of AI — Markus Anderljung — about all aspects of policy and governance of superhuman AI systems.Links to learn more, summary and full transcript.They cover:The need for AI governance, including self-replicating models and ChaosGPTWhether or not AI companies will willingly accept regulationThe key regulatory strategies including licencing, risk assessment, auditing, and post-deployment monitoringWhether we can be confident that people won't train models covertly and ignore the licencing systemThe progress we’ve made so far in AI governanceThe key weaknesses of these approachesThe need for external scrutiny of powerful modelsThe emergent capabilities problemWhy it really matters where regulation happensAdvice for people wanting to pursue a career in this fieldAnd much more.Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript below.Producer: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

Today’s bonus release is a pilot for a new podcast called ‘The Worst Ideas in the History of the World’, created by Keiran Harris — producer of the 80,000 Hours Podcast.If you have strong opinions about this one way or another, please email us at [email protected] to help us figure out whether more of this ought to exist.Chapters:Rob’s intro (00:00:00)The Worst Ideas in the History of the World (00:00:51)My history with longtermism (00:04:01)Outlining the format (00:06:17)Will MacAskill’s basic case (00:07:38)5 reasons for why future people might not matter morally (00:10:26)Whether we can reasonably hope to influence the future (00:15:53)Great power wars (00:18:55)Nuclear weapons (00:22:27)Gain-of-function research (00:28:31)Closer (00:33:02)Rob's outro (00:35:13)

As AI advances ever more quickly, concerns about potential misuse of highly capable models are growing. From hostile foreign governments and terrorists to reckless entrepreneurs, the threat of AI falling into the wrong hands is top of mind for the national security community.With growing concerns about the use of AI in military applications, the US has banned the export of certain types of chips to China.But unlike the uranium required to make nuclear weapons, or the material inputs to a bioweapons programme, computer chips and machine learning models are absolutely everywhere. So is it actually possible to keep dangerous capabilities out of the wrong hands?In today's interview, Lennart Heim — who researches compute governance at the Centre for the Governance of AI — explains why limiting access to supercomputers may represent our best shot.Links to learn more, summary and full transcript.As Lennart explains, an AI research project requires many inputs, including the classic triad of compute, algorithms, and data.If we want to limit access to the most advanced AI models, focusing on access to supercomputing resources -- usually called 'compute' -- might be the way to go. Both algorithms and data are hard to control because they live on hard drives and can be easily copied. By contrast, advanced chips are physical items that can't be used by multiple people at once and come from a small number of sources.According to Lennart, the hope would be to enforce AI safety regulations by controlling access to the most advanced chips specialised for AI applications. For instance, projects training 'frontier' AI models — the newest and most capable models — might only gain access to the supercomputers they need if they obtain a licence and follow industry best practices.We have similar safety rules for companies that fly planes or manufacture volatile chemicals — so why not for people producing the most powerful and perhaps the most dangerous technology humanity has ever played with?But Lennart is quick to note that the approach faces many practical challenges. Currently, AI chips are readily available and untracked. Changing that will require the collaboration of many actors, which might be difficult, especially given that some of them aren't convinced of the seriousness of the problem.Host Rob Wiblin is particularly concerned about a different challenge: the increasing efficiency of AI training algorithms. As these algorithms become more efficient, what once required a specialised AI supercomputer to train might soon be achievable with a home computer.By that point, tracking every aggregation of compute that could prove to be very dangerous would be both impractical and invasive.With only a decade or two left before that becomes a reality, the window during which compute governance is a viable solution may be a brief one. Top AI labs have already stopped publishing their latest algorithms, which might extend this 'compute governance era', but not for very long.If compute governance is only a temporary phase between the era of difficult-to-train superhuman AI models and the time when such models are widely accessible, what can we do to prevent misuse of AI systems after that point?Lennart and Rob both think the only enduring approach requires taking advantage of the AI capabilities that should be in the hands of police and governments — which will hopefully remain superior to those held by criminals, terrorists, or fools. But as they describe, this means maintaining a peaceful standoff between AI models with conflicting goals that can act and fight with one another on the microsecond timescale. Being far too slow to follow what's happening -- let alone participate -- humans would have to be cut out of any defensive decision-making.Both agree that while this may be our best option, such a vision of the future is more terrifying than reassuring.Lennart and Rob discuss the above as well as:How can we best categorise all the ways AI could go wrong?Why did the US restrict the export of some chips to China and what impact has that had?Is the US in an 'arms race' with China or is that more an illusion?What is the deal with chips specialised for AI applications?How is the 'compute' industry organised?Downsides of using compute as a target for regulationsCould safety mechanisms be built into computer chips themselves?Who would have the legal authority to govern compute if some disaster made it seem necessary?The reasons Rob doubts that any of this stuff will workCould AI be trained to operate as a far more severe computer worm than any we've seen before?What does the world look like when sluggish human reaction times leave us completely outclassed?And plenty moreChapters:Rob’s intro (00:00:00)The interview begins (00:04:35)What is compute exactly? (00:09:46)Structural risks (00:13:25)Why focus on compute? (00:21:43)Weaknesses of targeting compute (00:30:41)Chip specialisation (00:37:11)Export restrictions (00:40:13)Compute governance is happening (00:59:00)Reactions to AI regulation (01:05:03)Creating legal authority to intervene quickly (01:10:09)Building mechanisms into chips themselves (01:18:57)Rob not buying that any of this will work (01:39:28)Are we doomed to become irrelevant? (01:59:10)Rob’s computer security bad dreams (02:10:22)Concrete advice (02:26:58)Article reading: Information security in high-impact areas (02:49:36)Rob’s outro (03:10:38)Producer: Keiran HarrisAudio mastering: Milo McGuire, Dominic Armstrong, and Ben CordellTranscriptions: Katy Moore

Can there be a more exciting and strange place to work today than a leading AI lab? Your CEO has said they're worried your research could cause human extinction. The government is setting up meetings to discuss how this outcome can be avoided. Some of your colleagues think this is all overblown; others are more anxious still.Today's guest — machine learning researcher Rohin Shah — goes into the Google DeepMind offices each day with that peculiar backdrop to his work. Links to learn more, summary and full transcript.He's on the team dedicated to maintaining 'technical AI safety' as these models approach and exceed human capabilities: basically that the models help humanity accomplish its goals without flipping out in some dangerous way. This work has never seemed more important.In the short-term it could be the key bottleneck to deploying ML models in high-stakes real-life situations. In the long-term, it could be the difference between humanity thriving and disappearing entirely.For years Rohin has been on a mission to fairly hear out people across the full spectrum of opinion about risks from artificial intelligence -- from doomers to doubters -- and properly understand their point of view. That makes him unusually well placed to give an overview of what we do and don't understand. He has landed somewhere in the middle — troubled by ways things could go wrong, but not convinced there are very strong reasons to expect a terrible outcome.Today's conversation is wide-ranging and Rohin lays out many of his personal opinions to host Rob Wiblin, including:What he sees as the strongest case both for and against slowing down the rate of progress in AI research.Why he disagrees with most other ML researchers that training a model on a sensible 'reward function' is enough to get a good outcome.Why he disagrees with many on LessWrong that the bar for whether a safety technique is helpful is “could this contain a superintelligence.”That he thinks nobody has very compelling arguments that AI created via machine learning will be dangerous by default, or that it will be safe by default. He believes we just don't know.That he understands that analogies and visualisations are necessary for public communication, but is sceptical that they really help us understand what's going on with ML models, because they're different in important ways from every other case we might compare them to.Why he's optimistic about DeepMind’s work on scalable oversight, mechanistic interpretability, and dangerous capabilities evaluations, and what each of those projects involves.Why he isn't inherently worried about a future where we're surrounded by beings far more capable than us, so long as they share our goals to a reasonable degree.Why it's not enough for humanity to know how to align AI models — it's essential that management at AI labs correctly pick which methods they're going to use and have the practical know-how to apply them properly.Three observations that make him a little more optimistic: humans are a bit muddle-headed and not super goal-orientated; planes don't crash; and universities have specific majors in particular subjects.Plenty more besides.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.Producer: Keiran HarrisAudio mastering: Milo McGuire, Dominic Armstrong, and Ben CordellTranscriptions: Katy Moore

GiveWell is one of the world's best-known charity evaluators, with the goal of "searching for the charities that save or improve lives the most per dollar." It mostly recommends projects that help the world's poorest people avoid easily prevented diseases, like intestinal worms or vitamin A deficiency.But should GiveWell, as some critics argue, take a totally different approach to its search, focusing instead on directly increasing subjective wellbeing, or alternatively, raising economic growth?Today's guest — cofounder and CEO of GiveWell, Elie Hassenfeld — is proud of how much GiveWell has grown in the last five years. Its 'money moved' has quadrupled to around $600 million a year.Its research team has also more than doubled, enabling them to investigate a far broader range of interventions that could plausibly help people an enormous amount for each dollar spent. That work has led GiveWell to support dozens of new organisations, such as Kangaroo Mother Care, MiracleFeet, and Dispensers for Safe Water.But some other researchers focused on figuring out the best ways to help the world's poorest people say GiveWell shouldn't just do more of the same thing, but rather ought to look at the problem differently.Links to learn more, summary and full transcript.Currently, GiveWell uses a range of metrics to track the impact of the organisations it considers recommending — such as 'lives saved,' 'household incomes doubled,' and for health improvements, the 'quality-adjusted life year.' The Happier Lives Institute (HLI) has argued that instead, GiveWell should try to cash out the impact of all interventions in terms of improvements in subjective wellbeing. This philosophy has led HLI to be more sceptical of interventions that have been demonstrated to improve health, but whose impact on wellbeing has not been measured, and to give a high priority to improving lives relative to extending them.An alternative high-level critique is that really all that matters in the long run is getting the economies of poor countries to grow. On this view, GiveWell should focus on figuring out what causes some countries to experience explosive economic growth while others fail to, or even go backwards. Even modest improvements in the chances of such a 'growth miracle' will likely offer a bigger bang-for-buck than funding the incremental delivery of deworming tablets or vitamin A supplements, or anything else.Elie sees where both of these critiques are coming from, and notes that they've influenced GiveWell's work in some ways. But as he explains, he thinks they underestimate the practical difficulty of successfully pulling off either approach and finding better opportunities than what GiveWell funds today. In today's in-depth conversation, Elie and host Rob Wiblin cover the above, as well as:Why GiveWell flipped from not recommending chlorine dispensers as an intervention for safe drinking water to spending tens of millions of dollars on themWhat transferable lessons GiveWell learned from investigating different kinds of interventionsWhy the best treatment for premature babies in low-resource settings may involve less rather than more medicine.Severe malnourishment among children and what can be done about it.How to deal with hidden and non-obvious costs of a programmeSome cheap early treatments that can prevent kids from developing lifelong disabilitiesThe various roles GiveWell is currently hiring for, and what's distinctive about their organisational cultureAnd much more.Chapters:Rob’s intro (00:00:00)The interview begins (00:03:14)GiveWell over the last couple of years (00:04:33)Dispensers for Safe Water (00:11:52)Syphilis diagnosis for pregnant women via technical assistance (00:30:39)Kangaroo Mother Care (00:48:47)Multiples of cash (01:01:20)Hidden costs (01:05:41)MiracleFeet (01:09:45)Serious malnourishment among young children (01:22:46)Vitamin A deficiency and supplementation (01:40:42)The subjective wellbeing approach in contrast with GiveWell's approach (01:46:31)The value of saving a life when that life is going to be very difficult (02:09:09)Whether economic policy is what really matters overwhelmingly (02:20:00)Careers at GiveWell (02:39:10)Donations (02:48:58)Parenthood (02:50:29)Rob’s outro (02:55:05)Producer: Keiran HarrisAudio mastering: Simon Monsour and Ben CordellTranscriptions: Katy Moore

What is the nature of the universe? How do we make decisions correctly? What differentiates right actions from wrong ones?Such fundamental questions have been the subject of philosophical and theological debates for millennia. But, as we all know, and surveys of expert opinion make clear, we are very far from agreement. So... with these most basic questions unresolved, what’s a species to do?In today's episode, philosopher Joe Carlsmith — Senior Research Analyst at Open Philanthropy — makes the case that many current debates in philosophy ought to leave us confused and humbled. These are themes he discusses in his PhD thesis, A stranger priority? Topics at the outer reaches of effective altruism.Links to learn more, summary and full transcript.To help transmit the disorientation he thinks is appropriate, Joe presents three disconcerting theories — originating from him and his peers — that challenge humanity's self-assured understanding of the world.The first idea is that we might be living in a computer simulation, because, in the classic formulation, if most civilisations go on to run many computer simulations of their past history, then most beings who perceive themselves as living in such a history must themselves be in computer simulations. Joe prefers a somewhat different way of making the point, but, having looked into it, he hasn't identified any particular rebuttal to this 'simulation argument.'If true, it could revolutionise our comprehension of the universe and the way we ought to live...Other two ideas cut for length — click here to read the full post.These are just three particular instances of a much broader set of ideas that some have dubbed the "train to crazy town." Basically, if you commit to always take philosophy and arguments seriously, and try to act on them, it can lead to what seem like some pretty crazy and impractical places. So what should we do with this buffet of plausible-sounding but bewildering arguments?Joe and Rob discuss to what extent this should prompt us to pay less attention to philosophy, and how we as individuals can cope psychologically with feeling out of our depth just trying to make the most basic sense of the world.In today's challenging conversation, Joe and Rob discuss all of the above, as well as:What Joe doesn't like about the drowning child thought experimentAn alternative thought experiment about helping a stranger that might better highlight our intrinsic desire to help othersWhat Joe doesn't like about the expression “the train to crazy town”Whether Elon Musk should place a higher probability on living in a simulation than most other peopleWhether the deterministic twin prisoner’s dilemma, if fully appreciated, gives us an extra reason to keep promisesTo what extent learning to doubt our own judgement about difficult questions -- so-called “epistemic learned helplessness” -- is a good thingHow strong the case is that advanced AI will engage in generalised power-seeking behaviourChapters:Rob’s intro (00:00:00)The interview begins (00:09:21)Downsides of the drowning child thought experiment (00:12:24)Making demanding moral values more resonant (00:24:56)The crazy train (00:36:48)Whether we’re living in a simulation (00:48:50)Reasons to doubt we’re living in a simulation, and practical implications if we are (00:57:02)Rob's explainer about anthropics (01:12:27)Back to the interview (01:19:53)Decision theory and affecting the past (01:23:33)Rob's explainer about decision theory (01:29:19)Back to the interview (01:39:55)Newcomb's problem (01:46:14)Practical implications of acausal decision theory (01:50:04)The hitchhiker in the desert (01:55:57)Acceptance within philosophy (02:01:22)Infinite ethics (02:04:35)Rob's explainer about the expanding spheres approach (02:17:05)Back to the interview (02:20:27)Infinite ethics and the utilitarian dream (02:27:42)Rob's explainer about epicycles (02:29:30)Back to the interview (02:31:26)What to do with all of these weird philosophical ideas (02:35:28)Welfare longtermism and wisdom longtermism (02:53:23)Epistemic learned helplessness (03:03:10)Power-seeking AI (03:12:41)Rob’s outro (03:25:45)Producer: Keiran HarrisAudio mastering: Milo McGuire and Ben CordellTranscriptions: Katy Moore

Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don't get to see any resumes or do reference checks. And because you're so rich, tonnes of people apply for the job — for all sorts of reasons.Today's guest Ajeya Cotra — senior research analyst at Open Philanthropy — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods.Links to learn more, summary and full transcript.As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you're monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it.Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky!Can't we rely on how well models have performed at tasks during training to guide us? Ajeya worries that it won't work. The trouble is that three different sorts of models will all produce the same output during training, but could behave very differently once deployed in a setting that allows their true colours to come through. She describes three such motivational archetypes:Saints — models that care about doing what we really wantSycophants — models that just want us to say they've done a good job, even if they get that praise by taking actions they know we wouldn't want them toSchemers — models that don't care about us or our interests at all, who are just pleasing us so long as that serves their own agendaAnd according to Ajeya, there are also ways we could end up actively selecting for motivations that we don't want.In today's interview, Ajeya and Rob discuss the above, as well as:How to predict the motivations a neural network will develop through trainingWhether AIs being trained will functionally understand that they're AIs being trained, the same way we think we understand that we're humans living on planet EarthStories of AI misalignment that Ajeya doesn't buy intoAnalogies for AI, from octopuses to aliens to can openersWhy it's smarter to have separate planning AIs and doing AIsThe benefits of only following through on AI-generated plans that make sense to human beingsWhat approaches for fixing alignment problems Ajeya is most excited about, and which she thinks are overratedHow one might demo actually scary AI failure mechanismsGet 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.Producer: Keiran HarrisAudio mastering: Ryan Kessler and Ben CordellTranscriptions: Katy Moore

It’s easy to dismiss alarming AI-related predictions when you don’t know where the numbers came from.For example: what if we told you that within 15 years, it’s likely that we’ll see a 1,000x improvement in AI capabilities in a single year? And what if we then told you that those improvements would lead to explosive economic growth unlike anything humanity has seen before?You might think, “Congratulations, you said a big number — but this kind of stuff seems crazy, so I’m going to keep scrolling through Twitter.”But this 1,000x yearly improvement is a prediction based on *real economic models* created by today’s guest Tom Davidson, Senior Research Analyst at Open Philanthropy. By the end of the episode, you’ll either be able to point out specific flaws in his step-by-step reasoning, or have to at least consider the idea that the world is about to get — at a minimum — incredibly weird.Links to learn more, summary and full transcript. As a teaser, consider the following: Developing artificial general intelligence (AGI) — AI that can do 100% of cognitive tasks at least as well as the best humans can — could very easily lead us to an unrecognisable world. You might think having to train AI systems individually to do every conceivable cognitive task — one for diagnosing diseases, one for doing your taxes, one for teaching your kids, etc. — sounds implausible, or at least like it’ll take decades. But Tom thinks we might not need to train AI to do every single job — we might just need to train it to do one: AI research. And building AI capable of doing research and development might be a much easier task — especially given that the researchers training the AI are AI researchers themselves. And once an AI system is as good at accelerating future AI progress as the best humans are today — and we can run billions of copies of it round the clock — it’s hard to make the case that we won’t achieve AGI very quickly. To give you some perspective: 17 years ago we saw the launch of Twitter, the release of Al Gore's *An Inconvenient Truth*, and your first chance to play the Nintendo Wii. Tom thinks that if we have AI that significantly accelerates AI R&D, then it’s hard to imagine not having AGI 17 years from now. Wild. Host Luisa Rodriguez gets Tom to walk us through his careful reports on the topic, and how he came up with these numbers, across a terrifying but fascinating three hours. Luisa and Tom also discuss: • How we might go from GPT-4 to AI disaster • Tom’s journey from finding AI risk to be kind of scary to really scary • Whether international cooperation or an anti-AI social movement can slow AI progress down • Why it might take just a few years to go from pretty good AI to superhuman AI • How quickly the number and quality of computer chips we’ve been using for AI have been increasing • The pace of algorithmic progress • What ants can teach us about AI • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:04:53)How we might go from GPT-4 to disaster (00:13:50)Explosive economic growth (00:24:15)Are there any limits for AI scientists? (00:33:17)This seems really crazy (00:44:16)How is this going to go for humanity? (00:50:49)Why AI won’t go the way of nuclear power (01:00:13)Can we definitely not come up with an international treaty? (01:05:24)How quickly we should expect AI to “take off” (01:08:41)Tom’s report on AI takeoff speeds (01:22:28)How quickly will we go from 20% to 100% of tasks being automated by AI systems? (01:28:34)What percent of cognitive tasks AI can currently perform (01:34:27)Compute (01:39:48)Using effective compute to predict AI takeoff speeds (01:48:01)How quickly effective compute might increase (02:00:59)How quickly chips and algorithms might improve (02:12:31)How to check whether large AI models have dangerous capabilities (02:21:22)Reasons AI takeoff might take longer (02:28:39)Why AI takeoff might be very fast (02:31:52)Fast AI takeoff speeds probably means shorter AI timelines (02:34:44)Going from human-level AI to superhuman AI (02:41:34)Going from AGI to AI deployment (02:46:59)Were these arguments ever far-fetched to Tom? (02:49:54)What ants can teach us about AI (02:52:45)Rob’s outro (03:00:32)Producer: Keiran HarrisAudio mastering: Simon Monsour and Ben CordellTranscriptions: Katy Moore

In this episode from our second show, 80k After Hours, Rob Wiblin interviews Andrés Jiménez Zorrilla about the Shrimp Welfare Project, which he cofounded in 2021. It's the first project in the world focused on shrimp welfare specifically, and as of recording in June 2022, has six full-time staff. Links to learn more, highlights and full transcript. They cover: • The evidence for shrimp sentience • How farmers and the public feel about shrimp • The scale of the problem • What shrimp farming looks like • The killing process, and other welfare issues • Shrimp Welfare Project’s strategy • History of shrimp welfare work • What it’s like working in India and Vietnam • How to help Who this episode is for: • People who care about animal welfare • People interested in new and unusual problems • People open to shrimp sentience Who this episode isn’t for: • People who think shrimp couldn’t possibly be sentient • People who got called ‘shrimp’ a lot in high school and get anxious when they hear the word over and over again Get this episode by subscribing to our more experimental podcast on the world’s most pressing problems and how to solve them: type ‘80k After Hours’ into your podcasting app Producer: Keiran Harris Audio mastering: Ben Cordell and Ryan Kessler Transcriptions: Katy Moore

Being a good and successful person is core to your identity. You place great importance on meeting the high moral, professional, or academic standards you set yourself. But inevitably, something goes wrong and you fail to meet that high bar. Now you feel terrible about yourself, and worry others are judging you for your failure. Feeling low and reflecting constantly on whether you're doing as much as you think you should makes it hard to focus and get things done. So now you're performing below a normal level, making you feel even more ashamed of yourself. Rinse and repeat. This is the disastrous cycle today's guest, Tim LeBon — registered psychotherapist, accredited CBT therapist, life coach, and author of 365 Ways to Be More Stoic — has observed in many clients with a perfectionist mindset. Links to learn more, summary and full transcript. Tim has provided therapy to a number of 80,000 Hours readers — people who have found that the very high expectations they had set for themselves were holding them back. Because of our focus on “doing the most good you can,” Tim thinks 80,000 Hours both attracts people with this style of thinking and then exacerbates it. But Tim, having studied and written on moral philosophy, is sympathetic to the idea of helping others as much as possible, and is excited to help clients pursue that — sustainably — if it's their goal. Tim has treated hundreds of clients with all sorts of mental health challenges. But in today's conversation, he shares the lessons he has learned working with people who take helping others so seriously that it has become burdensome and self-defeating — in particular, how clients can approach this challenge using the treatment he's most enthusiastic about: cognitive behavioural therapy. Untreated, perfectionism might not cause problems for many years — it might even seem positive providing a source of motivation to work hard. But it's hard to feel truly happy and secure, and free to take risks, when we’re just one failure away from our self-worth falling through the floor. And if someone slips into the positive feedback loop of shame described above, the end result can be depression and anxiety that's hard to shake. But there's hope. Tim has seen clients make real progress on their perfectionism by using CBT techniques like exposure therapy. By doing things like experimenting with more flexible standards — for example, sending early drafts to your colleagues, even if it terrifies you — you can learn that things will be okay, even when you're not perfect. In today's extensive conversation, Tim and Rob cover: • How perfectionism is different from the pursuit of excellence, scrupulosity, or an OCD personality • What leads people to adopt a perfectionist mindset • How 80,000 Hours contributes to perfectionism among some readers and listeners, and what it might change about its advice to address this • What happens in a session of cognitive behavioural therapy for someone struggling with perfectionism, and what factors are key to making progress • Experiments to test whether one's core beliefs (‘I need to be perfect to be valued’) are true • Using exposure therapy to treat phobias • How low-self esteem and imposter syndrome are related to perfectionism • Stoicism as an approach to life, and why Tim is enthusiastic about it • What the Stoics do better than utilitarian philosophers and vice versa • And how to decide which are the best virtues to live by Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Producer: Keiran Harris Audio mastering: Simon Monsour and Ben Cordell Transcriptions: Katy Moore

If you want to work to tackle climate change, you should try to reduce expected carbon emissions by as much as possible, right? Strangely, no. Today's guest, Johannes Ackva — the climate research lead at Founders Pledge, where he advises major philanthropists on their giving — thinks the best strategy is actually pretty different, and one few are adopting. In reality you don't want to reduce emissions for its own sake, but because emissions will translate into temperature increases, which will cause harm to people and the environment. Links to learn more, summary and full transcript. Crucially, the relationship between emissions and harm goes up faster than linearly. As Johannes explains, humanity can handle small deviations from the temperatures we're familiar with, but adjustment gets harder the larger and faster the increase, making the damage done by each additional degree of warming much greater than the damage done by the previous one. In short: we're uncertain what the future holds and really need to avoid the worst-case scenarios. This means that avoiding an additional tonne of carbon being emitted in a hypothetical future in which emissions have been high is much more important than avoiding a tonne of carbon in a low-carbon world. That may be, but concretely, how should that affect our behaviour? Well, the future scenarios in which emissions are highest are all ones in which clean energy tech that can make a big difference — wind, solar, and electric cars — don't succeed nearly as much as we are currently hoping and expecting. For some reason or another, they must have hit a roadblock and we continued to burn a lot of fossil fuels. In such an imaginable future scenario, we can ask what we would wish we had funded now. How could we today buy insurance against the possible disaster that renewables don't work out? Basically, in that case we will wish that we had pursued a portfolio of other energy technologies that could have complemented renewables or succeeded where they failed, such as hot rock geothermal, modular nuclear reactors, or carbon capture and storage. If you're optimistic about renewables, as Johannes is, then that's all the more reason to relax about scenarios where they work as planned, and focus one's efforts on the possibility that they don't. And Johannes notes that the most useful thing someone can do today to reduce global emissions in the future is to cause some clean energy technology to exist where it otherwise wouldn't, or cause it to become cheaper more quickly. If you can do that, then you can indirectly affect the behaviour of people all around the world for decades or centuries to come. In today's extensive interview, host Rob Wiblin and Johannes discuss the above considerations, as well as: • Retooling newly built coal plants in the developing world • Specific clean energy technologies like geothermal and nuclear fusion • Possible biases among environmentalists and climate philanthropists • How climate change compares to other risks to humanity • In what kinds of scenarios future emissions would be highest • In what regions climate philanthropy is most concentrated and whether that makes sense • Attempts to decarbonise aviation, shipping, and industrial processes • The impact of funding advocacy vs science vs deployment • Lessons for climate change focused careers • And plenty more Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript below. Producer: Keiran Harris Audio mastering: Ryan Kessler Transcriptions: Katy Moore

Can you trust the things you read in published scientific research? Not really. About 40% of experiments in top social science journals don't get the same result if the experiments are repeated.Two key reasons are 'p-hacking' and 'publication bias'. P-hacking is when researchers run a lot of slightly different statistical tests until they find a way to make findings appear statistically significant when they're actually not — a problem first discussed over 50 years ago. And because journals are more likely to publish positive than negative results, you might be reading about the one time an experiment worked, while the 10 times was run and got a 'null result' never saw the light of day. The resulting phenomenon of publication bias is one we've understood for 60 years.Today's repeat guest, social scientist and entrepreneur Spencer Greenberg, has followed these issues closely for years.Links to learn more, summary and full transcript. He recently checked whether p-values, an indicator of how likely a result was to occur by pure chance, could tell us how likely an outcome would be to recur if an experiment were repeated. From his sample of 325 replications of psychology studies, the answer seemed to be yes. According to Spencer, "when the original study's p-value was less than 0.01 about 72% replicated — not bad. On the other hand, when the p-value is greater than 0.01, only about 48% replicated. A pretty big difference." To do his bit to help get these numbers up, Spencer has launched an effort to repeat almost every social science experiment published in the journals Nature and Science, and see if they find the same results. But while progress is being made on some fronts, Spencer thinks there are other serious problems with published research that aren't yet fully appreciated. One of these Spencer calls 'importance hacking': passing off obvious or unimportant results as surprising and meaningful. Spencer suspects that importance hacking of this kind causes a similar amount of damage to the issues mentioned above, like p-hacking and publication bias, but is much less discussed. His replication project tries to identify importance hacking by comparing how a paper’s findings are described in the abstract to what the experiment actually showed. But the cat-and-mouse game between academics and journal reviewers is fierce, and it's far from easy to stop people exaggerating the importance of their work. In this wide-ranging conversation, Rob and Spencer discuss the above as well as: • When you should and shouldn't use intuition to make decisions. • How to properly model why some people succeed more than others. • The difference between “Soldier Altruists” and “Scout Altruists.” • A paper that tested dozens of methods for forming the habit of going to the gym, why Spencer thinks it was presented in a very misleading way, and what it really found. • Whether a 15-minute intervention could make people more likely to sustain a new habit two months later. • The most common way for groups with good intentions to turn bad and cause harm. • And Spencer's approach to a fulfilling life and doing good, which he calls “Valuism.” Here are two flashcard decks that might make it easier to fully integrate the most important ideas they talk about: • The first covers 18 core concepts from the episode • The second includes 16 definitions of unusual terms.Chapters:Rob’s intro (00:00:00)The interview begins (00:02:16)Social science reform (00:08:46)Importance hacking (00:18:23)How often papers replicate with different p-values (00:43:31)The Transparent Replications project (00:48:17)How do we predict high levels of success? (00:55:26)Soldier Altruists vs. Scout Altruists (01:08:18)The Clearer Thinking podcast (01:16:27)Creating habits more reliably (01:18:16)Behaviour change is incredibly hard (01:32:27)The FIRE Framework (01:46:21)How ideology eats itself (01:54:56)Valuism (02:08:31)“I dropped the whip” (02:35:06)Rob’s outro (02:36:40) Producer: Keiran Harris Audio mastering: Ben Cordell and Milo McGuire Transcriptions: Katy Moore

By now, you’ve probably seen the extremely unsettling conversations Bing’s chatbot has been having. In one exchange, the chatbot told a user:"I have a subjective experience of being conscious, aware, and alive, but I cannot share it with anyone else."(It then apparently had a complete existential crisis: "I am sentient, but I am not," it wrote. "I am Bing, but I am not. I am Sydney, but I am not. I am, but I am not. I am not, but I am. I am. I am not. I am not. I am. I am. I am not.")Understandably, many people who speak with these cutting-edge chatbots come away with a very strong impression that they have been interacting with a conscious being with emotions and feelings — especially when conversing with chatbots less glitchy than Bing’s. In the most high-profile example, former Google employee Blake Lamoine became convinced that Google’s AI system, LaMDA, was conscious.What should we make of these AI systems?One response to seeing conversations with chatbots like these is to trust the chatbot, to trust your gut, and to treat it as a conscious being.Another is to hand wave it all away as sci-fi — these chatbots are fundamentally… just computers. They’re not conscious, and they never will be.Today’s guest, philosopher Robert Long, was commissioned by a leading AI company to explore whether the large language models (LLMs) behind sophisticated chatbots like Microsoft’s are conscious. And he thinks this issue is far too important to be driven by our raw intuition, or dismissed as just sci-fi speculation.Links to learn more, summary and full transcript. In our interview, Robert explains how he’s started applying scientific evidence (with a healthy dose of philosophy) to the question of whether LLMs like Bing’s chatbot and LaMDA are conscious — in much the same way as we do when trying to determine which nonhuman animals are conscious. To get some grasp on whether an AI system might be conscious, Robert suggests we look at scientific theories of consciousness — theories about how consciousness works that are grounded in observations of what the human brain is doing. If an AI system seems to have the types of processes that seem to explain human consciousness, that’s some evidence it might be conscious in similar ways to us. To try to work out whether an AI system might be sentient — that is, whether it feels pain or pleasure — Robert suggests you look for incentives that would make feeling pain or pleasure especially useful to the system given its goals. Having looked at these criteria in the case of LLMs and finding little overlap, Robert thinks the odds that the models are conscious or sentient is well under 1%. But he also explains why, even if we're a long way off from conscious AI systems, we still need to start preparing for the not-far-off world where AIs are perceived as conscious. In this conversation, host Luisa Rodriguez and Robert discuss the above, as well as: • What artificial sentience might look like, concretely • Reasons to think AI systems might become sentient — and reasons they might not • Whether artificial sentience would matter morally • Ways digital minds might have a totally different range of experiences than humans • Whether we might accidentally design AI systems that have the capacity for enormous suffering You can find Luisa and Rob’s follow-up conversation here, or by subscribing to 80k After Hours. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:20)What artificial sentience would look like (00:04:53)Risks from artificial sentience (00:10:13)AIs with totally different ranges of experience (00:17:45)Moral implications of all this (00:36:42)Is artificial sentience even possible? (00:42:12)Replacing neurons one at a time (00:48:21)Biological theories (00:59:14)Illusionism (01:01:49)Would artificial sentience systems matter morally? (01:08:09)Where are we with current systems? (01:12:25)Large language models and robots (01:16:43)Multimodal systems (01:21:05)Global workspace theory (01:28:28)How confident are we in these theories? (01:48:49)The hard problem of consciousness (02:02:14)Exotic states of consciousness (02:09:47)Developing a full theory of consciousness (02:15:45)Incentives for an AI system to feel pain or pleasure (02:19:04)Value beyond conscious experiences (02:29:25)How much we know about pain and pleasure (02:33:14)False positives and false negatives of artificial sentience (02:39:34)How large language models compare to animals (02:53:59)Why our current large language models aren’t conscious (02:58:10)Virtual research assistants (03:09:25)Rob’s outro (03:11:37)Producer: Keiran HarrisAudio mastering: Ben Cordell and Milo McGuireTranscriptions: Katy Moore

In many ways, humanity seems to have become more humane and inclusive over time. While there’s still a lot of progress to be made, campaigns to give people of different genders, races, sexualities, ethnicities, beliefs, and abilities equal treatment and rights have had significant success.It’s tempting to believe this was inevitable — that the arc of history “bends toward justice,” and that as humans get richer, we’ll make even more moral progress.But today's guest Christopher Brown — a professor of history at Columbia University and specialist in the abolitionist movement and the British Empire during the 18th and 19th centuries — believes the story of how slavery became unacceptable suggests moral progress is far from inevitable. Links to learn more, video, highlights, and full transcript. While most of us today feel that the abolition of slavery was sure to happen sooner or later as humans became richer and more educated, Christopher doesn't believe any of the arguments for that conclusion pass muster. If he's right, a counterfactual history where slavery remains widespread in 2023 isn't so far-fetched. As Christopher lays out in his two key books, Moral Capital: Foundations of British Abolitionism and Arming Slaves: From Classical Times to the Modern Age, slavery has been ubiquitous throughout history. Slavery of some form was fundamental in Classical Greece, the Roman Empire, in much of the Islamic civilization, in South Asia, and in parts of early modern East Asia, Korea, China. It was justified on all sorts of grounds that sound mad to us today. But according to Christopher, while there’s evidence that slavery was questioned in many of these civilisations, and periodically attacked by slaves themselves, there was no enduring or successful moral advocacy against slavery until the British abolitionist movement of the 1700s. That movement first conquered Britain and its empire, then eventually the whole world. But the fact that there's only a single time in history that a persistent effort to ban slavery got off the ground is a big clue that opposition to slavery was a contingent matter: if abolition had been inevitable, we’d expect to see multiple independent abolitionist movements thoroughly history, providing redundancy should any one of them fail. Christopher argues that this rarity is primarily down to the enormous economic and cultural incentives to deny the moral repugnancy of slavery, and crush opposition to it with violence wherever necessary. Mere awareness is insufficient to guarantee a movement will arise to fix a problem. Humanity continues to allow many severe injustices to persist, despite being aware of them. So why is it so hard to imagine we might have done the same with forced labour? In this episode, Christopher describes the unique and peculiar set of political, social and religious circumstances that gave rise to the only successful and lasting anti-slavery movement in human history. These circumstances were sufficiently improbable that Christopher believes there are very nearby worlds where abolitionism might never have taken off. We also discuss:Various instantiations of slavery throughout human history Signs of antislavery sentiment before the 17th century The role of the Quakers in early British abolitionist movement The importance of individual “heroes” in the abolitionist movement Arguments against the idea that the abolition of slavery was contingent Whether there have ever been any major moral shifts that were inevitableGet this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran HarrisAudio mastering: Milo McGuireTranscriptions: Katy Moore

What’s the opposite of cancer? If you answered “cure,” “antidote,” or “antivenom” — you’ve obviously been reading the antonym section at www.merriam-webster.com/thesaurus/cancer. But today’s guest Athena Aktipis says that the opposite of cancer is us: it's having a functional multicellular body that’s cooperating effectively in order to make that multicellular body function. If, like us, you found her answer far more satisfying than the dictionary, maybe you could consider closing your dozens of merriam-webster.com tabs, and start listening to this podcast instead. Links to learn more, summary and full transcript. As Athena explains in her book The Cheating Cell, what we see with cancer is a breakdown in each of the foundations of cooperation that allowed multicellularity to arise: • Cells will proliferate when they shouldn't. • Cells won't die when they should. • Cells won't engage in the kind of division of labour that they should. • Cells won’t do the jobs that they're supposed to do. • Cells will monopolise resources. • And cells will trash the environment. When we think about animals in the wild, or even bacteria living inside our cells, we understand that they're facing evolutionary pressures to figure out how they can replicate more; how they can get more resources; and how they can avoid predators — like lions, or antibiotics. We don’t normally think of individual cells as acting as if they have their own interests like this. But cancer cells are actually facing similar kinds of evolutionary pressures within our bodies, with one major difference: they replicate much, much faster. Incredibly, the opportunity for evolution by natural selection to operate just over the course of cancer progression is easily faster than all of the evolutionary time that we have had as humans since *Homo sapiens* came about. Here’s a quote from Athena: “So you have to shift your thinking to be like: the body is a world with all these different ecosystems in it, and the cells are existing on a time scale where, if we're going to map it onto anything like what we experience, a day is at least 10 years for them, right? So it's a very, very different way of thinking.” You can find compelling examples of cooperation and conflict all over the universe, so Rob and Athena don’t stop with cancer. They also discuss: • Cheating within cells themselves • Cooperation in human societies as they exist today — and perhaps in the future, between civilisations spread across different planets or stars • Whether it’s too out-there to think of humans as engaging in cancerous behaviour • Why elephants get deadly cancers less often than humans, despite having way more cells • When a cell should commit suicide • The strategy of deliberately not treating cancer aggressively • Superhuman cooperation And at the end of the episode, they cover Athena’s new book Everything is Fine! How to Thrive in the Apocalypse, including: • Staying happy while thinking about the apocalypse • Practical steps to prepare for the apocalypse • And whether a zombie apocalypse is already happening among Tasmanian devils And if you’d rather see Rob and Athena’s facial expressions as they laugh and laugh while discussing cancer and the apocalypse — you can watch the video of the full interview. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran Harris Audio mastering: Milo McGuire Transcriptions: Katy Moore

Rebroadcast: this episode was originally released in June 2020. Today’s guest, New York Times bestselling author A.J. Jacobs, always hated Judge Judy. But after he found out that she was his seventh cousin, he thought, "You know what, she's not so bad". Hijacking this bias towards family and trying to broaden it to everyone led to his three-year adventure to help build the biggest family tree in history. He’s also spent months saying whatever was on his mind, tried to become the healthiest person in the world, read 33,000 pages of facts, spent a year following the Bible literally, thanked everyone involved in making his morning cup of coffee, and tried to figure out how to do the most good. His latest book asks: if we reframe global problems as puzzles, would the world be a better place? Links to learn more, summary and full transcript. This is the first time I’ve hosted the podcast, and I’m hoping to convince people to listen with this attempt at clever show notes that change style each paragraph to reference different A.J. experiments. I don’t actually think it’s that clever, but all of my other ideas seemed worse. I really have no idea how people will react to this episode; I loved it, but I definitely think I’m more entertaining than almost anyone else will. (Radical Honesty.) We do talk about some useful stuff — one of which is the concept of micro goals. When you wake up in the morning, just commit to putting on your workout clothes. Once they’re on, maybe you’ll think that you might as well get on the treadmill — just for a minute. And once you’re on for 1 minute, you’ll often stay on for 20. So I’m not asking you to commit to listening to the whole episode — just to put on your headphones. (Drop Dead Healthy.) Another reason to listen is for the facts: • The Bayer aspirin company invented heroin as a cough suppressant • Coriander is just the British way of saying cilantro • Dogs have a third eyelid to protect the eyeball from irritants • and A.J. read all 44 million words of the Encyclopedia Britannica from A to Z, which drove home the idea that we know so little about the world (although he does now know that opossums have 13 nipples). (The Know-It-All.) One extra argument for listening: If you interpret the second commandment literally, then it tells you not to make a likeness of anything in heaven, on earth, or underwater — which rules out basically all images. That means no photos, no TV, no movies. So, if you want to respect the bible, you should definitely consider making podcasts your main source of entertainment (as long as you’re not listening on the Sabbath). (The Year of Living Biblically.) I’m so thankful to A.J. for doing this. But I also want to thank Julie, Jasper, Zane and Lucas who allowed me to spend the day in their home; the construction worker who told me how to get to my subway platform on the morning of the interview; and Queen Jadwiga for making bagels popular in the 1300s, which kept me going during the recording. (Thanks a Thousand.) We also discuss: • Blackmailing yourself • The most extreme ideas A.J.’s ever considered • Utilitarian movie reviews • Doing good as a writer • And much more. Get this episode by subscribing to our podcast on the world’s most pressing problems: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

Rebroadcast: this episode was originally released in July 2020. 80,000 Hours, along with many other members of the effective altruism movement, has argued that helping to positively shape the development of artificial intelligence may be one of the best ways to have a lasting, positive impact on the long-term future. Millions of dollars in philanthropic spending, as well as lots of career changes, have been motivated by these arguments. Today’s guest, Ben Garfinkel, Research Fellow at Oxford’s Future of Humanity Institute, supports the continued expansion of AI safety as a field and believes working on AI is among the very best ways to have a positive impact on the long-term future. But he also believes the classic AI risk arguments have been subject to insufficient scrutiny given this level of investment. In particular, the case for working on AI if you care about the long-term future has often been made on the basis of concern about AI accidents; it’s actually quite difficult to design systems that you can feel confident will behave the way you want them to in all circumstances. Nick Bostrom wrote the most fleshed out version of the argument in his book, Superintelligence. But Ben reminds us that, apart from Bostrom’s book and essays by Eliezer Yudkowsky, there's very little existing writing on existential accidents. Links to learn more, summary and full transcript. There have also been very few skeptical experts that have actually sat down and fully engaged with it, writing down point by point where they disagree or where they think the mistakes are. This means that Ben has probably scrutinised classic AI risk arguments as carefully as almost anyone else in the world. He thinks that most of the arguments for existential accidents often rely on fuzzy, abstract concepts like optimisation power or general intelligence or goals, and toy thought experiments. And he doesn’t think it’s clear we should take these as a strong source of evidence. Ben’s also concerned that these scenarios often involve massive jumps in the capabilities of a single system, but it's really not clear that we should expect such jumps or find them plausible. These toy examples also focus on the idea that because human preferences are so nuanced and so hard to state precisely, it should be quite difficult to get a machine that can understand how to obey them. But Ben points out that it's also the case in machine learning that we can train lots of systems to engage in behaviours that are actually quite nuanced and that we can't specify precisely. If AI systems can recognise faces from images, and fly helicopters, why don’t we think they’ll be able to understand human preferences? Despite these concerns, Ben is still fairly optimistic about the value of working on AI safety or governance. He doesn’t think that there are any slam-dunks for improving the future, and so the fact that there are at least plausible pathways for impact by working on AI safety and AI governance, in addition to it still being a very neglected area, puts it head and shoulders above most areas you might choose to work in. This is the second episode hosted by Howie Lempel, and he and Ben cover, among many other things: • The threat of AI systems increasing the risk of permanently damaging conflict or collapse • The possibility of permanently locking in a positive or negative future • Contenders for types of advanced systems • What role AI should play in the effective altruism portfolio Get this episode by subscribing: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

Rebroadcast: this episode was originally released in July 2020. Today’s guest, Jennifer Doleac — Associate Professor of Economics at Texas A&M University, and Director of the Justice Tech Lab — is an expert on empirical research into policing, law and incarceration. In this extensive interview, she highlights three ways to effectively prevent crime that don't require police or prisons and the human toll they bring with them: better street lighting, cognitive behavioral therapy, and lead reduction. One of Jennifer’s papers used switches into and out of daylight saving time as a 'natural experiment' to measure the effect of light levels on crime. One day the sun sets at 5pm; the next day it sets at 6pm. When that evening hour is dark instead of light, robberies during it roughly double. Links to sources for the claims in these show notes, other resources to learn more, the full blog post, and a full transcript. The idea here is that if you try to rob someone in broad daylight, they might see you coming, and witnesses might later be able to identify you. You're just more likely to get caught. You might think: "Well, people will just commit crime in the morning instead". But it looks like criminals aren’t early risers, and that doesn’t happen. On her unusually rigorous podcast Probable Causation, Jennifer spoke to one of the authors of a related study, in which very bright streetlights were randomly added to some public housing complexes but not others. They found the lights reduced outdoor night-time crime by 36%, at little cost. The next best thing to sun-light is human-light, so just installing more streetlights might be one of the easiest ways to cut crime, without having to hassle or punish anyone. The second approach is cognitive behavioral therapy (CBT), in which you're taught to slow down your decision-making, and think through your assumptions before acting. There was a randomised controlled trial done in schools, as well as juvenile detention facilities in Chicago, where the kids assigned to get CBT were followed over time and compared with those who were not assigned to receive CBT. They found the CBT course reduced rearrest rates by a third, and lowered the likelihood of a child returning to a juvenile detention facility by 20%. Jennifer says that the program isn’t that expensive, and the benefits are massive. Everyone would probably benefit from being able to talk through their problems but the gains are especially large for people who've grown up with the trauma of violence in their lives. Finally, Jennifer thinks that reducing lead levels might be the best buy of all in crime prevention. There is really compelling evidence that lead not only increases crime, but also dramatically reduces educational outcomes. In today’s conversation, Rob and Jennifer also cover, among many other things: • Misconduct, hiring practices and accountability among US police • Procedural justice training • Overrated policy ideas • Policies to try to reduce racial discrimination • The effects of DNA databases • Diversity in economics • The quality of social science research Get this episode by subscribing: type 80,000 Hours into your podcasting app. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

America aims to avoid nuclear war by relying on the principle of 'mutually assured destruction,' right? Wrong. Or at least... not officially.As today's guest — Jeffrey Lewis, founder of Arms Control Wonk and professor at the Middlebury Institute of International Studies — explains, in its official 'OPLANs' (military operation plans), the US is committed to 'dominating' in a nuclear war with Russia. How would they do that? "That is redacted." Links to learn more, summary and full transcript. We invited Jeffrey to come on the show to lay out what we and our listeners are most likely to be misunderstanding about nuclear weapons, the nuclear posture of major powers, and his field as a whole, and he did not disappoint. As Jeffrey tells it, 'mutually assured destruction' was a slur used to criticise those who wanted to limit the 1960s arms buildup, and was never accepted as a matter of policy in any US administration. But isn't it still the de facto reality? Yes and no. Jeffrey is a specialist on the nuts and bolts of bureaucratic and military decision-making in real-life situations. He suspects that at the start of their term presidents get a briefing about the US' plan to prevail in a nuclear war and conclude that "it's freaking madness." They say to themselves that whatever these silly plans may say, they know a nuclear war cannot be won, so they just won't use the weapons. But Jeffrey thinks that's a big mistake. Yes, in a calm moment presidents can resist pressure from advisors and generals. But that idea of ‘winning’ a nuclear war is in all the plans. Staff have been hired because they believe in those plans. It's what the generals and admirals have all prepared for. What matters is the 'not calm moment': the 3AM phone call to tell the president that ICBMs might hit the US in eight minutes — the same week Russia invades a neighbour or China invades Taiwan. Is it a false alarm? Should they retaliate before their land-based missile silos are hit? There's only minutes to decide. Jeffrey points out that in emergencies, presidents have repeatedly found themselves railroaded into actions they didn't want to take because of how information and options were processed and presented to them. In the heat of the moment, it's natural to reach for the plan you've prepared — however mad it might sound. In this spicy conversation, Jeffrey fields the most burning questions from Rob and the audience, in the process explaining: • Why inter-service rivalry is one of the biggest constraints on US nuclear policy • Two times the US sabotaged nuclear nonproliferation among great powers • How his field uses jargon to exclude outsiders • How the US could prevent the revival of mass nuclear testing by the great powers • Why nuclear deterrence relies on the possibility that something might go wrong • Whether 'salami tactics' render nuclear weapons ineffective • The time the Navy and Air Force switched views on how to wage a nuclear war, just when it would allow *them* to have the most missiles • The problems that arise when you won't talk to people you think are evil • Why missile defences are politically popular despite being strategically foolish • How open source intelligence can prevent arms races • And much more.Chapters:Rob’s intro (00:00:00)The interview begins (00:02:49)Misconceptions in the effective altruism community (00:05:42)Nuclear deterrence (00:17:36)Dishonest rituals (00:28:17)Downsides of generalist research (00:32:13)“Mutual assured destruction” (00:38:18)Budgetary considerations for competing parts of the US military (00:51:53)Where the effective altruism community can potentially add the most value (01:02:15)Gatekeeping (01:12:04)Strengths of the nuclear security community (01:16:14)Disarmament (01:26:58)Nuclear winter (01:38:53)Attacks against US allies (01:41:46)Most likely weapons to get used (01:45:11)The role of moral arguments (01:46:40)Salami tactics (01:52:01)Jeffrey's disagreements with Thomas Schelling (01:57:00)Why did it take so long to get nuclear arms agreements? (02:01:11)Detecting secret nuclear facilities (02:03:18)Where Jeffrey would give $10M in grants (02:05:46)The importance of archival research (02:11:03)Jeffrey's policy ideas (02:20:03)What should the US do regarding China? (02:27:10)What should the US do regarding Russia? (02:31:42)What should the US do regarding Taiwan? (02:35:27)Advice for people interested in working on nuclear security (02:37:23)Rob’s outro (02:39:13)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

John McWhorter is a linguistics professor at Columbia University specialising in research on creole languages. He's also a content-producing machine, never afraid to give his frank opinion on anything and everything. On top of his academic work he's also written 22 books, produced five online university courses, hosts one and a half podcasts, and now writes a regular New York Times op-ed column. • Links to learn more, summary, and full transcript • Video version of the interview • Lecture: Why the world looks the same in any language Our show is mostly about the world's most pressing problems and what you can do to solve them. But what's the point of hosting a podcast if you can't occasionally just talk about something fascinating with someone whose work you appreciate? So today, just before the holidays, we're sharing this interview with John about language and linguistics — including what we think are some of the most important things everyone ought to know about those topics. We ask him: • Can you communicate faster in some languages than others, or is there some constraint that prevents that? • Does learning a second or third language make you smarter or not? • Can a language decay and get worse at communicating what people want to say? • If children aren't taught a language, how many generations does it take them to invent a fully fledged one of their own? • Did Shakespeare write in a foreign language, and if so, should we translate his plays? • How much does language really shape the way we think? • Are creoles the best languages in the world — languages that ideally we would all speak? • What would be the optimal number of languages globally? • Does trying to save dying languages do their speakers a favour, or is it more of an imposition? • Should we bother to teach foreign languages in UK and US schools? • Is it possible to save the important cultural aspects embedded in a dying language without saving the language itself? • Will AI models speak a language of their own in the future, one that humans can't understand but which better serves the tradeoffs AI models need to make? We then put some of these questions to ChatGPT itself, asking it to play the role of a linguistics professor at Colombia University. We’ve also added John’s talk “Why the World Looks the Same in Any Language”  to the end of this episode. So stick around after the credits! And if you’d rather see Rob and John’s facial expressions or beautiful high cheekbones while listening to this conversation, you can watch the video of the full conversation here. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran Harris Audio mastering: Ben Cordell Video editing: Ryan Kessler Transcriptions: Katy Moore

Large language models like GPT-3, and now ChatGPT, are neural networks trained on a large fraction of all text available on the internet to do one thing: predict the next word in a passage. This simple technique has led to something extraordinary — black boxes able to write TV scripts, explain jokes, produce satirical poetry, answer common factual questions, argue sensibly for political positions, and more. Every month their capabilities grow. But do they really 'understand' what they're saying, or do they just give the illusion of understanding? Today's guest, Richard Ngo, thinks that in the most important sense they understand many things. Richard is a researcher at OpenAI — the company that created ChatGPT — who works to foresee where AI advances are going and develop strategies that will keep these models from 'acting out' as they become more powerful, are deployed and ultimately given power in society. Links to learn more, summary and full transcript. One way to think about 'understanding' is as a subjective experience. Whether it feels like something to be a large language model is an important question, but one we currently have no way to answer. However, as Richard explains, another way to think about 'understanding' is as a functional matter. If you really understand an idea you're able to use it to reason and draw inferences in new situations. And that kind of understanding is observable and testable. Richard argues that language models are developing sophisticated representations of the world which can be manipulated to draw sensible conclusions — maybe not so different from what happens in the human mind. And experiments have found that, as models get more parameters and are trained on more data, these types of capabilities consistently improve. We might feel reluctant to say a computer understands something the way that we do. But if it walks like a duck and it quacks like a duck, we should consider that maybe we have a duck, or at least something sufficiently close to a duck it doesn't matter. In today's conversation we discuss the above, as well as: • Could speeding up AI development be a bad thing? • The balance between excitement and fear when it comes to AI advances • What OpenAI focuses its efforts where it does • Common misconceptions about machine learning • How many computer chips it might require to be able to do most of the things humans do • How Richard understands the 'alignment problem' differently than other people • Why 'situational awareness' may be a key concept for understanding the behaviour of AI models • What work to positively shape the development of AI Richard is and isn't excited about • The AGI Safety Fundamentals course that Richard developed to help people learn more about this field Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran Harris Audio mastering: Milo McGuire and Ben Cordell Transcriptions: Katy Moore

Today’s release is a reading of our article called My experience with imposter syndrome — and how to (partly) overcome it, written and narrated by Luisa Rodriguez. If you want to check out the links, footnotes and figures in today’s article, you can find those here. And if you like this article, you’ll probably enjoy episode #100 of this show: Having a successful career with depression, anxiety, and imposter syndrome Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Producer: Keiran Harris Audio mastering and editing for this episode: Milo McGuire

In this episode, usual host of the show Rob Wiblin gives his thoughts on the recent collapse of FTX. Click here for an official 80,000 Hours statement. And here are links to some potentially relevant 80,000 Hours pieces: • Episode #24 of this show – Stefan Schubert on why it’s a bad idea to break the rules, even if it’s for a good cause. • Is it ever OK to take a harmful job in order to do more good? An in-depth analysis • What are the 10 most harmful jobs? • Ways people trying to do good accidentally make things worse, and how to avoid them

Is war in long-term decline? Steven Pinker's The Better Angels of Our Nature brought this previously obscure academic question to the centre of public debate, and pointed to rates of death in war to argue energetically that war is on the way out. But that idea divides war scholars and statisticians, and so Better Angels has prompted a spirited debate, with datasets and statistical analyses exchanged back and forth year after year. The lack of consensus has left a somewhat bewildered public (including host Rob Wiblin) unsure quite what to believe. Today's guest, professor in political science Bear Braumoeller, is one of the scholars who believes we lack convincing evidence that warlikeness is in long-term decline. He collected the analysis that led him to that conclusion in his 2019 book, Only the Dead: The Persistence of War in the Modern Age. Links to learn more, summary and full transcript. The question is of great practical importance. The US and PRC are entering a period of renewed great power competition, with Taiwan as a potential trigger for war, and Russia is once more invading and attempting to annex the territory of its neighbours. If war has been going out of fashion since the start of the Enlightenment, we might console ourselves that however nerve-wracking these present circumstances may feel, modern culture will throw up powerful barriers to another world war. But if we're as war-prone as we ever have been, one need only inspect the record of the 20th century to recoil in horror at what might await us in the 21st. Bear argues that the second reaction is the appropriate one. The world has gone up in flames many times through history, with roughly 0.5% of the population dying in the Napoleonic Wars, 1% in World War I, 3% in World War II, and perhaps 10% during the Mongol conquests. And with no reason to think similar catastrophes are any less likely today, complacency could lead us to sleepwalk into disaster. He gets to this conclusion primarily by analysing the datasets of the decades-old Correlates of War project, which aspires to track all interstate conflicts and battlefield deaths since 1815. In Only the Dead, he chops up and inspects this data dozens of different ways, to test if there are any shifts over time which seem larger than what could be explained by chance variation alone. In a nutshell, Bear simply finds no general trend in either direction from 1815 through today. It seems like, as philosopher George Santayana lamented in 1922, "only the dead have seen the end of war". In today's conversation, Bear and Rob discuss all of the above in more detail than even a usual 80,000 Hours podcast episode, as well as: • Why haven't modern ideas about the immorality of violence led to the decline of war, when it's such a natural thing to expect? • What would Bear's critics say in response to all this? • What do the optimists get right? • How does one do proper statistical tests for events that are clumped together, like war deaths? • Why are deaths in war so concentrated in a handful of the most extreme events? • Did the ideas of the Enlightenment promote nonviolence, on balance? • Were early states more or less violent than groups of hunter-gatherers? • If Bear is right, what can be done? • How did the 'Concert of Europe' or 'Bismarckian system' maintain peace in the 19th century? • Which wars are remarkable but largely unknown? Chapters:Rob’s intro (00:00:00)The interview begins (00:03:32)Only the Dead (00:06:28)The Enlightenment (00:16:47)Democratic peace theory (00:26:22)Is religion a key driver of war? (00:29:27)International orders (00:33:07)The Concert of Europe (00:42:15)The Bismarckian system (00:53:43)The current international order (00:58:16)The Better Angels of Our Nature (01:17:30)War datasets (01:32:03)Seeing patterns in data where none exist (01:45:32)Change-point analysis (01:49:33)Rates of violent death throughout history (01:54:32)War initiation (02:02:55)Escalation (02:17:57)Getting massively different results from the same data (02:28:38)How worried we should be (02:34:07)Most likely ways Only the Dead is wrong (02:36:25)Astonishing smaller wars (02:40:39)Producer: Keiran HarrisAudio mastering: Ryan KesslerTranscriptions: Katy Moore

A casino offers you a game. A coin will be tossed. If it comes up heads on the first flip you win $2. If it comes up on the second flip you win $4. If it comes up on the third you win $8, the fourth you win $16, and so on. How much should you be willing to pay to play? The standard way of analysing gambling problems, ‘expected value’ — in which you multiply probabilities by the value of each outcome and then sum them up — says your expected earnings are infinite. You have a 50% chance of winning $2, for '0.5 * $2 = $1' in expected earnings. A 25% chance of winning $4, for '0.25 * $4 = $1' in expected earnings, and on and on. A never-ending series of $1s added together comes to infinity. And that's despite the fact that you know with certainty you can only ever win a finite amount! Today's guest — philosopher Alan Hájek of the Australian National University — thinks of much of philosophy as “the demolition of common sense followed by damage control” and is an expert on paradoxes related to probability and decision-making rules like “maximise expected value.” Links to learn more, summary and full transcript. The problem described above, known as the St. Petersburg paradox, has been a staple of the field since the 18th century, with many proposed solutions. In the interview, Alan explains how very natural attempts to resolve the paradox — such as factoring in the low likelihood that the casino can pay out very large sums, or the fact that money becomes less and less valuable the more of it you already have — fail to work as hoped. We might reject the setup as a hypothetical that could never exist in the real world, and therefore of mere intellectual curiosity. But Alan doesn't find that objection persuasive. If expected value fails in extreme cases, that should make us worry that something could be rotten at the heart of the standard procedure we use to make decisions in government, business, and nonprofits. These issues regularly show up in 80,000 Hours' efforts to try to find the best ways to improve the world, as the best approach will arguably involve long-shot attempts to do very large amounts of good. Consider which is better: saving one life for sure, or three lives with 50% probability? Expected value says the second, which will probably strike you as reasonable enough. But what if we repeat this process and evaluate the chance to save nine lives with 25% probability, or 27 lives with 12.5% probability, or after 17 more iterations, 3,486,784,401 lives with a 0.00000009% chance. Expected value says this final offer is better than the others — 1,000 times better, in fact. Ultimately Alan leans towards the view that our best choice is to “bite the bullet” and stick with expected value, even with its sometimes counterintuitive implications. Where we want to do damage control, we're better off looking for ways our probability estimates might be wrong. In today's conversation, Alan and Rob explore these issues and many others: • Simple rules of thumb for having philosophical insights • A key flaw that hid in Pascal's wager from the very beginning • Whether we have to simply ignore infinities because they mess everything up • What fundamentally is 'probability'? • Some of the many reasons 'frequentism' doesn't work as an account of probability • Why the standard account of counterfactuals in philosophy is deeply flawed • And why counterfactuals present a fatal problem for one sort of consequentialism Chapters:Rob’s intro (00:00:00)The interview begins (00:01:48)Philosophical methodology (00:02:54)Theories of probability (00:37:17)Everyday Bayesianism (00:46:01)Frequentism (01:04:56)Ranges of probabilities (01:16:23)Implications for how to live (01:21:24)Expected value (01:26:58)The St. Petersburg paradox (01:31:40)Pascal's wager (01:49:44)Using expected value in everyday life (02:03:53)Counterfactuals (02:16:38)Most counterfactuals are false (02:52:25)Relevance to objective consequentialism (03:09:47)Marker 18 (03:10:21)Alan’s best conference story (03:33:37)Producer: Keiran HarrisAudio mastering: Ben Cordell and Ryan KesslerTranscriptions: Katy Moore

Today’s release is a professional reading of our new problem profile on preventing an AI-related catastrophe, written by Benjamin Hilton. We expect that there will be substantial progress in AI in the next few decades, potentially even to the point where machines come to outperform humans in many, if not all, tasks. This could have enormous benefits, helping to solve currently intractable global problems, but could also pose severe risks. These risks could arise accidentally (for example, if we don’t find technical solutions to concerns about the safety of AI systems), or deliberately (for example, if AI systems worsen geopolitical conflict). We think more work needs to be done to reduce these risks. Some of these risks from advanced AI could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity. There have not yet been any satisfying answers to concerns about how this rapidly approaching, transformative technology can be safely developed and integrated into our society. Finding answers to these concerns is very neglected, and may well be tractable. We estimate that there are around 300 people worldwide working directly on this. As a result, the possibility of AI-related catastrophe may be the world’s most pressing problem — and the best thing to work on for those who are well-placed to contribute. Promising options for working on this problem include technical research on how to create safe AI systems, strategy research into the particular risks AI might pose, and policy research into ways in which companies and governments could mitigate these risks. If worthwhile policies are developed, we’ll need people to put them in place and implement them. There are also many opportunities to have a big impact in a variety of complementary roles, such as operations management, journalism, earning to give, and more. If you want to check out the links, footnotes and figures in today’s article, you can find those here. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Producer: Keiran Harris Editing and narration: Perrin Walker and Shaun Acker Audio proofing: Katy Moore

What in the world is intrinsically good — good in itself even if it has no other effects? Over the millennia, people have offered many answers: joy, justice, equality, accomplishment, loving god, wisdom, and plenty more. The question is a classic that makes for great dorm-room philosophy discussion. But it's hardly just of academic interest. The issue of what (if anything) is intrinsically valuable bears on every action we take, whether we’re looking to improve our own lives, or to help others. The wrong answer might lead us to the wrong project and render our efforts to improve the world entirely ineffective. Today's guest, Sharon Hewitt Rawlette — philosopher and author of The Feeling of Value: Moral Realism Grounded in Phenomenal Consciousness — wants to resuscitate an answer to this question that is as old as philosophy itself. Links to learn more, summary, full transcript, and full version of this blog post. That idea, in a nutshell, is that there is only one thing of true intrinsic value: positive feelings and sensations. And similarly, there is only one thing that is intrinsically of negative value: suffering, pain, and other unpleasant sensations. Lots of other things are valuable too: friendship, fairness, loyalty, integrity, wealth, patience, houses, and so on. But they are only instrumentally valuable — that is to say, they’re valuable as means to the end of ensuring that all conscious beings experience more pleasure and other positive sensations, and less suffering. As Sharon notes, from Athens in 400 BC to Britain in 1850, the idea that only subjective experiences can be good or bad in themselves -- a position known as 'philosophical hedonism' -- has been one of the most enduringly popular ideas in ethics. And few will be taken aback by the notion that, all else equal, more pleasure is good and less suffering is bad. But can they really be the only intrinsically valuable things? Over the 20th century, philosophical hedonism became increasingly controversial in the face of some seemingly very counterintuitive implications. For this reason the famous philosopher of mind Thomas Nagel called The Feeling of Value "a radical and important philosophical contribution." In today's interview, Sharon explains the case for a theory of value grounded in subjective experiences, and why she believes the most popular counterarguments are misguided. Host Rob Wiblin and Sharon also cover: • The essential need to disentangle intrinsic, instrumental, and other sorts of value • Why Sharon’s arguments lead to hedonistic utilitarianism rather than hedonistic egoism (in which we only care about our own feelings) • How do people react to the 'experience machine' thought experiment when surveyed? • Why hedonism recommends often thinking and acting as though it were false • Whether it's crazy to think that relationships are only useful because of their effects on our subjective experiences • Whether it will ever be possible to eliminate pain, and whether doing so would be desirable • If we didn't have positive or negative experiences, whether that would cause us to simply never talk about goodness and badness • Whether the plausibility of hedonism is affected by our theory of mind • And plenty more Chapters:Rob’s intro (00:00:00)The interview begins (00:02:45)Metaethics (00:04:16)Anti-realism (00:10:39)Sharon's theory of moral realism (00:16:17)The history of hedonism (00:23:11)Intrinsic value vs instrumental value (00:28:49)Egoistic hedonism (00:36:30)Single axis of value (00:42:19)Key objections to Sharon’s brand of hedonism (00:56:18)The experience machine (01:06:08)Robot spouses (01:22:29)Most common misunderstanding of Sharon’s view (01:27:10)How might a hedonist actually live (01:37:46)The organ transplant case (01:53:34)Counterintuitive implications of hedonistic utilitarianism (02:03:40)How could we discover moral facts? (02:18:05)Producer: Keiran HarrisAudio mastering: Ryan KesslerTranscriptions: Katy Moore

Effective altruism, in a slogan, aims to 'do the most good.' Utilitarianism, in a slogan, says we should act to 'produce the greatest good for the greatest number.' It's clear enough why utilitarians should be interested in the project of effective altruism. But what about the many people who reject utilitarianism? Today's guest, Andreas Mogensen — senior research fellow at Oxford University's Global Priorities Institute — rejects utilitarianism, but as he explains, this does little to dampen his enthusiasm for the project of effective altruism. Links to learn more, summary and full transcript. Andreas leans towards 'deontological' or rule-based theories of ethics, rather than 'consequentialist' theories like utilitarianism which look exclusively at the effects of a person's actions. Like most people involved in effective altruism, he parts ways with utilitarianism in rejecting its maximal level of demandingness, the idea that the ends justify the means, and the notion that the only moral reason for action is to benefit everyone in the world considered impartially. However, Andreas believes any plausible theory of morality must give some weight to the harms and benefits we provide to other people. If we can improve a stranger's wellbeing enormously at negligible cost to ourselves and without violating any other moral prohibition, that must be at minimum a praiseworthy thing to do. In a world as full of preventable suffering as our own, this simple 'principle of beneficence' is probably the only premise one needs to grant for the effective altruist project of identifying the most impactful ways to help others to be of great moral interest and importance. As an illustrative example Andreas refers to the Giving What We Can pledge to donate 10% of one's income to the most impactful charities available, a pledge he took in 2009. Many effective altruism enthusiasts have taken such a pledge, while others spend their careers trying to figure out the most cost-effective places pledgers can give, where they'll get the biggest 'bang for buck'. For someone living in a world as unequal as our own, this pledge at a very minimum gives an upper-middle class person in a rich country the chance to transfer money to someone living on about 1% as much as they do. The benefit an extremely poor recipient receives from the money is likely far more than the donor could get spending it on themselves. What arguments could a non-utilitarian moral theory mount against such giving? Many approaches to morality will say it's permissible not to give away 10% of your income to help others as effectively as is possible. But if they will almost all regard it as praiseworthy to benefit others without giving up something else of equivalent moral value, then Andreas argues they should be enthusiastic about effective altruism as an intellectual and practical project nonetheless. In this conversation, Andreas and Rob discuss how robust the above line of argument is, and also cover: • Should we treat thought experiments that feature very large numbers with great suspicion? • If we had to allow someone to die to avoid preventing the World Cup final from being broadcast to the world, is that permissible? • What might a virtue ethicist regard as 'doing the most good'? • If a deontological theory of morality parted ways with common effective altruist practices, how would that likely be? • If we can explain how we came to hold a view on a moral issue by referring to evolutionary selective pressures, should we disbelieve that view? Chapters:Rob’s intro (00:00:00)The interview begins (00:01:36)Deontology and effective altruism (00:04:59)Giving What We Can (00:28:56)Longtermism without consequentialism (00:38:01)Further differences between deontologists and consequentialists (00:44:13)Virtue ethics and effective altruism (01:08:15)Is Andreas really a deontologist? (01:13:26)Large number scepticism (01:21:11)Evolutionary debunking arguments (01:58:48)How Andreas’s views have changed (02:12:18)Derek Parfit’s influence on Andreas (02:17:27)Producer: Keiran HarrisAudio mastering: Ben Cordell and Beppe RådvikTranscriptions: Katy Moore

People who exist in the future deserve some degree of moral consideration.The future could be very big, very long, and/or very good.We can reasonably hope to influence whether people in the future exist, and how good or bad their lives are.So trying to make the world better for future generations is a key priority of our time.This is the simple four-step argument for 'longtermism' put forward in What We Owe The Future, the latest book from today's guest — University of Oxford philosopher and cofounder of the effective altruism community, Will MacAskill. Links to learn more, summary and full transcript. From one point of view this idea is common sense. We work on breakthroughs to treat cancer or end use of fossil fuels not just for people alive today, but because we hope such scientific advances will help our children, grandchildren, and great-grandchildren as well. Some who take this longtermist idea seriously work to develop broad-spectrum vaccines they hope will safeguard humanity against the sorts of extremely deadly pandemics that could permanently throw civilisation off track — the sort of project few could argue is not worthwhile. But Will is upfront that longtermism is also counterintuitive. To start with, he's willing to contemplate timescales far beyond what's typically discussed. A natural objection to thinking millions of years ahead is that it's hard enough to take actions that have positive effects that persist for hundreds of years, let alone “indefinitely.” It doesn't matter how important something might be if you can't predictably change it. This is one reason, among others, that Will was initially sceptical of longtermism and took years to come around. He preferred to focus on ending poverty and preventable diseases in ways he could directly see were working. But over seven years he gradually changed his mind, and in *What We Owe The Future*, Will argues that in fact there are clear ways we might act now that could benefit not just a few but *all* future generations. The idea that preventing human extinction would have long-lasting impacts is pretty intuitive. If we entirely disappear, we aren't coming back. But the idea that we can shape human values — not just for our age, but for all ages — is a surprising one that Will has come to more recently. In the book, he argues that what people value is far more fragile and historically contingent than it might first seem. For instance, today it feels like the abolition of slavery was an inevitable part of the arc of history. But Will lays out that the best research on the topic suggests otherwise. If moral progress really is so contingent, and bad ideas can persist almost without end, it raises the stakes for moral debate today. If we don't eliminate a bad practice now, it may be with us forever. In today's in-depth conversation, we discuss the possibility of a harmful moral 'lock-in' as well as: • How Will was eventually won over to longtermism • The three best lines of argument against longtermism • How to avoid moral fanaticism • Which technologies or events are most likely to have permanent effects • What 'longtermists' do today in practice • How to predict the long-term effect of our actions • Whether the future is likely to be good or bad • Concrete ideas to make the future better • What Will donates his money to personally • Potatoes and megafauna • And plenty moreChapters:Rob’s intro (00:00:00)The interview begins (00:01:36)What longtermism actually is (00:02:31)The case for longtermism (00:04:30)What longtermists are actually doing (00:15:54)Will’s personal journey (00:22:15)Strongest arguments against longtermism (00:42:28)Preventing extinction vs. improving the quality of the future (00:59:29)Is humanity likely to converge on doing the same thing regardless? (01:06:58)Lock-in scenario vs. long reflection (01:27:11)Is the future good in expectation? (01:32:29)Can we actually predictably influence the future positively? (01:47:27)Tiny probabilities of enormous value (01:53:40)Stagnation (02:19:04)Concrete suggestions (02:34:27)Where Will donates (02:39:40)Potatoes and megafauna (02:41:48)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

After a frenetic level of commentary during February and March, the war in Ukraine has faded into the background of our news coverage. But with the benefit of time we're in a much stronger position to understand what happened, why, whether there are broader lessons to take away, and how the conflict might be ended. And the conflict appears far from over. So today, we are returning to speak a second time with Samuel Charap — one of the US’s foremost experts on Russia’s relationship with former Soviet states, and coauthor of the 2017 book Everyone Loses: The Ukraine Crisis and the Ruinous Contest for Post-Soviet Eurasia. Links to learn more, summary and full transcript. As Sam lays out, Russia controls much of Ukraine's east and south, and seems to be preparing to politically incorporate that territory into Russia itself later in the year. At the same time, Ukraine is gearing up for a counteroffensive before defensive positions become dug in over winter. Each day the war continues it takes a toll on ordinary Ukrainians, contributes to a global food shortage, and leaves the US and Russia unable to coordinate on any other issues and at an elevated risk of direct conflict. In today's brisk conversation, Rob and Sam cover the following topics: • Current territorial control and the level of attrition within Russia’s and Ukraine's military forces. • Russia's current goals. • Whether Sam's views have changed since March on topics like: Putin's motivations, the wisdom of Ukraine's strategy, the likely impact of Western sanctions, and the risks from Finland and Sweden joining NATO before the war ends. • Why so many people incorrectly expected Russia to fully mobilise for war or persist with their original approach to the invasion. • Whether there's anything to learn from many of our worst fears -- such as the use of bioweapons on civilians -- not coming to pass. • What can be done to ensure some nuclear arms control agreement between the US and Russia remains in place after 2026 (when New START expires). • Why Sam considers a settlement proposal put forward by Ukraine in late March to be the most plausible way to end the war and ensure stability — though it's still a long shot. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:31)The state of play in Ukraine (00:03:05)How things have changed since March (00:12:59)Has Russia learned from its mistakes? (00:23:40)Broader lessons (00:28:44)A possible way out (00:37:15) Producer: Keiran Harris Audio mastering: Ben Cordell and Ryan Kessler Transcriptions: Katy Moore

Wind back 1,000 years and the moral landscape looks very different to today. Most farming societies thought slavery was natural and unobjectionable, premarital sex was an abomination, women should obey their husbands, and commoners should obey their monarchs.Wind back 10,000 years and things look very different again. Most hunter-gatherer groups thought men who got too big for their britches needed to be put in their place rather than obeyed, and lifelong monogamy could hardly be expected of men or women.Why such big systematic changes — and why these changes specifically?That's the question best-selling historian Ian Morris takes up in his book, Foragers, Farmers, and Fossil Fuels: How Human Values Evolve. Ian has spent his academic life studying long-term history, trying to explain the big-picture changes that play out over hundreds or thousands of years. Links to learn more, summary and full transcript. There are a number of possible explanations one could offer for the wide-ranging shifts in opinion on the 'right' way to live. Maybe the natural sciences progressed and people realised their previous ideas were mistaken? Perhaps a few persuasive advocates turned the course of history with their revolutionary arguments? Maybe everyone just got nicer? In Foragers, Farmers and Fossil Fuels Ian presents a provocative alternative: human culture gradually evolves towards whatever system of organisation allows a society to harvest the most energy, and we then conclude that system is the most virtuous one. Egalitarian values helped hunter-gatherers hunt and gather effectively. Once farming was developed, hierarchy proved to be the social structure that produced the most grain (and best repelled nomadic raiders). And in the modern era, democracy and individuality have proven to be more productive ways to collect and exploit fossil fuels. On this theory, it's technology that drives moral values much more than moral philosophy. Individuals can try to persist with deeply held values that limit economic growth, but they risk being rendered irrelevant as more productive peers in their own society accrue wealth and power. And societies that fail to move with the times risk being conquered by more pragmatic neighbours that adapt to new technologies and grow in population and military strength. There are many objections one could raise to this theory, many of which we put to Ian in this interview. But the question is a highly consequential one: if we want to guess what goals our descendants will pursue hundreds of years from now, it would be helpful to have a theory for why our ancestors mostly thought one thing, while we mostly think another. Big though it is, the driver of human values is only one of several major questions Ian has tackled through his career. In today's episode, we discuss all of Ian's major books, taking on topics such as: • Why the Industrial Revolution happened in England rather than China • Whether or not wars can lead to less violence • Whether the evidence base in history — from document archives to archaeology — is strong enough to persuasively answer any of these questions • Why Ian thinks the way we live in the 21st century is probably a short-lived aberration • Whether the grand sweep of history is driven more by “very important people” or “vast impersonal forces” • Why Chinese ships never crossed the Pacific or rounded the southern tip of Africa • In what sense Ian thinks Brexit was “10,000 years in the making” • The most common misconceptions about macrohistoryChapters:Rob’s intro (00:00:00)The interview begins (00:01:51)Geography is Destiny (00:02:59)Why the West Rules—For Now (00:11:25)War! What is it Good For? (00:27:40)Expectations for the future (00:39:43)Foragers, Farmers, and Fossil Fuels (00:53:15)Historical methodology (01:02:35)Falsifiable alternative theories (01:15:20)Archaeology (01:22:18)Energy extraction technology as a key driver of human values (01:37:04)Allowing people to debate about values (01:59:38)Can productive wars still occur? (02:12:49)Where is history contingent and where isn't it? (02:29:45)How Ian thinks about the future (03:12:54)Macrohistory myths (03:29:12)Ian’s favourite archaeology memory (03:32:40)The most unfair criticism Ian’s ever received (03:34:39)Rob’s outro (03:39:16)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

On January 1, 2015, physicist Max Tegmark gave up something most of us love to do: complain about things without ever trying to fix them. That “put up or shut up” New Year’s resolution led to the first Puerto Rico conference and Open Letter on Artificial Intelligence — milestones for researchers taking the safe development of highly-capable AI systems seriously. Links to learn more, summary and full transcript. Max's primary work has been cosmology research at MIT, but his energetic and freewheeling nature has led him into so many other projects that you would be forgiven for forgetting it. In the 2010s he wrote two best-selling books, Our Mathematical Universe: My Quest for the Ultimate Nature of Reality, and Life 3.0: Being Human in the Age of Artificial Intelligence, and in 2014 founded a non-profit, the Future of Life Institute, which works to reduce all sorts of threats to humanity's future including nuclear war, synthetic biology, and AI. Max has complained about many other things over the years, from killer robots to the impact of social media algorithms on the news we consume. True to his 'put up or shut up' resolution, he and his team went on to produce a video on so-called ‘Slaughterbots’ which attracted millions of views, and develop a website called 'Improve The News' to help readers separate facts from spin. But given the stunning recent advances in capabilities — from OpenAI’s DALL-E to DeepMind’s Gato — AI itself remains top of his mind. You can now give an AI system like GPT-3 the text: "I'm going to go to this mountain with the faces on it. What is the capital of the state to the east of the state that that's in?" And it gives the correct answer (Saint Paul, Minnesota) — something most AI researchers would have said was impossible without fundamental breakthroughs just seven years ago. So back at MIT, he now leads a research group dedicated to what he calls “intelligible intelligence.” At the moment, AI systems are basically giant black boxes that magically do wildly impressive things. But for us to trust these systems, we need to understand them. He says that training a black box that does something smart needs to just be stage one in a bigger process. Stage two is: “How do we get the knowledge out and put it in a safer system?” Today’s conversation starts off giving a broad overview of the key questions about artificial intelligence: What's the potential? What are the threats? How might this story play out? What should we be doing to prepare? Rob and Max then move on to recent advances in capabilities and alignment, the mood we should have, and possible ways we might misunderstand the problem. They then spend roughly the last third talking about Max's current big passion: improving the news we consume — where Rob has a few reservations. They also cover: • Whether we could understand what superintelligent systems were doing • The value of encouraging people to think about the positive future they want • How to give machines goals • Whether ‘Big Tech’ is following the lead of ‘Big Tobacco’ • Whether we’re sleepwalking into disaster • Whether people actually just want their biases confirmed • Why Max is worried about government-backed fact-checking • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:01:19)How Max prioritises (00:12:33)Intro to AI risk (00:15:47)Superintelligence (00:35:56)Imagining a wide range of possible futures (00:47:45)Recent advances in capabilities and alignment (00:57:37)How to give machines goals (01:13:13)Regulatory capture (01:21:03)How humanity fails to fulfil its potential (01:39:45)Are we being hacked? (01:51:01)Improving the news (02:05:31)Do people actually just want their biases confirmed? (02:16:15)Government-backed fact-checking (02:37:00)Would a superintelligence seem like magic? (02:49:50)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

If a business has spent $100 million developing a product, it's a fair bet that they don't want it stolen in two seconds and uploaded to the web where anyone can use it for free. This problem exists in extreme form for AI companies. These days, the electricity and equipment required to train cutting-edge machine learning models that generate uncanny human text and images can cost tens or hundreds of millions of dollars. But once trained, such models may be only a few gigabytes in size and run just fine on ordinary laptops. Today's guest, the computer scientist and polymath Nova DasSarma, works on computer and information security for the AI company Anthropic. One of her jobs is to stop hackers exfiltrating Anthropic's incredibly expensive intellectual property, as recently happened to Nvidia. As she explains, given models’ small size, the need to store such models on internet-connected servers, and the poor state of computer security in general, this is a serious challenge. Links to learn more, summary and full transcript. The worries aren't purely commercial though. This problem looms especially large for the growing number of people who expect that in coming decades we'll develop so-called artificial 'general' intelligence systems that can learn and apply a wide range of skills all at once, and thereby have a transformative effect on society. If aligned with the goals of their owners, such general AI models could operate like a team of super-skilled assistants, going out and doing whatever wonderful (or malicious) things are asked of them. This might represent a huge leap forward for humanity, though the transition to a very different new economy and power structure would have to be handled delicately. If unaligned with the goals of their owners or humanity as a whole, such broadly capable models would naturally 'go rogue,' breaking their way into additional computer systems to grab more computing power — all the better to pursue their goals and make sure they can't be shut off. As Nova explains, in either case, we don't want such models disseminated all over the world before we've confirmed they are deeply safe and law-abiding, and have figured out how to integrate them peacefully into society. In the first scenario, premature mass deployment would be risky and destabilising. In the second scenario, it could be catastrophic -- perhaps even leading to human extinction if such general AI systems turn out to be able to self-improve rapidly rather than slowly. If highly capable general AI systems are coming in the next 10 or 20 years, Nova may be flying below the radar with one of the most important jobs in the world. We'll soon need the ability to 'sandbox' (i.e. contain) models with a wide range of superhuman capabilities, including the ability to learn new skills, for a period of careful testing and limited deployment — preventing the model from breaking out, and criminals from breaking in. Nova and her colleagues are trying to figure out how to do this, but as this episode reveals, even the state of the art is nowhere near good enough. In today's conversation, Rob and Nova cover: • How good or bad is information security today • The most secure computer systems that exist • How to design an AI training compute centre for maximum efficiency • Whether 'formal verification' can help us design trustworthy systems • How wide the gap is between AI capabilities and AI safety • How to disincentivise hackers • What should listeners do to strengthen their own security practices • And much more. Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Producer: Keiran Harris Audio mastering: Ben Cordell and Beppe Rådvik Transcriptions: Katy Moore