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.
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
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: Simon Monsour and Ben Cordell 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.
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.
As Tim stresses, perfectionism isn’t the same as being perfect, or simply pursuing excellence. What’s most distinctive about perfectionism is that a person’s standards don’t vary flexibly according to circumstance, meeting those standards without exception is key to their self-image, and they worry something terrible will happen if they fail to meet them.
It’s a mindset most of us have seen in ourselves at some point, or have seen people we love struggle with.
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
The pros and cons of perfectionism
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
How the Stoic approach to what we can can’t control can make it far easier to stay calm
What the Stoics do better than utilitarian philosophers and vice versa
What’s good about being guided by virtues as opposed to pursuing good consequences
How to decide which are the best virtues to live by
What the ancient Stoics got right from our point of view, and what they got wrong
And whether Stoicism has a place in modern mental health practice.
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: 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.
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.
To Johannes, another crucial thing to observe is that reducing local emissions in the near term is probably negatively correlated with one’s actual full impact. How can that be?
If you want to reduce your carbon emissions by a lot and soon, you’ll have to deploy a technology that is mature and being manufactured at scale, like solar and wind.
But 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.
And Johannes notes that in terms of speeding up technological advances and cost reductions, a million dollars spent on a very early-stage technology — one with few, if any, customers — packs a much bigger punch than buying a million dollars’ worth of something customers are already spending $100 billion on per year.
For instance, back in the early 2000’s, Germany subsidised the deployment of solar panels enormously. This did little to reduce carbon emissions in Germany at the time, because the panels were very expensive and Germany is not very sunny. But the programme did a lot to drive commercial R&D and increase the scale of panel manufacturing, which drove down costs and went on to increase solar deployments all over the world. That programme is long over, but continues to have impact by prompting solar deployments today that wouldn’t be economically viable if Germany hadn’t helped the solar industry during its infancy decades ago.
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.
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. (So far they’re two for three.)
According to Spencer, things are gradually improving. For example he sees more raw data and experimental materials being shared, which makes it much easier to check the work of other researchers.
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.
For instance, do you remember the sensational paper that claimed government policy was driven by the opinions of lobby groups and ‘elites,’ but hardly affected by the opinions of ordinary people? Huge if true! It got wall-to-wall coverage in the press and on social media. But unfortunately, the whole paper could only explain 7% of the variation in which policies were adopted. Basically the researchers just didn’t know what made some campaigns succeed while others didn’t — a point one wouldn’t learn without reading the paper and diving into confusing tables of numbers. Clever writing made their result seem more important and meaningful than it really was.
Another paper Spencer describes claimed to find that people with a history of trauma explore less. That experiment actually featured an “incredibly boring apple-picking game: you had an apple tree in front of you, and you either could pick another apple or go to the next tree. Those were your only options. And they found that people with histories of trauma were more likely to stay on the same tree. Does that actually prove anything about real-world behaviour?” It’s at best unclear.
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 what Spencer calls “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.
Spencer’s experiment to see 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 low-guilt approach to a fulfilling life and doing good, which he calls “Valuism.”
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: Ben Cordell and Milo McGuire Transcriptions: Katy Moore
By now, you’ve probably seen the extremely unsettling conversations Bing’s chatbot has been having (if you haven’t, check it out — it’s wild stuff). 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 Lemoine 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.
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.
Robert thinks there are a few different kinds of evidence we can draw from that are more useful than self-reports from the chatbots themselves.
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. Things like:
Having a physical or virtual body that you need to protect from damage
Being more of an “enduring agent” in the world (rather than just doing one calculation taking, at most, seconds)
Having a bunch of different kinds of incoming sources of information — visual and audio input, for example — that need to be managed
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.
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: Ben Cordell and Milo McGuire Transcriptions: 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.
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.
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.
Think of coal or oil today: we know that climate change is likely to cause huge harms, and we know that our coal and oil consumption contributes to climate change. But just believing that something is wrong doesn’t necessarily mean humanity stops doing it. We continue to use coal and oil because our whole economy is oriented around their use and we see it as too hard to stop.
Just as coal and oil are fundamental to the world economy now, for millennia slavery was deeply baked into the way the rich and powerful stayed rich and powerful, and it required a creative leap to imagine it being toppled.
More generally, 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.
Some disagree with Christopher, arguing that abolitionism was a natural consequence of the industrial revolution, which reduced Great Britain’s need for human labour, among other changes — and that abolitionism would therefore have eventually taken off wherever industrialization did. But as we discuss, Christopher doesn’t find that reply convincing.
If he’s right and the abolition of slavery was in fact contingent, we shouldn’t expect moral values to keep improving just because humanity continues to become richer. We might have to be much more deliberate than that if we want to ensure we keep moving moral progress forward.
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
Attitudes to slavery in other religions
The spread of antislavery in 18th century Britain
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 inevitable
Producer: Keiran Harris Audio mastering: Milo McGuire Transcriptions: Katy Moore
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.
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 go and kind of put yourself on a different spatial scale and time scale, and just 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. Then once you shift to that, you’re like, “Oh, wow, there’s so much that could be happening in terms of adaptation inside the body, how cells are actually evolving inside the body over the course of our lifetimes.” That shift just opens up all this potential for using evolutionary approaches in adaptationist thinking to generate hypotheses that then you can test.
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 our anti-contagious-cancer mechanisms are so successful
Why elephants get deadly cancers less often than humans, despite having way more cells
When a cell should commit suicide
When the human body deliberately produces tumours
The strategy of deliberately not treating cancer aggressively
Superhuman cooperation
And much more
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. Or read the transcript below.
Producer: Keiran Harris Audio mastering: Milo McGuire Video editing: Ryan Kessler Transcriptions: Katy Moore
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.”
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
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.
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: Ben Cordell Transcriptions: Katy Moore
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 get across?
If children aren’t taught any language at all, 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 the language we speak 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 the large language model ChatGPT, asking it to play the role of a linguistics professor at Colombia University.
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 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. Or read the transcript below.
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.
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.
One experiment conducted by AI researchers suggests that language models have some of this kind of understanding.
If you ask any of these models what city the Eiffel Tower is in and what else you might do on a holiday to visit the Eiffel Tower, they will say Paris and suggest visiting the Palace of Versailles and eating a croissant.
One would be forgiven for wondering whether this might all be accomplished merely by memorising word associations in the text the model has been trained on. To investigate this, the researchers found the part of the model that stored the connection between ‘Eiffel Tower’ and ‘Paris,’ and flipped that connection from ‘Paris’ to ‘Rome.’
If the model just associated some words with one another, you might think that this would lead it to now be mistaken about the location of the Eiffel Tower, but answer other questions correctly. However, this one flip was enough to switch its answers to many other questions as well. Now if you asked it what else you might visit on a trip to the Eiffel Tower, it will suggest visiting the Colosseum and eating pizza, among other changes.
Another piece of evidence comes from the way models are prompted to give responses to questions. Researchers have found that telling models to talk through problems step by step often significantly improves their performance, which suggests that models are doing something useful with that extra “thinking time”.
Richard argues, based on this and other experiments, 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, host Rob Wiblin and Richard 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
Why 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
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: Milo McGuire and Ben Cordell Transcriptions: Katy Moore
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.
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.
Among other metrics, Bear looks at:
Battlefield deaths alone, as a percentage of combatants’ populations, and as a percentage of world population.
The total number of wars starting in a given year.
Rates of war initiation as a fraction of all country pairs capable of fighting wars.
How likely it was during different periods that a given war would double in size.
In a nutshell, and taking in the full picture painted by these different measures, 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”.
That’s not to say things are the same in all periods. Depending on which indication of warlikeness you give the greatest weight, you can point to some periods that seem violent or pacific beyond what might be explained by random variation.
For instance, Bear points out that war initiation really did go down a lot at the end of the Cold War, with peace probably fostered by a period of unipolar US dominance, and the end of great power funding for proxy wars.
But that drop came after a period of somewhat above-average warlikeness during the Cold War. And surprisingly, the most peaceful period in Europe turns out not to be 1990–2015, but rather 1815–1855, during which the monarchical ‘Concert of Europe,’ scarred by the Napoleonic Wars, worked together to prevent revolution and interstate aggression.
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? Bear is no Enlightenment scholar, but his book notes (among other reasons) that while modernity threw up new reasons to embrace pacifism, it also gave us new reasons to embrace violence: as a means to overthrow monarchy, distribute the means of production more equally, or protect people a continent away from ethnic cleansing — all motives that would have been foreign in the 15th century.
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:
What would Bear’s critics say in response to all this?
What do the optimists get right?
What are the biggest problems with the Correlates of War dataset?
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?
What’s the connection between individual attitudes and group behaviour?
Is it a problem that this dataset looks at just the ‘state system’ and ‘battlefield deaths’?
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
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.”
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.
Insisting that people give up a sure thing in favour of a vanishingly low chance of a very large impact strikes some people as peculiar or even fanatical. But one of Alan’s PhD students, Hayden Wilkinson, discovered that rejecting expected value on this basis requires you to swallow even more bitter pills, like giving up on the idea that if A is better than B, and B is better than C, then A is also better than C.
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
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: Ben Cordell and Ryan Kessler Transcriptions: 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.
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.”
So what convinces Sharon that philosophical hedonism deserves another go?
Stepping back for a moment, any answer to the question “What has intrinsic value?” faces a serious challenge: “How do we know?” It’s far from clear how something having intrinsic value can cause us to believe that it has intrinsic value. And if there’s no causal or rational connection between something being valuable and our believing that it has value, we could only get the right answer by some extraordinary coincidence. You may feel it’s intrinsically valuable to treat people fairly, but maybe there’s just no reason to trust that intuition.
Since the 1700s, many philosophers working on so-called ‘metaethics’ — that is, the study of what ethical claims are and how we could know if they’re true — have despaired of us ever making sense of or identifying the location of ‘objective’ or ‘intrinsic’ value. They conclude that when we say things are ‘good,’ we aren’t really saying anything about their nature, but rather just expressing our own attitudes, or intentions, or something else.
Sharon disagrees. She says the answer to all this has been right under our nose all along.
We have a concept of value because of our experiences of positive sensations — sensations that immediately indicate to us that they are valuable and that if someone could create more of them, they ought to do so. Similarly, we have a concept of badness because of our experience of suffering — sensations that scream to us that if suffering were all there were, it would be a bad thing.
How do we know that pleasure is valuable, and that suffering is the opposite of valuable? Directly!
While I might be mistaken that a painting I’m looking at is in real life as it appears to me, I can’t be mistaken about the nature of my perception of it. If it looks red to me, it may or may not be red, but it’s definitely the case that I am perceiving redness. Similarly, while I might be mistaken that a painting is intrinsically valuable, I can’t be mistaken about the pleasurable sensations I’m feeling when I look at it, and the fact that among other qualities those sensations have the property of goodness.
While intuitive on some level, this arguably implies some very strange things. Most famously, the philosopher Robert Nozick challenged it with the idea of an ‘experience machine’: if you could enter into a simulated world and enjoy a life far more pleasurable than the one you experience now, should you do so, even if it would mean none of your accomplishments or relationships would be ‘real’? Nozick and many of his colleagues thought not.
The idea has also been challenged for failing to value human freedom and autonomy for its own sake. Would it really be OK to kill one person to use their organs to save the lives of five others, if doing so would generate more pleasure and less suffering? Few believe so.
In today’s interview, Sharon explains the case for a theory of value grounded in subjective experiences, and why she believes these counterarguments are misguided. A philosophical hedonist shouldn’t get in an experience machine, nor override an individual’s autonomy, except in situations so different from the classic thought experiments that it no longer seems strange they would do so.
Host Rob Wiblin and Sharon cover all that, as well as:
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
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
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 — does reject utilitarianism, but as he explains, this does little to dampen his enthusiasm for effective altruism.
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?
Perhaps it could interfere with the achievement of other important moral goals? In response to this Andreas notes that alleviating the suffering of people in severe poverty is an important goal that should compete with alternatives. And furthermore, giving 10% is not so much that it likely disrupts one’s ability to, for instance, care for oneself or one’s family, or participate in domestic politics.
Perhaps it involves the violation of important moral prohibitions, such as those on stealing or lying? In response Andreas points out that the activities advocated by effective altruism researchers almost never violate such prohibitions — and if a few do, one can simply rule out those options and choose among the rest.
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 philosophical thought experiments that feature very large numbers with great suspicion?
If we had to allow someone to die to avoid preventing the football World Cup final from being broadcast to the world, is that permissible or not? If not, what might that imply?
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?
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: Ben Cordell and Beppe Rådvik Transcriptions: 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.
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:
If we last as long as a typical mammal species, that’s another 700,000 years. If we last until the Earth is no longer habitable, that’s hundreds of millions of years. If we manage one day to take to the stars and build a civilisation there, we could live for hundreds of trillions of years. […] Future people [could] outnumber us a thousand or a million or a trillion to one.
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.
He highlights two effects that could be very enduring: “…reducing risks of extinction of human beings or of the collapse of civilisation, and ensuring that the values and ideas that guide future society are better ones rather than worse.”
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.
For thousands of years, almost everyone — from philosophers to slaves themselves — regarded slavery as acceptable in principle. At the time the British Empire ended its participation in the slave trade, the industry was booming and earning enormous profits. It’s estimated that abolition cost Britain 2% of its GDP for 50 years.
So why did it happen? The global abolition movement seems to have originated within the peculiar culture of the Quakers, who were the first to argue slavery was unacceptable in all cases and campaign for its elimination, gradually convincing those around them with both Enlightenment and Christian arguments. If a few such moral pioneers had fallen off their horses at the wrong time, maybe the abolition movement never would have gotten off the ground and slavery would remain widespread today.
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 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: Ben Cordell Transcriptions: 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.
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.
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: Ben Cordell 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.
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 Why the West Rules—For Now, he set out to understand why the Industrial Revolution happened in England and Europe went on to dominate much of the rest of the world, rather than industrialisation kicking off somewhere else like China, with China going on to establish colonies in Europe. (In a word: geography.)
In War! What is it Good For?, he tried to explain why it is that violent conflicts often lead to longer lives and higher incomes (i.e. wars build empires which suppress interpersonal violence internally), while other times they have the exact opposite effect (i.e. advances in military technology allow nomads to raid and pull apart these empires).
In today’s episode, we discuss all of Ian’s major books, taking on topics such as:
Whether the evidence base in history — from document archives to archaeology — is strong enough to persuasively answer any of these questions
Whether or not wars can still lead to less violence today
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 macrohistory
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: Ben Cordell Transcriptions: Katy Moore
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 podcast and 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?”
His favourite MIT project so far involved taking a bunch of data from the 100 most complicated or famous physics equations, creating an Excel spreadsheet with each of the variables and the results, and saying to the computer, “OK, here’s the data. Can you figure out what the formula is?”
For general formulas, this is really hard. About 400 years ago, Johannes Kepler managed to get hold of the data that Tycho Brahe had gathered regarding how the planets move around the solar system. Kepler spent four years staring at the data until he figured out what the data meant: that planets orbit in an ellipse.
Max’s team’s code was able to discover that in just an hour.
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 would be able to 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
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: Ben Cordell Transcriptions: 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 with the security team. 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.
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, something we can only speculate on at this point.
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 information security is today
The most secure computer systems that exist today
How to design an AI training compute centre for maximum efficiency
Whether ‘formal verification’ can help us design trustworthy systems
How wide the practical gap is between AI capabilities and AI safety
How to disincentivise hackers
What should listeners do to strengthen their own security practices
Jobs at Anthropic
And a few more things as well
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: Ben Cordell and Beppe Rådvik Transcriptions: Katy Moore
“We’re leaving these 16 contestants on an island with nothing but what they can scavenge from an abandoned factory and apartment block. Over the next 365 days, they’ll try to rebuild as much of civilisation as they can — from glass, to lenses, to microscopes. This is: The Knowledge!”
If you were a contestant on such a TV show, you’d love to have a guide to how basic things you currently take for granted are done — how to grow potatoes, fire bricks, turn wood to charcoal, find acids and alkalis, and so on.
But in the aftermath of a nuclear war or incredibly deadly pandemic that kills most people, many of the ways we do things today will be impossible — and even some of the things people did in the past, like collect coal from the surface of the Earth, will be impossible the second time around.
As Lewis points out, there’s “no point telling this band of survivors how to make something ultra-efficient or ultra-useful or ultra-capable if it’s just too damned complicated to build in the first place. You have to start small and then level up, pull yourself up by your own bootstraps.”
So it might sound good to tell people to build solar panels — they’re a wonderful way of generating electricity. But the photovoltaic cells we use today need pure silicon, and nanoscale manufacturing — essentially the same technology as microchips used in a computer — so actually making solar panels would be incredibly difficult.
Instead, you’d want to tell our group of budding engineers to use more appropriate technologies like solar concentrators that use nothing more than mirrors — which turn out to be relatively easy to make.
A disaster that unravels the complex way we produce goods in the modern world is all too possible. Which raises the question: why not set dozens of people to plan out exactly what any survivors really ought to do if they need to support themselves and rebuild civilisation? Such a guide could then be translated and distributed all around the world.
The goal would be to provide the best information to speed up each of the many steps that would take survivors from rubbing sticks together in the wilderness to adjusting a thermostat in their comfy apartments.
This is clearly not a trivial task. Lewis’s own book (at 300 pages) only scratched the surface of the most important knowledge humanity has accumulated, relegating all of mathematics to a single footnote.
And the ideal guide would offer pretty different advice depending on the scenario. Are survivors dealing with a radioactive ice age following a nuclear war? Or is it an eerily intact but near-empty post-pandemic world with mountains of goods to scavenge from the husks of cities?
If we take catastrophic risks seriously and want humanity to recover from a devastating shock as far and fast as possible, producing such a guide before it’s too late might be one of the higher-impact projects someone could take on.
As a brand-new parent, Lewis couldn’t do one of our classic three- or four-hour episodes — so this is an unusually snappy one-hour interview, where Rob and Lewis are joined by Luisa Rodriguez to continue the conversation from her episode of the show last year.
They cover:
The biggest impediments to bouncing back
The reality of humans trying to actually do this
The most valuable pro-resilience adjustments we can make today
How to recover without much coal or oil
How to feed the Earth in disasters
And the most exciting recent findings in astrobiology
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: Ben Cordell Transcriptions: Katy Moore
