The most important conversations about artificial intelligence you won’t hear anywhere else.
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Hosted by Rob Wiblin, Luisa Rodriguez, Zershaaneh Qureshi, and Tom Reed.
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The most important conversations about artificial intelligence you won’t hear anywhere else.
Subscribe by searching for ‘80000 Hours’ wherever you get podcasts.
Hosted by Rob Wiblin, Luisa Rodriguez, Zershaaneh Qureshi, and Tom Reed.
Copyright: © All rights reserved
"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 Morris
In 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.
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Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire
Transcriptions: 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 Kell
Links 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.
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Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Dominic Armstrong and Milo McGuire
Additional content editing: Luisa Rodriguez and Katy Moore
Transcriptions: 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 Collins
In 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.
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Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: 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 Sandberg
In 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.
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Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour and Milo McGuire
Transcriptions: 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 Esvelt
In 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:
Producer and editor: Keiran Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: 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:
Audio mastering and editing for this episode: Dominic Armstrong
Audio Engineering Lead: Ben Cordell
Producer: 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:
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 Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Simon Monsour
Transcriptions: 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.
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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 safety
2. Instituting capability audits for AI models
3. Buying time by exploiting hardware choke points
4. Getting critics involved in directly engineering AI models
5. Getting AI labs to be guided by motives other than profit
6. Radically increasing governments’ understanding of AI and their capabilities to sensibly regulate it
7. Creating international treaties to prevent proliferation of the most dangerous AI capabilities
8. Building a self-critical culture in AI labs of openly accepting when the status quo isn't working
9. Creating a mass public movement that understands AI and can demand the necessary controls
10. Not relying too much on delay, but instead seeking to move into a new somewhat-stable equilibria
As 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:
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 Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire
Transcriptions: 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 Webb
In 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:
If 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 Harris
Audio Engineering Lead: Ben Cordell
Technical editing: Milo McGuire and Dominic Armstrong
Additional content editing: Katy Moore and Luisa Rodriguez
Transcriptions: Katy Moore