#48 – Brian Christian on computer science algorithms that tackle fundamental and universal problems — and whether they can help us live better in practice

Ever felt that you were so busy you spent all your time paralysed trying to figure out where to start, and couldn’t get much done? Computer scientists have a term for this – thrashing – and it’s a common reason our computers freeze up. The solution, for people as well as laptops, is to ‘work dumber’: pick something at random and finish it, without wasting time thinking about the bigger picture.

Ever wonder why people reply more if you ask them for a meeting at 2pm on Tuesday, than if you offer to talk at whatever happens to be the most convenient time in the next month? The first requires a two-second check of the calendar; the latter implicitly asks them to solve a vexing optimisation problem.

What about estimating the probability of something you can’t model, and which has never happened before? Math has got your back: the likelihood is no higher than 1 in the number of times it hasn’t happened, plus one. So if 5 people have tried a new drug and survived, the chance of the next one dying is at most 1 in 6.

Bestselling author Brian Christian studied computer science, and in the book Algorithms to Live By he’s out to find the lessons it can offer for a better life. In addition to the above he looks into when to quit your job, when to marry, the best way to sell your house, how long to spend on a difficult decision, and how much randomness to inject into your life.

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#47 – PhD or programming? Fast paths into aligning AI as a machine learning engineer, according to ML engineers Catherine Olsson & Daniel Ziegler

After dropping out of his ML PhD at Stanford, Daniel Ziegler needed to decide what to do next. He’d always enjoyed building stuff and wanted to help shape the development of AI, so he thought a research engineering position at an org dedicated to aligning AI with human interests could be his best option.

He decided to apply to OpenAI, spent 6 weeks preparing for the interview, and actually landed the job. His PhD, by contrast, might have taken 6 years. Daniel thinks this highly accelerated career path may be possible for many others.

On today’s episode Daniel is joined by Catherine Olsson, who has also worked at OpenAI, and left her computational neuroscience PhD to become a research engineer at Google Brain. They share this piece of advice for those interested in this career path: just dive in. If you’re trying to get good at something, just start doing that thing, and figure out that way what’s necessary to be able to do it well.

To go with this episode, Catherine has even written a simple step-by-step guide to help others copy her and Daniel’s success.

Daniel thinks the key for him was nailing the job interview.

OpenAI needed him to be able to demonstrate the ability to do the kind of stuff he’d be working on day-to-day. So his approach was to take a list of 50 key deep reinforcement learning papers, read one or two a day, and pick a handful to actually reproduce. He spent a bunch of time coding in Python and TensorFlow, sometimes 12 hours a day, trying to debug and tune things until they were actually working.

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#46 – Philosopher Hilary Greaves on moral cluelessness, population ethics, probability within a multiverse, & harnessing the brainpower of academia to tackle the most important research questions

The barista gives you your coffee and change, and you walk away from the busy line. But you suddenly realise she gave you $1 less than she should have. Do you brush your way past the people now waiting, or just accept this as a dollar you’re never getting back? According to philosophy professor Hilary Greaves – Director of Oxford University’s Global Priorities Institute, which is hiring now – this simple decision will completely change the long-term future by altering the identities of almost all future generations.

How? Because by rushing back to the counter, you slightly change the timing of everything else people in line do during that day — including changing the timing of the interactions they have with everyone else. Eventually these causal links will reach someone who was going to conceive a child.

By causing a child to be conceived a few fractions of a second earlier or later, you change the sperm that fertilizes their egg, resulting in a totally different person. So asking for that $1 has now made the difference between all the things that this actual child will do in their life, and all the things that the merely possible child – who didn’t exist because of what you did – would have done if you decided not to worry about it.

As that child’s actions ripple out to everyone else who conceives down the generations, ultimately the entire human population will become different, all for the sake of your dollar. Will your choice cause a future Hitler to be born, or not to be born? Probably both!

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#45 – Economist Tyler Cowen says our overwhelming priorities should be maximising economic growth and making civilisation more stable. Is he right?

I’ve probably spent more time reading Tyler Cowen – Professor of Economics at George Mason University – than any other author. Indeed it’s his incredibly popular blog Marginal Revolution that prompted me to study economics in the first place. Having spent thousands of hours absorbing Tyler’s work, it was a pleasure to be able to question him about his latest book and personal manifesto: Stubborn Attachments: A Vision for a Society of Free, Prosperous, and Responsible Individuals.

Tyler makes the case that, despite what you may have heard, we can make rational judgments about what is best for society as a whole. He argues:

  1. Our top moral priority should be preserving and improving humanity’s long-term future
  2. The way to do that is to maximise the rate of sustainable economic growth
  3. We should respect human rights and follow general principles while doing so.

We discuss why Tyler believes all these things, and I push back where I disagree. In particular: is higher economic growth actually an effective way to safeguard humanity’s future, or should our focus really be elsewhere?

In the process we touch on many of moral philosophy’s most pressing questions: Should we discount the future? How should we aggregate welfare across people? Should we follow rules or evaluate every situation individually? How should we deal with the massive uncertainty about the effects of our actions? And should we trust common sense morality or follow structured theories?

After covering the book, the conversation ranges far and wide. Will we leave the galaxy, and is it a tragedy if we don’t? Is a multi-polar world less stable? Will humanity ever help wild animals? Why do we both agree that Kant and Rawls are overrated?

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#44 – Paul Christiano on how OpenAI is developing real solutions to the ‘AI alignment problem’, and his vision of how humanity will progressively hand over decision-making to AI systems

Paul Christiano is one of the smartest people I know and this episode has one of the best explanations for why AI alignment matters and how we might solve it. After our first session produced such great material, we decided to do a second recording, resulting in our longest interview so far. While challenging at times I can strongly recommend listening – Paul works on AI himself and has a very unusually thought through view of how it will change the world. Even though I’m familiar with Paul’s writing I felt I was learning a great deal and am now in a better position to make a difference to the world.

A few of the topics we cover are:

  • Why Paul expects AI to transform the world gradually rather than explosively and what that would look like
  • Several concrete methods OpenAI is trying to develop to ensure AI systems do what we want even if they become more competent than us
  • Why AI systems will probably be granted legal and property rights
  • How an advanced AI that doesn’t share human goals could still have moral value
  • Why machine learning might take over science research from humans before it can do most other tasks
  • Which decade we should expect human labour to become obsolete, and how this should affect your savings plan.

Here’s a situation we all regularly confront: you want to answer a difficult question, but aren’t quite smart or informed enough to figure it out for yourself. The good news is you have access to experts who are smart enough to figure it out. The bad news is that they disagree.

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#43 – Daniel Ellsberg on the creation of nuclear doomsday machines, the institutional insanity that maintains them, and a practical plan for dismantling them

In Stanley Kubrick’s iconic film Dr. Strangelove, the American president is informed that the Soviet Union has created a secret deterrence system which will automatically wipe out humanity upon detection of a single nuclear explosion in Russia. With US bombs heading towards the USSR and unable to be recalled, Dr Strangelove points out that “the whole point of this Doomsday Machine is lost if you keep it a secret – why didn’t you tell the world, eh?” The Soviet ambassador replies that it was to be announced at the Party Congress the following Monday: “The Premier loves surprises”.

Daniel Ellsberg – leaker of the Pentagon Papers which helped end the Vietnam War and Nixon presidency – claims in his new book The Doomsday Machine: Confessions of a Nuclear War Planner that Dr. Strangelove might as well be a documentary. After attending the film in Washington DC in 1964, he and a military colleague wondered how so many details of the nuclear systems they were constructing had managed to leak to the filmmakers.

The USSR did in fact develop a doomsday machine, Dead Hand, which probably remains active today.

If the system can’t contact military leaders, it checks for signs of a nuclear strike. Should its computers determine that an attack occurred, it would automatically launch all remaining Soviet weapons at targets across the northern hemisphere.

As in the film, the Soviet Union long kept Dead Hand completely secret, eliminating any strategic benefit, and rendering it a pointless menace to humanity.

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#42 – Amanda Askell on tackling the ethics of infinity, being clueless about the effects of our actions, and having moral empathy for intellectual adversaries

Consider two familiar moments at a family reunion.

Our host, Uncle Bill, is taking pride in his barbequing skills. But his niece Becky says that she now refuses to eat meat. A groan goes round the table; the family mostly think of this as an annoying picky preference. But were it viewed as a moral position rather than personal preference – as they might if instead Becky were avoiding meat on religious grounds – it would usually receive a very different reaction.

An hour later Bill expresses a strong objection to abortion. Again, a groan goes round the table: the family mostly think that he has no business in trying to foist his regressive preferences on other people’s personal lives. But if considered not as a matter of personal taste, but rather as a moral position – that Bill genuinely believes he’s opposing mass-murder – his comment might start a serious conversation.

Amanda Askell, who recently completed a PhD in philosophy at NYU focused on the ethics of infinity, thinks that we often betray a complete lack of moral empathy. Across the political spectrum, we’re unable to get inside the mindset of people who expresses views that we disagree with, and see the issue from their point of view.

A common cause of conflict, as above, is confusion between personal preferences and moral positions. Assuming good faith on the part of the person you disagree with, and actually engaging with the beliefs they claim to hold, is perhaps the best remedy for our inability to make progress on controversial issues.

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#41 – If the US put fewer people in prison, would crime go up? Not at all, according to Open Philanthropy’s renowned researcher David Roodman.

With 698 inmates per 100,000 citizens, the U.S. is the world’s leader in incarcerating people. But what effect does this actually have on crime?

According to David Roodman, Senior Advisor to Open Philanthropy, the marginal effect is zero.

This stunning rebuke to the American criminal justice system comes from the man Holden Karnofsky called “the gold standard for in-depth quantitative research”. His other investigations include the risk of geomagnetic storms, whether deworming improves health and test scores, and the development impacts of microfinance – all of which we also cover in this episode.

In his comprehensive review of the evidence, David says the effects of crime can be split into three categories; before, during, and after.

Does having tougher sentences deter people from committing crime? After reviewing studies on gun laws and ‘three strikes’ in California, David concluded that the effect of deterrence is zero.

Does imprisoning more people reduce crime by incapacitating potential offenders? Here he says yes, noting that crimes like motor vehicle theft have gone up in a way that seems pretty clearly connected with recent Californian criminal justice reforms (though the effect on violent crime is far lower).

Finally, do the after-effects of prison make you more or less likely to commit future crimes?

This one is more complicated.

His literature review suggested that more time in prison made people substantially more likely to commit future crimes when released. But concerned that he was biased towards a comfortable position against incarceration, David did a cost-benefit analysis using both his favoured reading of the evidence and the devil’s advocate view; that there is deterrence and that the after-effects are beneficial.

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#40 – How well can we actually predict the future? Katja Grace on why expert opinion isn’t a great guide to AI’s impact and how to do better

Experts believe that artificial intelligence will be better than humans at driving trucks by 2027, working in retail by 2031, writing bestselling books by 2049, and working as surgeons by 2053. But how seriously should we take these predictions?

Katja Grace, lead author of ‘When Will AI Exceed Human Performance?’, thinks we should treat such guesses as only weak evidence. But she also says there might be much better ways to forecast transformative technology, and that anticipating such advances could be one of our most important projects.

Note: Katja’s organisation AI Impacts is currently hiring part- and full-time researchers.

There’s often pessimism around making accurate predictions in general, and some areas of artificial intelligence might be particularly difficult to forecast.

But there are also many things we’re now able to predict confidently — like the climate of Oxford in five years — that we no longer give ourselves much credit for.

Some aspects of transformative technologies could fall into this category. And these easier predictions could give us some structure on which to base the more complicated ones.

One controversial debate surrounds the idea of an intelligence explosion; how likely is it that there will be a sudden jump in AI capability?

And one way to tackle this is to investigate a more concrete question: what’s the base rate of any technology having a big discontinuity?

A significant historical example was the development of nuclear weapons. Over thousands of years, the energy density of explosives didn’t increase by much. Then within a few years, it got thousands of times better. Discovering what leads to such anomalies may allow us to better predict the possibility of a similar jump in AI capabilities.

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#39 – How much should you change your beliefs based on new evidence? Spencer Greenberg on the scientific approach to solving difficult everyday questions

Will Trump be re-elected? Will North Korea give up their nuclear weapons? Will your friend turn up to dinner?

Spencer Greenberg, founder of ClearerThinking.org, has a process for working out such real life problems.

Let’s work through one here: how likely is it that you’ll enjoy listening to this episode?

The first step is to figure out your ‘prior probability’: your estimate of how likely you are to enjoy the interview before getting any further evidence.

Other than applying common sense, one way to figure this out is ‘reference class forecasting’. That is, looking at similar cases and seeing how often something is true, on average.

Spencer is our first ever return guest (Dr Anders Sandberg appeared on episodes 29 and 33 – but only because his one interview was so fascinating that we split it into two).

So one reference class might be, how many Spencer Greenberg episodes of the 80,000 Hours Podcast have you enjoyed so far? Being this specific limits bias in your answer, but with a sample size of just one – you’ll want to add more data points to reduce the variance of the answer (100% or 0% are both too extreme answers).

Zooming out, how many episodes of the 80,000 Hours Podcast have you enjoyed? Let’s say you’ve listened to 10, and enjoyed 8 of them. If so 8 out of 10 might be a reasonable prior.

If we want a bigger sample we can zoom out further: what fraction of long-form interview podcasts have you ever enjoyed?

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#38 – Yew-Kwang Ng on ethics and how to create a much happier world

Will people who think carefully about how to maximize welfare eventually converge on the same views?

The effective altruism community has spent the past 10 years debating how best to increase happiness and reduce suffering, and gradually narrowed in on the world’s poorest people, all sentient animals, and future generations.

Yew-Kwang Ng, Professor of Economics at Nanyang Technological University in Singapore, worked totally independently on this exact question since the 70s. Many of his early conclusions are now conventional wisdom within effective altruism – though other views he holds remain controversial or little-known.

For instance, he thinks we ought to explore increasing pleasure via direct brain stimulation, and that genetic engineering may be an important tool for increasing happiness in the future.

His work has suggested that the welfare of most wild animals is on balance negative and he hopes that in the future this is a problem humanity will work to solve. Yet he thinks that greatly improved conditions for farm animals could eventually justify eating meat.

And he has spent most of his life forcefully advocating for the view that happiness, broadly construed, is the only intrinsically valuable thing.

If it’s true that careful researchers will converge as Prof Ng believes, these ideas may prove as prescient as his other, now widely accepted, opinions.

See below for our summary and appreciation of Kwang’s top publications and insights throughout a lifetime of research.

Born in Japanese-occupied Malaya during WW2, Kwang has led an exceptional life. While in high school he was drawn to physics, mathematics, and philosophy, yet he chose to study economics because of his dream: to establish communism in an independent Malaysia.

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#37 – Finding the best charity requires estimating the unknowable. Here’s how GiveWell tries to do that, according to researcher James Snowden.

What’s the value of preventing the death of a 5-year-old child, compared to a 20-year-old, or an 80-year-old?

The global health community has generally regarded the value as proportional to the number of health-adjusted life-years the person has remaining – but GiveWell, one of the world’s foremost charity evaluators, no longer uses that approach. They found that contrary to the years-remaining’ method, many of their staff actually value preventing the death of an adult more than preventing the death of a young child. But there’s plenty of disagreement, with the team’s estimates spanning a four-fold range.

As James Snowden – a research consultant at GiveWell – explains in this episode, there’s no way around making these controversial judgement calls based on limited information. If you try to ignore a question like this, you just implicitly take an unreflective stance on it instead. And for each charity they investigate there’s 1 or 2 dozen of these highly uncertain parameters that need to be estimated.

GiveWell has been working to find the best way to make these decisions since its inception in 2007. Lives hang in the balance, so they want their staff to say what they really believe and bring whatever private knowledge they have to the table, rather than just defer to their managers, or an imaginary consensus.

Their strategy is to have a massive spreadsheet that lists dozens of things they need to know, and to ask every staff member to give a figure and justification. Then once a year, the GiveWell team gets together to identify what they really disagree about and think through what evidence it would take to change their minds.

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#36 – Tanya Singh on ending the operations management bottleneck in effective altruism

Almost nobody is able to do groundbreaking physics research themselves, and by the time his brilliance was appreciated, Einstein was hardly limited by funding. But what if you could find a way to unlock the secrets of the universe like Einstein nonetheless?

Today’s guest, Tanya Singh, sees herself as doing something like that every day. She’s Executive Assistant to one of her intellectual heroes who she believes is making a huge contribution to improving the world: Professor Bostrom at Oxford University’s Future of Humanity Institute (FHI).

She couldn’t get more work out of Bostrom with extra donations, as his salary is already easily covered. But with her superior abilities as an Executive Assistant, Tanya frees up hours of his time every week, essentially ‘buying’ more Bostrom in a way nobody else can. She also help manage FHI more generally, in so doing freeing up more than an hour of staff time for each hour she works. This gives her the leverage to do more good than other people or other positions.

In our previous episode, Tara Mac Aulay objected to viewing operations work as predominately a way of freeing up other people’s time:

“A good ops person doesn’t just allow you to scale linearly, but also can help figure out bottlenecks and solve problems such that the organization is able to do qualitatively different work, rather than just increase the total quantity”, Tara said.

Tara’s right that buying time for people at the top of their field is just one path to impact, though it’s one Tanya says she finds highly motivating. Other paths include enabling complex projects that would otherwise be impossible, allowing you to hire and grow much faster, and preventing disasters that could bring down a whole organisation – all things that Tanya does at FHI as well.

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#35 – How the audacity to fix things without asking permission can change the world, demonstrated by Tara Mac Aulay

How broken is the world? How inefficient is a typical organisation? Looking at Tara Mac Aulay’s life, the answer seems to be ‘very’.

At 15 she took her first job – an entry-level position at a chain restaurant. Rather than accept her place, Tara took it on herself to massively improve the store’s shambolic staff scheduling and inventory management. After cutting staff costs 30% she was quickly promoted, and at 16 sent in to overhaul dozens of failing stores in a final effort to save them from closure.

That’s just the first in a startling series of personal stories that take us to a hospital drug dispensary where pharmacists are wasting a third of their time, a chemotherapy ward in Bhutan that’s killing its patients rather than saving lives, and eventually the Centre for Effective Altruism, where Tara becomes CEO and leads it through start-up accelerator Y Combinator.

In this episode – available in audio and summary or transcript below – Tara demonstrates how the ability to do practical things, avoid major screw-ups, and design systems that scale, is both rare and precious.

People with an operations mindset spot failures others can’t see and fix them before they bring an organisation down. This kind of resourcefulness can transform the world by making possible critical projects that would otherwise fall flat on their face.

But as Tara’s experience shows they need to figure out what actually motivates the authorities who often try to block their reforms.

We explore how people with this skill set can do as much good as possible, what 80,000 Hours got wrong in our article ‘Why operations management is one of the biggest bottlenecks in effective altruism’, as well as:

  • Tara’s biggest mistakes and how to deal with the delicate politics of organizational reform.
  • How a student can save a hospital millions with a simple spreadsheet model.
  • The sociology of Bhutan and how medicine in the developing world often makes things worse rather than better.
  • What most people misunderstand about operations, and how to tell if you have what it takes.
  • And finally, operations jobs people should consider applying for, such as those open now at the Centre for Effective Altruism.

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#34 – Politics is so much worse because we use an atrocious 18th century voting system. Aaron Hamlin has a viable plan to fix it.

In 1991 Edwin Edwards won the Louisiana gubernatorial election. In 2001, he was found guilty of racketeering and received a 10 year invitation to Federal prison. The strange thing about that election? By 1991 Edwards was already notorious for his corruption. Actually, that’s not it.

The truly strange thing is that Edwards was clearly the good guy in the race. How is that possible?

His opponent was former Ku Klux Klan Grand Wizard David Duke.

How could Louisiana end up having to choose between a criminal and a Nazi sympathiser?

It’s not like they lacked other options: the state’s moderate incumbent governor Buddy Roemer ran for re-election. Polling showed that Roemer was massively preferred to both the career criminal and the career bigot, and would easily win a head-to-head election against either.

Unfortunately, in Louisiana every candidate from every party competes in the first round, and the top two then go on to a second – a so-called ‘jungle primary’. Vote splitting squeezed out the middle, and meant that Roemer was eliminated in the first round.

Louisiana voters were left with only terrible options, in a run-off election mostly remembered for the proliferation of bumper stickers reading “Vote for the Crook. It’s Important.”

We could look at this as a cultural problem, exposing widespread enthusiasm for bribery and racism that will take generations to overcome. But according to Aaron Hamlin, Executive Director of The Center for Election Science (CES), there’s a simple way to make sure we never have to elect someone hated by more than half the electorate: change how we vote.

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#33 – Oxford’s Anders Sandberg on solar flares, the annual risk of nuclear war, and what if dictators could live forever?

Joseph Stalin had a life-extension program dedicated to making himself immortal. What if he had succeeded?

According to our last guest, Bryan Caplan, there’s an 80% chance that Stalin would still be ruling Russia today. Today’s guest disagrees.

Like Stalin he has eyes for his own immortality – including an insurance plan that will cover the cost of cryogenically freezing himself after he dies – and thinks the technology to achieve it might be around the corner.

Fortunately for humanity though, that guest is probably one of the nicest people on the planet: Dr Anders Sandberg of Oxford University.

The potential availability of technology to delay or even stop ageing means this disagreement matters, so he has been trying to model what would really happen if both the very best and the very worst people in the world could live forever – among many other questions.

Anders, who studies low-probability high-stakes risks and the impact of technological change at the Future of Humanity Institute, is the first guest to appear twice on the 80,000 Hours Podcast and might just be the most interesting academic at Oxford.

His research interests include more or less everything, and bucking the academic trend towards intense specialization has earned him a devoted fan base.

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app.

Last time we asked him why we don’t see aliens, and how to most efficiently colonise the universe. In today’s episode we ask about Anders’ other recent papers, including:

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#32 – Economist Bryan Caplan thinks education is mostly pointless showing off. We test the strength of his case.

Bryan Caplan’s claim in The Case Against Education is striking: education doesn’t teach people much, we use little of what we learn, and college is mostly about trying to seem smarter than other people – so the government should slash education funding.

It’s a dismaying – almost profane – idea, and one most are inclined to dismiss out of hand. But having read the book, I have to admit that Bryan can point to a surprising amount of evidence in his favour.

After all, imagine this dilemma: you can have either a Princeton education without a diploma, or a Princeton diploma without an education. Which is the bigger benefit of going to Princeton – learning, or convincing people you’re smart? It’s not so easy to say.

For this interview, I searched for the best counterarguments I could find and challenged Bryan on what seem like the book’s weakest or most controversial claims.

Wouldn’t defunding education be especially bad for capable but low income students? Shouldn’t we just make incremental rather than radical changes to policy? If you reduced funding for education, wouldn’t that just lower prices, and not actually change the number of years people study? Is it really true that students who drop out in their final year of college earn about the same as people who never go to college at all?

And while we’re at it, don’t Bryan and I actually use what we learned at college every day? What about studies that show that extra years of education boost IQ scores? And surely the early years of primary school, when you learn reading and arithmetic, are useful even if college isn’t.

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#31 – Allan Dafoe on trying to prepare the world for the possibility that AI will destabilise global politics

The debate around the impacts of artificial intelligence often centres on ‘superintelligence’ – a general intellect that is much smarter than the best humans, in practically every field.

But according to Allan Dafoe – Senior Research Fellow in the International Politics of AI at Oxford University – even if we stopped at today’s AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including:

  • Mass labor displacement, unemployment, and inequality;
  • The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order;
  • Imagery intelligence and other mechanisms for revealing most of the ballistic missile-carrying submarines that countries rely on to be able to respond to nuclear attack;
  • Ubiquitous sensors and algorithms that can identify individuals through face recognition, leading to universal surveillance;
  • Autonomous weapons with an independent chain of command, making it easier for authoritarian regimes to violently suppress their citizens.

Allan is Director of the Center for the Governance of AI, at the Future of Humanity Institute within Oxford University. His goals have been to understand the causes of world peace and stability, which in the past has meant studying why war has declined, the role of reputation and honor as drivers of war, and the motivations behind provocation in crisis escalation. His current focus is helping humanity safely navigate the invention of advanced artificial intelligence.

I ask Allan:

  • What are the distinctive characteristics of artificial intelligence from a political or international governance point of view?
  • Is Allan’s work just a continuation of previous research on transformative technologies, like nuclear weapons?
  • How can AI be well-governed?
  • How should we think about the idea of arms races between companies or countries?
  • What would you say to people skeptical about the importance of this topic?
  • How urgently do we need to figure out solutions to these problems? When can we expect artificial intelligence to be dramatically better than today?
  • What’s the most urgent questions to deal with in this field?
  • What can people do if they want to get into the field?
  • Is there anything unusual that people can look for in themselves to tell if they’re a good fit to do this kind of research?

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#30 – Eva Vivalt’s research suggests social science findings don’t generalize. So evidence-based development – what is it good for?

If we have a study on the impact of a social program in a particular place and time, how confident can we be that we’ll get a similar result if we study the same program again somewhere else?

Dr Eva Vivalt is a lecturer in the Research School of Economics at the Australian National University. She compiled a huge database of impact evaluations in global development – including 15,024 estimates from 635 papers across 20 types of intervention – to help answer this question.

Her finding: not confident at all.

The typical study result differs from the average effect found in similar studies so far by almost 100%. That is to say, if all existing studies of an education program find that it improves test scores by 0.5 standard deviations – the next result is as likely to be negative or greater than 1 standard deviation, as it is to be between 0-1 standard deviations.

She also observed that results from smaller studies conducted by NGOs – often pilot studies – would often look promising. But when governments tried to implement scaled-up versions of those programs, their performance would drop considerably.

For researchers hoping to figure out what works and then take those programs global, these failures of generalizability and ‘external validity’ should be disconcerting.

Is ‘evidence-based development’ writing a cheque its methodology can’t cash?

Should we invest more in collecting evidence to try to get reliable results?

Or, as some critics say, is interest in impact evaluation distracting us from more important issues, like national economic reforms that can’t be tested in randomised controlled trials?

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#29 – Where are the aliens? Anders Sandberg on three new resolutions to the Fermi Paradox and how we could easily colonise the whole universe

Update April 2019: The key theory Dr Sandberg puts forward for why aliens may delay their activities has been strongly disputed in a new paper, which claims it is based on an incorrect understanding of the physics of computation.

The universe is so vast, yet we don’t see any alien civilizations. If they exist, where are they? Oxford University’s Anders Sandberg has an original answer: they’re ‘sleeping’, and for a very compelling reason.

Because of the thermodynamics of computation, the colder it gets, the more computations you can do. The universe is getting exponentially colder as it expands, and as the universe cools, one Joule of energy gets worth more and more. If they wait long enough this can become a 10,000,000,000,000,000,000,000,000,000,000x gain. So, if a civilization wanted to maximize its ability to perform computations – its best option might be to lie in wait for trillions of years.

Why would a civilization want to maximise the number of computations they can do? Because conscious minds are probably generated by computation, so doing twice as many computations is like living twice as long, in subjective time. Waiting will allow them to generate vastly more science, art, pleasure, or almost anything else they are likely to care about.

But there’s no point waking up to find another civilization has taken over and used up the universe’s energy. So they’ll need some sort of monitoring to protect their resources from potential competitors like us.

It’s plausible that this civilization would want to keep the universe’s matter concentrated, so that each part would be in reach of the other parts, even after the universe’s expansion. But that would mean changing the trajectory of galaxies during this dormant period. That we don’t see anything like that makes it more likely that these aliens have local outposts throughout the universe, and we wouldn’t notice them until we broke their rules. But breaking their rules might be our last action as a species.

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