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Speeding up social science 10-fold, how to do research that’s actually useful, & why plenty of startups cause harm

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What is the best, state-of-the-art therapy for depression? Do most meat eaters think it’s wrong to hurt animals? How likely do Americans think climate change is to cause human extinction? How do we make academics more intellectually honest, so we can actually trust their findings? How can we speed up social science research 10-fold? Do most startups improve the world, or make it worse? Why is research in top journals less reliable?

If you’re interested in these questions, this interview is for you.

A scientist, entrepreneur, writer and mathematician, Spencer Greenberg is constantly working to create tools to speed up and improve research and critical thinking. These include:

  • Rapid public opinion surveys – which he has used to learn public opinion on animal consciousness, farm animal welfare, the impact of developing world charities and the likelihood of extinction by various different means;
  • Tools to enable social science research to be run en masse very cheaply by anyone;
  • ClearerThinking.org, a highly popular site for improving people’s judgement and decision-making;
  • Ways to transform data analysis methods to ensure that papers only show true findings;
  • Ways to decide which research projects are actually worth pursuing.

In this episode of the show, Spencer discusses all of these and more. If you don’t feel like listening, that just shows that you have poor judgement and need to benefit from his wisdom even more!

Enjoy:

If you subscribe to our podcast,

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Podcast: You want to do as much good as possible and have billions of dollars. What do you do?

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What if you were in a position to give away billions of dollars to improve the world? What would you do with it? This is the problem facing Program Officers at the Open Philanthropy Project – people like Dr Nick Beckstead.

Following a PhD in philosophy, Nick works to figure out where money can do the most good. He’s been involved in major grants in a wide range of areas, including ending factory farming through technological innovation, safeguarding the world from advances in biotechnology and artificial intelligence, and spreading rational compassion.

This episode is a tour through some of the toughest questions ‘effective altruists’ face when figuring out how to best improve the world, including:

  • Should we mostly try to help people currently alive, or future generations? Nick studied this question for years in his PhD thesis, On the Overwhelming Importance of Shaping the Far Future. (The first 31 minutes is a snappier version of my conversation with Toby Ord.)
  • Is clean meat (aka in vitro meat) technologically feasible any time soon, or should we be looking for plant-based alternatives?
  • To stop malaria is it more cost-effective to use technology to eliminate mosquitos than to distribute bed nets?
  • What are the greatest risks to human civilisation continuing?
  • Should people who want to improve the future work for changes that will be very useful in a specific scenario,

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New problem profile: Improving institutional decision-making

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A few weeks ago we released a new problem profile focussed on improving decision-making in major societal institutions:

When powerful people make dumb choices it hurts us all. Here’s how to fix it.

In 2003, the United States chose to invade Iraq. Most now agree this decision was deeply flawed, costing trillions of dollars and thousands of lives.

Exactly what went wrong here is a contested and controversial issue. At best, the decision-making process severely lacked rigour, and at worst, it was heavily biased.

The government justified the invasion thanks to the intelligence community’s claim that it was “highly probable” that Iraq possessed Weapons of Mass Destruction (WMD) – but this statement was ambiguous. Policymakers took that to indicate near-100% certainty, and made decisions accordingly.1 But “highly probable” could easily also be interpreted as 80% certainty, or 70% – carrying very different practical implications. Those involved didn’t really think through the relevant probabilities, or consider how likely the estimates were to be wrong, or the implications if they were.

Others have suggested that the US had already decided to invade Iraq, and that this decision influenced intelligence collection – not the other way around. This a particularly extreme example of what’s known as motivated reasoning – a tendency to reason in ways that support whatever conclusion one wants to be true.

The call to invade hinged on the subjective impressions of a few key people – subjective impressions that later turned out to be wrong,

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New career review: Policy-oriented civil service (with a UK focus)

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We have a new career review focussed on government jobs developing policy, with a focus on the UK:

Working in the government you can have a big impact on pressing global problems. Here’s how to get started.

On the Sunday after Labour’s landslide victory in 1997, Tony Blair rang Alan Milburn to tell him he was going to be a minister in the Department of Health. Blair said: ‘We haven’t got a health policy. Your job is to get us one.’1

Milburn ‘was hungry for ideas’1 and met with a civil servant: Graham Winyard, the Deputy Chief Medical Officer. According to Winyard, this meeting was where the National Institute for Health and Care Excellence (NICE) was born.1 NICE helps the National Health Service decide which treatments are evidence-based and cost-effective.

Although its approach is controversial,2 NICE is seen internationally as a role model for how to make evidence-based decisions about health spending.3 The editor of the British Medical Journal described it as ‘conquering the world’ and thought it might ‘prove to be one of Britain’s greatest cultural exports’.4

What if you could have this kind of impact?

There are civil servants working on some of the world’s most urgent problems, from how to prevent nuclear proliferation to encouraging economic growth in the developing world. Like Winyard, they often have opportunities to play a central role in solving these problems.

In this profile we cover why it’s possible to have a significant impact as a civil servant,

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The space colonisation and nanotech focussed Silicon Valley community of the 70s and 80s

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One tricky thing about lengthy podcasts is that you cover a dozen issues, but when you give the episode a title you only get to tell people about one. With Christine Peterson’s interview I went with a computer security angle, which turned out to not be that viral a topic. But people who listened to the episode kept telling me how much they loved it. So I’m going to try publishing the interview in pieces, each focussed on a single theme we covered.

Christine Peterson co-founded the Foresight Institute in the 90s. In the lightly edited transcript below we talk about a community she was part of in her youth, whose idealistic ambition bears some similarity to effective altruism today. We also cover a controversy from that time about whether nanotechnology would change the world or was impossible. Finally we think about what lessons we can learn from that whole era.

If you subscribe to our podcast, you can listen at leisure on your phone, speed up the conversation if you like, and get notified about future episodes. You can do so by searching ‘80,000 Hours’ wherever you get your podcasts (RSS, SoundCloud, iTunes, Stitcher).

The community that dreamed of space settlement and atomic factories

Robert Wiblin: Tell us a bit about how you ended up where you are today.

Christine Peterson: Wow. When I was growing up,

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Our computers are fundamentally insecure. Here’s why that could lead to global catastrophe.

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Take a trip to Silicon Valley in the 70s and 80s, when going to space sounded like a good way to get around environmental limits, people started cryogenically freezing themselves, and nanotechnology looked like it might revolutionise industry – or turn us all into grey goo.

In this episode of the 80,000 Hours Podcast Christine Peterson takes us back to her youth in the Bay Area, the ideas she encountered there, and what the dreamers she met did as they grew up. We also discuss how she came up with the term ‘open source software’ (and how she had to get someone else to propose it).

Today Christine helps runs the Foresight Institute, which fills a gap left by for-profit technology companies – predicting how new revolutionary technologies could go wrong, and ensuring we steer clear of the downsides.

We dive into:

  • Can technology ‘move fast and break things’ without eventually breaking the world? Would it be better for technology to advance more quickly, or more slowly?
  • Whether the poor security of computer systems poses a catastrophic risk for the world.
  • Could all our essential services be taken down at once? And if so, what can be done about it? Christine makes a radical proposal for solving the problem.
  • Will AIs designed for wide-scale automated hacking make computers more or less secure?
  • Would it be good to radically extend human lifespan?

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Ending factory farming as soon as possible

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Every year tens of billions of animals are raised in terrible conditions in factory farms before being killed for human consumption. Despite the enormous scale of suffering this causes, the issue is largely neglected, with only about $50 million dollars spent each year tackling the problem globally.

Over the last two years Lewis Bollard – Project Officer for Farm Animal Welfare at the Open Philanthropy Project – has conducted extensive research into the best ways to eliminate animal suffering in farms as soon as possible.

This has resulted in $30 million in grants, making the Open Philanthropy Project one of the largest funders in the area.

Our conversation covers almost every approach being taken, which ones work, how individuals can best contribute through their careers, as well as:

  • How young people can set themselves up to contribute to scientific research into meat alternatives
  • How genetic manipulation of chickens has caused them to suffer much more than their ancestors, but could also be used to make them better off
  • Why Lewis is skeptical of vegan advocacy
  • Open Phil’s grants to improve animal welfare in China, India and South America
  • Why Lewis thinks insect farming would be worse than the status quo, and whether we should look for ‘humane’ insecticides
  • Why Lewis doubts that much can be done to tackle factory farming through legal advocacy or electoral politics
  • Which species of farm animals is best to focus on first
  • Whether fish and crustaceans are conscious,

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Is it time for a new scientific revolution? Julia Galef on how to make humans smarter, why Twitter isn’t all bad, and where effective altruism is going wrong

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The scientific revolution in the 16th century was one of the biggest societal shifts in human history, driven by the discovery of new and better methods of figuring out who was right and who was wrong.

Julia Galef – a well-known writer and researcher focused on improving human judgment, especially about high stakes questions – believes that if we could develop new techniques to resolve disagreements, predict the future and make sound decisions together, we could again dramatically improve the world. We brought her in to talk about her ideas.

Julia has hosted the Rationally Speaking podcast since 2010, co-founded the Center for Applied Rationality in 2012, and is currently working for the Open Philanthropy Project on an investigation of expert disagreements.

This interview complements a new detailed review of whether and how to follow Julia’s career path

We ended up speaking about a wide range of topics, including:

  • Her research on how people can have productive intellectual disagreements.
  • Why she once planned on becoming an urban designer.
  • Why she doubts people are more rational than 200 years ago.
  • What the effective altruism community is doing wrong.
  • What makes her a fan of Twitter (while I think it’s dystopian).
  • Whether more people should write books.
  • Whether it’s a good idea to run a podcast, and how she grew her audience.
  • Why saying you don’t believe X often won’t convince people you don’t.

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How Alex GB earned millions for charity within years by working in quant trading

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Quantitative financial trading is one of the highest paying parts of the world’s highest paying industry. 25 to 30 year olds with outstanding maths skills can earn millions a year in an obscure set of ‘quant trading’ firms, where they program computers with predefined algorithms to trade very quickly and effectively.

This makes it an attractive workplace for people who want to ‘earn to give’, and we know several people who are able to donate over a million dollars a year to effective charities by working in quant trading. Who are these people? What is the job like? And is there a risk that their trading work directly harms the world?

To learn about all this I spoke at length with Alexander Gordon-Brown, who has worked as a quant trader in London for the last three and a half years and donated hundreds of thousands of pounds. We covered:

  • What quant traders do and how much they earn;
  • Whether their work is beneficial or harmful for the world;
  • How to figure out if you’re a good fit for quant trading, and if so how to break into the industry;
  • Whether Alex enjoys the work and finds it motivating, as well as what alternatives careers he considered;
  • What variety of positions are on offer in quant trading, and what the culture is like in the various firms;
  • How he decides where to donate, and whether he has persuaded his colleagues to join him in becoming major philanthropists.

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Podcast: We aren’t that worried about the next pandemic. Here’s why we should be – and specifically what we can do to stop it.

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What natural disaster is most likely to kill more than 10 million human beings in the next 20 years?

Terrorism? Famine? An asteroid?

Actually it’s probably a pandemic: a deadly new disease that spreads out of control. We’ve recently seen the risks with Ebola and swine flu, but they pale in comparison to the Spanish flu which killed 3% of the world’s population in 1918 to 1920. If a pandemic of that scale happened again today, 200 million would die.

Looking back further, the Black Death killed 30 to 60% of Europe’s population, which would today be two to four billion globally.

The world is woefully unprepared to deal with new diseases. Many countries have weak or non-existent health services. Diseases can spread worldwide in days due to air travel. And international efforts to limit the spread of new diseases are slow, if they happen at all.

Even more worryingly, scientific advances are making it easier to create diseases much worse than anything nature could throw at us – whether by accident or deliberately.

In this in-depth interview I speak to Howie Lempel, who spent years studying pandemic preparedness for the Open Philanthropy Project. We spend the first 20 minutes covering his work as a foundation grant-maker, then discuss how bad the pandemic problem is, why it’s probably getting worse, and what can be done about it. In the second half of the interview we go through what you personally could study and where you could work to tackle one of the worst threats facing humanity.

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Podcast: How to train for a job developing AI at OpenAI or DeepMind

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Just two years ago OpenAI didn’t exist. It’s now among the most elite groups of machine learning researchers. They’re trying to make an AI that’s smarter than humans and have $1b at their disposal.

Even stranger for a Silicon Valley start-up, it’s not a business, but rather a non-profit founded by Elon Musk and Sam Altman among others, to ensure the benefits of AI are distributed broadly to all of society.

I did a long interview with one of its first machine learning researchers, Dr Dario Amodei, to learn about:

  • OpenAI’s latest plans and research progress.
  • His paper Concrete Problems in AI Safety, which outlines five specific ways machine learning algorithms can act in dangerous ways their designers don’t intend – something OpenAI has to work to avoid.
  • How listeners can best go about pursuing a career in machine learning and AI development themselves.

We suggest subscribing, so you can listen at leisure on your phone, speed up the conversation if you like, and get notified about future episodes. You can subscribe by searching ‘80,000 Hours’ wherever you get your podcasts (RSS, SoundCloud, iTunes, Stitcher).

The audio, summary, extra resources and full transcript are below.

Overview of the discussion

1m33s – What OpenAI is doing, Dario’s research and why AI is so important
15m50s –

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Which professions are paid too much given their value to society?

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Many jobs have spillover effects on the rest of society. For instance, the value of new treatments discovered by biomedical researchers is far greater than what they or their employers get paid, so they have positive spillovers. Other jobs have negative spillovers, such as those that generate pollution.

A forthcoming paper, by economists at UPenn and Yale, reports a survey of the economic literature on these spillover benefits for the 11 highest-earning professions.

There’s very little literature, so all these estimates are very, very uncertain, and should be not be taken literally. But it’s interesting reading – it represents a survey of what economists think they know about this topic, and it’s surprisingly little.

Here are the bottom lines – see more detail on the estimates below. (Note that we already discussed an older version of this paper, but the estimates have been updated since then.)

We calculated mean income for 2005 in an earlier article. We increased income by 30% to account for nominal wage growth since then.

The paper uses the expressions spillover and ‘externality’. An ‘externality’ is a technical term for a ‘cost or benefit that affects a party who did not choose to incur that cost or benefit.’ The authors of the paper call it an ‘externality’ when someone who buys a service does (or does not) benefit after taking account of the cost of purchasing it. This is a nonstandard usage,

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Podcast: Prof David Spiegelhalter on risk, statistics and improving the public understanding of science

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My colleague Jess Whittlestone and I spoke with Prof David Spiegelhalter, the Winton Professor of the Public Understanding of Risk at the University of Cambridge.

Prof Spiegelhalter tries to help people prioritise and respond to the many hazards we face, like getting cancer or dying in a car crash. To make the vagaries of life more intuitive he has had to invent concepts like the microlife, or a 30-minute change in life expectancy. He’s regularly in the UK media explaining the numbers that appear in the news, trying to assist both ordinary people and politicians to make sensible decisions based in the best evidence available.

We wanted to learn whether he thought a lifetime of work communicating science had actually had much impact on the world, and what advice he might have for people planning their careers today.

Subscribe to the podcast on iTunes or by searching 80,000 Hours wherever you get your podcasts. Links and a transcript are below.

“…What do we hear in the news? We hear about Ebola, we hear about terrorism, we hear about the latest threat that might be in what we eat and the way we travel, and we get very concerned about this, whether it’s a plane crash or whatever. Because that’s what’s in the news, that’s what is available to us. That’s what’s so prominent, but of course, so many of these risks are actually very small indeed…
…
What I am proud of is being part of a general community that’s very strong in Britain, to do with public engagement in science, which I’m just a small part of that because it covers material on the radio, stuff on television, stuff in some newspapers, and in various agencies. For example, in Statistics Authority, which is just trying to take a much more critical attitude to the way that numbers and evidence are used in society. I think it works. In Britain, we’re rather good compared with most people about, I don’t know, we don’t have these massive fears of vaccinations and nuclear power, of even GMOs. I think this is a sign that we in this country have developed quite a good public engagement with science community.”

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Podcast: The world desperately needs AI strategists. Here’s how to become one.

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If a smarter-than-human AI system were developed, who would decide when it was safe to deploy? How can we discourage organisations from deploying such a technology prematurely to avoid being beaten to the post by a competitor? Should we expect the world’s top militaries to try to use AI systems for strategic advantage – and if so, do we need an international treaty to prevent an arms race?

Questions like this are the domain of AI policy experts.

We recently launched a detailed guide to pursuing careers in AI policy and strategy, put together by Miles Brundage at the University of Oxford’s Future of Humanity Institute.

It complements our article outlining the importance of positively shaping artificial intelligence and a podcast with Dr Dario Amodei of OpenAI on more technical artificial intelligence safety work which builds on this one. If you are considering a career in artificial intelligence safety, they’re all essential reading.

I interviewed Miles to ask remaining questions I had after he finished his career guide. We discuss the main career paths; what to study; where to apply; how to get started; what topics are most in need of research; and what progress has been made in the field so far.

The audio, summary and full transcript are below.

We suggest subscribing, so you can listen at leisure on your phone,

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The Schwarzman Scholarship: An exciting opportunity to learn more about China and get a Masters in Global Affairs

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Co-authored with Helen Toner.

In general, living in a foreign country – for example, by studying there – is a great way to learn about the country, its language, people, and culture.

There’s one country in particular we think it would be very valuable for some of our readers to become knowledgable about: China. It’s the world’s largest country by population, and gets closer every year to being the largest global economy too. On the international stage, China is the probably world’s second most influential country. Its influence in economic, geopolitical, and cultural terms looks set to continue to grow throughout the course of the 21st century.

All of this means that China is likely to play a larger and larger role in all kinds of areas, including topics we care a great deal about, like factory farming, pandemic preparedness, preserving international peace and cooperation, and AI research. Being familiar with China is likely to provide unique and high-value opportunities to people working in those areas. What’s more, there aren’t yet many westerners with deep (or even passing) familiarity with China, which suggests that now is an especially valuable time to be learning more.

If you’d like to become knowledgable about China, there is a promising new way to do that: the Schwarzman Scholarship program.

Modelled on the Rhodes Scholarship, the Schwarzman Scholarship is targeted at high achieving students from around the world with an interest in leadership,

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How much do hedge fund traders earn?

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Hedge fund trading may be the highest paying job in the world, so to learn more, we spoke with a former manager at one of the world’s leading hedge funds. They gave us the following information, which allowed us to make a rough estimate of the typical earnings of hedge fund traders.

We also ran this document past several other people in the industry and asked them to point out mistakes.

We found that junior traders typically earn $300k – $3m per year, and it’s possible to reach these roles in 4 – 8 years. Senior portfolio managers can easily earn over $10m per year, though average earnings are probably lower. Read on for the details.

How do hedge funds make money and how is it shared among the employees?

Hedge funds trade in financial markets on behalf of clients in exchange for annual fees, and a cut of the profits. They’re similar to mutual funds but face fewer restrictions on what they can invest in, and can only be used by accredited investors.

The revenue of a hedge fund comes from the fees on the assets it manages. The typical fund charges a fee of 2% of assets under management per year, plus a performance fee. The performance fee is typically 20% of any returns it makes for the clients over and above the 2% base fee. So, if a fund makes 10% returns in a year,

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Most people report believing it’s incredibly cheap to save lives in the developing world

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One way that people can have a social impact with their career is to donate money to effective charities. We mention this path in our career guide, suggesting that people donate to evidence-backed charities such as the Against Malaria Foundation, which is estimated by GiveWell to save the lives of children in the developing world for around $7,500.

Alyssa Vance told me that many people may see this as highly ineffective relatively to their optimistic expectations about how much it costs to improve the lives of people in people. I thought the reverse would be true – folks would be skeptical that charities in the developing world were effective at all. Fortunately Amazon Mechanical Turk makes it straightforward to survey public opinion at a low cost, so there was no need for us to sit around speculating. I suggested a survey on this question to someone in the effective altruism community with a lot of experience using Mechanical Turk – Spencer Greenberg of Clearer Thinking – and he went ahead and conducted one in just a few hours.

You can work through the survey people took yourself here and we’ve put the data and some details about the method in a footnote. The results clearly vindicated Alyssa:

It turns out that most Americans believe a child can be prevented from dying of preventable diseases for very little –

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How accurately does anyone know the global distribution of income?

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World income distributionHow much should you believe the numbers in figures like this?

People in the effective altruism community often refer to the global income distribution to make various points:

  • The richest people in the world are many times richer than the poor.
  • People earning professional salaries in countries like the US are usually in the top 5% of global earnings and fairly often in the top 1%. This gives them a disproportionate ability to improve the world.
  • Many people in the world live in serious absolute poverty, surviving on as little as one hundredth the income of the upper-middle class in the US.

Measuring the global income distribution is very difficult and experts who attempt to do so end up with different results. However, these core points are supported by every attempt to measure the global income distribution that we’ve seen so far.

The rest of this post will discuss the global income distribution data we’ve referred to, the uncertainty inherent in that data, and why we believe our bottom lines hold up anyway.

Will MacAskill had a striking illustration of global individual income distribution in his book Doing Good Better, that has ended up in many other articles online, including our own career guide:
 
 

 
The data in this graph was put together back in 2012 using an approach suggested by Branko Milanovic,

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What skills are effective altruist organisations short of? Results from our survey.

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In August 2016, we surveyed 16 organisations in the effective altruism community about their hiring needs, and to what extent they are constrained by talent compared to funding.

What follows is a summary of the results, grouped by question asked. You can see the list of organisations surveyed in the footnotes.

Note that since the survey was carried out over six months ago, some of the information may no longer be up to date. We intend to repeat the survey in August 2017, and will report back on how the situation has changed.

What types of talent does your organisation need?

Here are the options provided on the survey, along with the number of organisations which stated that they were looking to hire people for these roles:

(Note that this table is not weighted by budget or team size, although we do not expect that this would materially affect the results.)

In open feedback, several respondents also mentioned that the community is most in need of specialist researchers rather than generalist researchers. Some other skill sets which were not included as options, but which were mentioned more than twice in the open feedback include:

  1. Economists, in 3 cases.
  2. Math and AI researchers, in 2 cases.
  3. Policy experts, in 2 cases.
  4. Scientists, especially biologists, in 3 cases.

Also see the longer list of skill sets needed by the community at the end of this summary.

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    5 reasons not to go into education

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    First published June 2015. Updated February 2017.

    When we first speak to people interested in doing good with their careers, they often say they want to get involved in education in the US or the UK. This could mean donating to a school, doing education policy work, or becoming a teacher.

    However, we haven’t prioritised careers in education at 80,000 Hours. We don’t dispute that education is a highly important problem – a more educated population could enable us to solve many other global challenges, as well as yield major economic benefits. The problem is that it doesn’t seem to be very easy to solve or neglected (important elements of our problem framework). So, it looks harder to have a large impact in education compared to many other areas. In the rest of this post, we’ll give five reasons why.

    The following isn’t the result of in-depth research; it’s just meant to explain why we’ve deprioritised education so far. Our views could easily change. Note that in this post we’re not discussing education in the developing world.

    1. It’s harder to help people in the US or UK

    Everyone in the US or UK is rich by global standards: the poorest 5% of Americans are richer than the richest 5% of Indians (and that’s adjusted for the difference in purchasing power, see an explanation and the full data).

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