New releases - Page 28 of 50 - 80,000 Hours

#7 – Julia Galef on making humanity more rational, what EA does wrong, and why Twitter isn’t all bad

Podcast by Robert Wiblin · Published September 13th, 2017

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 Open Philanthropy 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.
Why she started a PhD in economics but then quit.
Whether she would recommend an unconventional ‘public intellectual’ career like her own.
Whether the incentives in the intelligence community actually support sound thinking.
Whether big institutions will actually pick up new tools for improving decision-making if they are developed.
How to start out pursuing a career in which you also try to enhance human judgement and foresight.

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#6 – Toby Ord on why the long-term future of humanity matters more than anything else, and what we should do about it

Podcast by Robert Wiblin · Published September 6th, 2017

Of all the people whose well-being we should care about, only a small fraction are alive today. The rest are members of future generations who are yet to exist. Whether they’ll be born into a world that is flourishing or disintegrating – and indeed, whether they will ever be born at all – is in large part up to us. As such, the welfare of future generations should be our number one moral concern.

This conclusion holds true regardless of whether your moral framework is based on common sense, consequences, rules of ethical conduct, cooperating with others, virtuousness, keeping options open – or just a sense of wonder about the universe we find ourselves in.

That’s the view of Dr Toby Ord, a philosophy Fellow at the University of Oxford and co-founder of the effective altruism community. In this episode of the 80,000 Hours podcast Dr Ord makes the case that aiming for a positive long-term future is likely the best way to improve the world.

We then discuss common objections to long-termism, such as the idea that benefits to future generations are less valuable than those to people alive now, or that we can’t meaningfully benefit future generations beyond taking the usual steps to improve the present.

Later the conversation turns to how individuals can and have changed the course of history, what could go wrong and why, and whether plans to colonise Mars would actually put humanity in a safer position than it is today.

This episode goes deep into one of the most distinctive features of 80,000 Hours’ advice on doing good. It’s likely the most in-depth discussion of how we and the effective altruism community think about the long term future, and why we so often give it top priority.

If you prefer a book, Dr Toby Ord, an Oxford philosopher and advisor to 80,000 Hours, has recently published The Precipice: Existential Risk and the Future of Humanity which gives an overview of the moral importance of future generations, and what we can do to help them today.

We’ll mail you the book, for free

Join the 80,000 Hours newsletter and we’ll send you a free copy of the book.

We’ll also send you updates on our latest research, opportunities to work on existential risk, and news from the author.

If you’re already on our newsletter, email us at [email protected] to get a copy.

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#5 – Alex Gordon-Brown on making millions for charity each year by working in quant finance

Podcast by Robert Wiblin · Published August 28th, 2017

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, a Giving What We Can member 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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#4 – Howie Lempel on why we aren’t worried enough about the next pandemic — and specifically what we can do to stop it.

Podcast by Robert Wiblin and Howie Lempel · Published August 23rd, 2017

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 Open Philanthropy. 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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#3 – Dario Amodei on OpenAI and how AI will change the world for good and ill

Podcast by Robert Wiblin · Published July 21st, 2017

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 nonprofit 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.

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

Blog post by Benjamin Todd · Published June 27th, 2017

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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#2 – David Spiegelhalter on risk, statistics, and improving the public understanding of science

Podcast by Robert Wiblin · Published June 21st, 2017

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.

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#1 – Miles Brundage on the world’s desperate need for AI strategists and policy experts

Podcast by Robert Wiblin · Published June 6th, 2017

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.

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How much do hedge fund traders earn?

Blog post by Benjamin Todd · Published May 10th, 2017

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, then the performance fee is 20% of (10% –

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

Blog post by Robert Wiblin · Published May 9th, 2017

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 [Update: now $2,300 as of 2020].

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. 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?

Blog post by Robert Wiblin · Published April 6th, 2017

How 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.

Blog post by Peter McIntyre · Published March 19th, 2017

Note we’ve replaced this survey with one that’s more comprehensive and up-to-date.

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:

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

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5 reasons not to go into education

Blog post by Benjamin Todd · Published January 16th, 2017

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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The effective altruism guide to donating this giving season

Blog post by Robert Wiblin · Published December 28th, 2016

People in the effective altruism community aim to use evidence and careful reasoning to work out how to best promote the wellbeing of all. To find the highest-impact charities this giving season, they’ve done tens of thousands of hours of research and published over 50,000 words of analysis this month. We read it all, and summed up the main recommendations by area.

But which of the 9 problem areas listed should you personally give to? We’ve got you covered here too. This tool asks you six questions and adjusts the ranking based on your beliefs:¹

Quiz: Which problem should you give to? →

In the full post, you can find (i) how we came up with the list, (ii) more advice on how to narrow down the list, (iii) more information on each charity.

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80,000 Hours annual review Dec 2016

Blog post by Benjamin Todd · Published December 24th, 2016

Summary

2016 was an excellent year for 80,000 Hours. Here are some highlights – full details follow.

Metrics

In our last review in May 2015, we set the goal of 50 significant plan changes per month by October 2016. That month, we actually recorded over 200.
To make it harder to grow by adding lots of small plan changes, in October 2015 we started “impact rating” the plan changes, and tracking the impact-weighted total. 31 Dec 2015, we set the target of tripling the monthly rate of impact-adjusted plan changes over the year, which we achieved in November 2016. We now track about 150 impact-adjusted significant plan changes (IASPC) per month.

Impact and cost-effectiveness

Our costs in 2016 were £250,000, up 13% on 2015. Considering that our staff could have earned to give instead, the total opportunity cost is perhaps £350,000 – £500,000.
Since our last review, the ratio of costs to IASPC fell almost 3-fold.
In 2016, we caused 115 people to take the Giving What We Can (GWWC) 10% pledge. GWWC estimates this is worth about £5 million in donations to their recommended charities (counterfactually-adjusted, time-discounted, dropout adjusted). So this alone plausibly justifies our costs, although our aim is to solve talent gaps rather than funding gaps.
In addition, the plan changes since our last review now include three people who each intend to donate over $100m over their lifetimes,

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Has 80,000 Hours justified its costs?

Blog post by Benjamin Todd · Published December 23rd, 2016

We set up 80,000 Hours because we thought it could become one of the most effective charities in the world. The idea was to achieve a multiplier – with a small amount of our time, we could enable thousands of others to spend their careers on whatever is most effective, and achieve thousands of times as much as we could individually.

In this post, we examine whether 80,000 Hours has generated enough impact to justify its costs over our history, and make some rough estimates of our multiplier.

Because it’s hard to estimate what would have happened if 80,000 Hours had never existed, all of these estimates are very uncertain, and can be debated. However, there are multiple ways we’ve plausibly justified our costs to date. In this document, we sketch out some of these pathways. We’re not aiming to be fully rigorous. Rather, consider the examples as a group. If only a few turn out to be genuine cases of impact, we’ll have justified our costs many times over.

Is this the wrong question?

80,000 Hours is a startup. Asking whether we’ve justified our costs to date is like asking whether Google was profitable in 2000. The aim of 80,000 Hours is to grow, and have a far larger impact years in the future.

Most of the value of donations to 80,000 Hours comes from the chance that these donations enable us to grow 10-times or 100-times.

That said,

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End of year update on plan changes

Blog post by Roman Duda · Published December 22nd, 2016

This is an update on the number of significant plan changes we’ve caused as of the end of Nov 2016.

We define a significant plan change as:

Someone tells us that 80,000 Hours caused them to change the career path they intend to pursue, in a way that they think increases their lifetime impact.

More on what counts as a significant plan change here.

Our total number of plan changes as of the end of Nov 2016 is 1,854, and after impact-adjusting these it’s 1,504.8.

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80,000 Hours has a funding gap

Blog post by Benjamin Todd · Published December 8th, 2016

Over the past three years, we’ve grown almost 36-fold, more than tripling each year. This is measured in terms of our key metric – the number of impact-adjusted significant plan changes each month. At the same time, our budget has only increased 27% per year.

Given this success, we think it’s time to take 80,000 Hours to the next level of funding.

Over the next few weeks, we’ll be preparing our full annual review and fundraising documents, but here’s a preview.

Overall, the 2017 target is to triple, measured in terms of impact-adjusted significant plan changes per month (which will mean over 3,000 over the year). We’ll do this by continuing to improve the advice, and starting to scale up marketing, with the aim of becoming the default source of career advice for talented, socially-motivated graduates.

Concretely, here’s some priorities we could pursue:

Dramatically improve the career reviews and problem profiles, so we have in-depth profiles of all the best options. This will help our existing users make better changes, and bring in more traffic.
Upgrading – develop mentors and specialist content for the most high-potential users, such as those who want to work on AI risk, policy, EA orgs and so on. We now have a large base of engaged users (1300+ through the workshop, 80,000+ on newsletter), so there’s a lot of follow-up we could do to get more valuable plan changes from them.

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Update on 80,000 Hours May 2015 – June 2016

Blog post by Benjamin Todd · Published September 8th, 2016

This is a quick update on our progress over the last year. Our next in-depth annual review, in which we’ll vet everything in more depth, will be in January 2017.

Our impact across the year

Here’s our key metrics for the top of our funnel. Ultimately we care about significant plan changes, which we report right below.

Our newsletter now has a total of over 50,000 subscribers, which we think makes it the largest in the effective altruism community (most others have about 10,000). Our total traffic also just overtook GiveWell, which we think is the next largest by traffic (we had 880,000 users over the 12 months ending June, compared to 860,000 when calculated the same way.)

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