#103 – Max Roser on building the world's best source of COVID-19 data at Our World in Data

#103 – Max Roser on building the world's best source of COVID-19 data at Our World in Data

History is filled with stories of great people stepping up in times of crisis. Presidents averting wars; soldiers leading troops away from certain death; data scientists sleeping on the office floor to launch a new webpage a few days sooner.

That last one is barely a joke — by our lights, people like today’s guest Max Roser should be viewed with similar admiration by historians of COVID-19.

Links to learn more, summary and full transcript.

Max runs Our World in Data, a small education nonprofit which began the pandemic with just six staff. But since last February his team has supplied essential COVID statistics to over 130 million users — among them BBC, The Financial Times, The New York Times, the OECD, the World Bank, the IMF, Donald Trump, Tedros Adhanom, and Dr. Anthony Fauci, just to name a few.

An economist at Oxford University, Max Roser founded Our World in Data as a small side project in 2011 and has led it since, including through the wild ride of 2020. In today's interview Max explains how he and his team realized that if they didn't start making COVID data accessible and easy to make sense of, it wasn't clear when anyone would.

Our World in Data wasn't naturally set up to become the world's go-to source for COVID updates. Up until then their specialty had been long articles explaining century-length trends in metrics like life expectancy — to the point that their graphing software was only set up to present yearly data.

But the team eventually realized that the World Health Organization was publishing numbers that flatly contradicted themselves, most of the press was embarrassingly out of its depth, and countries were posting case data as images buried deep in their sites where nobody would find them. Even worse, nobody was reporting or compiling how many tests different countries were doing, rendering all those case figures largely meaningless.

Trying to make sense of the pandemic was a time-consuming nightmare. If you were leading a national COVID response, learning what other countries were doing and whether it was working would take weeks of study — and that meant, with the walls falling in around you, it simply wasn't going to happen. Ministries of health around the world were flying blind.

Disbelief ultimately turned to determination, and the Our World in Data team committed to do whatever had to be done to fix the situation. Overnight their software was quickly redesigned to handle daily data, and for the next few months Max and colleagues like Edouard Mathieu and Hannah Ritchie did little but sleep and compile COVID data.

In this episode Max tells the story of how Our World in Data ran into a huge gap that never should have been there in the first place — and how they had to do it all again in December 2020 when, eleven months into the pandemic, there was nobody to compile global vaccination statistics.

We also talk about:

• Our World in Data's early struggles to get funding
• Why government agencies are so bad at presenting data
• Which agencies did a good job during the COVID pandemic (shout out to the European CDC)
• How much impact Our World in Data has by helping people understand the world
• How to deal with the unreliability of development statistics
• Why research shouldn't be published as a PDF
• Why academia under-incentivises data collection
• The history of war
• And much more

Chapters:
• Rob’s intro (00:00:00)
• The interview begins (00:01:41)
• Our World In Data (00:04:46)
• How OWID became a leader on COVID-19 information (00:11:45)
• COVID-19 gaps that OWID filled (00:27:45)
• Incentives that make it so hard to get good data (00:31:20)
• OWID funding (00:39:53)
• What it was like to be so successful (00:42:11)
• Vaccination data set (00:45:43)
• Improving the vaccine rollout (00:52:44)
• Who did well (00:58:08)
• Global sanity (01:00:57)
• How high-impact is this work? (01:04:43)
• Does this work get you anywhere in the academic system? (01:12:48)
• Other projects Max admires in this space (01:20:05)
• Data reliability and availability (01:30:49)
• Bringing together knowledge and presentation (01:39:26)
• History of war (01:49:17)
• Careers at OWID (02:01:15)
• How OWID prioritise topics (02:12:30)
• Rob's outro (02:21:02)

Producer: Keiran Harris.
Audio mastering: Ryan Kessler.
Transcriptions: Sofia Davis-Fogel.

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#3 - Dario Amodei on OpenAI and how AI will change the world for good and ill

#3 - Dario Amodei on OpenAI and how AI will change the world for good and ill

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. Full transcript, apply for personalised coaching to work on AI safety, see what questions are asked when, and read extra resources to learn more. 1m33s - What OpenAI is doing, Dario’s research and why AI is important  13m - Why OpenAI scaled back its Universe project  15m50s - Why AI could be dangerous  24m20s - Would smarter than human AI solve most of the world’s problems?  29m - Paper on five concrete problems in AI safety  43m48s - Has OpenAI made progress?  49m30s - What this back flipping noodle can teach you about AI safety  55m30s - How someone can pursue a career in AI safety and get a job at OpenAI  1h02m30s - Where and what should people study?  1h4m15s - What other paradigms for AI are there?  1h7m55s - How do you go from studying to getting a job? What places are there to work?  1h13m30s - If there's a 17-year-old listening here what should they start reading first?  1h19m - Is this a good way to develop your broader career options? Is it a safe move?  1h21m10s - What if you’re older and haven’t studied machine learning? How do you break in?  1h24m - What about doing this work in academia?  1h26m50s - Is the work frustrating because solutions may not exist?  1h31m35s - How do we prevent a dangerous arms race?  1h36m30s - Final remarks on how to get into doing useful work in machine learning

21 Juli 20171h 38min

#2 - David Spiegelhalter on risk, stats and improving understanding of science

#2 - David Spiegelhalter on risk, stats and improving understanding of science

Recorded in 2015 by Robert Wiblin with colleague Jess Whittlestone at the Centre for Effective Altruism, and recovered from the dusty 80,000 Hours archives. David Spiegelhalter is a statistician at the University of Cambridge and something of an academic celebrity in the UK. Part of his role is to improve the public understanding of risk - especially everyday risks we face like getting cancer or dying in a car crash. As a result he’s regularly in the media explaining numbers in the news, trying to assist both ordinary people and politicians focus on the important risks we face, and avoid being distracted by flashy risks that don’t actually have much impact. Summary, full transcript and extra links to learn more. To help make sense of the uncertainties we face in life he has had to invent concepts like the microlife, or a 30-minute change in life expectancy. (https://en.wikipedia.org/wiki/Microlife) 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.

21 Juni 201733min

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

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

Robert Wiblin, Director of Research at 80,000 Hours speaks with Miles Brundage, research fellow at the University of Oxford's Future of Humanity Institute. Miles studies the social implications surrounding the development of new technologies and has a particular interest in artificial general intelligence, that is, an AI system that could do most or all of the tasks humans could do. This interview complements our profile of the importance of positively shaping artificial intelligence and our guide to careers in AI policy and strategy Full transcript, apply for personalised coaching to work on AI strategy, see what questions are asked when, and read extra resources to learn more.

5 Juni 201755min

#0 – Introducing the 80,000 Hours Podcast

#0 – Introducing the 80,000 Hours Podcast

80,000 Hours is a non-profit that provides research and other support to help people switch into careers that effectively tackle the world's most pressing problems. This podcast is just one of many things we offer, the others of which you can find at 80000hours.org. Since 2017 this show has been putting out interviews about the world's most pressing problems and how to solve them — which some people enjoy because they love to learn about important things, and others are using to figure out what they want to do with their careers or with their charitable giving. If you haven't yet spent a lot of time with 80,000 Hours or our general style of thinking, called effective altruism, it's probably really helpful to first go through the episodes that set the scene, explain our overall perspective on things, and generally offer all the background information you need to get the most out of the episodes we're making now. That's why we've made a new feed with ten carefully selected episodes from the show's archives, called 'Effective Altruism: An Introduction'. You can find it by searching for 'Effective Altruism' in your podcasting app or at 80000hours.org/intro. Or, if you’d rather listen on this feed, here are the ten episodes we recommend you listen to first: • #21 – Holden Karnofsky on the world's most intellectual foundation and how philanthropy can have maximum impact by taking big risks • #6 – Toby Ord on why the long-term future of humanity matters more than anything else and what we should do about it • #17 – Will MacAskill on why our descendants might view us as moral monsters • #39 – Spencer Greenberg on the scientific approach to updating your beliefs when you get new evidence • #44 – Paul Christiano on developing real solutions to the 'AI alignment problem' • #60 – What Professor Tetlock learned from 40 years studying how to predict the future • #46 – Hilary Greaves on moral cluelessness, population ethics and tackling global issues in academia • #71 – Benjamin Todd on the key ideas of 80,000 Hours • #50 – Dave Denkenberger on how we might feed all 8 billion people through a nuclear winter • 80,000 Hours Team chat #3 – Koehler and Todd on the core idea of effective altruism and how to argue for it

1 Maj 20173min

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