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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 allow them to trade very quickly and effectively.
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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.
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
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.
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