80,000 Hours Podcast21 Heinä 2017

#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

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