#48 - Brian Christian on better living through the wisdom of computer science

11/22/18 • 195 min

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Ever felt that you were so busy you spent all your time paralysed trying to figure out where to start, and couldn't get much done? Computer scientists have a term for this - thrashing - and it's a common reason our computers freeze up. The solution, for people as well as laptops, is to 'work dumber': pick something at random and finish it, without wasting time thinking about the bigger picture.

Bestselling author Brian Christian studied computer science, and in the book Algorithms to Live By he's out to find the lessons it can offer for a better life. He investigates into when to quit your job, when to marry, the best way to sell your house, how long to spend on a difficult decision, and how much randomness to inject into your life. In each case computer science gives us a theoretically optimal solution, and in this episode we think hard about whether its models match our reality.

Links to learn more, summary and full transcript.

One genre of problems Brian explores in his book are 'optimal stopping problems', the canonical example of which is ‘the secretary problem’. Imagine you're hiring a secretary, you receive *n* applicants, they show up in a random order, and you interview them one after another. You either have to hire that person on the spot and dismiss everybody else, or send them away and lose the option to hire them in future.

It turns out most of life can be viewed this way - a series of unique opportunities you pass by that will never be available in exactly the same way again.

So how do you attempt to hire the very best candidate in the pool? There's a risk that you stop before finding the best, and a risk that you set your standards too high and let the best candidate pass you by.

Mathematicians of the mid-twentieth century produced an elegant optimal approach: spend exactly one over *e*, or approximately 37% of your search, just establishing a baseline without hiring anyone, no matter how promising they seem. Then immediately hire the next person who's better than anyone you've seen so far.

It turns out that your odds of success in this scenario are also 37%. And the optimal strategy and the odds of success are identical regardless of the size of the pool. So as *n* goes to infinity you still want to follow this 37% rule, and you still have a 37% chance of success. Even if you interview a million people.

But if you have the option to go back, say by apologising to the first applicant and begging them to come work with you, and you have a 50% chance of your apology being accepted, then the optimal explore percentage rises all the way to 61%.

Today’s episode focuses on Brian’s book-length exploration of how insights from computer algorithms can and can't be applied to our everyday lives. We cover:

Computational kindness, and the best way to schedule meetings
* How can we characterize a computational model of what people are actually doing, and is there a rigorous way to analyse just how good their instincts actually are?
* What’s it like being a human confederate in the Turing test competition?
* Is trying to detect fake social media accounts a losing battle?
* The canonical explore/exploit problem in computer science: the multi-armed bandit
* What’s the optimal way to buy or sell a house?
* Why is information economics so important?
* What kind of decisions should people randomize more in life?
* How much time should we spend on prioritisation?

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The 80,000 Hours Podcast is produced by Keiran Harris.

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Links to learn more, summary and full transcript.

It turns out most of life can be viewed this way - a series of unique opportunities you pass by that will never be available in exactly the same way again.

Today’s episode focuses on Brian’s book-length exploration of how insights from computer algorithms can and can't be applied to our everyday lives. We cover:

Get this episode by subscribing: type '80,000 Hours' into your podcasting app.

The 80,000 Hours Podcast is produced by Keiran Harris.

Previous Episode

#47 - Catherine Olsson & Daniel Ziegler on the fast path into high-impact ML engineering roles

After dropping out of a machine learning PhD at Stanford, Daniel Ziegler needed to decide what to do next. He’d always enjoyed building stuff and wanted to shape the development of AI, so he thought a research engineering position at an org dedicated to aligning AI with human interests could be his best option.

He decided to apply to OpenAI, and spent about 6 weeks preparing for the interview before landing the job. His PhD, by contrast, might have taken 6 years. Daniel thinks this highly accelerated career path may be possible for many others.

On today’s episode Daniel is joined by Catherine Olsson, who has also worked at OpenAI, and left her computational neuroscience PhD to become a research engineer at Google Brain. She and Daniel share this piece of advice for those curious about this career path: just dive in. If you're trying to get good at something, just start doing that thing, and figure out that way what's necessary to be able to do it well.

Catherine has even created a simple step-by-step guide for 80,000 Hours, to make it as easy as possible for others to copy her and Daniel's success.

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Blog post with links to learn more, a summary & full transcript.

Daniel thinks the key for him was nailing the job interview.

OpenAI needed him to be able to demonstrate the ability to do the kind of stuff he'd be working on day-to-day. So his approach was to take a list of 50 key deep reinforcement learning papers, read one or two a day, and pick a handful to actually reproduce. He spent a bunch of time coding in Python and TensorFlow, sometimes 12 hours a day, trying to debug and tune things until they were actually working.

Daniel emphasizes that the most important thing was to practice *exactly* those things that he knew he needed to be able to do. His dedicated preparation also led to an offer from the Machine Intelligence Research Institute, and so he had the opportunity to decide between two organisations focused on the global problem that most concerns him.

Daniel’s path might seem unusual, but both he and Catherine expect it can be replicated by others. If they're right, it could greatly increase our ability to get new people into important ML roles in which they can make a difference, as quickly as possible.

Catherine says that her move from OpenAI to an ML research team at Google now allows her to bring a different set of skills to the table. Technical AI safety is a multifaceted area of research, and the many sub-questions in areas such as reward learning, robustness, and interpretability all need to be answered to maximize the probability that AI development goes well for humanity.

Today’s episode combines the expertise of two pioneers and is a key resource for anyone wanting to follow in their footsteps. We cover:

What are OpenAI and Google Brain doing?
* Why work on AI?
* Do you learn more on the job, or while doing a PhD?
* Controversial issues within ML
* Is replicating papers a good way of determining suitability?
* What % of software developers could make similar transitions?
* How in-demand are research engineers?
* The development of Dota 2 bots
* Do research scientists have more influence on the vision of an org?
* Has learning more made you more or less worried about the future?

Get this episode by subscribing: type '80,000 Hours' into your podcasting app.

The 80,000 Hours Podcast is produced by Keiran Harris.