Imagine that – one day – humanity dies out. At some point, many millions of years later, intelligent life might well evolve again. Is there any message we could leave that would reliably help them out?
In his second appearance on the 80,000 Hours Podcast, machine learning researcher and polymath Paul Christiano suggests we try to answer this question with a related thought experiment: are there any messages we might want to send back to our ancestors in the year 1700 that would have made history likely to go in a better direction than it did? It seems there probably are.
• Links to learn more, summary, and full transcript.
• Paul's first appearance on the show in episode 44.
• An out-take on decision theory.
We could tell them hard-won lessons from history; mention some research questions we wish we'd started addressing earlier; hand over all the social science we have that fosters peace and cooperation; and at the same time steer clear of engineering hints that would speed up the development of dangerous weapons.
But, as Christiano points out, even if we could satisfactorily figure out what we'd like to be able to tell our ancestors, that's just the first challenge. We'd need to leave the message somewhere that they could identify and dig up. While there are some promising options, this turns out to be remarkably hard to do, as anything we put on the Earth's surface quickly gets buried far underground.
But even if we figure out a satisfactory message, and a ways to ensure it's found, a civilization this far in the future won't speak any language like our own. And being another species, they presumably won't share as many fundamental concepts with us as humans from 1700. If we knew a way to leave them thousands of books and pictures in a material that wouldn't break down, would they be able to decipher what we meant to tell them, or would it simply remain a mystery?
That's just one of many playful questions discussed in today's episode with Christiano — a frequent writer who's willing to brave questions that others find too strange or hard to grapple with.
We also talk about why divesting a little bit from harmful companies might be more useful than I'd been thinking. Or whether creatine might make us a bit smarter, and carbon dioxide filled conference rooms make us a lot stupider.
Finally, we get a big update on progress in machine learning and efforts to make sure it's reliably aligned with our goals, which is Paul's main research project. He responds to the views that DeepMind's Pushmeet Kohli espoused in a previous episode, and we discuss whether we'd be better off if AI progress turned out to be most limited by algorithmic insights, or by our ability to manufacture enough computer processors.
Some other issues that come up along the way include:
• Are there any supplements people can take that make them think better?
• What implications do our views on meta-ethics have for aligning AI with our goals?
• Is there much of a risk that the future will contain anything optimised for causing harm?
• An out-take about the implications of decision theory, which we decided was too confusing and confused to stay in the main recording.
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below.
The 80,000 Hours Podcast is produced by Keiran Harris.
