📚 Quinn's Highlights

Join 📚 Quinn's Highlights

A batch of the best highlights from what Quinn's read, .

The Self-Reinforcing Stigmatization of Public Spaces (Like Libraries) Transcript: Speaker 1 One of the problems we have now is most cities, suburbs, towns in America have public libraries there. There's neighborhood libraries. The building is there. The buildings are generally not updated. They need to have new HVACs. They need new bathrooms. They need new furniture, but a lot of new books. Stomachs still not accessible to people in wheelchairs. There's all kinds of problems with libraries, just physically because we've under-invested in them. Libraries, unfortunately, have become the place of last resort for everyone who falls through the safety net. If you wake up in the morning in the American city and you don't have a home, you're told to go to a library. If you wake up in the morning and you're suffering from an addiction problem, you need a warm place. They'll send you to a library. If you need to use a bathroom, you'll go to a library. If you don't have child care for your kid, you might send your kid to a library. If you're old and you're alone, you might go to the library. We've used the library to try to solve all these problems that deserve actual treatment. How many times have you talked to someone who said it's basically a homeless shelter? What's happened is we've stigmatized our public spaces because we've done so little to address core problems that we've turned them into spaces of last resort for people who need a Hand. As we do that, we send another message to affluent middle-class Americans, and that is if you want a gathering place, build your own in the private sector.

The Infrastructure of Community

How to Know What's Real

Explore v.s. Exploit: Finding Solutions Quickly Can Get You Stuck in a Local Optimum Transcript: Speaker 1 So when I started doing the work in AI, one of the really, very, very general ideas that comes across again and again in computer science is this idea of the explore, exploit trade on. And the idea is that you can't get a system that is simultaneously going to optimize for actually being able to do things effectively. That's the exploit part. And being able to figure out, search through all the possibilities. So let me try to describe it this way. I guess we're a podcast. So you're going to have to imagine this usually I wave my arms around a lot here. So imagine that you have some problem you want to solve or some hypothesis that you want to discover. And you can think about it as if there's a big box full of all the possible hypotheses and all the possible solutions to your problem or possible policies that you could have, for instance, Your reinforcement learning context. And now you're in a particular space in that box. That's what you know now. That's the hypotheses you have now. That's the policies you have now. Now what you want to do is get somewhere else. You want to be able to find a new idea, a new solution. And the question is how do you do that? And the idea is that there are actually two different kinds of strategies you could use. One of them is you could just search for solutions that are very similar to the ones you already have. And you could just make small changes in what you already think to accommodate new evidence or a new problem. And that has the advantage that you're going to be able to find a pretty good solution pretty quickly. But it has a disadvantage. And the disadvantage is that there might be a much better solution that's much further away in that high dimensional space. And any interesting space is going to be too large to just search completely systematically. You're always going to have to choose which kinds of possibilities you want to consider. So it could be that there's a really good solution, but it's much more different from where you currently are. And the trouble is that if you just do something like what's called hill climbing, you just look locally, you're likely to get stuck in what's called a local optimum.

Alison Gopnik on Child Development, Elderhood, Caregiving, and A.I.

COMPLEXITY: Physics of Life

How Measurability/Mathematical Bias Limits the Scope of Scientific Inquiry and Human Discovery Transcript: Speaker 1 So there's this old paper from the, I think, 1960s by Eugene Vigner, the Nobel Prize physicist. It's called something like, on the unreasonable effectiveness of mathematics. The fun paper, and he's like, there's no good reason why mathematics should work as well as it does. And there's no good reason why there should be a tool that allows humans to predict things as well as math does. There's no good reason. It's kind of nuts. And we should all just be grateful. And he says some other things, but he's basically just kind of being all about how great mathematics is and how there's no good reason why it should be. And it's pretty cool that it does work so well. I think that there's a counter to that, which is that not everything is that easily described that mathematics. And there's lots of things for which mathematics is not that effective at describing. And it's actually just the things that were well described or easily described by mathematics are the things that were discovered using mathematical tools. They're the things that lend themselves that were amenable to mathematical inquiry. And a lot of the things that we're interested in terms of social science and cognitive science and the related philosophical inquiry are things that are much less tangible in terms Of this kind of specification. And you can see it like in a physics equation, right, a physical theory, whether it's about mass or electricity or something else, right, you have a theory about how things work. And then you can write out equations. And all the terms in the equations have units. And they are all directly related to the things that are measurable. The theories are directly about relationships between things that are measured. And in social theories and cognitive theories, so often our theories are about relating constructs. And then we have proxy measurements, but the theory isn't about the relationship between the proxy measures. The theory is about the constructs and the relationships between the constructs that are social in nature, that are cognitive in nature, but aren't the things that are being measured. And so there's this gap. And I don't know the extent to which that gap can be overcome.

Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale

COMPLEXITY: Physics of Life

...catch up on these, and many more highlights

Readwise.

Join 📚 Quinn's Highlights

A batch of the best highlights from what Quinn's read, .

LEARN

EXPLORE

CONTACT