📚 Quinn's Highlights

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

The danger, and you see it often in investing, is when people become too McNamara-like – so obsessed with data and so confident in their models that they leave no room for error or surprise. No room for things to be crazy, dumb, unexplainable, and to remain that way for a long time. Always asking, “Why is this happening?” and expecting there to be a rational answer. Or worse, always mistaking what happened for what you think should have happened. The ones who thrive long term are those who understand the real world is a neverending chain of absurdity, confusions, messy relationships, and imperfect people.

Does Not Compute

collabfund.com

The Self-Reinforcing Stigmatization of Public Spaces (Like Libraries) Summary: Public libraries are facing various physical problems due to under-investment. They are often the last option for people who lack access to basic services. Libraries are used as shelters for the homeless, warm places for those suffering from addiction, and even childcare centers. This over-reliance on libraries to solve societal issues has stigmatized these public spaces. The lack of investment in addressing core problems has turned libraries into spaces of last resort. This sends a message to affluent Americans that if they want a gathering place, they should build their own in the private sector. 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

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