📚 Quinn's Highlights

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Most of us really enjoy the building aspect but start to get a little shy when it comes to telling people about the stuff we’ve built. That could be for any number of reasons: fear, embarrassment, self-preservation, or an aversion to being perceived as hawking your wares. It’s a valuable exercise to investigate whether or not you resonate with any of those reasons. Are you afraid people are going to make fun of what you built? Are you embarrassed that it isn’t up to your own (admittedly high) standards? Are you waiting for some elusive perfect moment? Do you have an aversion to “marketing” and don’t want to become the thing you hate? Whatever it is for you, I encourage you to really dig into it and see if that fear is worth keeping around.

Publishing your work increases your luck

https://github.com/readme/

Inversion: Avoiding stupidity is easier than trying to be brilliant. Instead of asking, “How can I help my company?” you should ask, “What’s hurting my company the most and how can I avoid it?” Identify obvious failure points, and steer clear of them.

50 Ideas That Changed My Life - David Perell

perell.com

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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