📚 Quinn's Highlights

Join 📚 Quinn's Highlights

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

Balancing Intellectual Exploration and Action • There is an anti-pattern in certain podcasts that overemphasizes intellect and underemphasizes action. • Consuming knowledge from brilliant people can be stimulating, but it may lead to overthinking and under-practicing. • It is important to balance intellectual comprehension with taking action, initiating projects, and practicing. • Encouraging agency, initiative, entrepreneurship, and proactive energy is crucial. Transcript: Speaker 1 One piece of the puzzle, I think, is that there's an anti-pattern of podcasts, especially in the game, B space and related sort of sense making intellectual philosophical spaces, Which is I'm concerned about an overdoing the intellect and an underdoing the action. You know, there's all of the people that you interview on your show. They're brilliant people. You know, and it's like, every time I can get a new episode of my favorite podcast and listen to this person and be like, wow, they're so smart. And it's really stimulating to listen to these smart people that can communicate really clearly. And the concern that I have is that people get into a habit of just consuming knowledge, just listening to more and more different people and assembling this sort of like pristine map Of how they think reality works. And maybe they start a little bit to think about how they might initiate some kind of community or some project or something that they're interested in, but still they do this thing of Like way over engineering and overthinking it and under practicing, under experimenting. And so my energy is to try and interfere with that tendency and push people more towards their agency, more towards their initiative, their entrepreneurship, their get up and do it Kind of energy.

EP51 Richard Bartlett on Self-Organizing Collaboration

The Jim Rutt Show

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

Ambiguity in Communication is Both a Feature and a Bug Summary: In 1984, Eisenberg proposed that ambiguity in communication is important and influential. This idea suggests that being too clear can limit interpretation and hinder coalition-building. Ambiguity can be used to evade accountability, but it is also a general principle of communication. Transcript: Speaker 1 It's Eisenberg in 1984 in communication monographs or something. It's this great rambling paper and this idea has been massively influential to me, but he's basically arguing that it would seem like the point of communication should be clarity, To be as clear as possible. For me to say, I mean this and you do know exactly what I mean and that's the goal and ambiguity is therefore a bad thing. He argues that actually no ambiguity is a really important thing and other people have expanded on this. Now the way I think about this is like a blend of Eisenberg and then other people who've come a bit later, but that in a lot of ways if you're trying to get let's say a coalition, you don't Want to say this is exactly what our goal is and this is what we're trying to do. You want to use vague terms so that a bunch of people can sort of map whatever they think that the goal is onto and say that's consistent. It also leads to a reduction in accountability because after you do something and someone says, you said you were going to do this and you say, nah-ah listen to what I said, it's consistent With what I did because what I said was ambiguous. So it's pernicious in a way too. It's used nefariously in a lot of ways by let's say politicians and other kinds of leaders to avoid accountability, but it's also just a general principle of communication I think.

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

COMPLEXITY: Physics of Life

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