📚 Quinn's Highlights

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

The Golden Rule Doesn't Account for Other's Preferences and Interests The Golden Rule fails to consider individual differences, particularly in power dynamics. It assumes treating others as we want to be treated is ethical, but ignores the possibility that they may have different preferences based on their unique characteristics and circumstances. Transcript: Speaker 1 The golden rule does not adequately take into account these differences, especially of power. People commonly say, why is it ethical to treat others as we would want to be treated? They may be different than us and may want to be treated differently.

The Golden Rule

In Good We Trust

Bad Norms and Policies Produce "Legislatice Mediocrity" in Organizations Summary: Encouraging a culture of being teachable and open to listening to others is crucial for innovation and improvement in organizations. While standard operating procedures (SOPs) and efficient systems are appreciated, they should not create taboos or hinder learning, leading to what the speaker refers to as 'legislative mediocrity.' The speaker advocates for a focus on innovation and continuous improvement, rather than being stifled by rigid norms and policies. Transcript: Speaker 1 You want to be teachable and you want to have a culture of being teachable and listening to others. Yeah. That's that's really important. And so I love SOPs. I love I love it when you get a system working well and efficient. But I don't like it when it creates taboos and when it stops people learning. Legislative mediocrity. It drives me nuts. I'm very much let's do innovation. Let's improve.

Organizational Structures That Enable Knowledge Flow With Stuart French

Because You Need to Know Podcast ™

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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