📚 Quinn's Highlights

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Organizational Entropy: the tendency for artifacts you produce to start rotting immediately Summary: Any artifact produced within an organization immediately begins to deteriorate, much like a new car losing its value. Once published, such as a memo, it begins to become outdated. Entropy, in this context, always increases, requiring continual input of energy to prevent deterioration. This demands the creation of reinforcement mechanisms to ensure that all content remains current and functional, such as periodically checking and updating a database of memos. Transcript: Speaker 1 Organizational entropy, which is any artifact that you produce immediately starts rotting the moment that you have created it. Speaker 2 It's like driving a new car after a lot. Speaker 1 Yeah, the moment that anything is published in the company, you write a memo, it is already rotting. It is already going to be out of date. And so the concept of entropy is it is always increasing. And so the only way to keep entropy at bay is you have to add more energy into the system. So you have to create reinforcement mechanisms for any piece of content that you have. If you have a database of all your memos, you have to check them every once in a while to make sure they're up to date. You need to create more energy always has to go in in order to keep things fresh and functional.

#694 — Sam Corcos, Co-Founder of Levels — The Ultimate Guide to Virtual Assistants, 10x Delegation, and Winning Freedom by Letting Go

The Tim Ferriss Show

When business leaders describe their organisational cultures as embracing what it means to ‘fail fast’ or suggesting that we are all in a ‘learning organisation’, it rings hollow if the aspirations are not lived and structurally supported. Employees see through the public relations of an organisation that markets itself as innovative yet executes the same playbook year after year.

Driving Data Projects: A Comprehensive Guide

Haskell, Christine

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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