📚 Quinn's Highlights

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

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 Once published, such as a memo, it begins to become outdated.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 It is already going to be out of date.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

Pol.is: An Example of Tools for Facilitating Non-Adverserial Debate at Scale Summary: A twitter-like system in Taiwan guides conversations towards consensual outcomes by using k-means clustering. It's a simple proof of concept for fact checking and has been effective in large-scale conversations. The science of plurality can advance to help navigate complexity in diverse opinions. Transcript: Speaker 2 Pol.is i don't know if you guys are familiar with that but it's a system used in Taiwan it's a twitter like format but it deliberately guides conversations towards consensual or partially Consensual outcomes while highlighting the differences that exist in the conversations in a non-judgmental way and it's just a wonderful system and at the same time it's like the Most simplistic proof of concept of the general direction it uses k-means clustering of stated opinions it doesn't use any natural language processing it's like the bargain basement Version of what it's trying to achieve but it still has been transformatively effective for these types of conversations at scale in Taiwan and is being adopted if it survives by the Twitter bird watch folks as the foundations of what they're trying to do for fact checking so i do believe that there is a science here that can advance dramatically i think that we have Not chosen to apply ourselves to it because we've been seduced by oh we're going to do the unbiased algorithm that's going to predict the truth the right way rather than saying no people Are diverse you have a lot of different opinions how do we actually help people navigate that complexity so i really am hopeful that this science what i would call plurality really can Advance and and help us do these things much better and again i'll put in the plug if you're a researcher interested in these things we're trying to build an academic community that really Wants to work on them right to me at when at pluralitynetwork.org

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

The need for transparent and democratic decision-making: Human bullshit and algorithmic bullshit are two sides of the same coin Summary: Data and algorithms are not inherently bad, but they should be used in a transparent and democratic way that empowers everyone. Instead of arguing about whether computer or human decision-making is better, we should focus on accountable and transparent decision-making. This means avoiding human biases and stereotypes as well as naive machine learning without considering its real-world implications. Transcript: Speaker 1 So the point is that it's not that data and algorithms are bad it's that they need to be applied in a way which is transparent and which is democratic and which empowers all of us to carry On these debates rather than simply being tools which accurately or inaccurately are being used to buy the powerful to control the rest of us it's silly to argue about which is better You know computer decision making or human decision making that's really not the point I mean the point is we should have accountable transparent decision making instead of bs there's Human bs which comes in the form of stereotypes in ideology and there's algorithmic bs which comes in the form of naive machine learning without thinking enough about its applications

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

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