The Nonlinear Library

The Nonlinear Fund
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Jun 26, 2024 • 2min

EA - In favour of exploring nagging doubts about x-risk by Owen Cotton-Barratt

Expert Owen Cotton-Barratt discusses the importance of exploring nagging doubts about x-risk. Encourages individuals to align intellectual beliefs with gut skepticism, emphasizing the need for public discourse on existential security and AI risk concerns.
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Jun 25, 2024 • 10min

AF - What is a Tool? by johnswentworth

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What is a Tool?, published by johnswentworth on June 25, 2024 on The AI Alignment Forum. Throughout this post, we're going to follow the Cognition -> Convergence -> Consequences methodology[1]. That means we'll tackle tool-ness in three main stages, each building on the previous: Cognition: What does it mean, cognitively, to view or model something as a tool? Convergence: Insofar as different minds (e.g. different humans) tend to convergently model the same things as tools, what are the "real patterns" in the environment which give rise to that convergence? Consequences: Having characterized the real patterns convergently recognized as tool-ness, what other properties or consequences of tool-ness can we derive? What further predictions does our characterization make? We're not going to do any math in this post, though we will gesture at the spots where proofs or quantitative checks would ideally slot in. Cognition: What does it mean, cognitively, to view or model something as a tool? Let's start with a mental model of (the cognition of) problem solving, then we'll see how "tools" naturally fit into that mental model. When problem-solving, humans often come up with partial plans - i.e. plans which have "gaps" in them, which the human hasn't thought through how to solve, but expects to be tractable. For instance, if I'm planning a roadtrip from San Francisco to Las Vegas, a partial plan might look like "I'll take I-5 down the central valley, split off around Bakersfield through the Mojave, then get on the highway between LA and Vegas". That plan has a bunch of gaps in it: I'm not sure exactly what route I'll take out of San Francisco onto I-5 (including whether to go across or around the Bay), I don't know which specific exits to take in Bakersfield, I don't know where I'll stop for gas, I haven't decided whether I'll stop at the town museum in Boron, I might try to get pictures of the airplane storage or the solar thermal power plant, etc. But I expect those to be tractable problems which I can solve later, so it's totally fine for my plan to have such gaps in it. How do tools fit into that sort of problem-solving cognition? Well, sometimes similar gaps show up in many different plans (or many times in one plan). And if those gaps are similar enough, then it might be possible to solve them all "in the same way". Sometimes we can even build a physical object which makes it easy to solve a whole cluster of similar gaps. Consider a screwdriver, for instance. There's a whole broad class of problems for which my partial plans involve unscrewing screws. Those partial plans involve a bunch of similar "unscrew the screw" gaps, for which I usually don't think in advance about how I'll unscrew the screw, because I expect it to be tractable to solve that subproblem when the time comes. A screwdriver is a tool for that class of gaps/subproblems[2]. So here's our rough cognitive characterization: Humans naturally solve problems using partial plans which contain "gaps", i.e. subproblems which we put off solving until later Sometimes there are clusters of similar gaps A tool makes some such cluster relatively easy to solve. Convergence: Insofar as different minds (e.g. different humans) tend to convergently model the same things as tools, what are the "real patterns" in the environment which give rise to that convergence? First things first: there are limits to how much different minds do, in fact, convergently model the same things as tools. You know that thing where there's some weird object or class of objects, and you're not sure what it is or what it's for, but then one day you see somebody using it for its intended purpose and you're like "oh, that's what it's for"? () From this, we learn several things about tools: Insofar as different humans convergently model the same thing...
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Jun 25, 2024 • 3min

EA - Open Sourcing Metaculus by christian

The podcast discusses the decision to open source forecasting tools, seeking feedback from the EA community. It highlights plans for utilizing GitHub, improving accuracy, enhancing transparency, and encouraging professional contributions.
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Jun 25, 2024 • 4min

EA - Extraordinary cost-effectiveness analyses call for theoretical buttressing by Seth Ariel Green

Writer and Rationalist in the EA community, Seth Ariel Green, discusses the importance of theoretical foundations in cost-effectiveness analyses. He delves into the comparative cost-effectiveness of Pure Earth and GiveDirectly, highlighting the need for a compelling theoretical basis behind extraordinary results in effective altruism.
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Jun 25, 2024 • 4min

EA - Distancing EA from rationality is foolish by Jan Kulveit

Jan Kulveit discusses the growing tendency within EA to dissociate from Rationality, emphasizing the importance of differentiating between 'capital R' Rationality and 'small r' rationality. He explains the common ground of evidence-based decision-making and clear thinking between the two communities, urging a closer look at the relationship between Effective Altruism and Rationality.
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Jun 25, 2024 • 3min

EA - Responses to Manifest are not about Manifest by timunderwood

The podcast delves into the root fears behind arguments about excluding certain individuals, touching on issues of minorities being unwelcome, the connection of important identities to racists, and the fear of racial injustice. It also emphasizes the need for balanced solutions and intellectual freedom in addressing these concerns.
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Jun 25, 2024 • 12min

LW - Mistakes people make when thinking about units by Isaac King

Isaac King, author of 'Mistakes people make when thinking about units,' discusses common errors in unit calculations. He critiques Matt Parker's analysis of unit errors and emphasizes the importance of unit awareness in calculations. The podcast delves into unit conversions and dimensional analysis in physics.
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Jun 25, 2024 • 15min

AF - Formal verification, heuristic explanations and surprise accounting by Jacob Hilton

Jacob Hilton, ARC's current research focuses on combining mechanistic interpretability and formal verification. The podcast discusses formal verification and heuristic explanations for neural networks, exploring ARC's approach to safety and interpretability. They delve into challenges of formal verification for large neural networks and introduce heuristic explanations as an alternative approach. 'Surprise accounting' is discussed to assess the efficacy of heuristic explanations in understanding neural network properties.
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Jun 25, 2024 • 4min

LW - I'm a bit skeptical of AlphaFold 3 by Oleg Trott

Oleg Trott, author skeptical of AlphaFold 3, discusses dataset redundancy in predicting molecular bindings, neural network training anecdotes, and the importance of avoiding data redundancy for generalization in predictive models.
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Jun 25, 2024 • 59min

LW - Book Review: Righteous Victims - A History of the Zionist-Arab Conflict by Yair Halberstadt

Author Yair Halberstadt discusses the complexities of the Israeli-Palestinian conflict, including historical events from the Zionist settlement in Palestine to the Second Intifada. The podcast explores concrete steps for addressing security concerns, analyzes historical perspectives, and delves into strategic, historical, and religious aspects of the conflict.

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