Zotero Catalogue

The Solow model has its uses, but it fails when it comes to major changes stemming from AI.  Consider instead an economy with (at least) two factors of production: 1. Intelligence.  Yes, formal smarts.  Playing chess, proving math theorems, and doing well on evals.  Don’t forget humans can do those things too, though AIs are […]

At time of writing, large language models (LLMs) are beginning to obtain the ability to design, execute, write up, and referee scientific projects on the data-science side of astrophysics. What implications does this have for our profession? In this white paper, I list - and argue for - a set of facts or "points of agreement" about what astrophysics is, or should be; these include considerations of novelty, people-centrism, trust, and (the lack of) clinical value. I then list and discuss every possible benefit that astrophysics can be seen as bringing to us, and to science, and to universities, and to the world; these include considerations of love, weaponry, and personal (and personnel) development. I conclude with a discussion of two possible (extreme and bad) policy recommendations related to the use of LLMs in astrophysics, dubbed "let-them-cook" and "ban-and-punish." I argue strongly against both of these; it is not going to be easy to develop or adopt good moderate policies.

What’s the point of content knowledge when you can teach skills instead? Scotland decided to find out.

I am proud to be among the list of 25 initial signatories — all Fields Medallists — to the declaration below, which grew out of discussions between ourselves over the last week. We have…

In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey). Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that "substantial" or "extreme" concern is warranted about six different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more.

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Advances in medieval economic theory had greater real-world consequences than many know. Among economists, Joseph Schumpeter’s economic history mostly starts economic theory with the Salamanc…

Solcoa produces NdPr (neodymium-praseodymium) rare earth metal in America — the metal behind every electric motor, generator, and speaker on Earth.

A model of human behavior, and new ways to measure how faithfully AI can simulate people.

Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance. However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts. We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context. CIMemories uses synthetic user profiles with over 100 attributes per user, paired with diverse task contexts in which each attribute may be essential for some tasks but inappropriate for others. Our evaluation reveals that frontier models exhibit up to 69% attribute-level violations (leaking information inappropriately), with lower violation rates often coming at the cost of task utility. Violations accumulate across both tasks and runs: as usage increases from 1 to 40 tasks, GPT-5's violations rise from 0.1% to 9.6%, reaching 25.1% when the same prompt is executed 5 times, revealing arbitrary and unstable behavior in which models leak different attributes for identical prompts. Privacy-conscious prompting does not solve this - models overgeneralize, sharing everything or nothing rather than making nuanced, context-dependent decisions. These findings reveal fundamental limitations that require contextually aware reasoning capabilities, not just better prompting or scaling.

A model of human behavior, and new ways to measure how faithfully AI can simulate people.

Currently: working on hybrid schools, Senior Fellow at educationprogress.org, and reading both economics and history of science. Sebastian graduated from Wabash College with a Major in Classical La…

Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm has been used to explain many systems such as biological processes, traffic simulation, computer networks, etc. In games, cellular automata have been used in games such as SimCity and for the generation of spatial content such as caves or dungeons. However, creating effective local rules is hard and unintuitive. Cellular automata can be effectively evolved, but may still be hard to interpret. In this work, we introduce the concept of programmable cellular automata, where we represent the system as Python code. We also modularize the cellular automata into local functions and a decision function. Local functions take a local neighborhood and return a value, while the decision function takes the output of the local functions and decides the value of the next state. Separating the cellular automata into modules written in Python helps with understanding how these systems are working. We also explore adding global functions where they take the whole state and compute a function from it. We tested generating levels for three different games from the PCG Benchmark. The results showed that global functions decrease the number of iterations that cellular automata need to solve a problem, and that we cannot find solutions for some problems with purely local functions. Looking into the generated functions, we can see common functions that have been used in different experiments, which not only helps us understand the generator but also helps us understand these games better and what is important for them.

The full story about why procrastinators routinely ruin their own lives.

Part 2. Where does a procrastinator go wrong and how can you actually improve your procrastination habits?

Life is never easy with a monkey in your head.

On-platform customers receive an average 25% realized discount on compute through resale.

As Hugo Duminil-Copin wrote recently at https://proofsandprompts.com/2026/08/30/care-for-a-little-more-ai/ , the indiscriminate automated strip-mining of open problems for solutions may destroy the ecosystem from which the next generation of mathematical techniques, problems, and practitioners would have developed, similarly to how using excavators to dig out treasures from an archeological site destroys the rich historical context and data that gives such treasures so much of their meaning and value. It may become necessary to declare certain classes of mathematical problems off-limits to automated solvers, in order to preserve their broader value to the mathematical ecosystem (for instance, through the training of future mathematicians). Admittedly this can be hard to enforce when such tools are both powerful and widely available. But then again, society has been somewhat successful at using social pressure to discourage, say, widespread spoiling of upcoming movies. Perhaps it can become viable to maintain such protected mathematical sites if one simultaneously opens up other classes of problems as suitable for directing this new AI capability towards. (3/3)

Tiny Tapeout makes it more accessible than ever to get your designs manufactured on a real chip!

🎬 Now available, the follow up to AlphaGo: The Thinking Game    • The Thinking Game | Full documentary | Tri...   🎬 The Thinking Game takes you on a journey into the heart of DeepMind, capturing a team striving to unravel the mysteries of int

Free and open access to global development data

A web game and interactive explorer from Paradigm. Bootstrap an AI lab from scratch and see how compute, data, and labor shape the trajectory of AI progress.

Matt Huang is Co-Founder and Managing Partner at Paradigm. He serves on the boards of Stripe and Kalshi, and is the founder and project lead of Tempo, a payments network co-founded by Paradigm and Stripe. Before founding Paradigm in 2018, Matt was a partner at Sequoia Capital, where he led investments across internet, mobile, frontier technologies, and the firm’s crypto efforts. Earlier, Matt founded Hotspots, a Y Combinator company acquired by Twitter in 2012, and was an early investor in ByteDance, Instacart, and Benchling. He holds a B.S. in Mathematics from MIT.

Vividly imagined, stunningly prophetic, and epic in scope, The Diamond Age is a major novel from one of the most visionary writers of our time

We’re on a journey to advance and democratize artificial intelligence through open source and open science.