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Creator of Claude Code at Anthropic.

• Our readingkorrents.com

  Working with models has stopped being a theoretical science and become an empirical one, so your priors from computer science class are a hindrance.

  Their words

  And it's really about being empirical. So forget all of the things that you learned about past models. Forget everything that you've learned about computer science theory in class. Look at the model, try to do a task, see where it struggles, and then based on that adjust. So it's just like very much become it's not a theoretical science, it's become an empirical science.

  Boris Cherny: We Cut 80% of Claude Code's Promptyoutube.com

Senior economist at the Foundation for American Innovation; writes Second Best, and was the Niskanen Center's director of social policy before that.

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  People who extrapolate from first-principles models have a stronger track record predicting AI progress than those who wait for empirical proof.

  Their words

  Extrapolators have a remarkable track record in the AI field, being repeatedly early to trends and capabilities that empiricists believed were still decades away.

  Empiricists vs. Extrapolatorssecondbest.ca

Co-founder and CEO of Anthropic; previously VP of research at OpenAI.

• Our readingkorrents.com

  Training a model on principles rather than a list of rules is not an ideology but an empirical finding: its behaviour is more consistent and covers edge cases.

  Their words

  it's it's kind of purely a practical and empirical thing that we've observed that by teaching the model principles, getting it to learn from principles, its behavior is more consistent, it's easier to cover edge cases, and the model is more likely to do what people want it to do.

  youtube.com

Demographer; writes on fertility, marriage and population, and directs the Pro-Family Initiative at the Institute for Family Studies.

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  Something is broken in how people pair up in the West, and that shortage of couples and marriages, not money, is why there are too few children.

  Their words

  The empirical evidence strongly refutes the idea that rising opportunity costs of childbearing are driving low fertility. Declining marriage due to exogenous technology and economics shocks is tanking fertility irrespective of child direct or opportunity costs.

  Falling Fertility Is Not About Opportunity Costlymanstone.substack.com

Reinforcement-learning researcher; co-wrote the field's standard textbook, wrote The Bitter Lesson, and shared the 2024 Turing Award.

• Our readingkorrents.com

  The bitter lesson is only an empirical observation about a particular seventy years of history, and need not hold for the next seventy.

  Their words

  The bitter lesson. Oh, who cares about that? That's that's an empirical observation about a particular period in history. 70 years in history no longer doesn't necessarily have to apply the next 70 years.

  youtube.com

Senior economist at the Foundation for American Innovation; writes Second Best, and was the Niskanen Center's director of social policy before that.

• Our readingkorrents.com

  Qualitative leaps in AI capability from merely scaling up models are a rational necessity rather than an empirical mystery.

  Their words

  While the fact that merely scaling up AI models often results in qualitative leaps in performance may seem empirically mysterious, in retrospect it should be seen as a rational necessity.

  Hegel and the AI Mindsecondbest.ca

Historian at Stanford and the Hoover Institution; wrote Milton Friedman: The Last Conservative and Goddess of the Market, on Ayn Rand.

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  Objectivism's fundamental flaw is that it is justified by a fictional world rather than the real one, which makes it useless for understanding where we actually live.

  Their words

  The big flaw is that it's justified through a fictional world. It's not justified through reference to the real world. It's not empirical in a way. It's not... Rand herself would say this. She's not writing about things how they are, but how they should be. That idealism just really undermines it as a mechanism to understand where we're actually living.

  youtube.com

Machine-learning researcher; has written the Lil'Log survey posts on how a model technique works since 2017, and worked at OpenAI from 2018 to 2024, latterly leading its safety systems team.

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  Using supervised fine-tuning to teach a language model new knowledge risks increasing its hallucination rate.

  LLMs

  Their words

  These empirical results from Gekhman et al. (2024) point out the risk of using supervised fine-tuning for updating LLMs' knowledge.

  Extrinsic Hallucinations in LLMslilianweng.github.io

Urban planner, co-author with Charles Marohn of Escaping the Housing Trap, and policy director at the Parking Reform Network. Wrote for Strong Towns from 2015, mostly about how housing markets actually set prices.

From one piece Shortages and Spillovers: How People Misunderstand the Housing Crisis 2 beliefs, in the piece's order there

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  How dense a neighbourhood ought to be is a moral and political argument, not an empirical question with a discoverable right answer.

  Their words

  But understand that it’s ultimately a moral and political argument, not an empirical question of the ideal population or the exact level of housing demand in Santa Monica.

  strongtowns.org

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  Rents rise when vacancy is low and stabilise when vacancy is high, which makes the vacancy rate the housing metric that actually predicts prices.

  Their words

  Empirical data drawn from many different regions clearly shows that, on a metropolitan scale, rents tend to rise when vacancy rates are low and stabilize when vacancy rates are high.

  strongtowns.org

Economist at George Mason University, co-author of Marginal Revolution, host of Conversations with Tyler. Writes in short, dense claims about economics, culture and AI, and has moved position on AI in public more than once.

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  Virtually all economics of contemporary significance is empirical work.

  Their words

  Virtually all contemporary economics of note is empirical work

  tylercowen.com

Economist; former chief economist of the International Monetary Fund.

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  There is no tight relation between growth rates and interest rates, either theoretically or empirically.

  interest rates

  Their words

  while the growth rate may well affect saving and investment, there is no tight relation between growth rates and interest rates, either on theoretical or empirical grounds.

  Fiscal Policy under Low Interest Ratesdirect.mit.edu

Associate Professor at MIT EECS and Distinguished Scientist at Google DeepMind; first author of the Deep Residual Learning (ResNet) paper, among the most-cited papers of the century.

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  Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases.

  Their words

  We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

  arxiv.org

Computer vision researcher and former Chief Scientist of Megvii, previously at Microsoft Research Asia; co-author of ResNet and Faster R-CNN. Died in 2022; this page reflects his complete public record.

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  Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases.

  Their words

  We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

  arxiv.org