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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://profmarkfabian.substack.com/p/airing-my-grievances-with-wellbeing"/>
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	<rdf:li rdf:resource="https://arxiv.org/abs/2501.13813"/>
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	<rdf:li rdf:resource="https://onlinelibrary.wiley.com/doi/10.1111/tops.12644"/>
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  </channel><item rdf:about="https://stackingthebricks.com/how-blogs-broke-the-web/">
    <title>How the Blog Broke the Web - Stacking the Bricks</title>
    <dc:date>2026-05-04T14:22:35+00:00</dc:date>
    <link>https://stackingthebricks.com/how-blogs-broke-the-web/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- On the one hand, probably true.  On the other hand, how much of my life, cumulatively, has been spent typing '<a href="'?
]]></description>
<dc:subject>the_web_we_have_lost blogging we_shape_our_tools_and_our_tools_shape_us the_present_before_it_was_widely_distributed via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4d5cd0b780a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_web_we_have_lost"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blogging"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:we_shape_our_tools_and_our_tools_shape_us"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
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<item rdf:about="https://henrich.fas.harvard.edu/sites/g/files/omnuum5811/files/henrich/files/hong_henrich_-_2021_-_the_cultural_evolution_of_epistemic_practices.pdfd">
    <title>The Cultural Evolution of Epistemic Practices: The case of Diviniation</title>
    <dc:date>2026-04-16T17:38:56+00:00</dc:date>
    <link>https://henrich.fas.harvard.edu/sites/g/files/omnuum5811/files/henrich/files/hong_henrich_-_2021_-_the_cultural_evolution_of_epistemic_practices.pdfd</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although a substantial literature in anthropology and comparative religion explores
divination across diverse societies and back into history, little research has integrated
the older ethnographic and historical work with recent insights on human learning,
cultural transmission, and cognitive science. Here we present evidence showing that
divination practices are often best viewed as an epistemic technology, and we formally model the scenarios under which individuals may overestimate the efficacy of
divination that contribute to its cultural omnipresence and historical persistence. We
found that strong prior belief, underreporting of negative evidence, and misinferring
belief from behavior can all contribute to biased and inaccurate beliefs about the
effectiveness of epistemic technologies. We finally suggest how scientific epistemology, as it emerged in Western societies over the past few centuries, has influenced
the importance and cultural centrality of divination practices."]]></description>
<dc:subject>to:NB divination superstition cultural_evolution epistemology via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:55ac0f0a6d48/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:divination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:superstition"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epistemology"/>
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<item rdf:about="https://www.academia.edu/106616966/Landemore_Can_AI_bring_deliberation_to_the_masses">
    <title>Landemore: Can AI bring deliberation to the masses</title>
    <dc:date>2026-04-16T17:33:41+00:00</dc:date>
    <link>https://www.academia.edu/106616966/Landemore_Can_AI_bring_deliberation_to_the_masses</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A core problem in deliberative democracy is the tension between two seemingly equally important conditions of democratic legitimacy: deliberation, on the one hand, and mass participation, on the other. Might artificial intelligence help bring quality deliberation to the masses? The answer is a qualified yes. The chapter first examines the conundrum in deliberative democracy around the trade-off between deliberation and mass participation by returning to the seminal debate between Joshua Cohen and Jürgen Habermas. It then turns to an analysis of the 2019 French Great National Debate, a low-tech attempt to involve millions of French citizens in a two-month-long structured exercise of collective deliberation. Building on the shortcomings of this process, the chapter then considers two different visions for an algorithm-powered form of mass deliberation-Mass Online Deliberation (MOD), on the one hand, and Many Rotating Mini-publics (MRMs), on the other-theorizing various ways artificial intelligence could play a role in them. To the extent that artificial intelligence makes the possibility of either vision more likely to come to fruition, it carries with it the promise of deliberation at the very large scale."

--- Can't find this anywhere except this ridiculous parasitic site...]]></description>
<dc:subject>to:NB democracy large_language_models_(so_called) deliberative_democracy landemore.helene via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:046e2daa4d7c/</dc:identifier>
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<item rdf:about="https://theoryandpractice.org/2024/10/Yes,%20we%20did%20discover%20the%20Higgs!/">
    <title>Yes, we did discover the Higgs! - Theory And Practice</title>
    <dc:date>2026-04-16T17:30:22+00:00</dc:date>
    <link>https://theoryandpractice.org/2024/10/Yes,%20we%20did%20discover%20the%20Higgs!/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>cranmer.kyle particle_physics hypothesis_testing statistics philosophy_of_science via:? sociology_of_science science_as_a_social_process have_read to:blog</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86fa85118401/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:particle_physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
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<item rdf:about="https://www.percepta.ai/blog/can-llms-be-computers">
    <title>Can LLMs Be Computers? | Percepta</title>
    <dc:date>2026-03-22T04:02:11+00:00</dc:date>
    <link>https://www.percepta.ai/blog/can-llms-be-computers</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read large_language_models_(so_called) via:? slightly_mad</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ecee8ee1f1a2/</dc:identifier>
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<item rdf:about="https://karpathy.github.io/2026/02/12/microgpt/">
    <title>microgpt</title>
    <dc:date>2026-03-06T03:46:35+00:00</dc:date>
    <link>https://karpathy.github.io/2026/02/12/microgpt/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This is a brief guide to my new art project microgpt, a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd, makemore, nanogpt, etc.) and a decade-long obsession to simplify LLMs to their bare essentials, and I think it is beautiful 🥹. It even breaks perfectly across 3 columns:"

--- Very nice.  I should have The Kids re-do this in R...]]></description>
<dc:subject>large_language_models_(so_called) to_teach:statistics_and_generative_ai via:? in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9ba05b34a85a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
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<item rdf:about="https://arxiv.org/abs/2602.22271">
    <title>[2602.22271] Support Tokens, Stability Margins, and a New Foundation for Robust LLMs</title>
    <dc:date>2026-03-03T21:12:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2602.22271</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Self-attention is usually described as a flexible, content-adaptive way to mix a token with information from its past. We re-interpret causal self-attention transformers, the backbone of modern foundation models, within a probabilistic framework, much like how classical PCA is extended to probabilistic PCA. However, this re-formulation reveals a surprising and deeper structural insight: due to a change-of-variables phenomenon, a barrier constraint emerges on the self-attention parameters. This induces a highly structured geometry on the token space, providing theoretical insights into the dynamics of LLM decoding. This reveals a boundary where attention becomes ill-conditioned, leading to a margin interpretation similar to classical support vector machines. Just like support vectors, this naturally gives rise to the concept of support tokens.
"Furthermore, we show that LLMs can be interpreted as a stochastic process over the power set of the token space, providing a rigorous probabilistic framework for sequence modeling. We propose a Bayesian framework and derive a MAP estimation objective that requires only a minimal modification to standard LLM training: the addition of a smooth log-barrier penalty to the usual cross-entropy loss. We demonstrate that this provides more robust models without sacrificing out-of-sample accuracy and that it is straightforward to incorporate in practice."]]></description>
<dc:subject>to:NB large_language_models_(so_called) via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6f60e1cacd05/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hegemon.substack.com/p/the-epstein-files-and-russiagate">
    <title>The Epstein Files and Russiagate are the Same Thing</title>
    <dc:date>2026-02-12T17:04:26+00:00</dc:date>
    <link>https://hegemon.substack.com/p/the-epstein-files-and-russiagate</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This is depressingly persuasive.  For more on something Gutinsky mentions in passing, [https://archive.is/2026.02.06-235924/https://www.washingtonpost.com/technology/2026/02/06/epsteins-network-included-russian-tech-investors-with-past-kremlin-ties/]]]></description>
<dc:subject>our_decrepit_institutions corruption the_continuing_crises have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5cdd90f2a2f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:corruption"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_continuing_crises"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
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<item rdf:about="https://www.themathesontrust.org/library/hagakure-book-of-the-samurai">
    <title>Hagakure: Book of the Samurai - The Matheson TrustThe Matheson Trust</title>
    <dc:date>2026-02-07T20:28:02+00:00</dc:date>
    <link>https://www.themathesontrust.org/library/hagakure-book-of-the-samurai</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Honestly a lot of this is an old man peeving (and giving make-up tips).]]></description>
<dc:subject>books:noted have_read bushido via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fd0e80711393/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bushido"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41593-025-02196-7">
    <title>Investigating the methodological foundation of lesion network mapping | Nature Neuroscience</title>
    <dc:date>2026-02-07T20:26:58+00:00</dc:date>
    <link>https://www.nature.com/articles/s41593-025-02196-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Lesion network mapping (LNM) is a neuroimaging framework that uses normative functional connectivity (FC) data to link heterogeneous brain lesions and functional alterations to brain networks implicated in neurological and psychiatric conditions. However, many of the networks identified by LNM and related methods appear to be highly similar across diverse conditions such as addiction, depression, psychosis and epilepsy. To understand this similarity, we re-examined the data from multiple LNM studies and assessed the methodological roots of the method. Our findings reveal a foundational limitation: at its core, LNM involves a repetitive sampling of one and the same FC matrix. As a result, it systematically maps sets of local brain changes—whether they are patient lesions, magnetic resonance imaging-derived alterations, synthetic or random—onto the same nonspecific properties of the used FC data, producing highly similar networks across conditions. This central limitation cautions the use of LNM as a method for studying distinct biological networks underlying brain disorders. Our work may aid the development of a new generation of network-mapping methods from first principles."

--- Utterly devastating.]]></description>
<dc:subject>to:NB neuroscience network_data_analysis evisceration functional_connectivity via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2dc87b1031bb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:functional_connectivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.politico.com/news/magazine/2026/01/16/civil-war-university-of-austin-bari-weiss-00729688?media_author_id=63379887135&amp;media_id=3811554621182285242_63379887135&amp;ranking_info_token=gcayztq2zjixmja1zdy0nmu5ymmynjy0zmjizdzizmfhnix+m9ydfdgefv7h3zyngbm">
    <title>They Wanted a University Without Cancel Culture. Then Dissenters Were Ousted. - POLITICO</title>
    <dc:date>2026-02-07T20:26:17+00:00</dc:date>
    <link>https://www.politico.com/news/magazine/2026/01/16/civil-war-university-of-austin-bari-weiss-00729688?media_author_id=63379887135&amp;media_id=3811554621182285242_63379887135&amp;ranking_info_token=gcayztq2zjixmja1zdy0nmu5ymmynjy0zmjizdzizmfhnix+m9ydfdgefv7h3zyngbm</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Among many other juicy bits, I'll highlight this, for a couple of reasons:

"In a last-ditch effort of sorts, Rauch, Haidt and Strossen organized a call with Carvalho. The discussion didn’t inspire confidence in the group, said someone with knowledge of the call who was granted anonymity to speak candidly. Carvalho basically told them that UATX was a right-wing project, and that they’d known this when they signed up. But that wasn’t what any of them had believed."

1. I've met Carvalho and liked him, and respect his technical work, and am disappointed to see him involved in something like this.  (I'm sure he's crushed.)
2. This bit about Rauch, Haidt, and Strossen nags at me.  Like them or hate them --- and I have by far the most respect for Strossen's work, out of those three --- they are all mature, experienced public intellectuals.  They didn't think they were participating in a project of reactionary culture-warring, but in a real university.  I do not think that a Great-Books focused school needs to be conservative or right wing, let alone reacitonary.  (I think such a school will struggle to teach STEM subjects, or have to quitely abandon Great Books for that part of its curriculum, but that's a separate issue.)  I even think there can be conservative universities which really _are_ universities.  (Historically, after all, most of them have been, most of the time.)  But it was obvious to me, from the first time I heard about the University of Austin and the people sponsoring it, that it was going to be a reactionary culture-war project.  So why wasn't it obvious to Haidt, Rauch, and Strossen?  How could they not see that?
3. (Not directly related to this quote) _The Language of God_, while not a _good_ book, was in fact written by a real scientist (Francis Collins, at the time the director of the NIH [when that meant something]), and argues for theistic evolution, rather than creationism.  Honestly, if I was teaching a basic science course and wanted to not make very conservative teenagers' heads explode, I might assign it, or something similar, to give them a path towards accepting the elementary facts of life.  (This may or may not be why it was on the syllabus.)
4. That bust looks nothing at all like Bari Weiss.]]></description>
<dc:subject>academia running_dogs_of_reaction have_read via:? us_culture_wars</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d2ce6b3e890/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:running_dogs_of_reaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_culture_wars"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://profmarkfabian.substack.com/p/airing-my-grievances-with-wellbeing">
    <title>Airing my grievances with wellbeing science</title>
    <dc:date>2026-02-07T20:08:01+00:00</dc:date>
    <link>https://profmarkfabian.substack.com/p/airing-my-grievances-with-wellbeing</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Last tag is if I ever find myself teaching a surveys or measurement class. 
]]></description>
<dc:subject>measurement psychometrics social_measurement evisceration have_read via:? to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fdbb6da02779/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://julesevans.medium.com/iggy-pop-on-gibbons-decline-and-fall-of-the-roman-empire-11b14786035a">
    <title>Iggy Pop on Gibbon’s Decline and Fall of the Roman Empire | by Jules Evans | Medium</title>
    <dc:date>2025-12-29T01:48:52+00:00</dc:date>
    <link>https://julesevans.medium.com/iggy-pop-on-gibbons-decline-and-fall-of-the-roman-empire-11b14786035a</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In 1982, horrified by the meanness, tedium and depravity of my existence as I toured the American South playing rock and roll music and going crazy in public, I purchased an abridged copy of The Decline and Fall of the Roman Empire (Dero Saunders, Penguin).
"The grandeur of the subject appealed to me, as did the cameo illustration of Edward Gibbon, the author, on the front cover. He looked like a heavy dude.
"Being in a political business, I had long made a habit of reading biographies of wilful characters — Hitler, Churchill, MacArthur, Brando — with large profiles, and I also enjoyed books on war and political intrigue, as I could relate the action to my own situation in the music business, which is not about music at all, but is a kind of religion-rental.
"I would read with pleasure around 4 am, with my drugs and whisky in cheap motels, savouring the clash of beliefs, personalities and values, played out on antiquity’s stage by crowds of the vulgar, led by huge archetypal characters...."

Alternate link: [https://www.jstor.org/stable/25528281]]]></description>
<dc:subject>gibbon.edward roman_empire pop.iggy via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:13bf862dce31/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gibbon.edward"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:roman_empire"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pop.iggy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://jwmason.substack.com/p/actual-intelligence">
    <title>Actual Intelligence - by JW Mason - Money and Things</title>
    <dc:date>2025-10-25T19:59:58+00:00</dc:date>
    <link>https://jwmason.substack.com/p/actual-intelligence</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>via:? have_read mason.j.w. large_language_models_(so_called) re:gopnikism computer_networks_as_provinces_of_the_commonwealth_of_letters political_economy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc2cd5516c96/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mason.j.w."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:gopnikism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computer_networks_as_provinces_of_the_commonwealth_of_letters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_economy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2406.04089">
    <title>[2406.04089] On Limitation of Transformer for Learning HMMs</title>
    <dc:date>2025-09-17T13:23:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2406.04089</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite the remarkable success of Transformer-based architectures in various sequential modeling tasks, such as natural language processing, computer vision, and robotics, their ability to learn basic sequential models, like Hidden Markov Models (HMMs), is still unclear. This paper investigates the performance of Transformers in learning HMMs and their variants through extensive experimentation and compares them to Recurrent Neural Networks (RNNs). We show that Transformers consistently underperform RNNs in both training speed and testing accuracy across all tested HMM models. There are even challenging HMM instances where Transformers struggle to learn, while RNNs can successfully do so. Our experiments further reveal the relation between the depth of Transformers and the longest sequence length it can effectively learn, based on the types and the complexity of HMMs. To address the limitation of transformers in modeling HMMs, we demonstrate that a variant of the Chain-of-Thought (CoT), called block CoT in the training phase, can help transformers to reduce the evaluation error and to learn longer sequences at a cost of increasing the training time. Finally, we complement our empirical findings by theoretical results proving the expressiveness of transformers in approximating HMMs with logarithmic depth."

--- I guess it's good to have what's theoretically obvious empirically confirmed.]]></description>
<dc:subject>to:NB have_skimmed neural_networks time_series automata_theory state-space_models large_language_models_(so_called) via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a9f4e9105545/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:automata_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:state-space_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://openreview.net/forum?id=SBE2q9qwZj">
    <title>Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression | OpenReview</title>
    <dc:date>2025-09-02T19:30:09+00:00</dc:date>
    <link>https://openreview.net/forum?id=SBE2q9qwZj</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We describe a fast computation method for leave-one-out cross-validation (LOOCV) for 
$k$-nearest neighbours ($k$-NN) regression. We show that, under a tie-breaking condition for nearest neighbours, the LOOCV estimate of the mean square error for $k$
-NN regression is identical to the mean square error of $(k+1)$-NN regression evaluated on the training data, multiplied by the scaling factor $(k+1)^2/𝑘^2$. Therefore, to compute the LOOCV score, one only needs to fit $(k+1)$-NN regression only once, and does not need to repeat training-validation of $k$-NN regression for the number of training data. Numerical experiments confirm the validity of the fast computation method."

!!!]]></description>
<dc:subject>to:NB to_read nearest_neighbors to_teach:data-mining to_teach:undergrad-ADA via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7ddd207a4f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nearest_neighbors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.futurehouse.org/research-announcements/hle-exam">
    <title>About 30% of Humanity’s Last Exam chemistry/biology answers are likely wrong | FutureHouse</title>
    <dc:date>2025-08-18T01:49:48+00:00</dc:date>
    <link>https://www.futurehouse.org/research-announcements/hle-exam</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Ummm.  Never mind whether or not the answers are _right_.  The quoted examples seem to fundamentally mis-understand what "Ph.D.-level knowledge" is about; it's not a comprehensive listing of very small facts about some part of science.  It'd be a _bit_ of an overstatement to say that any particular fact can be taught to anyone arbitrarily ignorant of a field, but not by much.  I am not an entomologist, but I could, in fact, memorize whether snakeflies do or do not eat nectar (despite not being able to identify a snakefly in the flesh if my life depended on it).  Anyone familiar with algebraic symbols could memorize the answer to "what is the minimax rate of convergence for nonparametric regression, when the regression function is defined on a compact subset of 3-dimensional Euclidean space and has 2 continuous derivatives?", though they might not be able to actually explain "compact" or "minimax".  Ph.D.-level knowledge of nonparametric regression not only entails being able to give those explanations, but to relate that particular formula to a larger structure of thought, re-derive the result when you forget the formula, know how to start doing similar derivations if assumptions are slightly changed, etc., etc.  Actual expert knowledge of entomology likewise is not about memorizing trivia questions.  (Cf. [https://arxiv.org/abs/2506.21521].)
--- Shorter me: This may indeed be humanity's last exam, but for totally different reasons than its creators had in mind.]]></description>
<dc:subject>large_language_models_(so_called) measurement via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c84f369a7c21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://web.archive.org/web/20241117232629/https://www.reddit.com/r/theprimeagen/comments/1gqv4vc/teaching_computer_science_in_the_age_of_gippity/">
    <title>Teaching Computer Science in the age of Gippity : r/theprimeagen</title>
    <dc:date>2025-08-16T13:20:08+00:00</dc:date>
    <link>https://web.archive.org/web/20241117232629/https://www.reddit.com/r/theprimeagen/comments/1gqv4vc/teaching_computer_science_in_the_age_of_gippity/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I haven't seen _exactly_ this, but I have seen things far too much like this.]]></description>
<dc:subject>have_read teaching our_decrepit_institutions via:? academia education</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1e025c3062b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academia"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://data4democracy.substack.com/p/the-mothership-vortex-an-investigation">
    <title>The Mothership Vortex: An Investigation Into the Firm at the Heart of the Democratic Spam Machine</title>
    <dc:date>2025-08-05T15:25:51+00:00</dc:date>
    <link>https://data4democracy.substack.com/p/the-mothership-vortex-an-investigation</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>us_politics corruption our_decrepit_institutions have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f10ec195087/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:corruption"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780198250579.003.0005">
    <title>A Chinese Room that Understands | Views into the Chinese Room: New Essays on Searle and Artificial Intelligence | Oxford Academic</title>
    <dc:date>2025-07-28T15:19:22+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780198250579.003.0005</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The thesis of this chapter is that a computer can be programmed (and has been programmed) to understand natural language, in the sense of being able to translate natural language text, to answer in natural language questions put to it in language, and to perform similar tasks which, if performed by a human, would be taken as convincing evidence that the human understood the same language. We will call this thesis Empirical Strong AI (SAI-E) to distinguish it from Logical Strong AI (SAI-L), the thesis that Searle refutes, and which asserts that ‘an appropriately programmed digital computer with the right inputs and outputs, one that satisfies the Turing Test, would thereby necessarily have a mind’ (Searle 1999: 115)."

--- Slightly rough draft version in Simon's archive [https://iiif.library.cmu.edu/file/Simon_box00021_fld01492_bdl0001_doc0001/Simon_box00021_fld01492_bdl0001_doc0001.pdf]
]]></description>
<dc:subject>to:NB have_read artificial_intelligence philosophy_of_mind simon.herbert via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:82f9da509279/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simon.herbert"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/17588928.2025.2523875">
    <title>ROSE: A Universal Neural Grammar: Cognitive Neuroscience: Vol 0, No 0 - Get Access</title>
    <dc:date>2025-07-28T14:42:37+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/17588928.2025.2523875</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Processing natural language syntax requires a negotiation between symbolic and subsymbolic representations. Building on the recent representation, operation, structure, encoding (ROSE) neurocomputational architecture for syntax that scales from single units to inter-areal dynamics, I discuss the prospects of reconciling the neural code for hierarchical syntax with predictive processes. Here, the higher levels of ROSE provide instructions for symbolic phrase structure representations (S/E), while the lower levels provide probabilistic aspects of linguistic processing (R/O), with different types of cross-frequency coupling being hypothesized to interface these domains. I argue that ROSE provides a possible infrastructure for flexibly implementing distinct types of minimalist grammar parsers for the real-time processing of language. This perspective helps furnish a more restrictive ‘core language network’ in the brain than contemporary approaches that isolate general sentence composition. I define the language network as being critically involved in executing specific parsing operations (i.e. establishing phrasal categories, tree-structure depth, resolving dependencies, and retrieving proprietary lexical representations), capturing these network-defining operations jointly with probabilistic aspects of parsing. ROSE offers a ‘mesoscopic protectorate’ for natural language; an intermediate level of emergent organizational complexity that demands multi-scale modeling. By drawing principled relations across computational, algorithmic and implementational Marrian levels, ROSE offers new constraints on what a unified neurocomputational settlement for natural language syntax might look like, providing a tentative scaffold for a ‘Universal Neural Grammar’ – a species-specific format for neurally organizing the construction of compositional syntactic structures, which matures in accordance with a genetically determined biological matrix."]]></description>
<dc:subject>to:NB neuroscience neural_coding_and_decoding via:? linguistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:87db2ab27f50/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neuroscience"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_coding_and_decoding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linguistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://knightcolumbia.org/content/protocols-not-platforms-a-technological-approach-to-free-speech">
    <title>Protocols, Not Platforms: A Technological Approach to Free Speech | Knight First Amendment Institute</title>
    <dc:date>2025-07-28T14:37:40+00:00</dc:date>
    <link>https://knightcolumbia.org/content/protocols-not-platforms-a-technological-approach-to-free-speech</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Mike Masnick in 2019.
--- I don't disagree that shifting back from platforms to protocols would, in many ways, make for a healthier Internet.  But it's striking to me that this essay (like, I feel, most similar pieces, making similar pleas) fails to really reckon with a couple of basic points:
1. There are economies of scale in _running_ an online service, even if it had an open protocol at its base.  Think just of fighting spam (and, IMHO, spam is ultimately what killed Usenet).  (I used to think otherwise, back in the early- to mid- '90s, and some stuff I wrote then to that effect might even still be online somewhere, but I was very, very wrong.)
2. Normal people do not want to spend a lot of time comparison-shopping service providers.
These two together are going to tend to re-create centralization spontaneously, daily, hourly, and on a mass scale.  One could imagine _regulatory_ approaches to counter that, but crafting and implementing the regulation would be tricky.  And the imagination of these essays does not even go there.  Instead, it being 2019, the author makes some hopeful noises at using crypto coins (!) to support decentralized protocols.]]></description>
<dc:subject>the_web_we_have_lost internet market_failures_in_everything have_read via:? social_media networked_life redecentralization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:948555ae276d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_web_we_have_lost"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:internet"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:market_failures_in_everything"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_media"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:redecentralization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/4123261">
    <title>The Market for Quacks on JSTOR</title>
    <dc:date>2025-07-28T14:15:34+00:00</dc:date>
    <link>https://www.jstor.org/stable/4123261</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A group of n "quacks" plays a price-competition game, facing a continuum of "patients" who recover with probability a, whether they acquire a quack's "treatment". If patients chose rationally, the market would be inactive. I assume, however, that patients choose according to a boundedly rational procedure, which reflects "anecdotal" reasoning. This element of bounded rationality has significant implications. The market for quacks is active, and patients suffer a welfare loss which behaves non-monotonically w.r.t. n and a. In an extended model that endogenizes the quacks' choice of "treatments", the quacks minimize the force of price competition by offering maximally differentiated treatments. The patients' welfare loss is robust to market interventions, which would crowd out low-quality firms in standard models. Thus, as long as the patients' quality of reasoning is not lifted above the anecdotal level, ordinary competition policies may be ineffective."

--- One presumes this applies to educational policy, management consulting, etc., etc.]]></description>
<dc:subject>to:NB to_read psychoceramics market_failures_in_everything economics via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ef22bfe65165/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychoceramics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:market_failures_in_everything"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41587-025-02635-7">
    <title>Openness guides discovery | Nature Biotechnology</title>
    <dc:date>2025-06-18T16:28:38+00:00</dc:date>
    <link>https://www.nature.com/articles/s41587-025-02635-7</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Shorter Yanai and Lercher: Peter Medawar, "Is the Scientific Paper a Fraud?" (1963), an essay conspicuous by its absence from the bibliography here.  (Can there really not be a copy online, or is Google just too rotted to find it?)
--- Also, Y&L are systematically conflating "openness to new ideas", "openness to recognizing unexpected findings", "openness to other people's ideas", "doing 'open science' so-called", and the psychologists' "openness to experience" (which is about the adjectives you pick to describe yourself in certain multiple-choice tests).  I'd be prepared to believe these are all linked, but they slide far too easily from one meaning to another.


--- ETA: [https://classes.matthewjbrown.net/teaching-files/hps/medawar.pdf], via csantos, and cf. [https://pinboard.in/u:cshalizi/b:6ab946f434cf]]]></description>
<dc:subject>have_read science darwin_machines via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b1399b367222/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:darwin_machines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2413847122">
    <title>Reconciling ecology and evolutionary game theory or “When not to think cooperation” | PNAS</title>
    <dc:date>2025-04-22T15:32:57+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2413847122</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Evolutionary game theory (EGT)—overwhelmingly employed today for the study of cooperation in various systems, from microbes to cancer and from insect to human societies—started with the seminal 1973 paper by Maynard Smith and Price showing that limited animal conflict can be selected at the individual level. Owing to the explanatory potential of this paper and enabled by the powerful machinery of the soon-to-be-developed replicator dynamics, EGT took off at an accelerated pace and began to shape expectations across systems and scales. But, even as EGT has expanded its reach, and even as its mathematical foundations expanded with the development of adaptive dynamics and inclusion of stochastic processes, the replicator equation remains, half a century later, its most widely used equation. Owing to its early development and its staying power, the replicator dynamics has helped set both the baseline expectations and the terminology of the field. However, much like the original 1973 paper, replicator dynamics rests on the assumption that individual differences in reproduction are determined only by the payoff from the game (i.e., in isolation, all individuals, regardless of their strategy, have identical intrinsic growth rates). Here, we argue that this assumption limits the scope of replicator dynamics to such an extent as to warrant not just a more deliberative application process, but also a reconsideration of the broad predictions and terminology that it has generated. Simultaneously, we reestablish a dialog with ecology that can be mutually fruitful, e.g., by providing an explanation for how diverse ecological communities can assemble evolutionarily."]]></description>
<dc:subject>to:NB evolutionary_biology evolutionary_game_theory ecology evolution_of_cooperation via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3025bb8e6a9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolution_of_cooperation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.lesswrong.com/posts/oKAFFvaouKKEhbBPm/a-bear-case-my-predictions-regarding-ai-progress">
    <title>A Bear Case: My Predictions Regarding AI Progress — LessWrong</title>
    <dc:date>2025-03-16T19:00:03+00:00</dc:date>
    <link>https://www.lesswrong.com/posts/oKAFFvaouKKEhbBPm/a-bear-case-my-predictions-regarding-ai-progress</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I cannot believe I read something on this site with (mostly) approval.]]></description>
<dc:subject>have_read via:? large_language_models_(so_called) embers_of_autoregression</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:745aee9e0c24/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:embers_of_autoregression"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/journals/annals-of-statistics/volume-18/issue-3/No-Empirical-Probability-Measure-can-Converge-in-the-Total-Variation/10.1214/aos/1176347765.full">
    <title>No Empirical Probability Measure can Converge in the Total Variation Sense for all Distributions</title>
    <dc:date>2025-03-10T13:31:23+00:00</dc:date>
    <link>https://projecteuclid.org/journals/annals-of-statistics/volume-18/issue-3/No-Empirical-Probability-Measure-can-Converge-in-the-Total-Variation/10.1214/aos/1176347765.full</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["For any sequence of empirical probability measures $\left\{ \mu_n \right\}$ on the Borel sets of the real line and any $\delta < 0$, there exists a singular continuous probability measure $\mu$ such that $$\inf_{n}{\sup_{A}|\mu_n(A) - \mu(A)| \geq \frac{1}{2} − \delta$ almost surely."

--- This is weird.

]]></description>
<dc:subject>to:NB probability re:almost_none via:? have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5168b5d65b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.bostonreview.net/articles/what-ai-cant-do-for-democracy">
    <title>What AI Can't Do for Democracy - Boston Review</title>
    <dc:date>2025-03-02T15:25:11+00:00</dc:date>
    <link>https://www.bostonreview.net/articles/what-ai-cant-do-for-democracy</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read large_language_models_(so_called) democracy natural_language_processing via:? re:ai_as_social_technology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b87859285079/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:democracy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_language_processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ai_as_social_technology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41467-024-49711-1">
    <title>Complex behavior from intrinsic motivation to occupy future action-state path space | Nature Communications</title>
    <dc:date>2025-02-03T00:46:30+00:00</dc:date>
    <link>https://www.nature.com/articles/s41467-024-49711-1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high-dimensional controller, we show that complex behaviors such as “dancing”, hide-and-seek, and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization."]]></description>
<dc:subject>to:NB reinforcement_learning will_to_path_entropy via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:10f93a37bb31/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:will_to_path_entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2501.13813">
    <title>[2501.13813] Regularizing random points by deleting a few</title>
    <dc:date>2025-02-03T00:30:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2501.13813</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is well understood that if one is given a set $X \subset [0,1]$ of $n$ independent uniformly distributed random variables, then
$\sup_{0≤x≤1}{\left∣\frac{∣#{X \cap [0,x]}{#X}−x\right∣} \simleq \frac{\sqrt{\log{n}}{\sqrt{n}}$ with very high probability.
We show that one can improve the error term by removing a few of the points. For any $m\leq 0.001n$ there exists a subset $Y \subset X$ obtained by deleting at most $m$ points, so that the error term drops from $\sim \frac{\sqrt{\log{n}}}{n}$ to $\log(n)/m$ with high probability. When $m=cn$ for a small $0\leq c \leq 0.001$, this achieves the essentially optimal asymptotic order of discrepancy $\log{(n)}/n$. The proof is constructive and works in an online setting (where one is given the points sequentially, one at a time, and has to decide whether to keep or discard it). A change of variables shows the same result for any random variables on the real line with absolutely continuous density."]]></description>
<dc:subject>to:NB probability empirical_processes monte_carlo via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:97e6f84d0342/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:monte_carlo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://paleofloof.com/collections/plushies">
    <title>Plushies – PaleoFloof</title>
    <dc:date>2025-01-13T04:02:44+00:00</dc:date>
    <link>https://paleofloof.com/collections/plushies</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- OOH, LZS is starting to get in to dinosaurs.  OTOH, only one plushie has ever been even a partial success with him.]]></description>
<dc:subject>toys paleontology dinosaurs gifts via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d6a6aaf41afa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:toys"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:paleontology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dinosaurs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gifts"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.3-16am.co.uk/articles/noble-savage-redux-rousseau-meets-the-pirah%C3%A3">
    <title>Noble Savage Redux: Rousseau meets the Pirahã - 3:16</title>
    <dc:date>2025-01-01T01:28:08+00:00</dc:date>
    <link>https://www.3-16am.co.uk/articles/noble-savage-redux-rousseau-meets-the-pirah%C3%A3</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>linguistics anthropology have_read via:? evisceration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:797fb2d51297/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evisceration"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s42113-022-00166-x">
    <title>On Logical Inference over Brains, Behaviour, and Artificial Neural Networks | Computational Brain &amp; Behavior</title>
    <dc:date>2024-12-11T16:09:02+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s42113-022-00166-x</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference."]]></description>
<dc:subject>to:NB cognitive_science philosophy_of_science via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:179203deeef9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cognitive_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://proceedings.mlr.press/v139/simsek21a.html">
    <title>Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances</title>
    <dc:date>2024-12-09T21:37:04+00:00</dc:date>
    <link>https://proceedings.mlr.press/v139/simsek21a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study how permutation symmetries in overparameterized multi-layer neural networks generate ‘symmetry-induced’ critical points. Assuming a network with $L$ layers of minimal widths $r^*_1, \ldots r^*_{L-1}$ reaches a zero-loss minimum at $r*_1! \ldots r^*_{L-1}!$ isolated points that are permutations of one another, we show that adding one extra neuron to each layer is sufficient to connect all these previously discrete minima into a single manifold. For a two-layer overparameterized network of width $r^*+h =: m$,  we explicitly describe the manifold of global minima: it consists of $𝑇(𝑟^*,𝑚)$ affine subspaces of dimension at least $ℎ$ that are connected to one another. For a network of width $𝑚$, we identify the number $𝐺(𝑟,𝑚)$
of affine subspaces containing only symmetry-induced critical points that are related to the critical points of a smaller network of width $r < r^*$. Via a combinatorial analysis, we derive closed-form formulas for $T$ and $G$ and show that the number of symmetry-induced critical subspaces dominates the number of affine subspaces forming the global minima manifold in the mildly overparameterized regime (small $h$) and vice versa in the vastly overparameterized
regime ($h \gg r^*$). Our results provide new insights into the minimization of the non-convex loss function of overparameterized neural networks."

]]></description>
<dc:subject>to:NB to_read neural_networks via:? symmetries_of_neural_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d94a50e18b83/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:symmetries_of_neural_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.theverge.com/2024/12/5/24313222/chatgpt-pardon-biden-bush-esquire">
    <title>Stop using generative AI as a search engine - The Verge</title>
    <dc:date>2024-12-06T13:56:06+00:00</dc:date>
    <link>https://www.theverge.com/2024/12/5/24313222/chatgpt-pardon-biden-bush-esquire</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- The "to_teach" tags are just the courses I'm gearing up for in the next few semesters; I suspect I will be giving _this_ lesson (and having it ignored) for many years to come.]]></description>
<dc:subject>large_language_models_(so_called) to_teach to_teach:data-mining to_teach:undergrad-ADA to_teach:statistics_of_inequality_and_discrimination information_retrieval have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4dc951b569f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2590113323000226">
    <title>Moral controversies and academic public health: Notes on navigating and surviving academic freedom challenges - ScienceDirect</title>
    <dc:date>2024-09-05T18:01:42+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2590113323000226</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Schools of public health often serve both as public health advocacy organizations and as academic units within a university. These two roles, however, can sometimes come into conflict. I experienced this conflict directly at the Harvard T. H. Chan School of Public Health in holding and expressing unpopular minority viewpoints on certain moral controversies. In this essay I describe my experiences and their relation to questions of academic freedom, population health promotion, and efforts at working together across differing moral systems."]]></description>
<dc:subject>to_read academic_freedom us_culture_wars via:? vanderweele.tyler_j.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6a892c731473/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:academic_freedom"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_culture_wars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vanderweele.tyler_j."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://thegradient.pub/text-embedding-inversion/">
    <title>Do text embeddings perfectly encode text?</title>
    <dc:date>2024-08-01T18:50:33+00:00</dc:date>
    <link>https://thegradient.pub/text-embedding-inversion/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>via:? have_read large_language_models_(so_called) locality_sensitive_hashing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c78ed6ad4bda/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:locality_sensitive_hashing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.3758/s13423-013-0572-3">
    <title>Robust misinterpretation of confidence intervals | Psychonomic Bulletin &amp; Review</title>
    <dc:date>2024-07-11T14:03:21+00:00</dc:date>
    <link>https://link.springer.com/article/10.3758/s13423-013-0572-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students—all in the field of psychology—were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers’ performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data."

--- After skimming: On the one hand, I like a good "psychologists really do not understand anything about statistics" story as much as the next statistician, and I dare say more than most of my colleagues.

OTOH, I have my doubts about these survey items. Number 3 ("the hypothesis that the true mean is zero is likely to be incorrect") and number 5 ("we can be 95% confident that the true mean is between 0.1 and 0.4") are, IMHO, at least arguably true!  For #3: A parameter value falls outside a level-alpha confidence set iff the corresponding test rejects that parameter value with a p-value of at most 1-alpha, so 0 is rejected by this test, whatever it is, at the conventional 5% level.  Glossing "rejected by a reliable test" as "unlikely to be true" seems pragmatically fine.  (Frequentists do, after all, assign _likelihoods_ to hypotheses.)  As for #5, absent some clarification of what "confident" means, this is ambiguous.  As I explain in teaching [http://bactra.org/notebooks/confidence-sets.html], a confidence set offers the reader a dilemma: _either_ the true parameter is in the set, _or_ we got data that what really unlikely and unrepresentative under any parameter value.
That said, I am unable to come up with face-saving interpretations of the other four items.]]></description>
<dc:subject>to_read confidence_sets statistics teaching via:? have_skimmed in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1a4dda6ad6cb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://americanaffairsjournal.org/2024/05/the-nonprofit-industrial-complex-and-the-corruption-of-the-american-city/">
    <title>The Nonprofit Industrial Complex and the Corruption of the American City - American Affairs Journal</title>
    <dc:date>2024-06-18T17:25:18+00:00</dc:date>
    <link>https://americanaffairsjournal.org/2024/05/the-nonprofit-industrial-complex-and-the-corruption-of-the-american-city/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Of course the value here depends on the author getting the details right, and I am in no position to re-investigate (I'd almost say "re-litigate") every single controversy/scandal]]></description>
<dc:subject>have_read our_decrepit_institutions via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d0cd22fc0079/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:our_decrepit_institutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://madeinamericathebook.wordpress.com/2024/05/16/rooting-around-for-root-causes-can-be-a-big-distraction/">
    <title>Rooting Around for “Root Causes” Can Be a Big Distraction | MADE IN AMERICA</title>
    <dc:date>2024-06-18T17:19:25+00:00</dc:date>
    <link>https://madeinamericathebook.wordpress.com/2024/05/16/rooting-around-for-root-causes-can-be-a-big-distraction/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>public_policy causality sociology have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e783e7c5de2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:public_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://archive.ph/cJY3B">
    <title>Can the rich world escape its baby crisis?</title>
    <dc:date>2024-06-18T17:17:25+00:00</dc:date>
    <link>https://archive.ph/cJY3B</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read demography via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a45c954f4051/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:demography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-or-more-benchmarking-queries">
    <title>AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries</title>
    <dc:date>2024-06-18T17:16:45+00:00</dc:date>
    <link>https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-or-more-benchmarking-queries</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>large_language_models_(so_called) law artificial_intelligence your_favorite_deep_neural_network_sucks have_read via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e8b6a187cfbc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:law"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2405.18055v1">
    <title>[2405.18055v1] Dimension-free uniform concentration bound for logistic regression</title>
    <dc:date>2024-06-10T14:16:59+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.18055v1</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide a novel dimension-free uniform concentration bound for the empirical risk function of constrained logistic regression. Our bound yields a milder sufficient condition for a uniform law of large numbers than conditions derived by the Rademacher complexity argument and McDiarmid's inequality. The derivation is based on the PAC-Bayes approach with second-order expansion and Rademacher-complexity-based bounds for the residual term of the expansion."]]></description>
<dc:subject>to:NB to_read logistic_regression learning_theory to_teach:childs_garden_of_statistical_learning_theory via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2fb188076270/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:logistic_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2309.14316">
    <title>[2309.14316] Physics of Language Models: Part 3.1, Knowledge Storage and Extraction</title>
    <dc:date>2024-05-15T14:49:47+00:00</dc:date>
    <link>https://arxiv.org/abs/2309.14316</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large language models (LLMs) can store a vast amount of world knowledge, often extractable via question-answering (e.g., "What is Abraham Lincoln's birthday?"). However, do they answer such questions based on exposure to similar questions during training (i.e., cheating), or by genuinely learning to extract knowledge from sources like Wikipedia?
"In this paper, we investigate this issue using a controlled biography dataset. We find a strong correlation between the model's ability to extract knowledge and various diversity measures of the training data. Essentially, for knowledge to be reliably extracted, it must be sufficiently augmented (e.g., through paraphrasing, sentence shuffling) during pretraining. Without such augmentation, knowledge may be memorized but not extractable, leading to 0% accuracy, regardless of subsequent instruction fine-tuning.
"To understand why this occurs, we employ (nearly) linear probing to demonstrate a strong connection between the observed correlation and how the model internally encodes knowledge -- whether it is linearly encoded in the hidden embeddings of entity names or distributed across other token embeddings in the training text.
"This paper provides several key recommendations for LLM pretraining in the industry: (1) rewrite the pretraining data -- using small, auxiliary models -- to provide knowledge augmentation, and (2) incorporate more instruction-finetuning data into the pretraining stage before it becomes too late."]]></description>
<dc:subject>large_language_models_(so_called) via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39031f20b567/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.010401">
    <title>Phys. Rev. Lett. 128, 010401 (2022) - Eavesdropping on the Decohering Environment: Quantum Darwinism, Amplification, and the Origin of Objective Classical Reality</title>
    <dc:date>2024-05-14T15:15:23+00:00</dc:date>
    <link>https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.010401</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[“How much information about a system S can one extract from a fragment F of the environment E that decohered it?” is the central question of Quantum Darwinism. To date, most answers relied on the quantum mutual information of SF, or on the Holevo bound on the channel capacity of F to communicate the classical information encoded in S. These are reasonable upper bounds on what is really needed but much harder to calculate—the accessible information in the fragment F about S. We consider a model based on imperfect c-not gates where all the above can be computed, and discuss its implications for the emergence of objective classical reality. We find that all relevant quantities, such as the quantum mutual information as well as various bounds on the accessible information exhibit similar behavior. In the regime relevant for the emergence of objective classical reality this includes scaling independent of the quality of the imperfect c-not gates or the size of E, and even nearly independent of the initial state of S."

--- Ungated: [http://arxiv.org/abs/2107.00035]]]></description>
<dc:subject>information_theory quantum_mechanics decoherence via:? in_NB zurek.w.h.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:71c3fdd0e944/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:quantum_mechanics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decoherence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:zurek.w.h."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w32342">
    <title>Social Movements and Public Opinion in the United States | NBER</title>
    <dc:date>2024-05-14T13:03:25+00:00</dc:date>
    <link>https://www.nber.org/papers/w32342</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent social movements stand out by their spontaneous nature and lack of stable leadership, raising doubts on their ability to generate political change. This article provides systematic evidence on the effects of protests on public opinion and political attitudes. Drawing on a database covering the quasi-universe of protests held in the United States, we identify 14 social movements that took place from 2017 to 2022, covering topics related to environmental protection, gender equality, gun control, immigration, national and international politics, and racial issues. We use Twitter data, Google search volumes, and high-frequency surveys to track the evolution of online interest, policy views, and vote intentions before and after the outset of each movement. Combining national-level event studies with difference-in-differences designs exploiting variation in local protest intensity, we find that protests generate substantial internet activity but have limited effects on political attitudes. Except for the Black Lives Matter protests following the death of George Floyd, which shifted views on racial discrimination and increased votes for the Democrats, we estimate precise null effects of protests on public opinion and electoral behavior."

--- This paper looks interesting, but I am kind of blown away that it seems to not cite any social-movement scholars from sociology or political science.  (I haven't double-checked everyone's affiliation, maybe I'm being _slightly_ unfair there.)]]></description>
<dc:subject>to:NB social_movements causal_inference us_politics via:? economistic_imperialism</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc5b3281d19d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_movements"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:us_politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economistic_imperialism"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2311.17035">
    <title>[2311.17035] Scalable Extraction of Training Data from (Production) Language Models</title>
    <dc:date>2024-05-13T18:32:20+00:00</dc:date>
    <link>https://arxiv.org/abs/2311.17035</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization."]]></description>
<dc:subject>large_language_models_(so_called) via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1d0f65613041/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2402.18491">
    <title>[2402.18491] Dynamical Regimes of Diffusion Models</title>
    <dc:date>2024-03-05T15:29:45+00:00</dc:date>
    <link>https://arxiv.org/abs/2402.18491</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally. Our analysis reveals three distinct dynamical regimes during the backward generative diffusion process. The generative dynamics, starting from pure noise, encounters first a 'speciation' transition where the gross structure of data is unraveled, through a mechanism similar to symmetry breaking in phase transitions. It is followed at later time by a 'collapse' transition where the trajectories of the dynamics become attracted to one of the memorized data points, through a mechanism which is similar to the condensation in a glass phase. For any dataset, the speciation time can be found from a spectral analysis of the correlation matrix, and the collapse time can be found from the estimation of an 'excess entropy' in the data. The dependence of the collapse time on the dimension and number of data provides a thorough characterization of the curse of dimensionality for diffusion models. Analytical solutions for simple models like high-dimensional Gaussian mixtures substantiate these findings and provide a theoretical framework, while extensions to more complex scenarios and numerical validations with real datasets confirm the theoretical predictions."]]></description>
<dc:subject>your_favorite_deep_neural_network_sucks of_course_its_really_a_spin_glass neural_networks via:? mezard.marc stochastic_differential_equations mixture_models generative_diffusion_models in_NB to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c892f4f7dfb6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:your_favorite_deep_neural_network_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:of_course_its_really_a_spin_glass"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mezard.marc"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_differential_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixture_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:generative_diffusion_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.2466/pr0.1993.72.1.123">
    <title>Validity of the GRE without Restriction of Range - Bradley E. Huitema, Cheri R. Stein, 1993</title>
    <dc:date>2023-12-05T18:25:42+00:00</dc:date>
    <link>https://doi.org/10.2466/pr0.1993.72.1.123</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Restriction of range is a frequently acknowledged issue in estimating the validity of predictors of academic performance in graduate school. Data obtained from a doctoral program in a psychology department where graduate students were admitted without regard to Graduate Record Examination (GRE) scores yielded essentially identical standard deviations on this test for the 204 applicants and 138 enrolled students. The GRE-Total validity coefficients obtained on subjects in the enrolled sample ranged from .55 through .70; these values are considerably higher than those typically reported. The data are congruent with the argument that uncorrected GRE validity coefficients yield biased estimates of the unknown validity in unrestricted applicant pools."

--- I guess my position is that I think standardized tests like the GRE have evolved to be pretty good predictors of whether someone is ready for various educational programs (without major investments on the part of the program in remedial work, change in the structure of the program, etc.), which in no way implies the existence of a unitary ability being measured.  I also frankly think they're going to be _less_ gameable, and less likely to reproduce mere cultural capital, than the feasible alternatives...]]></description>
<dc:subject>to:NB to_read via:? standardized_testing mental_testing to_teach:statistics_of_inequality_and_discrimination psychometrics prediction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2250814cce70/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:standardized_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mental_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/abs/pii/S0167865513004145">
    <title>Alhazen and the nearest neighbor rule - ScienceDirect</title>
    <dc:date>2023-09-20T18:13:09+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/abs/pii/S0167865513004145</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show that a clear formulation of the nearest neighbor decision rule for pattern classification can be found, along with other remarkably modern ideas, in an influential medieval treatise on visual perception authored by Alhazen, one of the major figures in the so-called “Islamic golden age.” To put the work in context we provide also a brief description of some of the salient points of Alhazen’s theory."]]></description>
<dc:subject>to_read nearest_neighbors history_of_science islamic_civilization via:? al-haytham in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9d7a3175b2a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nearest_neighbors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:islamic_civilization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:al-haytham"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.santafenewmexican.com/news/local_news/rio-arriba-county-sheriff-sentenced-to-3-years-taken-into-custody/article_01955b14-5386-11ec-89da-53a270046f32.html">
    <title>Rio Arriba County Sheriff sentenced to 3 years, taken into custody | Local News | santafenewmexican.com</title>
    <dc:date>2023-09-20T17:36:08+00:00</dc:date>
    <link>https://www.santafenewmexican.com/news/local_news/rio-arriba-county-sheriff-sentenced-to-3-years-taken-into-custody/article_01955b14-5386-11ec-89da-53a270046f32.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- New Mexico will always have a special place in my heart, but there are times I am very glad not to have spent my life thre.]]></description>
<dc:subject>police corruption new_mexico via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2c6ed1b5e684/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:police"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:corruption"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:new_mexico"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1202.3699">
    <title>[1202.3699] Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search</title>
    <dc:date>2023-08-14T17:45:19+00:00</dc:date>
    <link>https://arxiv.org/abs/1202.3699</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-optimally in Markov Decision Processes (MDPs) with very large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is equivalent to optimal behavior in the known belief-space MDP, although the size of this belief-space MDP grows exponentially with the amount of history retained, and is potentially infinite. We show how an agent can use one particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an efficient way to act nearly Bayes-optimally for all but a polynomial number of steps, assuming that FSSS can be used to act efficiently in any possible underlying MDP."]]></description>
<dc:subject>in_NB reinforcement_learning learning_theory littman.michael_l. via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:db4b86ee8a65/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:littman.michael_l."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://medium.com/@Rationalist69/the-perpetual-helldump-d9043468f329">
    <title>The Perpetual Helldump</title>
    <dc:date>2023-08-10T20:06:00+00:00</dc:date>
    <link>https://medium.com/@Rationalist69/the-perpetual-helldump-d9043468f329</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- This is a good description of the dynamics, though the suggestion at the end that it's rare on the right is bizarre.]]></description>
<dc:subject>have_read networked_life re:actually-dr-internet-is-the-name-of-the-monsters-creator computer_networks_as_provinces_of_the_commonwealth_of_letters via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c2b6e7e866f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computer_networks_as_provinces_of_the_commonwealth_of_letters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.newsweek.com/2023/07/21/ai-scariest-beast-ever-created-says-sci-fi-writer-bruce-sterling-1809439.html">
    <title>AI is scariest beast ever created, says sci-fi author Bruce Sterling</title>
    <dc:date>2023-06-30T11:46:01+00:00</dc:date>
    <link>https://www.newsweek.com/2023/07/21/ai-scariest-beast-ever-created-says-sci-fi-writer-bruce-sterling-1809439.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- Good to see that bruces hasn't lost his toych; sad to see this in Zombie Newsweek, of all places.]]></description>
<dc:subject>artificial_intelligence sterling.bruce large_language_models_(so_called) via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:65ba3c4007da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sterling.bruce"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://mattbruenig.com/2023/03/05/equality-and-equity/">
    <title>Equality and Equity – Matt Bruenig Dot Com</title>
    <dc:date>2023-06-28T17:01:16+00:00</dc:date>
    <link>https://mattbruenig.com/2023/03/05/equality-and-equity/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I ... find myself agreeing with this?  (Last tag is arguable.)]]></description>
<dc:subject>political_philosophy equity_vs_equality via:? have_read to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dae6691c2c9f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:equity_vs_equality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/3773462">
    <title>Divination: &quot;Adaptive&quot; from Whose Perspective? on JSTOR</title>
    <dc:date>2023-06-15T19:45:51+00:00</dc:date>
    <link>https://www.jstor.org/stable/3773462</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB divination magic anthropology cultural_evolution via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:48687cf314f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:divination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:magic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:anthropology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_evolution"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286067">
    <title>Causal implicatures from correlational statements | PLOS ONE</title>
    <dc:date>2023-05-22T18:20:47+00:00</dc:date>
    <link>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286067</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Correlation does not imply causation, but this does not necessarily stop people from drawing causal inferences from correlational statements. We show that people do in fact infer causality from statements of association, under minimal conditions. In Study 1, participants interpreted statements of the form “X is associated with Y” to imply that Y causes X. In Studies 2 and 3, participants interpreted statements of the form “X is associated with an increased risk of Y” to imply that X causes Y. Thus, even the most orthodox correlational language can give rise to causal inferences."

--- Good to have this confirmed!]]></description>
<dc:subject>causal_inference psychology to_teach:undergrad-ADA gershman.samuel via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:86677951c9ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gershman.samuel"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/qje/article-abstract/134/4/1793/5492274?redirectedFrom=fulltext">
    <title>Food Deserts and the Causes of Nutritional Inequality* | The Quarterly Journal of Economics | Oxford Academic</title>
    <dc:date>2023-05-21T16:17:20+00:00</dc:date>
    <link>https://academic.oup.com/qje/article-abstract/134/4/1793/5492274?redirectedFrom=fulltext</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study the causes of “nutritional inequality”: why the wealthy eat more healthfully than the poor in the United States. Exploiting supermarket entry and household moves to healthier neighborhoods, we reject that neighborhood environments contribute meaningfully to nutritional inequality. We then estimate a structural model of grocery demand, using a new instrument exploiting the combination of grocery retail chains’ differing presence across geographic markets with their differing comparative advantages across product groups. Counterfactual simulations show that exposing low-income households to the same products and prices available to high-income households reduces nutritional inequality by only about 10%, while the remaining 90% is driven by differences in demand. These findings counter the argument that policies to increase the supply of healthy groceries could play an important role in reducing nutritional inequality."

--- Feel like I might have bookmarked this before, but not read it.
--- From the abstract, it sounds like they're taking food preferences as, if not exactly exogenous, then at least very directly caused by class/income.  But if food preferences are set early in life based on what's available, it could be that the existence of food deserts 20--40 years ago explains what they're seeing now...  (Perhaps they address this?)]]></description>
<dc:subject>to:NB via:? inequality economics food to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:feae50efd9b2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:food"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://osf.io/preprints/socarxiv/5ecfa/">
    <title>SocArXiv Papers | Artificially Precise Extremism: How Internet-Trained LLMs Exaggerate Our Differences</title>
    <dc:date>2023-05-10T13:24:00+00:00</dc:date>
    <link>https://osf.io/preprints/socarxiv/5ecfa/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Large Language Models (LLMs) offer new research possibilities for social scientists, but their potential as “synthetic data” is still largely unknown. In this note, we investigate the potential of using the popular closed-source LLM ChatGPT to measure human opinion. We show that although ChatGPT-generated opinions are similar to human opinion for some groups of US respondents, synthetic opinions also significantly exaggerate the extremity and certainty of partisan and social divisions. Responses from prompted “persona” profiles in ChatGPT produce measures of partisan and racial affective polarization that are seven times larger than the average opinion of humans who possess the same attributes as the prompted personas. Furthermore, synthetic data are artificially precise, with a standard deviation that is only 31% of the variation found in actual opinions among comparable humans. Because LLMs are proprietary, it is difficult to pinpoint the source of these biases, but our findings raise important questions about the appropriateness of replacing human opinion with synthetic responses generated by closed-source LLMs."]]></description>
<dc:subject>large_language_models_(so_called) re:actually-dr-internet-is-the-name-of-the-monsters-creator via:? as_if_for:_tags_were_still_a_thing in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8051eb541d1e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_language_models_(so_called)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:actually-dr-internet-is-the-name-of-the-monsters-creator"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:as_if_for:_tags_were_still_a_thing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.theguardian.com/us-news/2017/nov/15/the-gun-numbers-just-3-of-american-adults-own-a-collective-133m-firearms">
    <title>The gun numbers: just 3% of American adults own a collective 133m firearms | Gun crime | The Guardian</title>
    <dc:date>2023-05-08T21:15:57+00:00</dc:date>
    <link>https://www.theguardian.com/us-news/2017/nov/15/the-gun-numbers-just-3-of-american-adults-own-a-collective-133m-firearms</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>guns violence heavy_tails have_read via:? tracked_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:da4b214e694b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:guns"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:violence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tracked_down_references"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://networks.quantecon.org/">
    <title>Economic Networks: Theory and Computation</title>
    <dc:date>2023-05-02T20:58:44+00:00</dc:date>
    <link>https://networks.quantecon.org/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This textbook is an introduction to economic networks, intended for students and researchers in the fields of economics and applied mathematics. The textbook emphasizes quantitative modeling, with the main underlying tools being graph theory, linear algebra, fixed point theory and programming. The text is suitable for a one-semester course, taught either to advanced undergraduate students who are comfortable with linear algebra or to beginning graduate students."]]></description>
<dc:subject>economics social_networks via:? downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1cf821aecad0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.forbes.com/sites/amyfeldman/2023/04/02/the-thrill---and-the-mystery---of-a-1970s-bell-labs-ai-chatbot-known-as-red-father/?sh=1a95c2c62277">
    <title>The Thrill — And The Mystery — Of A 1970s Bell Labs AI Chatbot Known As ‘Red Father’</title>
    <dc:date>2023-05-02T20:34:33+00:00</dc:date>
    <link>https://www.forbes.com/sites/amyfeldman/2023/04/02/the-thrill---and-the-mystery---of-a-1970s-bell-labs-ai-chatbot-known-as-red-father/?sh=1a95c2c62277</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I _think_ I got this from Henry but not sure.  It's a bit inconclusive, but it doesn't surprise me in the least that something like this was running around Bell Labs back in the day.]]></description>
<dc:subject>have_read natural_language_processing artificial_intelligence the_present_before_it_was_widely_distributed bell_labs via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:403e384e9413/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:natural_language_processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:artificial_intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bell_labs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/10.1111/tops.12644">
    <title>The Emergence of Specialized Roles Within Groups - Goldstone - Topics in Cognitive Science - Wiley Online Library</title>
    <dc:date>2023-05-02T20:15:32+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/10.1111/tops.12644</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms—“Group Binary Search,” “Battles of the Exes,” and “Find the Unicorn”—that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework."]]></description>
<dc:subject>social_life_of_the_mind collective_cognition to_read via:? re:democratic_cognition goldstone.robert_l. in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c6010a66dc9d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:collective_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:democratic_cognition"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goldstone.robert_l."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://nymag.com/intelligencer/article/richard-walter-criminal-profiler-fraud.html">
    <title>Richard Walter, the ‘Living Sherlock Holmes,’ Was a Fraud</title>
    <dc:date>2023-05-02T19:26:39+00:00</dc:date>
    <link>https://nymag.com/intelligencer/article/richard-walter-criminal-profiler-fraud.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>fraud crime have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9392c287ec3f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fraud"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2209.04836">
    <title>[2209.04836] Git Re-Basin: Merging Models modulo Permutation Symmetries</title>
    <dc:date>2023-03-27T15:04:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2209.04836</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -- often variants of stochastic gradient descent -- exhibit surprising effectiveness in fitting large neural networks in practice. We argue that neural network loss landscapes often contain (nearly) a single basin after accounting for all possible permutation symmetries of hidden units a la Entezari et al. 2021. We introduce three algorithms to permute the units of one model to bring them into alignment with a reference model in order to merge the two models in weight space. This transformation produces a functionally equivalent set of weights that lie in an approximately convex basin near the reference model. Experimentally, we demonstrate the single basin phenomenon across a variety of model architectures and datasets, including the first (to our knowledge) demonstration of zero-barrier linear mode connectivity between independently trained ResNet models on CIFAR-10. Additionally, we identify intriguing phenomena relating model width and training time to mode connectivity. Finally, we discuss shortcomings of the linear mode connectivity hypothesis, including a counterexample to the single basin theory."

--- Ignorant speculation: that there's a permutation symmetry to this problem is obvious (?).  (Just shuffle the order of the internal nodes.)  But I suspect there might also (generally? always?) be more continuous symmetries as well.  Consider a very simple three-layer _linear_ network, with inputs X, hidden layer Z, outputs Y.  Since it's a linear network, Z= w X and Y = v Z.  Now pick any invertible matrix r and consider w' = rw, v' = vr^{-1}.  Clearly v' w' X = v w X so these new weights would leave the input-output mapping unchanged.  If we restricted r to being an orthogonal matrix, we'd even leave (lots) of norms on the internal layer activations unchanged.  This is of course just a version of the rotation problem for factor models.  Now obviously this gets more complicated if there are nonlinearities involved, so Z=f(wX) and Y=f(vZ).  I presume that people in the field have worried at this for a while, and I just need to find the right keywords...
---ETA after reading: track down references from Hecht-Nielsen in the 1990s.  ]]></description>
<dc:subject>optimization neural_networks via:? re:in_soviet_union_optimization_problem_solves_you have_read in_NB symmetry_of_neural_networks to_teach:statistics_and_generative_ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ff10a94f790/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:symmetry_of_neural_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_and_generative_ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.sagepub.com/doi/abs/10.3102/0013189X231155154">
    <title>The Effect-Size Benchmark That Matters Most: Education Interventions Often Fail - Matthew A. Kraft, 2023</title>
    <dc:date>2023-03-18T12:33:19+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/abs/10.3102/0013189X231155154</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["It is a healthy exercise to debate the merits of using effect-size benchmarks to interpret research findings. However, these debates obscure a more central insight that emerges from empirical distributions of effect-size estimates in the literature: Efforts to improve education often fail to move the needle. I find that 36% of effect sizes from randomized control trials of education interventions with standardized achievement outcomes are less than 0.05 SD. Publication bias surely masks many more failed efforts from our view. Recognizing the frequency of these failures should be at the core of any approach to interpreting the policy relevance of effect sizes. We can aim high without dismissing as trivial those effects sizes that represent more incremental improvement."

--- On the one hand, that's not much.  OTOH, imagine someone _did_ come up with a twist to teaching that made a big difference, like (wildly) 10SD.  Wouldn't it be adopted so quickly, without any randomized anything, that it would quickly become invisible in this sort of analysis?  (This'd be the social equivalent of a "selective sweep" in evolutionary genetics, and maybe detectable in similar ways.)]]></description>
<dc:subject>to:NB education psychology experimental_psychology meta-analysis via:? have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:982f28c7e81f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:experimental_psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:meta-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://thepointmag.com/politics/everything-is-hyperpolitical/">
    <title>Everything Is Hyperpolitical | The Point Magazine</title>
    <dc:date>2023-03-15T18:27:58+00:00</dc:date>
    <link>https://thepointmag.com/politics/everything-is-hyperpolitical/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- I dunno, I may have aged out of finding this sort of cultural criticism (based on the changing moods evoked by the authors' favorite  artists and novelists) worth reading.
--- For something which is otherwise so generous with name-dropping, the absence of any mention of Zeynep Tufecki (especially in the two paragraphs beginning "On the policy front") is something else.]]></description>
<dc:subject>have_read cultural_criticism the_continuing_crises via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bac202ef80c9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cultural_criticism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_continuing_crises"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1093/oso/9780192895844.003.0004">
    <title>What Does the Aristotelian Phronimos Know? | Virtue and Action: Selected Papers | Oxford Academic</title>
    <dc:date>2023-03-15T18:21:19+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780192895844.003.0004</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Although most of us have a rough-and-ready grasp of virtue and vice concepts, this article argues that the fully virtuous and practical wise agent also has a perfected and fine-grained grasp of these concepts, and other ethically relevant thick concepts like ‘the important’, ‘the fine’, and ‘the necessary’. This makes an important contribution to the debate between generalists and particularists about ethics, insofar as virtue terms often have general content but the ability to apply that general content requires the agent to have a refined ethical understanding which comes from experience and the individual’s own development of virtue."

--- This seems to presume that there are such things as agents who are virtuous across contexts.  The alternative would be that while it is possible to define ethical concepts in a context-independent way, actual agents can only learn the skills/habits that lead to virtuous behavior _in particular environments_, and that these skills, transposed to radically different contexts, would not lead to good results.  (I realize that I am making complaints about generalizability, over-fitting, and perhaps even computational complexity in an Aristotlean argument about virtue ethics.)]]></description>
<dc:subject>to:NB to_read moral_philosophy via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:499db94e976e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:moral_philosophy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://philpapers.org/rec/WHITEA">
    <title>Roger White, The epistemic advantage of prediction over accommodation - PhilPapers</title>
    <dc:date>2023-02-24T03:36:01+00:00</dc:date>
    <link>https://philpapers.org/rec/WHITEA</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["According to the thesis of Strong Predictionism, we typically have stronger evidence for a theory if it was used to predict certain data, than if it was deliberately constructed to accommodate those same data, even if we fully grasp the theory and all the evidence on which it was based. This thesis faces powerful objections and the existing arguments in support of it are seriously flawed. I offer a new defence of Strong Predictionism which overcomes the objections and provides a deeper understanding of the epistemic importance of prediction. I conclude by applying this account to strategies for defending scientific realism."]]></description>
<dc:subject>to:NB prediction philosophy_of_science re:phil-of-bayes_paper via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7dd0298aa71e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2301.09633">
    <title>[2301.09633] Prediction-Powered Inference</title>
    <dc:date>2023-02-24T03:35:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.09633</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We introduce prediction-powered inference – a framework for performing valid statistical inference when an experimental data set is supplemented with predictions from a machine-learning system. Our framework yields provably valid conclusions without making any assumptions on the machine-learning algorithm that supplies the predictions. Higher accuracy of the predictions translates to smaller confidence intervals, permitting more powerful inference. Prediction-powered inference yields simple algorithms for computing valid confidence intervals for statistical objects such as means, quantiles, and linear and logistic regression coefficients. We demonstrate the benefits of prediction-powered inference with data sets from proteomics, genomics, electronic voting, remote sensing, census analysis, and ecology."]]></description>
<dc:subject>to:NB confidence_sets jordan.michael_i. prediction via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2391d4d47bcc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jordan.michael_i."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>