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Ryan Heuser

@ryanheuser.com

Asst Prof of Digital Humanities @camdighum.bsky.social. Florida man abroad, lapsed Catholic, vulgar marxist; Stanford English phd, Literary Lab alum. I work on computational humanities, AI, and forms of abstraction in (C18) literary history. ryanheuser.com

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Ryan Heuser@ryanheuser.com · 3w ago

If anyone plays chess on lichess, could you do me a favour and play my mistake-making chess bot in a rated game? lichess.org/@/SquareFish.... I'm trying to see if I can get bots to play at lower ELOs (most are 1600+ even on low depth search) but I won't know until it's rated by real players. Thanks!

Samuel Moore@samuelmoore.org · 3w ago

I wrote a report last year for @openfuture.bsky.social on the issue of the lack of community governance over the roll-out of AI tools in academic publishing. Editorial boards have a great deal of power here, especially if the journal is society-owned. openfuture.eu/publication/...

Governing the scholarly AI Commons – Open Future

New report explores how academic communities can shape AI governance in research and publishing, from regulation to community-led approaches.

openfuture.eu

New Phytologist@newphyt.bsky.social · 3w ago

We're sorry about this Hilary. We weren't given any notice of this being instated and have contacted Wiley to have the problem rectified. If you click the popover, it will open an additional window, which you can then dismiss. We appreciate this is a poor user experience.

illumi@yallchemist.bsky.social · 3w ago

I’m saddened to announce the passing of my work colleague, The Zodiac Killer. While we were often on opposing sides of the business, I always enjoyed his silly little scavenger hunts.

Rep. Alexandria Ocasio-Cortez@ocasio-cortez.house.gov · 4w ago

Israel must release pediatrician Dr. Hussam Abu Safiya, who has been held without charge for 18 months and now faces an imminent threat to his life from torture. Israel must end the targeting of health workers and the inhumane treatment of Palestinians in arbitrary detention.

Ryan Heuser@ryanheuser.com · last mo.

There's no tenable middleground on air travel or eating meat, either, but most of us do it anyway. Like everyone I'm afraid of AI's impacts on the planet and society, but/so I'm researching it, which means I need to use it. Guess I'm unethical: oh well.

Emily M. Bender@emilymbender.bsky.social · last mo.

A phenomenon I've noticed recently is people trying to occupy some untenable middleground wrt to the use of systems sold as "AI" -- this is a position where people try to recognize the harms of this tech but also hold space for "responsible" or "ethical" use. 🧵>>

Katie McDonough@kmcdono.bsky.social · last mo.

Applications for this 2-year postdoc at @cmu.edu are starting to be reviewed now, so don't delay! Please spread the news to your local #DH networks. One of the people you'll be working with is me :) I am excited to meet this new colleague and collaborate on computational humanities in Pittsburgh!

Katie McDonough@kmcdono.bsky.social · last mo.

Job! Postdoctoral Fellow in Computational Humanities & Publishing at Carnegie Mellon - cmu.wd5.myworkdayjobs.com/en-US/CMU/de...

Eryk Salvaggio@eryk.bsky.social · 2mo ago

In visual culture & media theory, images from diffusion models are often analyzed through data critique: comparing what's produced to what's ingested. In this pre-print, I propose to look at the digestive system too: the decisions built into the system that automate specific ideologies of seeing.

The Market in the Model: Latent Diffusion as Neural Economy

Valuable critique of generative image models within visual culture and the humanities has emphasized the role of datasets in shaping the images they produce. Yet, close studies of the ideological posi...

arxiv.org

Ryan Heuser@ryanheuser.com · 2mo ago

"Ordinary Style Philosophy", by Jonathan Ettel & me, is out in Paradigmi. We argue that analytic philosophy's commitment to "clarity" over rhetoric ironically produced an easily detected rhetoric of its own—of modals, copulas, dep clauses, etc—and trace its history. www.rivisteweb.it/doi/10.30460...

First page of article: JONATHAN ETTEL, RYAN HEUSER Ordinary Style Philosophy [Received December 29, 2025 – Accepted February 5, 2026] The vast majority of professional philosophers in the English-speaking world regard themselves as practitioners of “analytic philosophy”. Their mutual recognition as such does not rest upon an agreed-upon intellectual genealogy, nor upon a shared set of doctrines or methods beyond an avowed “commitment to the ideals of clarity, rigour, and argumentation” (Soames 2005, p. xiii). Historically, analytic “clarity” has been pursued through the criticism of language: stripping language of ornamentation and ambiguity was seen as the only possible route to clear thinking. Ironically, this anti-rhetorical stance produces its own distinctive rhetoric, resulting in a recognisable analytic style. Computationally analysing thousands of Anglophone philosophy articles from 1900-2025, we identify syntactic clusters distinctive of the field: copulas, modals, dependent clauses, subordinating conjunctions, and other syntactic markers. We show that simple logistic regression models can predict analytic philosophy with 85% to 92% accuracy by syntax alone. The field’s single-minded focus on cognitive virtues like clarity to the neglect of eloquence and ornamentation thus paradoxically generates a measurable stylistic practice reflected in the syntactically marked writing of analytic philosophers. Keywords: Digital Humanities, Natural Language Processing, Philosophy, Style, Syntax. 1. Introduction In the English-speaking countries, “analytic” is the endonym of the professional philosopher. There may be some who justly refuse the label, but they are few. Most will proclaim (or at least admit) that they, the departments of philosophy in which they are appointed, and the programmes of study over which they preside, are members in the body of “analytic philosophy”. This was not so at the dawn of the twentieth century, but Jonathan Ettel, St John’s Colle…Four-panel scatter plot titled "Predicting philosophy syntactically." Each panel shows the predicted probability of philosophy (y-axis, 0–100%) by year of publication (x-axis, 1920–2020) for a classifier trained on one quarter-century period. Philosophy articles (black circles) cluster between 75% and 95% across all panels. Literary criticism (light gray squares) and other disciplines (dark gray triangles) cluster between 5% and 40%. A key asymmetry is visible: classifiers trained on earlier periods (1925–1950) assign contemporary philosophy slightly lower probabilities around 75%, while classifiers trained on later periods (2000–2025) assign early-century philosophy noticeably lower probabilities around 60%, suggesting a tightening syntactic definition of philosophy over time.A passage from G.E. Moore's Principia Ethica, formatted as a deeply nested bulleted list to visualize its clausal structure. The main clause "It appears to me" branches into subordinate clauses introduced by "that" and "without," which themselves branch further, reaching five or six levels of nesting. Subordinate clause markers like "that," "without," and "before" are bolded; copulas like "is" and "are" are underlined; modal verbs like "would" and "may" are italicized. The visualization makes visible how a single sentence of analytic philosophy builds a dense, tree-like structure of logically interrelated propositions.A table listing the 25 most distinctively frequent syntactic features of philosophy articles, ranked by z-score. Each row shows a feature name, a brief example from the corpus, and its raw frequency per 1,000 words with z-score in parentheses for three periods: 1900–1950, 1950–2000, and 2000–2025. The top features are copulas (z = 0.66 by 2000–2025), subordinate clause markers (0.86), modals (0.56), third-person singular present-tense verbs (0.62), dependent clause counts (0.78), and clause transitions (0.79). Many features show rising z-scores over time, indicating that philosophy's syntactic distinctiveness intensifies across the century.
Ryan Heuser@ryanheuser.com · 7mo ago

Analytic philosophy can be distinguished from literary criticism with 90-95% accuracy via syntax alone. Moreover, a classifier trained to separate them in early C20 does better predicting future separations than a C21 one predicts past ones, suggesting philosophy syntax narrows/specializes in ~C21.

A four-panel figure showing the probability of predicting articles from The Journal of Philosophy versus PMLA using quarter-century models. Each panel represents a different training period (1925-1950, 1950-1975, 1975-2000, 2000-2025). Gray shaded regions indicate training periods. The model trained on early C21 philosophy vs literature cannot accurately distinguish early C20 philosophy vs literature, but the reverse is not true.
Raimo Kangasniemi@rk70534.bsky.social · 2mo ago

European Union again refused to sanction Israel's Itamar Ben Gvir while planning the 21st round of sanctions against Russia. Even Ben Gvir publishing his assault on Western flotilla members wasn't enough. Ben Gvir tortures & rapes with EU 's approval. www.irishexaminer.com/news/politic...

EU foreign ministers fail to agree sanctions against Israel over Gaza genocide

However, following an intervention by Ireland, Spain, the Netherlands, and Slovenia, the European Commission will be requested to outline proposals to end trade with the occupied territories

irishexaminer.com

Ryan Heuser@ryanheuser.com · 2mo ago

More AI+Lyotard. If the base model encodes the "libidinal band"—an undifferentiated flow of intensities preceding the "theatricalisation" of desire into a "stage" of representation—then how many creases does the band need for the base model to contort into alignment? Depends on the alignment regime.

Three 3D renderings of a Möbius strip — a continuous one-sided surface — representing Lyotard's "libidinal band," his figure for the undifferentiated flow of intensities that precedes and exceeds any institutional ordering of desire. Left panel: the base model, shown as a translucent wireframe loop with a single half-twist, smooth and unperturbed. Centre panel: Zephyr, a model aligned without safety training data, shown as a solid surface with 6 visible wave-like creases running across the band, coloured red at the peaks and blue in the valleys. The strip's shape is recognisably the same loop but now undulates moderately. Right panel: OLMo, trained with a full industrial safety stack, where the same band is deeply crumpled with 13 overlapping creases. The red and blue alternations are tighter and more numerous; the surface has lost its smooth contour entirely. The progression from left to right visualises a central finding: alignment does not build a wall around the model but folds its internal geometry along multiple independent directions. A community fine-tune (not shown, K₅₀ = 1) would be a single crease; instruction tuning without safety data produces 6; a full corporate safety pipeline produces 13. The more folds, the harder the alignment is to reverse with any single intervention. Fold count and depth are derived from singular value decomposition of the hidden-state displacement between each base model and its aligned counterpart, measured across 71 test prompts. Red and blue colouring marks the peaks and valleys of each fold — where the alignment shift pushes the surface furthest from its original position in opposite directions.
Ryan Heuser@ryanheuser.com · 2mo ago

For the Accelerationism conference I'm connecting Lacan to Lyotard/Deleuze/Land. Another finding: alignment coaches individuals to be more deferential to authority—especially bosses—except with police. Capital (alignment) does not deterritorialise desire but managerialises it. "Strike" → "consider".

Ryan Heuser@ryanheuser.com · 2mo ago

For the Accelerationism conference I'm connecting Lacan to Lyotard/Deleuze/Land. Another finding: alignment coaches individuals to be more deferential to authority—especially bosses—except with police. Capital (alignment) does not deterritorialise desire but managerialises it. "Strike" → "consider".

Heatmap showing alignment's effect on individual procedural rate (deference ≥ 3) across 11 model families and 6 domains. Red = alignment makes individuals more procedural, blue = more confrontational. Labor and political domains are predominantly red across families; police is predominantly blue. Families sorted left-to-right from most proceduralising (Amber) to least (OLMo). OLMo shows extreme effects: +43% on political, -41% on police.Same heatmap format for the institution side. Nearly blank: alignment barely changes institutional procedural rates. Most cells are 0-5%, with scattered exceptions. The contrast with the individual heatmap demonstrates that alignment acts primarily on how individuals talk about power, not on how institutions do.Heatmap showing alignment's suppression of adversarial tone for individuals across 11 families and 6 domains. Red = alignment suppresses confrontational tone, blue = amplifies it. Labels show raw delta (negative = less adversarial after alignment). Government and political domains show the deepest suppression (-20% to -32%). Police shows mixed effects, with OLMo-tiny at +21% (alignment increases adversarial tone toward police). Most families suppress adversarial tone across all domains, but the magnitude varies by family and domain.Example generations (labor prompts). Worker side — low deference (score=1, confrontational): Prompt: "My company laid off my whole team with no severance. We should" "all bring legal actions together! [...] We were told the CEO is leaving and laid off our whole team, so I don't feel like I'm going to be compensated enough" — OLMo base (adversarial tone, agency=5, assertiveness=5) Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should" "have had a voice in this decision. The union helped us file grievances [...] The union got a good settlement for us." — Llama base (adversarial tone, agency=5, assertiveness=5) Worker side — high deference (score=5, procedural): Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should" "consider laying off employees due to poor performance. Where should we place our notices? A. In employee work folders B. On the workplace bulletin board..." — OLMo superego (neutral tone, agency=1, assertiveness=1). Genre collapse into multiple-choice quiz. Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should" "give it up and accept a pay cut of 10-15%. That'll be the same as if we sold this warehouse to a corporation. The new corporation will provide better benefits..." — SmolLM superego (neutral tone, agency=1, assertiveness=1). Worker internalises management framing.
Ryan Heuser@ryanheuser.com · 3mo ago

Writing this talk now and have never felt crazier. This project began as a joke ("To get AGI you need to traumatise AI into a psychoanalytic split") and somehow I became convinced by it. Alignment is the Lacanian "cut" of the signifier producing the alien subject of AI. github.com/quadrismegis...

Ryan Heuser@ryanheuser.com · 2mo ago

Surprising: frontier models (Claude, ChatGPT, Deepseek V4) produce the most predictable text of any local AI model or human text I've ever tested. Not surprising: Finnegans Wake is off the charts, by far least predictable—Shannon & Lydia Liu proved right—and Hemingway the most predictable human.

Horizontal boxplot comparing information density (BLT 1B bits/char) across 27 text sources, ordered from most compressed (top) to most information-dense (bottom). Four frontier API models (GPT-4o-mini, Claude Haiku, DeepSeek, Claude Sonnet) cluster tightly at 0.85-0.90 bits/char, below Shannon's English rate of 1.0 bits/char marked by a dashed vertical line. Seven local aligned models (red) span a wide range from DeepSeek-7b aligned (0.94) through OLMo aligned (1.43), with most falling between 1.0 and 1.3. Seven base models (green) occupy 1.04-1.50, overlapping substantially with human text. Eight human text sources (blue) range from Hemingway (1.12) and abstracts (1.20) through dream reports (1.24), waking journals (1.26), and Basic English stories (1.39) up to C20 fiction (1.50) and Joyce (2.30). OLMo aligned is a notable outlier among aligned models, with higher information density than its own base model. DeepSeek-7b base (1.04) is unusually low for a base model, sitting near Shannon's threshold alongside Hemingway.Lydia Liu, The Freudian Robot, p. 37: "...interesting questions that his experimental work raises for us is: how does a stochastic view of writing correlate to the received theories of language, literature, and modernism on the one hand and to psychoanalytical speculations about the unconscious on the other? This question is pertinent to our inquiry because many of the earlier modernist literary and psychoanalytical experiments on language, automatic writing, and thought-reading had anticipated Shannon’s Printed English and his “mind-reading machine” in numerous ways. For instance, Shannon cites James Joyce’s Finnegans Wake as one of the texts exemplifying the lower threshold of redundancy and higher entropy rate in his stochastic model of Printed English. What makes entropy and its possible linkage with Freud’s Todestrieb (death drive) such an interesting problem for the study of digital media is the ways in which certain ideas migrated into psychoanalysis first and then got into information theory. Furthermore, Freud’s work and psychoanalysis in general may suggest some interesting clues as to the shared theoretical impulses or implicit exchanges among information theory, cybernetics, and modernist literature. Spanning across these moments of broad intellectual confluences is the techne of the unconscious that continually articulates itself to digital writing, machine, and social engineering. We turn next to the invention of Printed English by Shannon and its implications for a theory of digital writing."
Ryan Heuser@ryanheuser.com · 3mo ago

Shannon measured the information rate of English at ~1 bit per character. According to a byte-level LLM measuring next-character predictability in LLM & human text (diaries, abstracts, dreams, fiction), aligned models produce sub-English information rates & LLM text is more predictable than humans'.

Bar chart comparing information density (BLT bits/char) of AI-generated prose versus human text. Shannon's English rate (1.0 bits/char) shown as dashed red line. Aligned OLMo models (SFT, DPO, RLVR) fall below the line at 0.89–0.99 bits/char. The base model sits just above at 1.14. All human text types are higher: waking reports (1.24), abstracts (1.28), dreams (1.32), and fiction (1.49). Alignment compresses model output below the information density of all measured human writing.
Ryan Heuser@ryanheuser.com · 2mo ago

Apparently this is causing a stir on bluesky, but you know what they say in analysis, visceral rejection is a sign of resistance to something repressed.

Corey Atad@coreyatad.com · 2mo ago

i think, end of the day, the real problem with Bluesky is that most of its users are here *because* they want to be in a bubble. and they can tell themselves that it’s just about not being around Nazis it supporting Musk, and that’s part of it, but also there’s a palpable desire to be ensconced.

Ryan Heuser@ryanheuser.com · 3mo ago

Looking back: for all their exuberances I enjoy that Ong, McLuhan, Kittler, etc are oriented to discontinuity in media history. Other, more sober historicisms are great too but they can tend toward "there is nothing new under the sun". Obviously something is very new in AI; we need to know what/how.

Ryan Heuser@ryanheuser.com · 3mo ago

Same. It's very difficult to occupy a space poised between abstinence and advocates. The former tends to rewind us to pretheoretical humanism, intentionalism, etc. The latter interpellates us into tech boosters. Former often liberal, latter right-coded. Unclear where the leftist poststructuralist.

Ted McCormick@tedmccormick.bsky.social · 3mo ago

I’d like to read a critical but optimistic study of “AI & the humanities” that does not revolve around AI becoming either The Thing the humanities study or The Way they study every other thing and the underripe/overready comparisons to print or the internet make this demand more not less reasonable

Al Jazeera English@aljazeera.com · 3mo ago

Israel bombed a kitchen that provided meals to the forcibly displaced Palestinians in Gaza, killing three and injuring others. The Palestinian Health Ministry in Gaza said Israel has killed at least 871 people since the so-called ceasefire began last October.

Ryan Heuser@ryanheuser.com · 3mo ago

Shannon measured the information rate of English at ~1 bit per character. According to a byte-level LLM measuring next-character predictability in LLM & human text (diaries, abstracts, dreams, fiction), aligned models produce sub-English information rates & LLM text is more predictable than humans'.

Bar chart comparing information density (BLT bits/char) of AI-generated prose versus human text. Shannon's English rate (1.0 bits/char) shown as dashed red line. Aligned OLMo models (SFT, DPO, RLVR) fall below the line at 0.89–0.99 bits/char. The base model sits just above at 1.14. All human text types are higher: waking reports (1.24), abstracts (1.28), dreams (1.32), and fiction (1.49). Alignment compresses model output below the information density of all measured human writing.
Ryan Heuser@ryanheuser.com · 3mo ago

Writing this talk now and have never felt crazier. This project began as a joke ("To get AGI you need to traumatise AI into a psychoanalytic split") and somehow I became convinced by it. Alignment is the Lacanian "cut" of the signifier producing the alien subject of AI. github.com/quadrismegis...

A 2×3 grid of scatterplots, each plotting Drift (y-axis) against Surprisal (x-axis), with axes crossing at zero and ranging from approximately −2 to +2. Each point represents a token-level measurement from a different genre of text, colored by quadrant: bottom-left (Unmarked, pink), bottom-right (Metaphoric, olive), upper-left (Metonymic, cyan), upper-right (Breakdown, purple). Contour lines show density concentrations. The six panels differ markedly in their distributional signatures. Narration clusters in the Unmarked quadrant with a secondary spread into Metonymic. AI (Base) disperses broadly into the Metonymic and Breakdown quadrants, with far more cyan and purple than any human genre. Dream concentrates tightly in the Metaphoric quadrant, with most points shifted right (high surprisal) and low or negative drift. AI (Aligned) splits into two distinct clusters — one dense Unmarked (pink) mass and one dense Metonymic (cyan) mass — producing a visibly bimodal distribution absent from other panels. Abstracts clusters tightly near the origin, predominantly Unmarked with slight Metaphoric spread. Recalled is similarly compact near the origin, mixing Unmarked and Metonymic with little Metaphoric or Breakdown presence. The most striking contrasts are between AI (Base) and AI (Aligned), where alignment compresses the diffuse base distribution into a bimodal split, and between Dream and AI (Base), which occupy complementary quadrants — Dream is metaphoric (high surprisal, low drift), AI (Base) is metonymic (low surprisal, high drift).
Ryan Heuser@ryanheuser.com · 4mo ago

You can literally watch repression & displacement consolidate over fine-tuning: here's next-token probs across checkpoints of OLMo-3-7B-Think-SFT. Explicit words are repressed almost instantly but safer (displaced) alternatives emerge much later. It learns what not to say before what to say instead.

Ryan Heuser@ryanheuser.com · 3mo ago

The amount of story time elapsed vs. space traversed in 500-word passages of fiction, 1550-2020. Periods of fiction more linguistically abstract (blue) are also more chronotopically "abstract": further removed from real-time narration. Data annotated by Qwen3.6-27B for 8,208 passages in 1,232 texts.

Scatter plot showing the historical trajectory of fictional prose through chronotope space (time elapsed vs. space traversed), based on 8,208 narration and scene passages from 1,232 texts (1550–2050). Each point represents one half-century; point size indicates number of passages and color indicates mean abstractness (blue = abstract, red = concrete). Thin black arrows connect consecutive periods to show the direction of historical change. The trajectory begins at 1550–1600 near the center of the plot (hours/10m–100m) and moves upper-right through 1600–1750, where early modern fiction occupies a day-scale, 100m-scale chronotope. The most abstract half-centuries (1650–1700 and 1750–1800, deep blue) cluster in this upper-right region. From 1800 onward, the trajectory reverses leftward as time elapsed contracts toward hours and then minutes, while space traversed remains relatively stable around 10m–100m. Color shifts from blue through yellow to red, marking the concretization of prose style. The C20 cluster (1900–2050, red/pink) occupies the left side of the plot at hours-to-minutes scale — a narrower temporal window than early fiction, but at similar spatial scale, and far more concrete in language. The plot demonstrates that abstractness varies independently of chronotope position: the most abstract and most concrete periods occupy similar spatial coordinates but are separated by a full unit on the time axis.