Volume 384 Issue 2320 | Philosophical Transactions of the Royal Society A | The Royal Society
Influential themed journal issues across the physical mathematical and engineering sciences.
On Thickets, Enclosure, and Leaving
Every platform I left, I left for the same pattern: enclosure by othering — systems that define what is shameful, create targets, and point an aggressive majority at them. Substack's AI-detection turn is the latest, and it is Dark Forestry in action. The escape is spatial: go where the platforms ar…
Teach Yourself Programming in Ten Years
The conclusion is that either people are in a big rush to learn about programming, or that programming is somehow fabulously easier to learn than anything else. Felleisen et al. give a nod to this trend in their book How to Design Programs, when they say "Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies." The Abtruse Goose comic also had their take.
People & Technology
To understand AI’s effect on moral character, ethicist Kwame Anthony Appiah goes back to John Stuart Mill, and the idea that people are shaped by their choices.
Thoughts about the Leiden Declaration
Last September I went to a workshop at the Lorentz Centre in Leiden to discuss mathematics and AI with historians, philosophers, computer scientists, AI researchers, and mathematicians of several d…
The Human-in-the-Loop is Tired
On reward functions, dopamine, and what it actually feels like when the code starts writing itself
This is Not the End of Reading. It's the End of Modernism.
Why academics and journalists are confusing Modernism with reading itself.
AI Safety Is a Narrative Problem · Special Issue 5: Grappling With the Generative AI Revolution
This op-ed explores power and narrative dynamics around AI. Drawing on pop-culture references, the professional experiences of the author and examples from 2023’s “Great AI Safety Hype Roadshow,” this piece draws on the literary criticism technique of practical criticism to consider how speeches and announcements from both Silicon Valley executives and research scientists to interrogate the media-friendly nature of p(doom) discourse—which focuses on the existential risks of AI (PauseAI, 2023)—and its likely consequences. The complexities of AI and its numerous social impacts can be difficult for even the most expert analyst to unpack. In spite of this, the potential of “existential threats” has successfully cut through to become a mainstay of mainstream media coverage over the last year. This piece will make the case that this is an effective narrative conceit that has achieved a number of ends that traditional science communication tends to find difficult, if not impossible, to achieve. Firstly, it is easy to understand. Simplification of this nature—that removes jargon and complexity and focuses on a single outcome—is much easier to fit on a TV rolling news ticker or on the cover of a tabloid newspaper than more well-balanced, representative opinions. Secondly, it inherits prior assumptions from well-known dramatic forms. P(doom) plays to stories familiar from Greek tragedy through to Marvel movies, in which lone male heroes battle ineluctable forces. Thirdly, it is imbued with urgency and so becomes difficult to ignore.
GainForest — Biodiversity Observations & Nature Projects
Explore field observations, biodiversity records, and nature projects from communities and organizations using GainForest.
On the Concept of the Political in AI-Generated Art – Open Assembly
Plotting is an online publication gathered and organized by LASP Rietveld.
Impeccable: Design skills for AI harnesses
1 skill, 23 commands, and curated anti-patterns for impeccable frontend design. Works with Cursor, Claude Code, GitHub Copilot, Gemini CLI, and Codex CLI.
How linguistics learned to stop worrying and love the language models
Language models (LMs) can produce fluent, grammatical text. Nonetheless, some maintain that language models don’t really learn language and also, even if they did, that would not be informative for the study of human learning and processing. On the other side, there have been claims that the success of LMs obviates the need for studying linguistic theory and structure. We argue that both extremes are wrong. LMs can contribute to fundamental questions about linguistic structure, language processing, and learning. They force us to rethink arguments and ways of thinking that have been foundational in linguistics. While they do not replace linguistic structure and theory, they serve as model systems and working proofs of concept for gradient, usage-based approaches to language. We offer an optimistic take on the relationship between language models and linguistics.
