AI elucidaria
A guided library for making sense of AI — not a news feed. Each piece here is one of the best things written (or recorded) on its corner of the subject. We link every work at its original home and send you there to read it; what we add is the guidance around it: a short account of what each piece is and why it matters, a reading companion you can keep open in a second tab, and learning streams that put the pieces in an order that fits where you're starting from.
Your progress is remembered only in this browser — no accounts, no tracking, nothing leaves your machine.
Thirty days, one idea a day, from nothing to following the argument.
In July a benchmark score nearly tripled without anything inside the model changing. The interesting question is not how the software was improved.
The best-known failure of large language models is that they cannot count the letters in a word, and the best-known explanation for it is that the word arrives broken into pieces.
What "predict the next token" actually means — and the half of the sentence that almost every popular account leaves out.
The operation that lets a word at the end of a sentence change what a pronoun in the middle refers to is nine years old, was named after something it does not do, and is now a minority of the layers in the models whose internals can be read.
A machine asked the same question a thousand times, with greedy decoding requested, returned eighty distinct completions. What decides which word comes next, and how much of the past the decision may consult, are two settings — and both are somebody's decision rather than a fact about the machine.
A trained model reads text and writes text. Everything a product appears to do besides that — searching, opening files, running commands, remembering a previous conversation — is ordinary software deciding what text to place in front of it and what to do with the text it returns. That division decides a great deal about what a system costs to run. It decides less than the industry's own marketing suggests about whether the system is right.
Four routes through the same library, ordered for different backgrounds. Choosing one highlights your path; you can wander off it freely.
No background assumed. The shortest path from 'I keep hearing about AI' to being genuinely conversant: what these systems are, what they do to work, and how to read the news about them.
For engineers and technical people who don't write software for a living. Start with how the systems work, end with what they mean for the person operating them.
For readers trained in philosophy, theology, or political thought. Start with the arguments and their genealogy; branch into the machinery when you want it.
For readers fluent in charts and economic reasoning but not in code. Start with work and incentives; branch into the machinery and the politics.
Twelve works, curated. Reading time refers to the original; every card links out to it.
The single best hour you can spend on what a large language model actually is. Karpathy — who helped build these systems at OpenAI and Tesla — explains a model as two files on a computer, shows what training does to…
The short essay that gave the AI-at-work debate its two most useful metaphors. Reporting a large field experiment he ran with colleagues at Boston Consulting Group, Mollick describes the "jagged frontier": AI is…
Nine hundred words that explain more of the last decade of AI than most books. Sutton looks back over seventy years of AI research and finds one pattern repeating: researchers build human knowledge into their systems,…
You have probably heard that every ChatGPT question "uses a bottle of water." Masley takes that claim, and the wave of news stories behind it, and works through the actual numbers: how much water data centers use…
The optimist's case, made properly. Smith walks through comparative advantage — the two-century-old idea that trade pays even when one side is better at everything — and applies it to a world where AI outperforms humans…
"We retired the horse — why should we be any different?" Krier takes the strongest version of that worry seriously and explains why the economics is more complicated. Comparative advantage can keep human work valuable…
The full version. Where the one-hour talk gives you the shape of a language model, this three-and-a-half-hour lecture opens the machine: how raw internet text becomes training data, what tokens are and why models see…
The most influential statement of the deflationary view: AI as a normal — transformative, but normal — technology like electricity or the internet, not a coming superintelligence. Narayanan and Kapoor argue that what…
The team whose AI 2027 scenario predicted that racing to superhuman AI ends in extinction or an irreversible concentration of power here writes the other branch: their positive plan. In this story, the US and China…
A former Bible-school student discovers, in the depths of losing her faith, that the transhumanist promise — minds uploaded, death defeated, a coming merge with the machine — is Christian eschatology wearing a lab coat.…
The Catholic Church's considered answer to the question the AI industry keeps begging: what is intelligence? Drawing on a philosophical tradition running from Aristotle through Aquinas, the note argues that human…
What should AI look like from the perspective of one person trying to stay productive — and safe — as the internet fills with machine-generated deception? Gwern's answer is the "guardian angel": an AI trained not to be…