Who was Alan Turing? The man behind modern computing
Alan Turing was a British mathematician who defined what a computer is, helped break Enigma in World War 2, and asked whether machines can think. His life and work.
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How today’s AI works, from the first idea of a machine that learns to the chatbots and assistants people use every day. Start with what AI is, then follow the links: each note answers one question and links to the ones it leads to.
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The note that answers the topic’s biggest question, then the two it leads to most directly.
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Artificial intelligence is software that does tasks we link with human thinking, mostly by learning patterns from data. What it is, how it learns, and what it can't do.
Graphite7 min read
Then
Machine learning is a way of building software that learns rules from examples instead of being given them. How it works, its three main kinds, and what overfitting means.
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And then
ChatGPT works by predicting the next piece of text, one token at a time, using a model trained on vast text and then tuned by people into an assistant. Here is each step.
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Every note
The three above, then the rest. Each answers one question and links to the notes it leads to.
Alan Turing was a British mathematician who defined what a computer is, helped break Enigma in World War 2, and asked whether machines can think. His life and work.
Graphite6 min read
A large language model is an AI system trained on vast amounts of text to predict what comes next, which lets it write, summarize and answer. What large means, and its limits.
Graphite6 min read
GPT stands for generative pre-trained transformer, a kind of AI model that writes text. What each word means, and how the transformer's attention works.
Graphite6 min read
AI slop is low-quality content made in bulk with AI, mostly to catch clicks or ad money. Where the word came from, why it pays, and how to tell it from careful AI-assisted work.
Graphite6 min read
Generative AI is AI that creates new text, images, audio or code by learning the patterns in huge sets of examples. How it differs from other AI, and how the two main kinds work.
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Roko's basilisk is a 2010 thought experiment about a future AI that punishes anyone who didn't help create it. What it claims, and why decision theorists reject it.
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Vibe coding is building software by describing what you want to an AI and accepting the code it writes without reading it closely. Where it works, where it breaks, and how to do it safely.
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AGI, artificial general intelligence, is AI that could learn and do most tasks people can, not just one. No system meets every definition, and experts disagree on what would count.
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The history of artificial intelligence runs from Turing's 1950 question and the 1956 Dartmouth workshop through two AI winters to deep learning, transformers and chatbots.
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A data center is a building full of networked computers, with the power and cooling to run them nonstop. AI needs far more computing, so it needs more of them, and more electricity.
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The Turing test asks whether a machine can hold a typed conversation so well that a judge can't reliably tell it from a person. What Turing proposed in 1950, and what passing shows.
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The Chinese room argument is John Searle's 1980 thought experiment claiming that a computer running a program can seem to understand language without understanding anything.
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A deepfake is a video, image or voice recording made or altered by AI to show someone saying or doing something they never did. How they're made, the harm they do, and how to check.
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MCP, the Model Context Protocol, is an open standard for connecting AI apps to tools and data. One connector works with any app that speaks it, instead of a custom one for each pair.
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The technological singularity is the idea that AI smarter than people could improve itself so fast that the future becomes impossible to predict. Where the idea came from, and its critics.
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Retrieval-augmented generation, or RAG, makes an AI model look things up before it answers: it finds relevant passages in a set of documents and hands them to the model with the question.
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A neural network is a program built from many simple units that each weigh their inputs and pass a signal on. It learns by adjusting those weights until its answers stop being wrong.
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AI chatbots hallucinate because they predict likely words rather than look facts up, and their training rewards a confident guess over saying they don't know.
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An AI agent is an AI system that works toward a goal in steps, choosing and using tools such as search, code and apps, instead of answering one message and stopping.
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Deep learning is machine learning with neural networks of many layers, each learning features from the one below, from edges up to whole objects or meanings.
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Whether AI is an existential risk is argued, not settled. The worry is that very capable future systems could pursue goals at odds with human interests; critics call this speculative.
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