AI (Artificial Intelligence)
AI is the field of building systems that perform tasks normally requiring human intelligence — reasoning, perception, language, and decision-making.
Artificial intelligence (AI) is the broad field concerned with building systems that perform tasks normally requiring human intelligence: reasoning, perception, language understanding, and decision-making. It's an umbrella term — machine learningMachine Learning (ML)Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules., deep learningDeep LearningDeep learning is machine learning using multi-layer neural networks, which learn their own features from raw data instead of relying on hand-engineered ones., and today's LLMsLLM (Large Language Model)An LLM is a large transformer trained to predict the next token on massive text corpora, then fine-tuned to follow instructions — the architecture behind GPT, Claude, Gemini, and Llama. are all specific approaches within AI, not synonyms for it. Symbolic, rule-based systems (expert systems) are also AI, even though they contain no learning at all.
How it works
There is no single AI algorithm — the term names a goal, and the field has pursued it through several distinct paradigms:
- Symbolic AI: explicit rules and logical inference over human-written knowledge bases, as in an expert systemExpert SystemAn expert system encodes a human expert's domain knowledge as hand-written if-then rules plus an inference engine, the dominant AI approach of the 1970s-80s.. Behaviour is inspectable but every rule must be authored.
- Statistical learning: fit parameters to data with a loss and an optimizer — the machine learningMachine Learning (ML)Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules. recipe.
- Representation learning: let a deep neural networkNeural NetworkA neural network is layers of simple weighted-sum-plus-nonlinearity units (neurons) chained together, trained by gradient descent and backpropagation. discover its own features from raw pixels or text rather than hand-engineered ones.
- Search and planning: enumerate future states and score them, as in game-playing systems.
Modern systems mix these. An agentAgentAn agent puts an LLM in a loop with tool access, letting it decide autonomously which tools to call and in what order to accomplish a multi-step goal. built on an LLM still uses search-like loops and external tools around a learned model.
When it breaks
- Terminology drift. "AI" in a product claim can mean anything from a regex to a frontier model. Ask what the system actually does before evaluating it.
- Benchmark ≠ capability. A system can top a benchmark and fail on slightly shifted inputs, because it matched dataset artifacts rather than the underlying task.
- Anthropomorphizing. Treating outputs as "understanding" or "intent" leads to misplaced trust; fluent text is not a claim of correctness.
- Assuming progress transfers. Gains in one modality or paradigm rarely carry over automatically to another.
See also: Machine LearningMachine Learning (ML)Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules., Deep LearningDeep LearningDeep learning is machine learning using multi-layer neural networks, which learn their own features from raw data instead of relying on hand-engineered ones., LLMLLM (Large Language Model)An LLM is a large transformer trained to predict the next token on massive text corpora, then fine-tuned to follow instructions — the architecture behind GPT, Claude, Gemini, and Llama.
Learn more: History & Landscape · Wikipedia: Artificial intelligence
Mentioned in
Lessons where this comes up in context.
- AI Safety & AlignmentWhether a model's own objectives match what we actually want, independent of any attacker — specification gaming, outer vs. inner alignment, why RLHF isn't a complete answer, and scalable oversight
- AI SecurityAdversarial misuse of a deployed AI system — prompt injection, jailbreaks, data exfiltration via tool use, adversarial examples, and the red-teaming practice that hunts for all of them
- Applied & Agentic SystemsHow prompting, RAG, and agents combine to turn a single trained LLM into a real, capable application
- History & LandscapeSymbolic AI to expert systems to statistical ML to deep learning to the LLM era
- How ChatGPT Was Actually BuiltA worked narrative tying pretraining, alignment, inference, and security together as one pipeline, instead of as separate topics — illustrative, synthesized from public research, not an insider account
- ML FundamentalsSupervised/unsupervised learning, loss functions, gradient descent
- Neural Networks & BackpropFrom Karpathy's micrograd approach — building a tiny neural net and stepping through forward/backward passes
- Tooling & The Dev StackLanguages, frameworks, and where they fit — what you'd actually touch to build and ship a model
word2vec
word2vec (2013) was a technique for learning dense vector representations of words from raw text, a direct precursor to modern token embeddings.
Machine Learning (ML)
Machine learning is the practice of writing programs that learn a function from data, via a loss function and an optimizer, instead of following hand-written rules.