Technology & Engineering
Systems that learn from data — the fastest-moving field in this index, and the one with the worst signal-to-noise ratio.
12 fields · 132 topics · 195 curated works
The statistical learning theory underneath everything else here.
Foundations & Overviews · Supervised Learning · Unsupervised Learning · Bias-Variance Tradeoff · Overfitting & Regularisation
Neural networks, and why depth turned out to matter so much.
Foundations & Overviews · Backpropagation · Multilayer Perceptrons · Convolutional Neural Networks · Recurrent Networks & LSTMs
Getting machines to handle language, which turned out to be most of the problem.
Foundations & Overviews · Tokenisation · Word & Sentence Embeddings · Language Modelling · Machine Translation
Extracting structure from images.
Foundations & Overviews · Image Classification · Object Detection · Semantic Segmentation · Pose Estimation
Learning by acting, with delayed and sparse feedback.
Foundations & Overviews · Markov Decision Processes · Dynamic Programming Methods · Q-Learning · Policy Gradient Methods
Models that produce samples rather than labels.
Foundations & Overviews · Autoencoders & VAEs · Generative Adversarial Networks · Diffusion Models · Autoregressive Models
Whether these systems do what we intend — the field's most consequential open problem.
Foundations & Overviews · The Alignment Problem · Specification Gaming · Reward Hacking · Interpretability for Safety
Learning with explicit uncertainty.
Foundations & Overviews · Graphical Models · Bayesian Networks · Hidden Markov Models · Gaussian Processes
The engineering that turns a model into a product.
Foundations & Overviews · Training Infrastructure · Distributed Training · Model Serving & Inference · Quantisation & Distillation
The half-century of AI that came before deep learning, much of it still useful.
Foundations & Overviews · Search Algorithms · Constraint Satisfaction · Knowledge Representation · Expert Systems
Who is accountable when the model is wrong.
Foundations & Overviews · Algorithmic Bias & Fairness · Transparency & Explainability · Privacy & Data Rights · Labour Market Effects
Recurring hype cycles, and the questions that never resolved.
Foundations & Overviews · The Dartmouth Conference · The AI Winters · The Turing Test · The Chinese Room Argument
No prior grounding assumed.
Computing Machinery and Intelligence
Alan Turing · 1950
Replaces 'can machines think' with an operational imitation game, and answers the standard objections in advance.
+25 more at this level
Assumes you know the vocabulary.
Hidden Technical Debt in Machine Learning Systems
Sculley et al. · 2015
The model is a tiny box in a large diagram; almost all the cost and almost all the failures live in the plumbing around it.
+60 more at this level
Primary sources and full treatments.
Speech and Language Processing
Daniel Jurafsky & James H. Martin · 2000
The standard NLP text across three decades of the field changing underneath it, which makes its revisions a history of the subject.
+107 more at this level
12 of 195 works
Alan Turing
Replaces 'can machines think' with an operational imitation game, and answers the standard objections in advance.
Assumes no mathematics and no philosophy training; Turing wrote it to be argued with
28 pageslink checked 17 Sept 2026Richard Sutton
General methods that leverage computation consistently beat hand-engineered human knowledge, and researchers keep relearning this.
≈1,100 wordslink checked 17 Sept 2026John McCarthy, Marvin L. Minsky, Nathaniel Rochester & Claude E. Shannon
Proposes that every aspect of learning and intelligence can in principle be described precisely enough to simulate on a machine, and asks funding for a summer workshop to find out — the document that named the field.
link checked 17 Sept 2026James Lighthill
Commissioned by the UK Science Research Council, concludes that AI research had failed to deliver on its grandiose promises and that most of its techniques did not scale past toy problems — the report credited with triggering the first AI winter's funding cuts.
link checked 17 Sept 2026Jonathon Shlens
Derives PCA from first principles as a change of basis that maximises variance and decorrelates the data, building the linear-algebra intuition that most treatments assume the reader already has.
link checked 17 Sept 2026Bo Pang & Lillian Lee
Surveys sentiment classification and opinion mining a decade into the field, organising the accumulated techniques by the linguistic phenomena — negation, comparison, subjectivity — that make bag-of-words classifiers fail.
link checked 17 Sept 2026Steven Bird, Ewan Klein & Edward Loper
Teaches language processing by making the reader build the pieces — tokenisers, taggers, parsers, classifiers — rather than by presenting the theory and its results.
link checked 17 Sept 2026Halevy, Norvig & Pereira
For messy human problems such as language, a simple model over far more data beats an elegant model over less, so effort is better spent on the corpus.
link checked 17 Sept 2026Pedro Domingos
Twelve lessons practitioners otherwise learn the hard way: generalisation is the only goal, more data beats a cleverer algorithm, and intuition fails in high dimensions.
link checked 17 Sept 2026Human Rights Watch & Harvard IHRC
Argues fully autonomous weapons that select and engage targets without a human in the loop cannot reliably comply with the laws of war's distinction and proportionality requirements, and calls for a preemptive international ban.
50 pageslink checked 17 Sept 2026Nils J. Nilsson
A biographical memoir tracing McCarthy's path from a politically radical Los Angeles childhood through Caltech, Dartmouth and Stanford to his founding role in AI — coining the term, inventing Lisp, and formalising common-sense reasoning.
17 pageslink checked 17 Sept 2026Michael Nielsen
Builds a working network from nothing and introduces each component only once the previous version has visibly failed, so the architecture arrives as a series of fixes.
link checked 17 Sept 2026