The Long Detour

Machine Learning Before the GPU, 1943–2011 · in three volumes
assembled and directed by Remy Ochei
written with an event-sourced multi-agent harness
1943 · McCulloch–Pitts 1958 · perceptron 1974 · expert systems 1986 · backprop 1995 · SVM 2011 · the eve of the GPU

▶ watch it being written — the tape, all 13,539 events, replayed

Volume I — A Narrative History of Machine Learning, 1943–2011
Two Founding Bets: Symbols and Connections
The Rule-Based Ascendancy: Expert Systems and the Knowledge Engineering Dream
The Connectionist Counterattack: Backpropagation and Parallel Distributed Processing
Neural Networks Go to Work
The Statistical Turn: Bayesian Networks and Probabilistic AI
The Kernel Insurgency: Vapnik and the Statistical Learning Theorists
Committees of Weak Learners: The Ensemble Revolution
Reinforcement Learning and the Behaviorist Thread
Evolution as Optimizer: Genetic and Swarm Algorithms
The Empiricist Turn: Text, Web, and Recommendation
Vision Without Understanding: Statistical Pattern Recognition
Epilogue: On the Eve of the GPU
Volume II — A Graduate Text
Linear Discriminants and the Perceptron
The Multilayer Perceptron and Backpropagation
Convolutional and Self-Organizing Architectures
Energy-Based Networks: Hopfield Nets, Boltzmann Machines, and the Helmholtz Machine
Recurrent Networks and Sequence Learning
Statistical Learning Theory and the VC Framework
Kernel Methods and the Support Vector Machine
Bayesian Networks and Probabilistic Graphical Models
The EM Algorithm and Latent Variable Models
Decision Trees and Rule Induction
Ensemble Methods: Bagging, Boosting, and Random Forests
Reinforcement Learning: Value Functions and Temporal-Difference Methods
Evolutionary and Swarm Algorithms
Unsupervised Learning: Clustering, Dimensionality Reduction, and Manifolds
Semi-Supervised and Transductive Learning
Volume III — A Practitioner's Guide to Small-Data, Low-Compute Learning
Learning from a Handful of Examples: Instance-Based and Local Methods
Feature Engineering and Selection Before End-to-End Learning
Trees and Forests: Interpretable Workhorses
Support Vector Machines for Small, Messy Data
Handling Imbalanced, Costly, and Drifting Data
Ensembles and Model Combination in Practice
Cross-Validation, Model Selection, and Honest Error Estimates
Case-Based and Analogical Reasoning as an Alternative to Learning from Scratch
Fuzzy and Neuro-Fuzzy Systems for Ill-Specified Domains
Practical Bayesian Networks and Expert Systems
Clustering and Unsupervised Discovery on a Budget
Recommender Systems, Time Series, and Domain Case Studies
HARPY · SPEECH UNDERSTANDING · CMU 1976
VOCABULARY ......... 1,011 WORDS
NETWORK STATES ..... 15,000
COMPILE TIME ....... OVER 13 HRS, DEC-10 (KL)
THROUGHPUT ......... 80 X REAL-TIME
PROCESSOR .......... 0.35 MIPS PDP-KA10
STATUS ............. IT WORKED.
$5
PER SENTENCE.
— cost target, HARPY speech understanding system, 1976
THE ALGORITHMS WERE READY.
THE MACHINES WERE NOT.
Concept mockup of the hardcover: greenbar jacket with die-cut sprocket holes, the red detour line, red-sprayed page edges
the print edition, concepted — die-cut sprocket holes, red-sprayed edges. A rendered mockup, not yet an object.

Colophon

This book was drafted, revised, and fact-checked by an event-sourced multi-agent system — an author, an adversarial critic, a planner, and an arbiter — working over a corpus of ~5,000 primary papers, directed and assembled by its human author. Nothing here rests on trust in the process: the process is published. Every sentence traces to logged reads of the primary sources; every quotation was machine-verified verbatim against the paper it cites; every number in an experimental rerun comes from an executed, independently reproduced computation; and the full construction record — the tape — replays to exactly this text.

Words176,659
Sections117
Construction events on the tape13,539
Executed experiments398
Critiques filed and resolved175

Preprint, text only — figures are forthcoming. Source, tape, lab, and harness: github.com/doInfinitely/long-detour.

Even the jacket keeps receipts: the cover concepts and the design transcript are in the repository, including the quote check that caught a mocked-up cover inventing archival telemetry. Jacket copy passes the same quote check as the prose.

remyochei.com · The Bracket Studio