Evolutionary computation
Evolutionary computation doesn’t mimic life—it weaponises abstraction to solve engineering problems.
Artificial intelligence
The field that has been five years away for seventy years, and then was not.
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All briefs
Ada Lovelace
Lovelace didn’t write code — she wrote the first argument for what code could mean.
AlexNet
Depth won the 2012 ImageNet challenge—not cleverness, not data, not theory, but raw, GPU-fuelled layer count.
Neural network (machine learning)
Geoffrey Hinton and others
Geoffrey Hinton and colleagues established core methods—backpropagation for representation learning, and Boltzmann machines for unsupervised deep generative modelling—between 1985 and 1995. Their work was grounded in statistical mechanics, experimentally verified on small-scale networks, and explicitly recognised as foundational for machine learning with artificial neural networks. It did not deliver production AI, nor claim to.
Perceptron
A 1958 Navy press conference promised a machine that would walk, talk and think; what Frank Rosenblatt had actually built was a simple trainable classifier, and a decade later a single technical proof about it nearly ended neural-network research.
Shakey the robot
Shakey didn’t think like us—but it proved machines could plan like us.
Dartmouth workshop
The Dartmouth workshop didn’t build AI — it invented the idea that AI could be built.
Turing test
Alan Turing
The Turing test is a precise, behaviour-first redefinition of intelligence—not proof of thought, but a standard for functional equivalence in dialogue. It endures because it is simple, testable, and ruthlessly limited.
Transformer (deep learning)
A 2017 Google paper argued attention alone, without the recurrent processing every prior language model relied on, could handle sequences better and in parallel, and that architectural bet underlies essentially every large language model built since.