The History of AI: How Have We Got Here?
The almost incredible power of Generative AI is already in the process of being taken for granted. The almost incredible power is the latest stage of an intellectual relay race that began almost two centuries.
Charles Babbage — The Machine Architect
Superpower: Building blueprints.
Babbage designed the Analytical Engine, a 19th-century plan for a general-purpose computer. The hardware never quite made it, but the idea did — and modern computers still follow the plan.
Key date: 1837 — sets out the full design of the Analytical Engine.
Ada Lovelace — The Visionary
Superpower: Seeing the future.
In the 1840s she imagined machines that could manipulate symbols and compose music, not just do sums. That spark — that computers could be creative — lies at the heart of today’s generative AI.
Key date: 1843 — publishes her famous “Notes” on the Analytical Engine.
Alan Turing — The Codebreaker
Superpower: Asking the right question.
Turing helped crack wartime codes and, earlier, showed what it means for a computation to be possible at all — laying AI’s theoretical foundations.
Key date: 1936 — publishes “On Computable Numbers…,” defining the universal machine.
Tommy Flowers — The Engineer Hero
Superpower: Making it real.
He built Colossus, the world’s first programmable electronic computer, to help decipher enemy messages. Proof that bold engineering can change history.
Key date: 1944 — Colossus becomes operational at Bletchley Park.
John von Neumann — The Systems Master
Superpower: Putting order into chaos.
He laid out the basic architecture for computers — memory, processing, instructions — the template your laptop (and the world’s supercomputers) still use.
Key date: 1945 — First Draft of a Report on the EDVAC defines the stored-program model.
Claude Shannon — The Information Wizard
Superpower: Turning messages into maths.
Shannon showed how to measure and transmit information reliably. Without his ideas on bits and signals, our digital world — and AI’s data diet — would be static and snow.
Key date: 1948 — publishes “A Mathematical Theory of Communication.”
John McCarthy — The Name-Giver
Superpower: Defining the mission.
He coined the term “Artificial Intelligence” and built early languages to make it happen. Naming a field helped summon a community.
Key date: 1956 — organises the Dartmouth Conference and coins “Artificial Intelligence.”
Yann LeCun — The Pattern Spotter
Superpower: Reading the world like a comic strip.
His “convolutional” ideas taught computers to recognise images — from cats to street signs — and kick-started the modern visual AI boom.
Key date: 1998 — LeNet-5 shows neural nets can read handwritten digits at scale.
Yoshua Bengio — The Learner’s Guide
Superpower: Teaching machines to learn more gently.
Bengio’s work helped deep learning move from clever demos to reliable tools by finding better ways for machines to practise and improve.
Key date: 2003 — introduces a neural probabilistic language model that revives data-driven learning.
Fei-Fei Li — The Data Catalyser
Superpower: Giving AI a photo album.
By creating ImageNet — a vast, labelled picture collection — she gave AI the training ground it needed to see. Results improved almost overnight.
Key date: 2009 — launches ImageNet.
Geoffrey Hinton — The Neural Network Whisperer
Superpower: Patience.
He kept faith in “neural nets” when they were unfashionable. Decades later, the approach powered breakthroughs in speech, vision and today’s large language models.
Key date: 2012 — his lab’s AlexNet wins ImageNet, igniting the deep-learning boom.
Ashish Vaswani & Team — The Attention Alchemists (Transformers)
Superpower: Focusing on what matters.
They discovered that letting models pay “attention” to the most relevant parts of language (and images) unlocks fluent, scalable AI — the magic ingredient enabling today’s Generative AI revolution.
Key date: 2017 — publish “Attention Is All You Need,” introducing the transformer.
How these AI superheroes made their impact
- Imagination (Lovelace, Turing): daring to ask “what if?”
- Infrastructure (Babbage, von Neumann, Flowers, Shannon): the scaffolding that all modern computing stands on.
- Learning at scale (McCarthy, LeCun, Bengio, Li, Hinton, Vaswani & team): the know-how, data and breakthroughs that make today’s AI feel almost magical.