AI Data Visualizations
aihistoryproject.org
Paradigm Shifts
Eight decades of AI research, grouped into the paradigms that defined them — symbolic AI rising and collapsing, the statistical turn, and the deep-learning era that swallowed the field.
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Each decade normalised to 100% — paradigm mix, free of the uneven item counts per decade.
Foundations & Cyberneticspeak 1940s
The theoretical groundwork — computability, feedback, and the mind as a machine.
Symbolic AI & Knowledgepeak 1960s
Intelligence as explicit rules and symbols — logic, expert systems, knowledge bases.
Statistical ML & Datapeak 2000s
The statistical turn — learning patterns from data rather than hand-coded rules.
Neural Nets & Deep Learningpeak 2010s
Connectionism and its revivals — perceptrons, backpropagation, deep architectures.
Generative AI & LLMspeak 2020s
Scale, pretraining and generation — language models, agents, and reasoning.
Perception, Language & Roboticspeak 2010s
The applied frontier every paradigm attacked — seeing, speaking, and moving.
Compute & Infrastructurepeak 1940s
The substrate — the machines, chips, and systems each era could run on.
Safety, Ethics & Governancepeak 2010s
The consequences thread — alignment, bias, policy, and who answers for it.
What the Shape Shows
30% → ~0
Symbolic AI’s arc
Takes nearly a third of the 1970s, then all but vanishes. One 2010s entry inherits it — knowledge graphs — and it survives as infrastructure rather than as a research programme.
1990s
The statistical turn
Statistical ML & Data reaches its high point as learning from data displaces hand-coded rules.
Twice
Neural nets return
A 1980s backpropagation revival, a dip through the 2000s, then the 2010s deep-learning surge.
40%
The generative era
Generative AI & LLMs go from nothing before the 1980s to the largest share of the 2020s.
Derived from the tags on all 446 timeline items · 426 carry at least one paradigm tagHover a band or a card to isolate it
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AI Careers in Motion
How 25 personalities moved across universities, labs, and companies — revealing the hidden network behind AI's biggest breakthroughs.
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Organization Groups
Academic(23)
Google / DeepMind(14)
OpenAI(9)
Anthropic(3)
Meta(1)
Microsoft(1)
NVIDIA(1)
Tesla(2)
Other Industry(9)
Startups / Independent(15)
Key Patterns
The Hinton Tree
1 professor → 3 revolutions
Sutskever (OpenAI/SSI), Krizhevsky (AlexNet), LeCun (Meta) all trained under Hinton at Toronto.
The OpenAI Diaspora
6 senior exits
Sutskever → SSI, Amodeis → Anthropic, Schulman → Anthropic, Karpathy → Eureka, Murati → startup.
Google as Finishing School
14 of 25 passed through
More than half of AI's top figures worked at Google/Brain/DeepMind at some point.
University of Toronto
The #1 AI feeder
Hinton, LeCun, Sutskever, Karpathy, Krizhevsky, Gomez — one department, six world-changers.
Built for aihistoryproject.org — To understand where AI is going, start with where it began.Click any name to explore · Hover to highlight connections
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Academic Lineage
Who taught whom. Doctoral advisors traced from Wikidata, revealing the schools AI grew out of — and that the field has no single family tree, but 14 separate ones.
On the timeline (85) — click to openTeachers outside it (98)· advisor → student, left to right
The John McCarthy line8 on the timeline · 17 people
The David Rumelhart line7 on the timeline · 12 people
The Herbert A. Simon line5 on the timeline · 9 people
The Geoffrey Hinton line5 on the timeline · 8 people
The John Holland line4 on the timeline · 8 people
The Fei-Fei Li line3 on the timeline · 7 people
The Leslie Kaelbling line2 on the timeline · 7 people
The Alain Colmerauer line2 on the timeline · 6 people
The Jürgen Schmidhuber line2 on the timeline · 5 people
The Roger Schank line2 on the timeline · 5 people
The John Hopfield line2 on the timeline · 4 people
The Marvin Minsky line2 on the timeline · 4 people
The Shane Legg line2 on the timeline · 4 people
The Yoshua Bengio line2 on the timeline · 4 people
What the Lines Show
Piaget
Papert’s teacher
Seymour Papert studied under the child psychologist Jean Piaget — which is why Logo was about how children think.
Simon
The CMU line
Herbert Simon taught Allen Newell and Edward Feigenbaum; between them they trained Richard Fikes and Douglas Lenat.
4 steps
Skinner to Abbeel
B. F. Skinner → William Estes → David Rumelhart → Michael I. Jordan → Andrew Ng: behaviourism to deep learning in four handovers.
No root
A forest, not a tree
The chains never meet. AI’s researchers descend from separate mathematical, psychological and linguistic traditions.
134 advisor links across 183 people · doctoral advisor data from Wikidata (CC0)Hover a name to trace its chain