A structured reference to the individuals shaping artificial intelligence across research, industry, governance, ethics, and public discourse. Curated as a resource for professionals navigating the AI landscape. For more see the full list at AI People.

Foundational Architects (1940s–1980s)

Alan Turing | Computation Theory, AI Foundations | United Kingdom The mathematician who defined computation itself. His 1936 paper introduced the Turing Machine-the theoretical basis for all modern computers-while his 1950 "Computing Machinery and Intelligence" proposed the Turing Test and asked whether machines could think. His work at Bletchley Park breaking the Enigma code demonstrated early machine-augmented intelligence in practice. Key works: "On Computable Numbers" (1936), "Computing Machinery and Intelligence" (1950)

John McCarthy | AI Foundations, Programming Languages | United States Coined the term "artificial intelligence" in 1955 and organised the 1956 Dartmouth Conference that launched the field. Created Lisp, which remained the dominant AI programming language for decades. His vision of AI emphasised logical reasoning and knowledge representation over statistical approaches. Key works: "Programs with Common Sense" (1959), Lisp programming language

Marvin Minsky | Cognitive Architecture, AI Foundations | United States Co-founded MIT's AI Laboratory and shaped how a generation thought about machine intelligence. His "Society of Mind" theory proposed that intelligence emerges from interactions between simple agents. The 1969 book "Perceptrons" (with Papert) demonstrated limitations of single-layer neural networks, inadvertently triggering the first "AI winter." Key works: "Perceptrons" (1969), "The Society of Mind" (1986)

Claude Shannon | Information Theory | United States Created information theory in a single paper, giving us the mathematical framework for understanding communication, compression, and entropy. His work at Bell Labs established the theoretical foundations for digital communication and, by extension, for how machines process and transmit information. Key works: "A Mathematical Theory of Communication" (1948)

Norbert Wiener | Cybernetics, Control Theory | United States Founded cybernetics-the study of feedback and control in machines and living systems. His interdisciplinary approach connecting engineering, biology, and computation influenced early AI thinking and anticipated concerns about autonomous systems that remain relevant today. Key works: "Cybernetics" (1948), "The Human Use of Human Beings" (1950)

John von Neumann | Computer Architecture, Game Theory | United States Designed the stored-program architecture that nearly all modern computers use. His work on game theory and self-replicating automata provided mathematical frameworks later applied to AI decision-making and evolutionary computation. Key works: "First Draft of a Report on the EDVAC" (1945), "Theory of Games and Economic Behavior" (1944)

Warren McCulloch & Walter Pitts | Neural Computation | United States Their 1943 paper created the first mathematical model of neural networks, showing how simple neuron-like units could perform logical operations. This work directly inspired later neural network research and established the computational metaphor for understanding brains. Key works: "A Logical Calculus of the Ideas Immanent in Nervous Activity" (1943)

Herbert A. Simon | Decision-Making, Cognitive Science | United States Nobel laureate who introduced "bounded rationality"-the insight that human decision-making is constrained by limited information and cognitive capacity. Co-created early AI programs that could prove theorems and solve problems, demonstrating that machines could perform tasks requiring intelligence. Key works: "The Sciences of the Artificial" (1969), "Human Problem Solving" (1972)

Allen Newell | Cognitive Architecture, Problem Solving | United States With Simon, created the Logic Theorist (1956) and General Problem Solver-programs that could reason and prove mathematical theorems. His later "unified theories of cognition" attempted to explain all human cognitive behaviour within a single framework. Key works: "Unified Theories of Cognition" (1990), Logic Theorist and GPS systems

Arthur Samuel | Machine Learning | United States Coined the term "machine learning" and created a checkers-playing program at IBM that learned from experience and eventually beat its creator. Demonstrated in the 1950s that machines could improve through self-play-a principle that would later power AlphaGo. Key works: "Some Studies in Machine Learning Using the Game of Checkers" (1959)

Edward Feigenbaum | Expert Systems, Knowledge Engineering | United States The "father of expert systems" who showed that encoding human expertise in rule-based systems could solve real problems. DENDRAL and MYCIN demonstrated AI's practical value in chemistry and medicine, launching the expert systems industry of the 1980s. Key works: DENDRAL, MYCIN expert systems

Seymour Papert | Educational Computing, Neural Networks | United States Created LOGO programming language to teach children computational thinking. His constructionist learning theory influenced decades of educational technology. Co-authored "Perceptrons" with Minsky, which shaped neural network research for twenty years. Key works: "Mindstorms" (1980), "Perceptrons" (1969)

Frank Rosenblatt | Neural Networks | United States Invented the perceptron in 1958-the first trainable neural network-and demonstrated machines that could learn to recognise patterns. Though limited, the perceptron established principles that underpin today's deep learning systems. Key works: "The Perceptron" (1958), "Principles of Neurodynamics" (1962)

Lotfi A. Zadeh | Fuzzy Logic | United States Created fuzzy set theory and fuzzy logic, providing mathematical tools for reasoning with imprecise or uncertain information. His work enabled AI systems to handle the ambiguity inherent in real-world problems and human language. Key works: "Fuzzy Sets" (1965)

How to Use This Directory

For research: Each entry includes key works and affiliations for deeper investigation.

For event planning: Filter by geographic base, domain, or public engagement experience.

For understanding the field: The categorisation reveals how different communities, from technical researchers, ethicists, policymakers, industry leaders all shape AI development.

For identifying perspectives: Note whose voices are included and whose might be missing from any particular AI conversation.

This directory is maintained as a resource for the AI age. Last updated: 2026.

Curated by Rahim Hirji for thesuperskills.com.

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