Who Coined the Term Artificial Intelligence? The History of AI in Brief

John McCarthy coined the term “artificial intelligence” in 1955 when he proposed the Dartmouth Summer Research Project on Artificial Intelligence. The workshop took place in 1956 at Dartmouth College, New Hampshire, establishing AI as a formal academic discipline. McCarthy defined AI as “the science and engineering of making intelligent machines.”

• John McCarthy named the field in 1955 and organised the 1956 Dartmouth Workshop.
• Alan Turing laid the conceptual groundwork in 1950 but never used the phrase “artificial intelligence.”
• AI research stalled twice, in the 1970s and the late 1980s, during periods now called the AI winters.
• Deep learning, from 2012 onward, changed everything by combining massive datasets with affordable GPU compute.
• India has produced world-class AI pioneers, with Raj Reddy among the most decorated in the field’s history.

Who Coined the Term Artificial Intelligence and Who Named the Field?

The question of who coined the term artificial intelligence has a clear answer: John McCarthy, in 1955, at age 27, when he wrote the proposal for the Dartmouth Summer Research Project. He chose the phrase deliberately, partly to distinguish the new project from existing work in cybernetics and automata theory. It was a branding decision as much as a scientific one, and it stuck.

Alan Turing deserves enormous credit, but for a different contribution. His 1950 paper “Computing Machinery and Intelligence,” published in the journal Mind, introduced the imitation game, later called the Turing Test, as a way of asking whether machines could exhibit intelligent behaviour. Turing asked the right question. McCarthy gave the question a name and an institutional home.

The Dartmouth Workshop of 1956

The Dartmouth Summer Research Project ran for roughly six weeks in the summer of 1956. McCarthy was joined by Marvin Minsky, Nathaniel Rochester and Claude Shannon, among others. The group believed, optimistically, that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

That optimism turned out to be premature, but the workshop produced something durable: a shared vocabulary and a community of researchers who would define the field for decades. If you want to understand the history of artificial intelligence, this is the room where it started.

Marvin Minsky’s Role

Minsky co-founded the MIT Artificial Intelligence Laboratory with McCarthy in 1959 and spent the next five decades shaping how researchers thought about perception, knowledge representation and neural networks. He is considered one of the founding fathers of AI alongside McCarthy, though McCarthy remains the person who coined the term artificial intelligence and organised the founding event.

From the First AI Wave to the AI Winters

The years between 1956 and the early 1970s were genuinely exciting. Programs like the Logic Theorist and ELIZA showed that computers could, at least superficially, reason and converse. Government funding flowed freely, especially from DARPA in the United States. Researchers made bold predictions about human-level AI arriving within a generation.

Then the money stopped. The first AI winter, roughly 1974 to 1980, arrived after a series of critical reports, most famously the 1973 Lighthill Report in the UK, concluded that AI had failed to deliver on its promises. The Lighthill Report led directly to the cancellation of most British AI funding. DARPA cut its AI budget in the US around the same time.

Expert Systems and the Second Winter

AI recovered in the early 1980s on the back of expert systems, rule-based programs that encoded human expertise in specific domains like medical diagnosis and financial analysis. Companies invested heavily. Japan launched its Fifth Generation Computer Project in 1982 with the explicit goal of building AI-powered machines.

By the late 1980s, expert systems had hit a wall. They were brittle, expensive to maintain and could not generalise beyond their narrow rules. The second AI winter, from roughly 1987 to 1993, saw the commercial AI market collapse. According to a widely cited industry estimate, the expert systems market peaked at over $400 million annually in the mid-1980s before shrinking dramatically as companies abandoned the technology.

Why Funding Kept Collapsing

Both AI winters share the same root cause: compute and data could not keep up with the ambition. Researchers were designing architectures that required far more processing power and far more labelled data than 1970s or 1980s hardware could provide. The ideas were not entirely wrong. They were just 30 years early.

Key milestones in the history of artificial intelligence

Period	Key Event	Outcome
1950	Turing publishes “Computing Machinery and Intelligence”	Turing Test proposed as a benchmark for machine intelligence
1955 to 1956	McCarthy coins “artificial intelligence”; Dartmouth Workshop held	AI established as a formal academic discipline
1974 to 1980	First AI winter; Lighthill Report published in the UK	Government funding cut across the US and UK
1980 to 1987	Expert systems boom	Commercial AI market peaks, then collapses
1987 to 1993	Second AI winter	Expert systems market shrinks; Japan’s Fifth Generation Project winds down
2012	AlexNet wins ImageNet competition by a wide margin	Deep learning era begins in earnest
2017	Google publishes “Attention Is All You Need”	Transformer architecture introduced; GPT era begins

How Modern AI Arrived and What Finally Changed

The turning point is usually dated to 2012, when a deep neural network called AlexNet, built by Geoffrey Hinton’s team at the University of Toronto, won the ImageNet Large Scale Visual Recognition Challenge by a margin that shocked the computer vision community. AlexNet’s error rate was 15.3%, compared to 26.2% for the second-place entry, according to the original ImageNet results published by Russakovsky et al. (2015) in the International Journal of Computer Vision.

Three things converged to make that possible: the availability of large labelled datasets, affordable GPU computing, and improved training techniques for deep neural networks. None of those three ingredients existed at the right scale during the AI winters. When they arrived together, progress accelerated fast.

In 2017, researchers at Google Brain published “Attention Is All You Need,” introducing the transformer architecture. That paper is the direct ancestor of GPT, BERT, Gemini and every large language model you have heard of since. According to Google Scholar, the paper has accumulated over 100,000 citations, making it one of the most cited papers in computer science history.

India’s AI Pioneers and the Father of AI in India

India’s contribution to AI is older and deeper than most people realise. Dabbala Rajagopal “Raj” Reddy, born in Katur, Andhra Pradesh, is widely regarded as the father of artificial intelligence in India. He became the first person of Asian origin to win the Turing Award, which he received in 1994 alongside Edward Feigenbaum, for his work on large-scale AI systems.

Reddy spent most of his career at Carnegie Mellon University, where he founded the Robotics Institute and worked on speech recognition systems decades before Alexa or Siri existed. His research group built some of the earliest continuous speech recognition systems in the world. He also served on India’s National Knowledge Commission and has been a vocal advocate for using AI to address challenges in education and governance in the developing world.

India’s institutional AI story has grown significantly since. IIT Bombay, IIT Delhi and IISc Bangalore all run active AI research programmes. The Indian government’s National AI Strategy, published by NITI Aayog in 2018, set out a framework for AI adoption across healthcare, agriculture, education and infrastructure. According to NASSCOM’s 2023 Technology and Leadership Report, India had over 420,000 AI and ML professionals, making it one of the largest AI talent pools in the world.

If you’re tracking the AI job market and skills landscape in India right now, the growth numbers are striking and the demand for credentialed professionals continues to outpace supply.

The Book That Defined AI Education: Rich and Knight

Artificial Intelligence by Elaine Rich and Kevin Knight, first published in 1983 and later updated with Shivashankar B. Nair, became the standard undergraduate textbook for AI courses across India and much of the world. “Artificial Intelligence by Rich and Knight” is still one of the most searched textbook titles among Indian engineering students. It covers search algorithms, knowledge representation, natural language processing and machine learning in a way that remains pedagogically sound even today.

The book’s longevity says something about the field: the foundational ideas covering tree search, logic and probabilistic reasoning have not been replaced by deep learning. They have been extended by it.

Understanding that history helps you pick the right skills to build. The future-proof your career in the age of AI guide on this site breaks down exactly which skills employers are paying for right now and which ones are likely to hold value as the technology keeps shifting.

Where India Stands on AI Education Today

Several institutions in India have positioned themselves specifically around AI education. The Indian Institute of Technology Hyderabad launched one of India’s first dedicated B.Tech programmes in Artificial Intelligence in 2019. While no single institution currently holds the title of “first artificial intelligence university in India” in a formal sense, IIIT Hyderabad and IIT Madras have been running AI-focused research centres since the early 2000s.

Private-sector edtech has moved faster. Platforms offering structured, industry-aligned AI curricula have seen enrolment surge. If you want to build real skills without a two-year full-time commitment, bootcamp training programs are worth exploring as a practical alternative.

Frequently Asked Questions

Who coined the term artificial intelligence?

John McCarthy coined the term “artificial intelligence” in 1955 when he wrote the proposal for the Dartmouth Summer Research Project. The workshop took place in 1956. McCarthy defined the field as “the science and engineering of making intelligent machines” and is universally credited with naming the discipline.

Who is the father of artificial intelligence?

John McCarthy is most commonly called the father of artificial intelligence, both for coining the term and for organising the 1956 Dartmouth Workshop that established AI as a formal field. Alan Turing and Marvin Minsky are also considered founding figures, but McCarthy’s role in naming and institutionalising the discipline gives him the primary title.

Who is called the father of AI in India?

Raj Reddy, born in Andhra Pradesh, is widely regarded as the father of AI in India. He won the Turing Award in 1994, the first person of Asian descent to do so, for his work on large-scale AI systems at Carnegie Mellon University. His contributions to speech recognition and AI policy in India remain foundational.

When did artificial intelligence begin?

Artificial intelligence as a named field began in 1956 with the Dartmouth Workshop organised by John McCarthy. Alan Turing’s 1950 paper on machine intelligence is the intellectual precursor. Early AI programs appeared in the late 1950s and 1960s, though practical progress was slow until the deep learning era began around 2012.

What were the AI winters?

The AI winters were two periods of drastically reduced funding and interest in AI research. The first ran roughly from 1974 to 1980, triggered by the UK’s Lighthill Report and unmet expectations. The second lasted from about 1987 to 1993, after the expert systems market collapsed. Both ended when new techniques and better hardware re-ignited progress.

AI’s history is a lesson in patience and compounding progress. The ideas that seemed to fail in the 1970s turned out to be correct; they just needed 40 more years of compute and data to prove it. Understanding that arc helps you think more clearly about where the technology is heading and where the real career opportunities sit.

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Last updated: June 2025. Reviewed by the 3University editorial team.