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When someone asks who invented AI, it sounds at first like an average quiz question on a Günther Jauch show with exactly one correct answer. Just like “Who invented the telephone?” – name, year, done.
With AI, it is different: AI is not a single device, but an idea, a field of research and a long chain of breakthroughs. And yes: in this chain there are a few names without whom we would have no chatbots, no image recognition and no smart assistance systems today. We trace the development for you.
Before we dive into dates and names, a small but useful piece of demystification: the question “Who invented AI?” is not a one-person hero story, but rather a relay race spanning decades.
Sometimes AI was conceived as a set of rules (“If X, then Y”), sometimes as a learning system that teaches itself patterns, and in between there were phases in which the industry spent its time more or less in hibernation.
That is exactly why it is worth looking at the key milestones: you will see how the development of AI progressed from philosophical foundations via mathematical models to today’s breakthroughs. And you will suddenly be able to answer the question of how long AI has actually existed in your sleep.
The “Dartmouth Summer Research Project on Artificial Intelligence” is widely regarded as the founding event of AI as a field of research. It was organised by, among others, John McCarthy, Marvin Minsky, C. E. Shannon and Nathaniel Rochester. Dartmouth itself records that the term “Artificial Intelligence” was coined there and that the conference is regarded as the “birth” of the field.
So if you have to answer in one sentence who “invented” AI, John McCarthy is the name that comes up most often, because he coined the term and defined the framework. But: a term is not yet a functioning AI. That required more.
Even before Dartmouth, in 1950 Alan Turing posed the question “Can machines think?” in his paper “Computing Machinery and Intelligence” and proposed the “Imitation Game” (later the “Turing test”). This was important because Turing thereby shifted the focus: away from hair-splitting (“What is thinking?”) and towards observable behaviour.
While the symbolists were thinking about rules, there were parallel early attempts to replicate learning. From 1957, Frank Rosenblatt worked on the perceptron, an early (very simple) learning model. This was not yet “modern AI”, but it shows that the development of AI ran on two tracks from early on, with rules and learning at the same time.
In the 1960s and 1970s, the idea of representing intelligence through symbols, logic and rules boomed. Later came expert systems: knowledge was modelled by hand. This worked in narrow domains, until it became too expensive, too fragile and too difficult to scale. Then came what so often comes in tech stories: a long winter.
“AI winter” describes phases in which interest in and funding for AI declined significantly, above all after promises that were too big and real performance that was too small. That is a good reminder for today: AI is powerful, but it remains a system with limits, risks and dependencies (data, quality, governance).
A major step for learning systems was the broad establishment of backpropagation (propagating errors backwards through the network and adjusting the weights). The classic Nature article by Rumelhart, Hinton & Williams (1986) is among the central references.
This is one of the reasons why names such as Geoffrey Hinton come up so often today in the context of “Who invented AI”: he strongly shaped the modern era of machine learning and neural networks.
In 2012, AlexNet (Krizhevsky, Sutskever, Hinton) won the ImageNet competition by a clear margin – a wake-up call that deep networks plus GPU power suddenly scale. From then on, AI in many companies was no longer “research”, but product.
The next big lever came in 2017 with “Attention Is All You Need”: the Transformer.
Transformers are (put simply) extremely good at learning relationships in sequences and form the backbone of many modern language and multimodal models. When someone asks today who invented AI, many actually mean: who made modern generative AI possible? And there, 2017 is a key year.
From this point on, AI did not just get “better”, it also suddenly became something the public could experience: models such as BERT (2018) showed how strong Transformers can become in language tasks, GPT-3 (2020) brought large-scale text generation to a wide audience, and with ChatGPT (2022) the whole thing finally arrived in everyday life.
The coming years will revolve less around “even bigger, even smarter” and more around integration: AI as part of processes, roles and chains of responsibility.
In practical terms, this means:
Instead of one-size-fits-all models for everything, more domain-specific systems and solutions are emerging: AI for support, AI for quality inspection, AI for knowledge management, AI for documents. When people in business ask “How long has AI existed?”, they often mean: since when has AI been genuinely relevant for us? For many, the answer is: since it became integrable into everyday operations and economically viable.
AI can do a great deal. But the benefit only emerges when someone cleanly bridges the gap between IT, business units and strategy. That is exactly where demo parts company with value creation.
“Who invented AI?” is the wrong question with the right curiosity behind it. AI is not a solo invention, but a relay: Turing provided the concept, Dartmouth gave the field its name, McCarthy coined the term, Rosenblatt & co. delivered early learning models, backprop made training more practicable, AlexNet brought the breakthrough in practice and Transformers ignited today’s generation.
If you tell this story in your company, this is the essence: AI has grown – and it keeps on growing. The difference comes not from magic, but from clear goals, good data and a plan that takes implementation and responsibility into account.
This is exactly where BE BRAVE comes in: as a partner that not only explains AI, but translates it into processes, teams and systems in such a way that it becomes a genuine business lever – pragmatic, cleanly set up and with an eye on Swiss standards, data protection and feasibility.
If you have to name one person: John McCarthy coined the term “Artificial Intelligence” and organised the Dartmouth workshop in 1956. In 1950, Alan Turing laid important foundations for the idea of machine intelligence and for the Turing test.
As a field of research, “officially” mostly since 1956 (Dartmouth). As an idea (machines that “think”), considerably longer – but 1950/1956 are the usual points of reference.
Because expectations and reality drifted apart at times: promises that were too big, too little computing power, too little data, systems that were too fragile. The result: less funding and interest.
There were several, but two markers are essential: AlexNet in 2012 (deep learning on a broad scale) and Transformers in 2017 (the basis of many of today’s language models).
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