AI originated in the 1950s, but the ideas behind it go back further. The modern history of AI formally began in 1950 with the work of Alan Turing, and was given a name in 1956 at the Dartmouth Conference in the United States.
How did the history of AI begin?
The idea that machines can think is ancient. Philosophy and mathematics have long considered formal reasoning, logic and the question of what exactly intelligence is. The concrete move towards artificial intelligence began with the emergence of digital computers in the 1940s.
In 1950, British mathematician Alan Turing published the influential article “Computing Machinery and Intelligence”. In it, he posed the question that became the basis of the entire field: “Can machines think?” To make that question measurable, he introduced the Turing test: a method for determining whether a machine exhibits intelligent behaviour indistinguishable from human behaviour. His work laid the theoretical foundation on which the field was built.
When did AI officially emerge?
The term “artificial intelligence” was first officially used in 1956, at the Dartmouth Conference at Dartmouth University in the United States. The conference was organised by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon.
McCarthy suggested calling the new field “artificial intelligence”. The conference did not produce any major scientific breakthroughs, but it had a different significance: it established AI as an independent field of research with its own goals. Machine learning, reasoning and problem solving became the central ambitions.
That is why 1956 counts as the official birth year of AI as a discipline, even though its theoretical roots lay six years earlier with Turing.
What were the first AI programmes?
Back in 1955, a year before the Dartmouth Conference, Allen Newell and Herbert Simon developed the Logic Theorist. This was the first computer programme that could prove mathematical theorems via symbolic manipulation, without the solution method being pre-programmed. It is considered the first true AI programme.
This was followed in the 1960s by ELIZA, developed by Joseph Weizenbaum at MIT. ELIZA simulated a conversation with a psychotherapist by rewriting sentences from the user as questions. It was the first chatbot in the history of AI and demonstrated something striking: people are quick to attribute human qualities to systems that recognise only simple patterns.
During the same period, the first neural networks emerged, and in 1986, David Rumelhart, Geoffrey Hinton and Ronald Williams described the backpropagation algorithm: a method by which neural networks could learn more efficiently. This algorithm would only show its full potential years later. How that technology evolved into the systems used today is explained in our article on how AI works from.
What were the AI winters in the history of AI?
The optimistic early years were followed by periods of stagnation that have become known as AI winters. The first AI winter began in the 1970s. Computers lacked sufficient computing power and memory to meet ambitious goals, and investors withdrew funding.
In the 1980s, there was a temporary revival through expert systems: programmes that captured the knowledge of human experts in rules and decision trees. MYCIN could diagnose bacterial infections and Digital Equipment Corporation's XCON configured customised computer systems. These systems worked well for specific, defined problems, but were fragile outside their own domain.
A second AI winter followed in the late 1980s. The limitations of expert systems became apparent, investments dried up again and hundreds of AI companies went bankrupt. It took until the 1990s for the tide to turn.
How did the internet change the history of AI?
The rise of the internet, cheaper storage and faster processors gave machine learning the fuel it needed. For the first time, there was widespread access to data, and the computing power to process that data increased rapidly.
In 1997, IBM's Deep Blue beat chess world champion Garry Kasparov. It was a milestone that attracted worldwide attention and demonstrated that computers could outperform humans in a specific, complex task. In the following years, AI began to penetrate everyday applications: search engines, spam filters and recommendation systems became the first AI applications that large groups of people encountered on a daily basis.
When did the deep learning revolution begin in the history of AI?
In 2012, the AlexNet system, developed by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, convincingly won an international image recognition competition. It performed so much better than the competition that it caught the attention of the entire research field. Deep learning, training deep neural networks on large data sets, became the dominant method in AI research from then on.
In the following years, speech recognition improved greatly, translation systems became more accurate and the available computing power continued to grow. That combination enabled ever larger and more powerful models. At LearnLLM, we see in training courses that professionals who understand how this period shaped today's tools are significantly better able to use those tools effectively. You can find an overview of the types of AI that emerged from this revolution in our article on the different types of AI.
When did generative AI emerge?
Generative AI has been around for some time, but broke through to a wider audience with the launch of ChatGPT in November 2022. Before that, OpenAI had released GPT-3 in 2020, a language model that could generate text of a quality not previously possible.
ChatGPT made that technology accessible to everyone through a simple chat interface. Within days, large numbers of users worldwide had created accounts. The rapid adoption led to an acceleration of investments and developments by competitors such as Google, Anthropic and Meta.
Generative AI is the most direct form of AI for professionals today. More on how ChatGPT works and what tasks you use it for, you can read in our detailed article. How generative AI as a technology works, explains our article on what generative AI is from.
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