The term 'artificial intelligence', or 'AI' for short, has become deeply embedded in our everyday lives. From films and TV shows to professional projects within organisations, it is everywhere. Its presence is undeniable, from virtual assistants and smart home devices to predictive analytics and autonomous vehicles.

Most people have at least a basic understanding of what it means: machines or systems that can perform tasks that would normally require human intelligence. As AI continues to evolve, it is not only shaping the way we live and work, but also challenging our understanding of intelligence itself.

Few people know that the term 'artificial intelligence' originated somewhere and has evolved over the years. So why not read this article I've written for you to find out the story of the definition of AI? I’ll try my best to answer that question!

Defining Intelligence: The 1956 Dartmouth Conference and the Origins of AI

The story of the definition of AI began in 1956 at Dartmouth University in New Hampshire. At that time, the world was still recovering from the devastation of World War II. The use of computing technology and machinery was a great asset to the Allies in their efforts to defeat the Nazi forces.

For example, Alan Turing - a British cryptanalyst and mathematician, designed a machine that was used to decrypt German codes by using methods and assumptions. Later on, he theorised his work into a model.  Progressively, a number of scientific projects have been launched in parallel with this momentum.

In that context, four American scientists decided to hold a conference at Dartmouth College, which is located in the city of Hanover in New Hampshire. The purpose was to define and establish a new research field that would encompass all work related to thinking machines and complex information processing. This provided an opportunity to develop a shared understanding of what would become artificial intelligence.

To be more specific, here is the exact proposal statement wrote by John McCarthy: 

We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture 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.

An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.

Initially, John McCarthy was an assistant professor of mathematics at Dartmouth College. He was seconded by Marvin Minsky, who would go on to found MIT's AI lab in Boston, as well as being a cognitive scientist and computer science professor at MIT.

The two of them were also supported by Nathaniel Rochester, an IBM architect and engineer, and Claude Shannon, who is considered the father of information theory — a branch of applied mathematics and electrical engineering that studies the quantification, storage, and communication of information.

Over the process, there were multiple presentations; work themes were discussed with different perspectives and approaches from the participants. These themes included machine learning, problem-solving, neural networks, language processing, and the possibility of machines improving themselves. Each researcher brought unique ideas—ranging from mathematical logic to cognitive psychology—helping to shape the early theoretical framework of artificial intelligence.

Dartmouth’s Legacy: The Formation of AI’s First Scientific Community

The Dartmouth workshop is widely regarded as the birthplace of artificial intelligence. It is commonly said that the workshop generated interest among researchers to begin studying the field.

Furthermore, it created a scientific community in which everyone involved could share their work and learn from others. It laid the foundations for the theoretical basis of the following decades.

The Dartmouth workshop is widely regarded as the birthplace of artificial intelligence. It is often said that the workshop sparked researchers' interest in beginning to study the field.

Furthermore, it fostered a scientific community in which participants could share their work and learn from each other. The workshop laid the theoretical foundations for the following decades, shaping key research areas such as symbolic reasoning, machine learning, natural language processing and problem-solving frameworks.

Many of the attendees went on to become leading figures in AI, establishing laboratories, mentoring new generations of researchers, and influencing academic and industrial approaches to intelligent systems. The collaborative spirit and ambitious vision of the Dartmouth workshop continue to inspire AI research to this day.

The Evolution of AI: Revisiting Dartmouth’s Dream in a Generative Age

The field of artificial intelligence is currently made up of dedicated individuals, both men and women, who devote their lives to advancing AI technologies across various industries.

In 2025, the rapid rise of generative AI sparked widespread public interest, prompting companies to recognise the strategic necessity of integrating AI solutions to optimise internal operations and external services.

However, AI still faces significant challenges. Ethical concerns, algorithmic bias, data privacy issues and the need for transparent and responsible development remain at the forefront of the conversation.

Although the technology offers great potential, its deployment must be guided by thoughtful regulation, inclusive collaboration and a commitment to human-centred design, to ensure that AI benefits society as a whole.

The original vision of artificial intelligence, as set out at the Dartmouth Conference in 1956, centred on the idea that machines could replicate all aspects of human intelligence. However, the field has since evolved far beyond this initial framework.

Today, AI is less about mimicking the full spectrum of human cognition and more about building specialised systems capable of performing narrowly defined tasks with superhuman efficiency.

The complexity of human thought, emotion, and context-awareness continues to elude even the most advanced models. As such, while we have made significant progress, we remain far from realising the original, ambitious definition of AI put forth at Dartmouth.