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The history of Artificial Intelligence begins long before 1955, with ideas and inventions that anticipated the creation of intelligent machines.
Artificial Intelligence (AI) is a modern concept, but its origins date back much further than 1955. In fact, even before the term was coined, there were glimpses of a future in which machines could perform complex tasks that would disable human intelligence.
These first steps towards IA have been featured in folklore, science fiction, and early technological innovations of the 19th and 20th centuries. Discoveries and ideas from inventors and thinkers of past generations were instrumental in shaping what we understand as AI today.
The first look at the intelligent machine
The concept of thinking machines did not arise with the first digital computers or with the invention of AI as a scientific field.
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In fact, it has appeared in a satirical way in the pages of literature. In 1726, Jonathan Swift, in his work Gulliver's Travels, dynamically the notion of a machine called “The Engine”.
Although the description was an ironic criticism of scholars of the time, the idea of a contraption that could generate new ideas from mechanically rearranged words already foreshadowed what would later become algorithmic text generation.
Swift ridiculed the pretensions of academics, but his vision was surprisingly futuristic, foreshadowing developments that, centuries later, would be possible with the advancement of AI.
Leonardo Torres and Quevedo's chess automatic
Already at the beginning of the 20th century, the Spanish engineer Leonardo Torres y Quevedo developed an automaton called El Ajedrecista (The Chess Player).
In 1912, he created a machine capable of playing a simplified chess endgame, with a king and a rook against a king.
The machine used electromagnets to move the pieces and could identify illegal moves, as well as being able to checkmate when it was in a winning position.
Although it was a mechanical machine, El Ajedrecista demonstrated the possibility of replicating behaviors that disable logical calculation, an essential principle for modern AI.
The emergence of computing
The 1940s marked a turning point on the path to the creation of modern AI. With the development of digital electronic computers, new possibilities for intelligent machines began to open up.
O Atanasoff-Berry Computer (ABC), created by John Vincent Atanasoff and Clifford Berry, was one of the first digital computers and an innovation in binary computing and electronic circuits — the essential foundations for the development of AI programs.
In 1943, scientists Warren McCulloch and Walter Pitts proposed a mathematical model of the human brain, revealing that neurons and synapses could be modeled as computational networks.
This idea of neural networks would inspire decades of research and become an important field within AI, reemerging with force in the 21st century.
Alan Turing
No figure is more central to the history of AI than Alan Turing. Born in 1912, Turing was a British mathematician whose work laid the foundation for modern computer science.
In 1950, he published his article “Computing Machinery and Intelligence” (Computing Machines and Intelligence), where he raised a fundamental question: “Machines can think? "
Turing did not delve into philosophical discussions about what it means to “think,” but he proposed a practical approach: the Turing Test.
In this test, a human judge interacts with two interlocutors — a human and a machine — through a text chat.
If the judge can’t tell which is which, the machine can be considered “intelligent.” The Turing Test became a landmark in AI evaluation and remains relevant to this day.
Turing's work also had a profound impact on how we understand computing. He suggested that computers, if programmed correctly, could simulate any mental process, given the right instructions.
Unfortunately, Turing passed away in 1954, without seeing the term “AI” formally adopted, but his contribution to the field is undeniable.
John McCarthy
In 1955, mathematician and computer scientist John McCarthy, along with other researchers, specified the Dartmouth Summer Research Project on Artificial Intelligence, an event that is often considered the starting point of AI as a formal research field.
During this event, McCarthy introduced the term “artificial intelligence,” which would become the basis for all future research in the field.
McCarthy also developed Lisp, a fundamental programming language for AI that would become a primary tool for researchers for many decades.
Furthermore, he believed that machines could reason logically and that formal logic would be the key to replicating human intelligence. His vision profoundly influenced the early development of Artificial Intelligence.
Marvin Minsky
Marvin Minsky, one of the organizers of the Dartmouth workshop, was one of the pioneers who made significant contributions to AI.
He was one of the founders of MIT Artificial Intelligence Laboratory, which would become a center of excellence in the development of Artificial Intelligence.
Minsky was also at the forefront of the creation of Society of Mind, a theory that suggested that intelligence is composed of a set of specialized agents that operate cooperatively.
Minsky also worked on the development of artificial neural networks, but in his critique of perceptron by Frank Rosenblatt, he highlighted that simple neural network models had limitations in more complex tasks.
Despite this, his contributions profoundly shaped the field by incorporating psychology and cognitive science into AI studies.
Herbert A. Simon and Allen Newell
While McCarthy and Minsky focused on formal logic and neural networks, Herbert A. Simon and Allen Newell approached AI from a cognitive perspective.
In 1956, Simon and Newell performed the Logic Theorist, considered the first functional Intelligence program, which proved mathematical theorems autonomously.
This was an important breakthrough as it demonstrated that machines could solve complex problems, such as humans face in logical judgment.
In 1957, they performed the General Problem Solver (GPS), an attempt to simulate how humans solve problems in general, using heuristics to guide the search for solutions.
The idea that machines could reason and solve problems like humans has been a central concept of AI for decades.
Arthur Samuel
In the 1950s, IBM's Arthur Samuel made one of the first major contributions to what we now call machine learning.
He created a checkers program that learned from its own games. With each game, the program improved its skills, adjusting its performance based on the feedback it received, a principle that would become central to machine learning.
His work was a milestone in the evolution of AI, as it showed that machines could learn and improve without direct human intervention.
Frank rosenblatt
Frank Rosenblatt was another fundamental name in the history of AI, mainly for the development of perceptron, a type of artificial neural network.
Created in 1957, the Perceptron was able to learn to classify objects based on labeled examples.
Although limited to linear problems, the Perceptron was the precursor to modern neural networks, and its ideas were fundamental to the renaissance of AI decades later, when more advanced neural networks evolved to be developed.
Joseph Weizenbaum and the ELIZA Chatbot
In the 1960s, Joseph Weizenbaum developed the ELIZA program, a chatbot that simulated human conversations based on a simple set of pattern-matching rules.
Although simple, the program demonstrated the possibility of convincing human-computer interaction.
ELIZA also raised ethical questions about interacting with machines, as many users felt the program had feelings and empathy, something that Weizenbaum had not expected.
The Winter of Artificial Intelligence
After initial optimism, AI research has gone through periods of discouragement, known as “AI winters.”
In the 1970s and 1980s, unfulfilled promises and limitations of the AI systems of the time led to a significant slowdown.
However, the ideas of pioneers such as Turing, McCarthy and Minsky have continued to influence subsequent generations of researchers.
Advances in the 21st century
From the 2000s onwards, with the increase in computer processing power and the emergence of new algorithms, AI began to develop at an impressive rate.
Deep learning, an advanced type of neural network, has begun to show its potential in areas such as computer vision, speech recognition and natural language processing.
In 2012, the victory of AlexNet on ImageNet was a major milestone, symbolizing the resurgence of the field of AI.
Today, AI is present in several areas of our daily lives, from personal assistants to self-driving cars.
Innovations are rapid, but we cannot forget that modern AI was built on the shoulders of the giants that came before it.
Turing, McCarthy, Minsky, Rosenblatt and their colleagues laid the foundations that allowed us to get to where we are today.
The story of AI, filled with challenges and discoveries, continues to unfold, and its impact on the modern world is undeniable.
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