The Philosophical Seed of Automatism
Artificial Intelligence (AI) is founded on the assumption that the human thought process can be mechanized. This age-old aspiration originated from the need to transfer human labor and characteristics—including cognitive ones—to man-made contraptions. The history of AI, which ultimately seeks to replicate human intelligence in computers, dates back to myths about artificial beings endowed with consciousness, but its technical roots are consolidated in philosophy and mathematics. From mythological automatons to rationalist treatises, the desire to emulate the human mind has been a cultural and scientific constant.
From Classical Logic to Combinatorics
The conceptual foundations were established in Ancient Greece, where Aristotle formalized the mechanics of deductive thought through the syllogism. This was the first systematic attempt to model human reasoning using predictable structures. Centuries later, during the Middle Ages, Ramon Llull (1232–1315) proposed the Ars Magna, conceiving of logical machines capable of generating knowledge by combining basic truths through simple logical operations. Llull anticipated the idea of a symbolic computational system, where knowledge could be derived by formal manipulation, a notion that would re-emerge centuries later in symbolic AI.
Rationalism as Calculation
In the 17th century, the view that reason could be reduced to calculation became explicit. Philosophers like Gottfried Leibniz envisioned a universal language of reasoning (characteristica universalis) and a calculus ratiocinator, postulating that argumentation could be reduced to simple calculations. This tradition already had precedents in Thomas Hobbes, who claimed that "reason is nothing but reckoning." These ideas transformed the mental operation into a "computation," equating the use of rational rules with the act of thinking. Logical thought thus became a mechanical operation, capable of being automated.
Boolean Algebra and the Programmable Machine
The decisive leap towards mechanization occurred in the 19th century. George Boole developed Boolean algebra, which formalized propositional logic into a binary system (1 and 0). This language is the essential mathematical substrate for electronic circuits and programming. In parallel, Charles Babbage designed the Analytical Engine, incorporating essential concepts such as memory, a processing unit, and flow control. His collaborator, Ada Lovelace, intuited that these machines would not only manipulate numbers but also symbols, music, or text, laying the foundations for general-purpose programming. Lovelace envisioned the creative potential of machines, anticipating generative AI.
The Formalist Program and Gödel's Crisis
In the early 20th century, mathematicians like Bertrand Russell and Alfred North Whitehead sought to reduce all of mathematics to formal logical principles in Principia Mathematica. Inspired by this formalism, David Hilbert posed the Entscheidungsproblem (decision problem), seeking a mechanical procedure capable of determining whether any mathematical proposition was true or false. However, in 1931, Kurt Gödel published his Incompleteness Theorem, demonstrating that in any consistent and sufficiently powerful formal system, there will always be true propositions that cannot be proven within the system. This revelation limited the ambition of completely automating logical reasoning.
Alan Turing and the Definition of Computability
The decisive answer to Hilbert's problem came from Alan Turing in 1936 with the introduction of the Turing Machine (TM) in his paper On Computable Numbers. The TM, an abstract model, rigorously defined the mathematical concept of a mechanical procedure or algorithm. The model, capable of simulating any calculation by manipulating symbols on an infinite tape, established the theoretical limits of what is computable. This culminated in the Church-Turing Thesis, which posits that anything computable by a human is also computable by a Turing Machine. This theoretical framework is the foundation of modern computer science and AI.
The Impetus of World War II and Cybernetics
World War II transformed theory into engineering. Turing was key in the development of Colossus at Bletchley Park, which demonstrated the ability of machines to perform complex intellectual tasks—such as code-breaking—at superhuman speed. This experience led Turing to reinterpret Gödel's limitations as a strategy to build increasingly powerful machines. At this time, John von Neumann laid the foundations for the architecture of modern computers, while Norbert Wiener founded Cybernetics, which studied control and communication systems, and Claude Shannon developed Information Theory, essential for digital data management.
Pioneers of Biological Connectionism
Parallel to the logicist approach, biologically inspired models were developed. The work of Santiago Ramón y Cajal on neural structure was fundamental to understanding distributed processing. In 1943, Warren McCulloch and Walter Pitts proposed the first mathematical model of an artificial neuron. This binary model (on-off state) demonstrated that a network of these units could perform complex logical functions, laying the groundwork for Artificial Neural Networks (ANNs). This work culminated in 1951, when Marvin Minsky and Dean Edmond built SNARC, the first neural network computer, anticipating the connectionist paradigm that would re-emerge strongly decades later.
The 1950 Question: Can Machines Think?
Turing's change of perspective, driven by the success of the war machines, was reflected in his 1950 paper, Computing Machinery and Intelligence. Turing posed the foundational question of AI: "Can machines think?" Considering the debate over the definitions of "machine" and "think" to be sterile and ambiguous, he decided to replace it with a more practical and concrete question about observable performance. Thus, intelligence ceased to be a metaphysical essence and became an evaluable functional capacity.
The Turing Test: A Behavioral Criterion
To evaluate a machine's ability to exhibit intelligent behavior indistinguishable from human, Turing proposed the Imitation Game, known as the Turing Test. In it, a human interrogator communicates via text with two hidden interlocutors: a machine and a human. If the machine consistently deceives the evaluator into believing it is the human, it is considered to exhibit intelligence. This test was established as a practical and measurable criterion for evaluating intelligence, focusing on the machine's ability to handle natural language, reasoning, knowledge, learning, and, notably, aesthetic sensitivity and empathy.
The Formal Birth of a Discipline
Although the 1950 Turing Test was the conceptual turning point, the research field was formally founded in the summer of 1956, during the Dartmouth Conference. It was there that John McCarthy coined the term "Artificial Intelligence." At this key meeting, Allen Newell and Herbert A. Simon presented the Logic Theorist, considered one of the first programs to exhibit intelligent behavior by proving 38 of the first 52 theorems of Principia Mathematica. This event formalized the discipline and the logicist-symbolic research line that dominated the following decades, marking the dawn of modern AI.
Conclusion: From Philosophy to the Thinking Machine
The history of AI up to the 1950s is a journey that connects classical philosophy, formal logic, computational mathematics, and electronic engineering. From Aristotelian syllogisms to the Turing Test, each stage represented a step towards the mechanization of thought. This journey not only reveals the technical origin of AI but also its cultural dimension: the human desire to understand and replicate its own intelligence. The dawn of AI was not an isolated event, but the result of centuries of reflection, abstraction, and experimentation.