A Historical Starting Point
Artificial Intelligence (AI), despite its growing prominence in daily life, remains an abstract notion for many. However, its history has a clear origin: the Dartmouth Summer Research Project, held in 1956. This meeting not only coined the term "Artificial Intelligence" but also established the conceptual and organizational foundations that still support the field's development today. The Dartmouth vision lives on in every algorithm, virtual assistant, and intelligent system we use.
The Founding Hypothesis
The conference was driven by four key figures: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. Their bold proposal was: "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." This statement not only defined the goal of AI but also laid out a roadmap for replicating human cognitive functions like language, abstraction, and autonomous improvement.
The Name and the First Paradigms
The term "Artificial Intelligence" was chosen by McCarthy for its neutrality and appeal, distinguishing it from concepts like "cybernetics." Although some pioneers preferred "complex information processing," AI prevailed. At Dartmouth, two approaches emerged: symbolic systems, like Newell and Simon's Logic Theorist, and biologically inspired models, like Rosenblatt's Perceptron, which mimicked neural networks.
The Influence of Turing and its Limits
The computational view of intelligence, influenced by Alan Turing, reduced the human mind to problem-solving ability. This simplification allowed for technical advances but also drew criticism for ignoring aspects like intuition, emotion, or consciousness. Some philosophers considered this view a distortion of human intelligence, useful for engineering but conceptually limited.
Philosophy of Mind and Semantic Critique
The philosophical debate intensified with arguments like John Searle's "Chinese room" experiment, which questioned whether a machine that manipulates symbols truly "understands." Hilary Putnam, initially a proponent of functionalism, eventually acknowledged that meaning is partly determined by the environment, suggesting that intentionality cannot be reduced to internal computational processes.
The Pioneers' Optimism
In 1956, the atmosphere was one of overflowing enthusiasm. It was thought that human-level AI would arrive in "a couple of summers." This optimism was supported by rapid hardware advancements. However, it soon became clear that replicating human intelligence was much more complex. Problems like the combinatorial explosion and failures in machine translation led to the so-called "AI winters," periods of stagnation and skepticism.
The Shift Towards Data
At the end of the 20th century, AI changed course. The search for perfect logic was abandoned, and the machine learning paradigm, based on large volumes of data, was adopted. Artificial neural networks resurged strongly, and deep learning enabled spectacular advances. AlphaGo, the Google system that defeated the world Go champion, symbolizes this new era based on statistics and massive training.
Adaptive and Embodied Intelligence
Today, many experts agree that the original definition of AI is insufficient. Intelligence is not just calculation: it is adaptation, embodiment, and social context. Antonio Damasio has highlighted that explicit intelligence (reason) arises from implicit intelligence (biological homeostasis). This approach suggests that true intelligence requires a body, environment, and culture, elements absent in current computational systems.
The Missing Ethics at Dartmouth
One of the most notorious gaps of the conference was the lack of ethical discussion. Currently, AI faces serious challenges: algorithmic biases, unexplained autonomous decisions, erosion of privacy, and manipulation of public opinion. These problems demand deep reflection on the social impact of technology and the need for solid regulatory frameworks.
Transparency and Governance
To face these challenges, an ethical, transparent, and robust AI is required. Accountability must be an integral part of the design of intelligent systems. Furthermore, it is essential that technological developments respect human autonomy and are subject to international governance frameworks that regulate their use in critical sectors like health, justice, and security.
Artificial Intelligence or Augmented Intelligence?
Some experts propose redefining AI as "Augmented Intelligence," emphasizing its complementary role. Instead of replacing the human being, technology should enhance our capabilities, facilitating decision-making, learning, and creativity. This vision promotes a collaborative relationship between humans and machines, more aligned with democratic and social values.
A Living and Evolving Legacy
The Dartmouth Conference was the starting point of an intellectual revolution. Although its predictions were optimistic and its omissions evident, its impact is unquestionable. Today, AI is at a crossroads: between technical progress and ethical responsibility. Understanding its origin, philosophy, and challenges is essential to building a future where artificial intelligence is at the service of human well-being.