Artificial intelligence (AI) is much more than a mere technological innovation; it represents a continuity in humanity's long quest to understand, replicate, and surpass the capabilities of the mind. This ambition has its roots in ancient myths like that of Prometheus, who stole fire from the gods to enlighten humanity. Over the centuries, this quest has transformed into an ambitious scientific project, blending philosophy, mathematics, and engineering. This text explores the intellectual foundations, technological advances, and ethical implications of AI, while shedding light on the challenges and promises it holds for the future.
The Founding Myths of Knowledge
1.1 Prometheus: The Symbol of Transgression
The myth of Prometheus, as recounted by Aeschylus, illustrates the duality inherent in humanity's quest for knowledge. Prometheus, whose name means "foresight," not only transgressed divine laws by stealing sacred fire but also enlightened humanity through nous (reason). This gift of fire symbolizes humanity’s ability to use knowledge to create tools and technologies that improve life. However, this transgression earned him the wrath of Zeus, who condemned him to eternal punishment. This myth reflects a deep tension in Western thought: the pursuit of knowledge is both a source of progress and an act of transgression against the natural or divine order.
1.2 Mary Shelley and Frankenstein: A Reflection on Artificial Creation
Mary Shelley, in her novel Frankenstein , draws directly from ancient myths to explore the dangers of artificial creation. Victor Frankenstein, the protagonist, seeks to bring a creature to life using the scientific knowledge of his time, particularly discoveries in electricity and biology. However, his creation turns against him, highlighting the risks of unchecked intellectual ambition. For Shelley, science, though it can liberate humanity from ignorance, can also lead to unforeseen and destructive consequences. This critical reflection on the ethical responsibility of creators remains relevant today, particularly in the context of AI.
The Philosophical Foundations of AI
2.1 Rationalism and Empiricism: Two Opposing Currents
Western philosophy has long been divided between two major schools of thought: rationalism and empiricism. Rationalists, such as René Descartes and Gottfried Wilhelm Leibniz, believed that knowledge comes from pure reason. For them, the human mind is capable of discovering universal truths through logic and introspective reflection. Conversely, empiricists, such as John Locke and David Hume, argued that all knowledge stems from sensory experience. According to them, the human mind is a tabula rasa (a blank slate) at birth, and our ideas are formed from the impressions we receive from the external world.
2.2 Immanuel Kant: A Bridge Between the Two Traditions
Immanuel Kant attempted to reconcile these two approaches by proposing an innovative synthesis. According to him, knowledge results from an interaction between a priori structures of the mind (such as time and space) and sensory data derived from experience. This perspective has had a lasting influence on modern philosophy and the cognitive theories underlying AI. Kant also emphasizes the active role of the mind in constructing reality. For example, when an individual observes someone approaching them, they do not merely perceive a series of isolated images but a coherent and identifiable object. This ability to organize perceptions into stable objects is essential for understanding how AI systems can model the world.
Scientific and Technological Advances
3.1 The Copernican and Galilean Revolutions
The scientific revolutions of the 16th and 17th centuries transformed our understanding of the world. Copernicus demonstrated that the Earth was not the center of the universe, while Galileo used mathematical tools to describe physical laws. These discoveries showed that observable reality could differ radically from our immediate intuitions. These advances also inspired thinkers like Thomas Hobbes, who asserted that "reasoning is a form of calculation." This idea that human thought can be formalized and mechanized forms the basis of early work on AI.
3.2 Calculating Machines: From Pascal to Babbage
In the 17th century, inventors like Blaise Pascal and Gottfried Wilhelm Leibniz developed calculating machines capable of performing complex arithmetic operations. These devices demonstrated that processes once reserved for the human mind could be automated. In the 19th century, Charles Babbage designed the "analytical engine," considered the ancestor of modern computers. Although his machine was never built during his lifetime, it introduced fundamental concepts such as the separation between memory and processing units.
3.3 George Boole and Symbolic Logic
George Boole played a crucial role in the development of symbolic logic, which became the cornerstone of modern computing. His Boolean system, based on simple operations like AND, OR, and NOT, allows logical relationships to be represented formally. This formalism is still used today in the design of electronic circuits and AI algorithms.
The Beginnings of Artificial Intelligence
4.1 Alan Turing and the Turing Test
In 1950, Alan Turing published an article titled Computing Machinery and Intelligence , in which he proposed a test to evaluate the intelligence of machines. The "Turing Test" involves asking an interrogator to distinguish between the responses of a human and those of a machine solely through text-based interactions. This test raised fundamental questions about the nature of intelligence and the criteria for judging whether a machine is "intelligent." Though controversial, it remains an important reference in the field of AI.
4.2 The Dartmouth Workshop (1956)
In 1956, a group of researchers, including John McCarthy, Marvin Minsky, and Claude Shannon, gathered at a workshop at Dartmouth to define the foundations of AI. They proposed several research themes, such as neural networks, programming languages, and machine learning methods. This event is considered the official starting point of AI as a scientific discipline.
Ethical and Philosophical Challenges
5.1 American Pragmatism and AI
William James and Charles Sanders Peirce, central figures in American pragmatism, argued that the truth of an idea should be evaluated based on its practical consequences. This approach is relevant to AI, as it encourages engineers to focus on concrete applications rather than abstract debates about the nature of intelligence.
5.2 The Risks and Limits of AI
Despite its promises, AI raises significant ethical questions. For example, how can we ensure that decisions made by automated systems are fair and transparent? How can we prevent human biases from being embedded in algorithms?
Conclusion: Toward an Intelligible Future
Artificial intelligence is the fruit of a long intellectual tradition that combines philosophy, science, and technology. While it offers extraordinary opportunities, it also requires deep reflection on its ethical and social implications. By understanding its roots and challenges, we will be better equipped to shape a future where AI serves humanity in a responsible and beneficial manner.