When we think about artificial intelligence (AI), it is easy to imagine recent advancements, such as virtual assistants and autonomous vehicles. However, the roots of AI are deeply connected to philosophical questions and scientific advancements spanning centuries. AI is the result of a rich intersection of ideas from multiple fields, each contributing to the formation of this domain. Let us explore some of these historical influences that have shaped the journey of AI.
Philosophy and Mathematics: The Cradle of Logical Thought
Since antiquity, philosophers have sought to understand the mind and thought. Aristotle, for instance, was a pioneer in proposing theories about logic, which later became fundamental to the formal representation of thought. In the 17th century, Descartes separated mind and body, raising questions about what could be mechanized and what was exclusively human. This perspective inspired a series of debates and advancements that culminated in mathematical systems, such as George Boole’s symbolic logic, used in the construction of the first computing circuits (Russell & Norvig, 2022; McCorduck, 2018).
Psychology and Cognitive Science: Modeling the Human Mind
With the advancement of mathematical logic, scientists began to study the human mind as a system that could be modeled. Freud, Binet, and other pioneers attempted to quantify and describe mental processes. However, it was with cognitive science that more robust models emerged, such as the Computational Theory of Mind, which compares the human mind to a computational system capable of processing information systematically. These concepts directly influenced the development of AI systems focused on solving complex problems (Rescorla, 2024).
Neuroscience and Neural Networks: Biological Inspiration
Observing the brain’s functioning was also crucial for AI development. In 1943, McCulloch and Pitts proposed a mathematical model for neurons, demonstrating that they could execute logical operations. This work formed the foundation for artificial neural networks, which today support technologies such as facial recognition and automatic translators. Modern research in deep learning continues to draw inspiration from the mechanisms of the human brain (LeCun, Bengio & Hinton, 2015).
Information Theory and Language: Understanding and Processing Data
In 1948, Claude Shannon established the foundations of Information Theory, defining how data could be quantified, stored, and transmitted. These principles are still used today for natural language processing, enabling AI systems to understand and generate language efficiently. Additionally, advancements in linguistics by Noam Chomsky brought new perspectives on modeling human language, essential for virtual assistants like Siri or Alexa (Floridi, 2010; Chomsky, 2002).
Evolutionary Theories and Evolutionary Computing: Seeking Natural Solutions
The idea that machines could “evolve” inspired the development of genetic algorithms, which simulate natural selection to solve complex problems. A practical example is the aerodynamic design of airplanes, optimized through evolutionary techniques. These algorithms continue to be used in applications for optimization and machine learning (Floreano & Mattiussi, 2008).
Social Sciences and Economics: Exploring Human and Systemic Implications
With the widespread adoption of AI, understanding its interaction with social and economic structures is essential. Social sciences analyze AI’s influence on cultural practices and human values, while economics provides analytical tools to explore resource redistribution and labor market impacts. Models like game theory help understand AI-based decision-making, while ethical frameworks are developed to ensure more inclusive and responsible technological progress (Brynjolfsson & McAfee, 2016; Zuboff, 2020).
Engineering and Implementation Technologies: Turning Ideas into Reality
Finally, advancements in engineering were essential to transform abstract ideas into real systems. From early automatons inspired by clockwork mechanisms to modern computers, engineering provided the technical support necessary to build autonomous machines. Technologies like feedback control and cybernetics played fundamental roles in creating adaptable and efficient devices (Wiener, 1970; Nilsson, 2009).
Conclusion: AI as an Interdisciplinary Field
Artificial intelligence reflects the convergence of ideas and discoveries from various fields of knowledge. From philosophical reflections on the mind to the technical advancements of engineering, AI’s journey is a fascinating story of interdisciplinary collaboration. This legacy reminds us that technological progress does not occur in isolation but is shaped by our ideas, dreams, and collective efforts.
REFERENCES
- Brynjolfsson, E., & McAfee, A. (2016). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
- Chomsky, N. (2002). Syntactic structures. Mouton de Gruyter.
- Floridi, L. (2010). Information: A very short introduction. Oxford University Press.
- Floreano, D., & Mattiussi, C. (2008). Bio-inspired artificial intelligence: Theories, methods, and technologies. MIT Press.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
- McCorduck, P. (2018). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. CRC Press.
- Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press.
- Rescorla, M. (2024). The computational theory of mind. Stanford Encyclopedia of Philosophy.
- Russell, S. J., & Norvig, P. (2022). Artificial intelligence: A modern approach. Pearson.
- Wiener, N. (1970). Cibernética e sociedade. Editora Cultrix.
- Zuboff, S. (2020). A era do capitalismo de vigilância: A luta por um futuro humano na nova fronteira do poder. Intrínseca.
