Have you ever wondered how we got to Artificial Intelligence?
Here we explore the fascinating discoveries that, through centuries of thought, gave rise to AI, just as we are now forging the Mind of the Future, to Artificiology, and to our everyday relationship with highly trained, humanized artifacts.
Although it may seem like a modern concept, the idea of creating beings or machines with artificial intelligence is not new. Its roots go back to ancient times, with myths and legends of automata and artificial beings capable of thought and action. Think of the Greek myth of Talos, a bronze giant who protected Crete, or the mechanical automata of Leonardo da Vinci.
Philosophers like Aristotle were already exploring the rules of logical reasoning, laying the foundations for what we call algorithms today. In the 17th century, thinkers like René Descartes and Gottfried Wilhelm Leibniz fantasized about machines that could imitate human thought. Leibniz, in fact, conceived of a "universal calculator" that would manipulate logical symbols, an astonishing premonition of modern computers.
These first sparks of human curiosity, the fascination with replicating intelligence, were the true starting point for everything that would come later.
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Neurophysiologists Warren McCulloch and Walter Pitts publish "A Logical "Calculus of Ideas Immanent in Nervous Activity." They describe how neurons in the brain could work in a binary (all-or-nothing) model to produce logical functions. It was the first conceptualization of an artificial neural network! Although rudimentary, it laid the foundation for machine learning.
Alan Turing, the British genius, publishes "Computing Machinery and Intelligence." In it, he proposed the famous Turing Test, a way to determine whether a machine can exhibit intelligent behavior indistinguishable from humans. His question "Can machines think?" opened the door to formal AI research.
The key milestone. At the famous Dartmouth Conference, a group of scientists led by John McCarthy coined the term "Artificial Intelligence." This defined the field of study and laid the groundwork for research in the following decades. Marvin Minsky, Nathaniel Rochester, and Claude Shannon also participated.
Impressive programs emerged, such as Joseph Weizenbaum's ELIZA (1966), an early chatbot that simulated conversation, and Terry Winograd's SHRDLU (1972), which understood natural language in a "block world". However, expectations were too high and computing resources were limited, leading to a period of skepticism and funding cuts known as the "AI Winter."
AI re-emerged with "Expert Systems," programs that emulated the knowledge of human specialists in specific domains. MYCIN, for example, diagnosed infectious diseases. Although limited, they were commercially successful and demonstrated the practical value of AI.
An iconic moment: IBM's Deep Blue computer defeats world chess champion Garry Kasparov. This milestone demonstrated the ability of machines to outperform humans at complex, well-defined cognitive tasks, capturing the public's imagination.
The massive availability of data (Big Data), the increase in computing power (especially GPUs), and advances in deep neural network (Deep Learning) algorithms are driving a new era. Voice recognition (Siri, Alexa), image recognition, and autonomous vehicles are beginning to take shape.
Milestones continue to happen every day. To stay up-to-date, subscribe to our exclusive AI Trends Newsletter!
Today, we live in an era of AI explosion. Generative AI, with models like GPT-3, GPT-4, DALL-E, and Midjourney, is redefining creativity, software development, and the way we interact with information. You can generate text, images, music, and even code with just a few instructions.
AI is embedded in our lives: from movie recommendations to voice assistants on our phones and route optimization in cities. Companies use it to analyze markets, predict trends, and improve the customer experience.
The future? AI is expected to continue advancing by leaps and bounds, tackling complex problems such as drug discovery, climate change, and space exploration. The lines between what machines can do and what humans can do are increasingly blurred.