Artificial Intelligence (AI) is now a cornerstone of modern innovation, influencing everything from our smartphones to scientific research. But its journey didn’t begin with sophisticated algorithms or cloud computing. The roots of AI stretch back centuries to philosophical inquiries about intelligence, logic, and the nature of the human mind. The formal birth of AI as a scientific discipline occurred in the mid-20th century, but its development has evolved through a complex interplay of theory, experimentation, and technological progress. Understanding when AI started requires exploring a tapestry of ideas, breakthroughs, and aspirations that have spanned generations.
The Philosophical Origins
The earliest ideas that eventually inspired artificial intelligence go back to ancient times. Philosophers in ancient Greece, such as Aristotle, pondered the nature of reasoning and developed the first formal system of logic. His work on syllogistic logic, which laid out rules for drawing conclusions from premises, would later influence computer science and AI.
In the 17th century, thinkers like René Descartes and Gottfried Wilhelm Leibniz speculated about the possibility of mechanizing thought. Leibniz dreamed of a “universal language of reasoning” that could be used to solve all human disputes, a vision that foreshadowed the idea of programmable logic.
In the 19th century, George Boole developed Boolean algebra, a mathematical framework for logic that would later become foundational for computer circuits and digital systems. Ada Lovelace, often considered the first computer programmer, theorized that Charles Babbage’s Analytical Engine could manipulate symbols in accordance with rules—laying the conceptual groundwork for machine logic.
Though these early efforts were theoretical, they raised the core question AI attempts to answer: Can the processes of human thought be replicated by machines?
The Birth of Modern Computing
The development of modern computing during the 20th century was a turning point in the quest to create artificial intelligence. In the 1930s, Alan Turing, a British mathematician and logician, introduced the concept of a “universal machine” capable of performing any calculation that could be represented as an algorithm. This theoretical construct—later called the Turing Machine—was a precursor to the modern computer.
Turing’s 1950 paper titled “Computing Machinery and Intelligence” posed the fundamental question: Can machines think? In it, he put out what is now called the Turing Test, a technique for figuring out whether a machine is capable of displaying intelligent behavior that is identical to that of a human. Though controversial, this paper is often regarded as a philosophical and technical foundation for AI.
During World War II, Turing built machines that could decode encrypted German messages, demonstrating the power of machines to perform complex, human-like tasks. Around the same time, advances in electrical engineering and circuit design led to the development of the first programmable digital computers such as ENIAC and EDVAC in the United States.
The Dartmouth Conference: AI Is Born
Artificial Intelligence as a formal field of study was born in the summer of 1956 at the Dartmouth Conference, organized by John McCarthy (who came up with the term “artificial intelligence”), Nathan Rochester, the late Claude Shannon and Erwin Minsky. “In theory, every facet of learning or any other characteristic of intelligence can be so accurately described that a machine can be made to simulate it,” the plan said.
This conference marked the official beginning of AI as a scientific discipline. The researchers involved believed that significant progress could be made toward building intelligent machines within a generation. This optimism led to early explorations in natural language processing, game playing, and machine learning.
The First AI Programs
During the late 1950s and early 1960s, researchers developed some of the first AI programs. Allen Newell and Herbert A. Newell founded the Logic Theorist. Simon in 1955, was designed to mimic human problem-solving skills. It could prove mathematical theorems from Principia Mathematica and is considered the first artificial intelligence program.
Another milestone came with the General Problem Solver (GPS), also developed by Newell and Simon. GPS could solve a broad class of problems using means-ends analysis, mimicking human problem-solving strategies.
In parallel, McCarthy developed LISP (LISt Processing language) in 1958, which became the dominant language for AI programming for decades. It enabled symbolic reasoning and manipulation, key techniques in early AI research.
AI’s First Boom: 1956–1974
The first few decades following the Dartmouth Conference were marked by rapid progress and high expectations. AI programs could solve algebra problems, prove theorems, and play games like checkers. Fully intelligent machines, according to researchers, would be constructed within a few decades.
During this period, early successes included:
ELIZA (1966): A natural language processing program developed by Joseph Weizenbaum that mimicked a Rogerian psychotherapist.
SHRDLU (early 1970s): A program by Terry Winograd that could interact with objects in a virtual world using natural language.
However, as AI systems failed to generalize beyond narrow tasks and struggled with real-world complexity, disillusionment set in.
The First AI Winter
By the mid-1970s, AI research entered its first “AI winter.” Funding dried up as initial promises were not fulfilled.Stasis was exacerbated by the limits of symbolic AI and a lack of processing power. Early systems were brittle, unable to handle ambiguity or adapt to new information. Scientists discovered that imitating human intelligence was much more difficult than they had first thought.
Expert Systems and the Second Boom: 1980s
AI experienced a resurgence in the 1980s with the development of expert systems—computer programs that mimicked decision-making of human experts. One of the most famous, XCON, was used by Digital Equipment Corporation to configure computer systems.
These systems used rule-based logic and could outperform humans in narrowly defined domains. Corporations began investing in AI, and governments renewed funding.Expert systems were challenging to uphold and demanded a lot of user input. Ultimately, an additional Intelligence freeze in the late 1980s and early 1990s was brought on by the drawbacks of rule-based systems as well as growing expenses.
The Rise of Machine Learning
AI’s trajectory changed dramatically with the emergence of machine learning, particularly in the 1990.Instead of manually creating rules, machines started to learn from data.This paradigm shift made AI more flexible and scalable.
One milestone was IBM’s Deep Blue defeating world chess champion Garry Kasparov in 1997. This was a powerful demonstration of how AI could outperform humans in complex strategic tasks.
Advances in neural networks, statistical learning, and support vector machines drove progress. Still, limitations in computing power and data availability constrained wider adoption.
AI in the 21st Century
The 2000s and 2010s witnessed an explosion in AI capabilities, driven by three key factors:
Big Data: The internet, sensors, and mobile devices produced massive volumes of data.
Increased Computing Power: GPUs and cloud computing enabled training of large-scale models.
Deep Learning: Multilayered neural networks, especially convolutional neural networks (CNNs), revolutionized image and speech recognition.
Breakthroughs included:
Siri and Alexa: Voice assistants that brought AI into consumer products.
AlphaGo (2016): Developed by DeepMind, it defeated the world champion in Go, a game previously thought too complex for machines.
GPT and Transformer Models: Language models that can write text, answer questions, and generate human-like responses.
Conclusion
So, when did artificial intelligence start? While the official birth of AI as a field occurred in 1956 at the Dartmouth Conference, its intellectual roots go back centuries, and its evolution has been shaped by milestones in philosophy, mathematics, computing, and neuroscience. From Aristotle’s logic to Alan Turing’s theoretical machines and today’s neural networks, AI has traveled a long road.
AI’s journey is a story of ambition, failure, reinvention, and resurgence. Though still far from achieving artificial general intelligence, AI has already begun to redefine what machines—and humans—are capable of. The seeds planted decades ago have grown into a powerful force that continues to shape our world in profound and sometimes unpredictable ways.
