The study of mechanical or “formal” reasoning dates back to ancient philosophers and mathematicians. This exploration of logic laid the groundwork for Alan Turing’s theory of computation, which proposed that a machine manipulating simple symbols like “0” and “1” could simulate any form of mathematical reasoning. Alongside advances in cybernetics, information theory, and neurobiology, this idea spurred researchers to explore the possibility of building an “electronic brain.” Early milestones in artificial intelligence (AI) included McCulloch and Pitts’ 1943 model of “artificial neurons” and Turing’s influential 1950 paper Computing Machinery and Intelligence, which introduced the Turing test and demonstrated the feasibility of machine intelligence.

AI research formally emerged as a field in 1956 during a workshop at Dartmouth College. The participants became key figures in the field, leading research efforts in the 1960s. Their teams developed programs that astonished the public—machines were learning checkers strategies, solving algebraic word problems, proving logical theorems, and even communicating in English. By the late 1950s and early 1960s, AI research labs had been established at leading universities in the U.S. and the U.K.

During the 1960s and 1970s, researchers were optimistic that AI would soon achieve general intelligence. In 1965, Herbert Simon predicted that within two decades, machines would be capable of any task a human could perform. Marvin Minsky echoed this sentiment in 1967, claiming that AI would be largely solved within a generation. However, the challenges proved more formidable than expected. In 1974, funding for AI research was drastically reduced in both the U.S. and the U.K., following criticism from Sir James Lighthill and pressure from Congress to prioritize more practical projects. Additionally, the 1969 book Perceptrons by Minsky and Papert cast doubt on neural networks, leading to widespread skepticism about their potential. This period of disillusionment, known as the “AI winter,” made securing funding for AI projects difficult.

AI experienced a resurgence in the early 1980s, driven by the commercial success of expert systems—software that mimicked human expert reasoning. By 1985, the AI market exceeded a billion dollars. Inspired by Japan’s ambitious Fifth Generation Computer Project, the U.S. and U.K. governments renewed their support for academic AI research. However, the collapse of the Lisp Machine market in 1987 triggered another prolonged AI winter.

Until this point, most AI research had focused on symbolic representations of cognition, modeling intelligence with high-level symbols to represent concepts such as goals, beliefs, and logical facts. By the late 1980s, some researchers began questioning whether this symbolic approach could truly capture human cognition, particularly in areas like perception, robotics, learning, and pattern recognition. This led to a shift toward “sub-symbolic” methods. Rodney Brooks challenged the need for internal representations, focusing instead on engineering machines that could interact with their environments. Meanwhile, Judea Pearl and Lotfi Zadeh pioneered methods for reasoning under uncertainty. The most significant breakthrough, however, was the revival of connectionism—research into artificial neural networks—led by Geoffrey Hinton and others. In 1990, Yann LeCun demonstrated that convolutional neural networks (CNNs) could effectively recognize handwritten digits, setting the stage for modern deep learning.

AI regained credibility in the late 1990s and early 2000s through mathematically rigorous approaches and targeted problem-solving. This narrow focus allowed researchers to produce measurable results and collaborate with fields like statistics, economics, and applied mathematics. By 2000, AI-driven solutions were widely used, though often not labeled as “AI” due to the so-called “AI effect.” Some researchers, concerned that AI was straying from its goal of general intelligence, established the subfield of artificial general intelligence (AGI) around 2002. By the 2010s, AGI research had gained substantial funding.

The modern AI boom began in 2012, when deep learning started outperforming traditional methods on industry benchmarks. This success was fueled by advancements in hardware—such as faster processors, GPUs, and cloud computing—and access to vast datasets like ImageNet. As deep learning rapidly surpassed other techniques, interest and investment in AI skyrocketed. Between 2015 and 2019, machine learning research publications increased by 50%.

By the mid-2010s, concerns over fairness, ethics, and AI alignment became central topics in machine learning research. The field expanded dramatically, with funding surging and more researchers dedicating their careers to these issues. The alignment problem emerged as a critical area of academic study.

In the late 2010s and early 2020s, AGI research produced high-profile breakthroughs that captured global attention. In 2015, DeepMind’s AlphaGo defeated the world champion Go player, demonstrating the power of reinforcement learning. OpenAI’s GPT-3, released in 2020, showcased unprecedented natural language capabilities. The launch of ChatGPT on November 30, 2022, marked a turning point—becoming the fastest-growing consumer software in history with over 100 million users in two months. AI was now firmly embedded in public consciousness.

This surge in AI capabilities led to an investment frenzy. By 2022, annual AI investment in the U.S. alone reached approximately $50 billion, with AI specialists making up 20% of new computer science PhDs. Job postings related to AI numbered around 800,000 in the U.S. that year. By 2024, 22% of newly funded startups identified as AI companies, cementing AI’s role as a dominant force in technology and business.