2.2: The Birth of AI (1950s – 1960s)

1956: The Dartmouth Conference

In the summer of 1956, a small group of visionary researchers gathered at Dartmouth College for what would become a pivotal moment in scientific history. John McCarthy, a young mathematician with a passion for mechanical reasoning, joined forces with Marvin Minsky, Nathaniel Rochester from IBM, and Claude Shannon, the father of information theory. Together, they orchestrated a groundbreaking symposium that would formally launch artificial intelligence as a distinct academic discipline.

The conference proposal boldly declared their intention to explore "how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." This ambitious gathering, though lasting only two months, established the foundational principles and research directions that would guide AI development for decades to come. McCarthy coined the term "artificial intelligence" itself, giving a name to humanity's quest to create thinking machines.

1950s – 1960s: Early Optimism

The years following Dartmouth were marked by infectious enthusiasm and remarkable breakthroughs that seemed to validate the researchers' boldest predictions. Allen Newell and Herbert Simon developed the Logic Theorist, recognized as the inaugural operational AI program, which could prove mathematical theorems by mimicking human reasoning processes. When the program successfully proved 38 of the first 52 theorems in Whitehead and Russell's "Principia Mathematica," it demonstrated that machines could engage in what appeared to be genuine logical thinking.

Joseph Weizenbaum's ELIZA program captured public imagination by conducting seemingly intelligent conversations, using simple pattern matching to respond to human input in a way that felt remarkably natural. Though ELIZA's methods were relatively simple, users often attributed human-like understanding to the program, revealing both the power of well-designed interfaces and the human tendency to anthropomorphize responsive systems.

During this golden age of early AI, researchers maintained optimistic projections that general human-level artificial intelligence was not just possible, but imminent. Herbert Simon famously predicted in 1965 that "machines will be capable, within twenty years, of doing any work a man can do." This confidence, while ultimately premature, drove unprecedented innovation and attracted significant funding to the field.