The First AI Boom

Introduction:
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‎The Rise of Symbolic AI
‎The late 1950s and 1960s witnessed the first major wave of excitement in Artificial Intelligence. With the conceptual groundwork laid by Alan Turing and the invention of the digital computer, researchers now had the tools — and the optimism — to attempt building intelligent machines. The dominant approach during this time was symbolic AI, also known as "good old-fashioned AI" (GOFAI), based on the idea that intelligence could be replicated by manipulating symbols using formal logic.
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‎1. The Dartmouth Conference:
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‎ AI is Born
‎In 1956, a small group of scientists gathered at Dartmouth College in New Hampshire for a summer workshop. The proposal, written by John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester, boldly claimed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” This ‎2. The Logconference didn’t produce any breakthroughs during its session, but it succeeded in giving birth to a new field: Artificial Intelligence.

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‎This moment marked the start of what we now call the first AI boom — a time when many believed that human-level machine intelligence was just a few decades away heorist:
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‎The First Intelligent Program
‎One of the earliest and most impressive achievements in AI was the development of the Logic Theorist by Allen Newell and Herbert A. Simon in 1955. This program was designed to mimic the problem-solving skills of a human mathematician. It could prove theorems from “Principia Mathematica,” a foundational text in symbolic logic, by applying formal reasoning rules.
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‎The Logic Theorist wasn't just a technical success; it was a philosophical statement. It suggested that reasoning — long considered a unique hallmark of human intelligence — could be simulated by a machine following logical rules.
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‎3. LISP and the Growth of AI Research.
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‎In 1958, John McCarthy, one of the founding fathers of AI, created LISP, a programming language specifically designed for artificial intelligence work. LISP allowed for the manipulation of symbolic expressions and became the go-to language for AI researchers for many years.
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‎The same year, McCarthy established the Artificial Intelligence Laboratory at MIT, which would become a leading center for AI research. Other institutions, like Stanford and Carnegie Mellon, also launched their own AI labs, sparking a wave of funding and excitement.
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‎4. Early Successes and High Hopes
‎Throughout the 1960s.
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‎researchers created various programs that could solve algebra problems, play simple games, or understand limited natural language. These included systems like ELIZA (a simple chatbot simulating a therapist), SHRDLU (which could manipulate virtual blocks using typed commands), and General Problem Solver — all demonstrating the early potential of AI.
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‎These achievements led to a sense of overconfidence. AI scientists made bold predictions, claiming that general-purpose intelligent machines might exist within a generation. Governments and universities poured money into AI research with great expectations.
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‎5. Limitations Emerge
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‎Despite early excitement, serious limitations began to appear. Most of these systems worked well only in narrow, highly controlled environments. They struggled with real-world ambiguity, incomplete data, and the flexibility that human thinking requires. These programs could not “understand” context the way people do — a critical flaw.
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‎The symbolic AI approach also depended heavily on hand-coded rules and domain knowledge, which proved difficult to scale. As the hype began to outpace the results, cracks started forming in the AI community’s optimism.
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‎Conclusion:
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‎A Dream Challenged
‎The first AI boom laid essential foundations — programming languages, research labs, and ambitious ideas — that would carry the field forward. But by the late 1960s, it became clear that true intelligence was far more complex than early researchers had imagined. The high hopes of this era would soon give way to a more sobering reality.