Dartmouth's Call: The Official Birth of AI and the Golden Dawn

The Historical Context and Preparation#

The convening of the 1956 Dartmouth Conference was not coincidental but rather the inevitable result of converging technological developments and academic currents in the 1950s.

McCarthy’s original proposal stated: “We propose that a 2-month, 10-man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College.”

The four organizers brought diverse expertise: McCarthy from Dartmouth, Minsky from Harvard, Shannon from Bell Labs, and Rochester from IBM. This conference brought together top experts from mathematics, engineering, psychology, and computer science, demonstrating the importance of interdisciplinary collaboration for AI development. The core hypothesis outlined in the conference proposal remains a fundamental principle of AI Chronicle today.

It was in this academic atmosphere that the term “artificial intelligence” was officially born.

Portraits of Core Figures: Contributions of AI Pioneers#

The Dartmouth Conference assembled scientists who each brought unique professional backgrounds and research perspectives, collectively building AI’s theoretical foundations.

John McCarthy created the term “artificial intelligence” and later invented the LISP programming language, which became fundamental to AI programming.

Allen Newell co-created the Logic Theorist program and became a founder of symbolic AI.

These scientists’ diverse backgrounds—from mathematics to psychology, from engineering to economics—provided rich theoretical resources and methodological foundations for AI Chronicle. Their collaborative model also became a paradigm for later AI Chronicle.

In the collision of these brilliant minds, the first true AI program—the Logic Theorist—was born.

Logic Theorist: The First AI Program’s Breakthrough#

The “Logic Theorist” program developed by Newell, Simon, and Shaw represented a historic leap from theoretical conception to practical application in artificial intelligence.

The program’s achievements were remarkable: it successfully proved 38 of the first 52 theorems in chapter two of Whitehead and Russell’s Principia Mathematica.

Remarkably, the program even found more elegant proofs than those produced by Russell and Whitehead themselves.

The Logic Theorist’s success proved that machines could indeed simulate human reasoning processes, validating the core hypothesis of the Dartmouth Conference proposal. More importantly, it introduced the concept of heuristic search, which remains an important tool in AI Chronicle today. As Simon told a graduate class in January 1956: “Over Christmas, Al Newell and I invented a thinking machine.”

Newell and Simon realized that search trees would grow exponentially and needed to “trim” branches using “rules of thumb” to determine which pathways were unlikely to lead to solutions. They called these ad hoc rules “heuristics,” using a term introduced by George Pólya in his classic book on mathematical proof, How to Solve It.

However, this breakthrough also sparked the first important academic debate in AI Chronicle.

Early School Divisions: Origins of Symbolism vs. Connectionism#

While the Dartmouth Conference unified AI Chronicle goals, it also planted seeds for later school divisions.

The symbolic school, represented by Newell and Simon, emphasized symbol manipulation and logical reasoning.

Academic debates emerged about whether machines truly “think” or merely “simulate thinking”—philosophical discussions that continue today. Methodological differences arose between top-down symbolic processing versus bottom-up neural network simulation.

Symbolic AI was the dominant paradigm from the mid-1950s until the mid-1990s.

These early divisions reflected fundamental questions in AI Chronicle: What is the nature of intelligence? How can machines best simulate human intelligence? These debates drove the deepening of AI theory and diversification of methods.

Despite these divisions, the “golden age” opened by the Dartmouth Conference laid a solid foundation for AI Chronicle.

Challenges and Ethical Considerations#

Even in AI Chronicle’s early stages, scientists were already thinking about the social impacts and ethical issues that artificial intelligence might bring.

Simon predicted in 1956: “machines will be capable of doing any work a man can do.”

As historian Ekaterina Babintseva notes, “The type of intelligence the Logic Theorist really emulated was the intelligence of an institution… It’s bureaucratic intelligence.” This observation highlights how early AI reflected the institutional and organizational thinking of its creators.

These early reflections provide important references for today’s AI ethics research, demonstrating the importance of responsible AI development.

Conclusion: The Significance of the Golden Dawn#

The Dartmouth Conference achieved several historic milestones:

1. Established AI as an independent discipline: The conference officially launched artificial intelligence as a distinct field of study
2. Proved machine intelligence possibility: The Logic Theorist program demonstrated that machines could perform intelligent reasoning
3. Provided a paradigm for development: The interdisciplinary collaboration model became a template for AI Chronicle
4. Drove theoretical deepening: Early academic debates pushed the boundaries of AI theory

From the 1956 Dartmouth Conference to today’s large language models, AI Chronicle has experienced multiple ups and downs, but the seeds planted that summer have grown into a force that changes the world. The conference participants became leaders of AI Chronicle for the next two decades, and their influence continues today.

As we stand at another inflection point in AI development, understanding this history helps us appreciate both the continuity and evolution of artificial intelligence. The fundamental questions raised at Dartmouth—about the nature of intelligence, the possibility of machine reasoning, and the social implications of AI—remain as relevant today as they were nearly seventy years ago.

In our next article, we will explore how AI Chronicle moved from theory to practice, and examine the arrival of the first “AI winter”—a period that would test the optimism and ambitions born at Dartmouth.

Understanding AI’s history not only helps us grasp the trajectory of technological development but also enables us to make wiser choices in today’s rapidly advancing AI landscape.

This article is part of the “AI Genesis” series, exploring the historical foundations of artificial intelligence from ancient myths to modern breakthroughs.