AI and Science of Reasoning(2)

Sherlockian Way of Thinking

The Dartmouth Conference: The Birth of a Name

In the summer of 1956, at Dartmouth College in New Hampshire, a small group of visionary scientists convened what has since been remembered as the birth of modern AI: the Dartmouth Summer Research Project on Artificial Intelligence.

Its organizers—John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester—were young pioneers from mathematics, computer science, and information theory. Their proposal was audacious: “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.” For the first time, the phrase “Artificial Intelligence” was coined and set forth as a research field in its own right.

A small group of visionary scientists convened at Dartmouth College in 1956.

Though the conference produced no immediate breakthroughs, its historical significance was immense. First, it gave AI its name and its identity as a distinct discipline, uniting disparate efforts in machine translation, control systems, and neural theory under a single banner. Second, it crystallized the conviction that human intelligence could be technically analyzed and re-created. That conviction has fueled AI research for nearly seventy years, leading to the chatbots, self-driving cars, and generative systems we use today.

The Winters of AI: Two Long Silences

Yet the path of AI was not smooth. Twice, hopes froze into disappointment during periods now called the AI winters.

The first, in the early 1970s, came when the extravagant promises of early AI researchers collapsed against the hard limits of contemporary hardware and software. Machine translation fumbled meaning, automated reasoning systems worked only in narrow contexts, and the U.S. government cut funding when expectations went unmet.

The second, in the late 1980s, followed the brief rise of expert systems. These rule-based programs, once hailed as a corporate miracle, proved brittle, costly, and unable to adapt. When businesses abandoned them, investment dried up, and once again AI fell into disrepute.

From these two winters, researchers learned a sobering truth: intelligence could not be conjured by mere symbols and rules; genuine progress would require vast resources, data, and fundamentally new approaches.

AI’s Quiet Resurrection: The Power of Data and Machine Learning

With the dawn of the twenty-first century, AI quietly returned.

With the dawn of the twenty-first century, AI quietly returned—not with grand proclamations, but with steady infiltration into science, industry, and daily life. Two forces lay behind this revival: the exponential growth of computing power, and the explosion of data in the internet age.

Computers became exponentially faster and more capacious, making it possible to run algorithms that were once unthinkable. Meanwhile, the rise of the internet and smartphones produced oceans of text, images, audio, and video—raw material for machines to study. At last, AI had the “textbooks” it had always lacked.

This environment gave rise to machine learning: systems that no longer relied solely on rules painstakingly coded by humans, but that learned patterns directly from data. Like children exploring the world, machines could now learn from experience, rather than merely follow instructions.

Yet even this was only the prelude. True transformation still awaited the advent of deep learning, a phrase that would soon reshape the landscape entirely.