How a Mathematician Asked a Question and Changed the World
This is the first part in our blog series on AI's developmental history, Minds and Machines.
Few scientific questions have created new industries and had a decisive influence on humanity's future. In 1950, a British mathematician posed exactly such a question in the academic journal Mind: Can machines think?
Alan Turing's exploration of machine intelligence laid the conceptual foundation for what we now call artificial intelligence, though he would not live to see his vision materialize.
The genius of Turing's approach was not only theoretical—it was practical. Instead of getting lost in philosophical debates about consciousness, he created a concrete framework for evaluating machine intelligence, which researchers still reference today.
The Imitation Game
What we now call the "Turing Test" began as a simple thought experiment labeled "the imitation game." Turing avoided endless debates about defining thinking by focusing instead on observable behavior. If a machine could fool human judges into believing they were talking with another person, wouldn't that machine be considered intelligent?
This approach proved revolutionary. By setting a clear, measurable goal, Turing gave early AI researchers something concrete to work toward. The test established a practical North Star for the field—create systems that could perform tasks typically requiring human intelligence.
Behavioral assessment replaced philosophical speculation as the driving force behind AI development.
While modern AI development has moved beyond the Turing Test as the sole measure of success, the principle of judging machines by what they can do, not by their internal mechanisms, remains the foundation for how we evaluate AI today.
The German Enigma Code
Turing did not arrive at his AI concepts in isolation. His earlier work during World War II at Bletchley Park, where he helped break the German Enigma code, provided crucial experience with mechanizing aspects of human thought.
The massive electromechanical computers used for codebreaking showed the first signs that machines could handle complex problems previously considered uniquely human.
Even before the war, Turing had demonstrated foresight. His 1936 paper introducing the concept of a Universal Turing Machine established the theoretical foundation for modern computing. This abstract device—capable of simulating any other computer's logic—laid an essential groundwork for both practical computing and artificial intelligence.
"I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted," Turing wrote in 1950.
Predicting the Year 2000
Among Turing's most remarkable contributions were his specific predictions about machine capabilities. In his 1950 paper, he predicted that computers with around 100 MB of memory would be able to pass his test around the year 2000—a timeline that proved optimistic, though not far off.
Turing also predicted several AI approaches that would later become central:
Learning machines that improve through experience rather than explicit programming
The importance of child-machine teaching, corresponding to today's training methods
The eventual need for machines to interact with their environment, anticipating embodied AI
His vision extended beyond pure technical possibilities. Turing recognized that machines might reach their goals through methods entirely different from human approaches—a concept that presaged how modern machine learning often develops solutions humans would not devise.
Not all of Turing's predictions hit the mark. He underestimated the complexity of natural language understanding and overestimated how quickly truly general machine intelligence would emerge. Still, the breadth and accuracy of his foresight remains astonishing given how little computing power existed in his lifetime.
The Turing Test Still Inspires
Over seventy years after Turing's groundbreaking paper, his influence on artificial intelligence remains profound. The Turing Test continues to inspire our research and imagination, though it has evolved significantly. Modern variants include specialized tests for creativity, visual understanding, and morality.
Competitive benchmarks throughout AI often reflect Turing's way of evaluating through behavior. From image recognition competitions to robot tournaments, we continue to measure AI progress through observable performance rather than internal structure.
The philosophical questions Turing raised also remain central to AI discourse. When a large language model generates coherent text indistinguishable from human writing, are we witnessing genuine understanding or merely sophisticated mimicry? Turing would argue that the distinction ultimately may matter less than what these systems enable us to achieve.
Yes, Machines Can Probably Learn to Think
What makes Turing's contribution special is how it transcended his era's technological limitations. He worked with computers thousands of times less powerful than today's smartphones and envisioned capabilities we are only now beginning to realize.
As generative artificial intelligence, autonomous systems, and machine learning transform our world, we are witnessing the continued unfolding of a vision first articulated by a mathematician wondering if machines could think. The answer increasingly appears to be "yes"—albeit in ways even Turing might find surprising.