The Long Road to Artificial Intelligence

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Science, War, Computation, and the Making of Machine Intelligence

Artificial intelligence is often given a convenient birthday: the summer of 1956, when a small group of scientists gathered at Dartmouth College to investigate whether aspects of human intelligence could be reproduced by machines. The meeting was organized by John McCarthy, and the proposal—written with Marvin Minsky, Nathaniel Rochester, and Claude Shannon—introduced the term artificial intelligence.

But artificial intelligence did not suddenly appear in 1956.

By then, many of its essential ingredients had already been developing for decades: formal logic, theories of computation, electronic computers, information theory, neural models, automatic control, simulation, radar, and research into human decision-making.

Dartmouth did something extremely important: it gave a name and an explicit research program to an emerging field.

The road to AI, however, had begun much earlier.

Before computers: making thought mechanical

One of the oldest dreams behind artificial intelligence is not actually artificial intelligence. It is the possibility that reasoning itself might be formalized.

Leibniz imagined a calculus ratiocinator, a system in which reasoning could, in principle, be reduced to symbolic calculation. In the nineteenth century George Boole went substantially further, showing that logical propositions could be represented and manipulated algebraically.

For decades this remained primarily mathematics. Then an extraordinary connection occurred.

In 1937, Claude Shannon showed that Boolean algebra could be applied to electrical switching circuits. Logical operations could therefore be implemented physically with relays. The Computer History Museum describes Shannon's work as a crucial bridge between Boole's nineteenth-century logic and digital circuitry.

At almost exactly the same historical moment, Alan Turing was approaching the problem from the opposite direction. His 1936 work on computable numbers described an abstract machine capable of executing formal procedures through the manipulation of symbols.

The conceptual pieces were converging.

Logic could be formalized.

Procedures could be formalized.

And electrical machines could implement logic.

Modern computing was becoming possible.

Then came the war

World War II did not create these scientific ideas. But it dramatically changed the environment in which they developed.

Scientific questions suddenly became military necessities.

Radar had to detect objects humans could not see. Cryptanalysis required machines and mathematicians to search enormous spaces of possibilities. Fire-control systems had to predict the future position of moving aircraft. Ballistics demanded rapid numerical calculation. Operations research attempted to optimize enormously complicated military systems.

Governments were also willing to mobilize scientific resources on a previously extraordinary scale.

MIT's Radiation Laboratory illustrates the transformation. During the war it grew to thousands of researchers and engineers, developed more than 100 radar systems, and generated designs that resulted in roughly $1.5 billion in industrial radar production. MIT describes it as one of the largest scientific research enterprises of the war.

The Manhattan Project did something similar with nuclear physics. Bletchley Park did it with cryptanalysis and computation. Rocket programs did it with propulsion, guidance, aerodynamics, and control.

The important historical change was therefore not merely a collection of inventions.

A new mechanism had emerged for connecting:

science → government → industry → engineering → computation → operational systems.

And that mechanism survived the war.

From wartime laboratories to computational systems

After 1945, the scientific and technological infrastructure created during the war did not simply disappear.

The Cold War supplied new problems and new funding.

One of the clearest examples emerged from air defense.

The Soviet atomic test of 1949 created an urgent American problem: how could a continental territory detect and respond to a possible attack by Soviet bombers?

Radar alone was insufficient. Information from many radar stations had to be transmitted, combined, interpreted, displayed, and acted upon fast enough to intercept an aircraft.

That requirement helped produce one of the most extraordinary computer systems of the twentieth century: SAGE, the Semi-Automatic Ground Environment.

Its ancestor, MIT's Whirlwind computer, had originally grown out of a Navy-sponsored flight-simulator project. It was subsequently redirected toward air defense because it could process information in real time. In a 1950 demonstration, digitized radar information was transmitted to Whirlwind over telephone lines and displayed almost immediately.

This represented a significant change in what computers were being asked to do.

A traditional calculation could be imagined as:

input → calculation → result

SAGE required something closer to:

world → sensors → data → computer → representation → human decision → action

Its computers continuously received information from multiple radars, constructed tracks representing aircraft, displayed the resulting air situation to operators, and helped direct interceptors toward potential targets. The eventual system connected hundreds of radars and air-defense facilities to large digital computers and interactive operator stations.

SAGE also forced developments in magnetic-core memory, modems, real-time software, computer graphics, human-computer interaction, and large-scale software engineering. Lincoln Laboratory argues that the project became an important driver of the emerging American computer industry.

This was not yet artificial intelligence.

But it is an important part of its environment.

Machines were no longer being built merely to calculate numbers. They were increasingly being built to process information about changing situations in the real world.

From calculating the world to reasoning about it

Another important connection developed at RAND.

Researchers including Allen Newell and Herbert Simon became interested in decision-making, organizations, simulation, and eventually the possibility that human problem solving itself could be represented computationally.

With Cliff Shaw, Newell and Simon created the Logic Theorist in the mid-1950s. Instead of calculating a trajectory or processing radar data, the program attempted something recognizably cognitive: proving mathematical theorems through symbolic manipulation.

This represented another transition.

Computers had already demonstrated that they could calculate.

Now researchers were asking whether they could reason.

At Dartmouth in 1956, these emerging lines of research were brought together under an extraordinarily ambitious proposition. McCarthy and his collaborators proposed investigating the idea that features of learning and intelligence might be described precisely enough that machines could simulate them.

Artificial intelligence had acquired its name.

Two competing ideas of intelligence

Early AI did not develop along a single path.

One approach treated intelligence primarily as symbolic manipulation. Knowledge could be represented through symbols, relationships, rules, and logical operations. Programs could search through possible solutions, prove theorems, solve puzzles, and construct plans.

Another approach attempted to reproduce aspects of learning.

Frank Rosenblatt's perceptron, developed in the 1950s, represented an early attempt to build systems capable of learning from examples rather than relying entirely on explicitly programmed symbolic rules.

The tension between these traditions—roughly, reasoning through explicit representations versus learning statistical representations—would recur throughout the history of AI.

Symbolic AI initially achieved remarkable results in constrained environments. Programs played games, solved mathematical problems, manipulated language, and planned sequences of actions.

Robotics then forced AI to confront the physical world.

SRI's Shakey, developed from the late 1960s into the early 1970s, integrated cameras and other sensors with planning and action. Its research contributed to methods for connecting perception, planning, and execution.

A machine could now, in a limited environment, perceive something about its surroundings, reason about what to do, and physically act.

The problem was that reality was vastly more complicated than the simplified worlds in which early AI performed well.

The limits become visible

The optimism of early AI repeatedly collided with computational limits.

Real environments contained ambiguity, incomplete information, enormous numbers of possible states, and forms of knowledge that were extraordinarily difficult to encode manually.

Expert systems demonstrated during the 1970s and 1980s that substantial amounts of specialist knowledge could be represented explicitly. But building and maintaining those representations was expensive and difficult.

Periods of optimism were followed by periods of disappointment and reduced funding—the so-called AI winters.

Yet another research tradition was developing.

Instead of specifying intelligence almost entirely through rules, researchers increasingly investigated machines that could learn useful representations from data.

Neural networks returned.

Backpropagation made multilayer networks more practically trainable. Probabilistic methods became increasingly important. Computing power increased enormously. Digital data accumulated.

Eventually these developments collided with another technology whose origins lay partly outside AI: massively parallel graphics processors.

Machines learn to perceive

In 2012, AlexNet dramatically outperformed previous approaches in the ImageNet image-recognition competition using a deep convolutional neural network trained with GPUs.

The result helped trigger the rapid adoption of deep learning throughout computer vision. A major 2015 Nature review described deep learning as enabling computational systems to learn multiple levels of representation and documented major improvements across image recognition, speech, audio, and other domains.

Something fundamental had changed.

Researchers no longer needed to specify every feature that a machine should use to recognize an object.

The machine could learn useful internal representations from data.

Within a few years, deep learning transformed speech recognition, machine translation, computer vision, game playing, and many other areas.

Then the same underlying revolution reached language.

From language to general-purpose models

The transformer architecture introduced in 2017 provided an unusually scalable mechanism for learning relationships within sequences.

When transformers were trained on increasingly large collections of text, code, images, and eventually other modalities, another unexpected transition occurred.

Language models became capable of performing tasks that had not been individually programmed into them.

Writing.

Translation.

Programming.

Question answering.

Summarization.

Reasoning across many domains.

Image interpretation.

Increasingly, interaction with software and external tools.

For most of the history of computing, humans had needed to translate their intentions into languages machines could execute.

Large language models partially reversed that relationship.

The machine was learning to interpret ours.

AI leaves the chatbot

The latest transition may prove as important as the previous ones.

AI models are increasingly being connected to tools, memory, software, sensors, computers, and robots.

Instead of simply producing an answer, an AI system can increasingly perform sequences of actions toward an objective.

The concept itself is not new. Agents have been part of AI research for decades. What has changed is their practical capability.

Contemporary systems can combine language, vision, planning, external tools, code execution, and interaction with digital environments. Robotics research is simultaneously connecting increasingly capable learned models to physical machines.

AI is therefore returning to a problem that existed long before modern language models:

How does an intelligent system perceive a situation, understand it sufficiently to make a decision, and act?

Curiously, this echoes some of the architecture encountered much earlier in the history.

Radar observed the world.

SAGE constructed a computational representation.

Humans and machines processed that representation.

Actions followed.

Modern AI operates at a radically different level of sophistication and generality, but the historical continuity is worth noticing.

A different way of seeing the history of AI

Seen this way, artificial intelligence is not a sequence of isolated inventions.

It is a convergence.

Formal logic made reasoning mathematically describable.

The theory of computation made general symbolic procedures conceivable.

Electronics made those procedures physically executable.

War accelerated radar, control, communications, computation, and large-scale systems engineering.

Cold War programs connected computers to continuously changing real-world information.

Early AI attempted to reproduce reasoning.

Machine learning increasingly allowed systems to acquire patterns from experience.

Deep learning transformed perception.

Transformers transformed language processing.

And contemporary agents are reconnecting intelligence with action.

The historical progression is therefore not simply:

Turing → Dartmouth → neural networks → ChatGPT.

It is considerably broader:

logic → computation → information → control → simulation → reasoning → learning → perception → language → action.

The boundaries between these traditions were never as clean as their later academic disciplines make them appear.

Conclusion

Dartmouth remains a crucial moment in the history of artificial intelligence. It provided a name, a community, and an explicit research agenda for the idea that intelligence itself could become an engineering problem.

But it was not the beginning of the story.

AI emerged from a much longer attempt to formalize processes that had previously belonged exclusively to human thought and action.

And one of the most striking features of that history is its acceleration during the middle decades of the twentieth century.

World War II and the Cold War did not magically invent artificial intelligence. But they concentrated scientific talent, money, industrial capacity, strategic urgency, and computational problems on an unprecedented scale. Radar, cryptanalysis, automatic control, simulation, operations research, and real-time computing created both technologies and research cultures from which parts of AI subsequently emerged.

That makes the history of artificial intelligence larger than the history of artificial intelligence itself.

It is also a history of mathematics and physics.

Of war and institutions.

Of machines and humans.

Of attempts to understand intelligence by trying to reproduce pieces of it.

For centuries, humans asked whether thought could be reduced to rules.

During the twentieth century, we discovered that some of those rules could be executed by machines.

The question then became whether machines could learn.

Then whether they could perceive.

Then whether they could understand language.

And now, increasingly, whether they can act.

The remarkable thing about artificial intelligence may therefore not be that it suddenly appeared.

It may be how many different histories had to converge before it could.