The Long Road to Intelligence

Artificial Intelligence is often treated as a modern phenomenon—a sudden eruption of math that happened around 2022. But the quest to build a mechanical brain is nearly as old as the computer itself. It is a story marked by brilliant breakthroughs, bitter corporate rivalries, devastating "Winters," and a few moments where humanity realized it was no longer the smartest thing on the planet.

---

1. 1956: The Summer that Started it All

In 1956, a young mathematician named John McCarthy persuaded the Rockefeller Foundation to fund a summer workshop at Dartmouth College. He invited a group of men who would become the "Founding Fathers" of the field: Marvin Minsky, Claude Shannon, Nathaniel Rochester, Allen Newell, and Herbert Simon.

The Mission Statement

McCarthy’s 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."

They believed that a two-month, ten-man study would make significant progress on things like language and "self-improvement." While they didn't solve AI that summer, they did something more important: they gave the field a name (Artificial Intelligence) and a home.

The First Programs

Newell and Simon arrived with the Logic Theorist, a program that could prove mathematical theorems. It was the first "AI" ever written. When it proved a theorem that was more elegant than the one found in the Principia Mathematica, Russell and Whitehead (the authors) were reportedly impressed and slightly annoyed.

---

2. The Golden Age and the First Winter (1960s - 1974)

The 60s were a time of unbridled optimism. Minsky famously predicted in 1967: "Within a generation... the problem of creating 'artificial intelligence' will substantially be solved."

The Breakthroughs

• ELIZA (1966): The first chatbot, created by Joseph Weizenbaum. It mimicked a therapist by reflecting the user's questions. People became so emotionally attached to it that Weizenbaum eventually turned against AI, fearing its power to deceive.
• Shakey the Robot (1970): The first "general-purpose" mobile robot that could reason about its actions.

The Crash

By 1973, the promises of the Dartmouth group had failed to materialize. Computers were too slow to process language, and "combinatorial explosion" meant that adding one more variable to a problem made it impossible for a machine to solve.

• The Lighthill Report (1973): In the UK, Sir James Lighthill published a scathing review of AI research, calling it "grandiose" and "misleading."
• Funding Cuts: DARPA and the UK government pulled their funding, leading to the First AI Winter.

---

3. The 1980s: The Rise and Fall of the Lisp Machines

In the late 70s, AI was resurrected through Expert Systems. Instead of trying to simulate a human brain, researchers built programs that followed thousands of "If-Then" rules to help doctors diagnose diseases or geologists find oil.

The Symbolics vs. LMI War

To run these complex expert systems, researchers needed specialized hardware. This led to the creation of Lisp Machines—computers hardwired to execute the Lisp programming language. The MIT AI Lab split into two rival companies: Symbolics and Lisp Machines Incorporated (LMI).

• Symbolics became the "Apple" of the 80s, producing the 3600 High-Resolution Workstations.
• Genera, their operating system, was decades ahead of its time, featuring bit-mapped graphics and object-oriented programming.

The Connection Machine: Danny Hillis's Dream

While Symbolics was building better serial processors, Danny Hillis was co-founding Thinking Machines Corporation. He built the Connection Machine (CM-1), a supercomputer with 65,536 processors connected in a 12-dimensional hypercube. His goal was to simulate the parallel nature of the human brain. The CM-1, with its glowing red LEDs and cube-of-cubes design, remains one of the most iconic pieces of hardware in history.

The Second Winter (1987)

By 1987, the "Expert System" bubble burst. They were too expensive to maintain, and general-purpose PCs (from Sun and Apple) became fast enough to run Lisp without specialized hardware. Symbolics filed for bankruptcy, and AI entered its Second Winter.

---

4. 1997: Deep Blue and the Brute Force Victory

In 1996, World Chess Champion Garry Kasparov beat IBM's Deep Blue. He laughed at the machine, calling it "clumsy."

IBM went back to the lab. They upgraded Deep Blue to evaluate 200 million positions per second. In May 1997, in New York City, the rematch took place.

The Moment of Truth

In Game 2, Deep Blue played a "positional" move (avoiding a trap) that Kasparov believed only a human could make. He grew paranoid, accusing IBM of having a human grandmaster secretly feeding moves to the machine. Deep Blue won the match 3.5 to 2.5.

Kasparov was devastated. It was the first time a computer had beaten a reigning world champion in a match. However, the victory was one of "brute force" calculation, not "intelligence." It didn't "understand" chess; it just searched a massive tree of possibilities.

---

5. 2012: The AlexNet Revolution

AI remained a niche field of "Machine Learning" (the "detective" work) until 2012. Geoffrey Hinton, the "Godfather of AI," and his students entered the ImageNet competition with AlexNet, a deep convolutional neural network.

They used GPUs (Graphic Processing Units) for the first time. The results were shocking: they cut the error rate in half. Suddenly, the "Neural Network" theory that everyone had laughed at in the 80s was the only thing that worked.

---

6. 2017 - Present: The Transformer Age

In 2017, a team of eight researchers at Google published "Attention Is All You Need." They were frustrated with the slow speed of RNNs. They proposed a system called the Transformer.

The Backstory

The title was a playful nod to the Beatles' "All You Need Is Love." The authors (Vaswani, Shazeer, Jones, etc.) designed a system that didn't read words one by one. It looked at the entire context through "Self-Attention."

This architecture was so efficient that it could be trained on the entire public internet.

• 2018: OpenAI launches GPT-1.
• 2020: GPT-3 shows that scaling these models creates "emergent" abilities (coding, logic).
• 2023: ChatGPT becomes the fastest-growing application in human history.

---

7. 2025 and Beyond: The Agentic Web

Today, we are moving beyond the "Chatbot." We are entering the era of Autonomous Agents—AI that doesn't just talk, but uses computers, books flights, and conducts scientific research.

We are coming full circle. The 1956 dream of a "thinking machine" that improves itself is no longer a workshop proposal. It is a reality running in datacenters across the globe.

---

Conclusion

The history of AI is not a history of machines; it is a history of human ambition. From the lopsided optimism of Dartmouth to the hardware-fueled madness of Symbolics and the mathematical elegance of the Transformer, we have spent 70 years trying to build a mirror for our own minds.

Now that we have it, the next chapter of history will be written by both of us.