WHEN ARTIFICIAL INTELLIGENCE STARTED ?

1. Philosophical Seeds: Before Computers

Long before “AI” was a term, thinkers pondered if machines could replicate human thought. Aristotle’s syllogistic logic in the 4th century BCE laid the foundations for symbolic reasoning, a key concept in modern computing . In the 17th century, Leibniz envisioned a “universal calculus” to mechanize reasoning, and in the 19th century, George Boole developed Boolean algebra—making binary logic possible . These intellectual advances were critical to later digital computing.

2. The Advent of Computing: Turing’s Vision

By the early 20th century, the theoretical groundwork for modern computers and AI began to take shape. Alan Turing introduced the concept of a “universal machine”—what we now call a Turing Machine—in 1936, and in 1950 he asked, “Can machines think?” . His proposed “Imitation Game,” now known as the Turing Test, became the benchmark for machine intelligence

3. The Birth of AI: Dartmouth, 1956

The formal founding of AI as an academic discipline occurred in 1956. John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon hosted the Dartmouth Summer Research Project on Artificial Intelligence—a pioneering workshop lasting six to eight weeks at Dartmouth College. It was here that the term “artificial intelligence” was coined, marking the official birth of the field . This event—often called the “Constitutional Convention of AI”—set the stage for decades of research.

4. Early AI Programs: Logic, Games, and Learning

Logic Theorist (1955)

A year before the Dartmouth conference, Allen Newell, Herbert Simon, and Cliff Shaw developed the Logic Theorist, often regarded as the first true AI program. It automated theorem proving by solving 38 out of 52 theorems from Principia Mathematica and even found improved proofs . It demonstrated that machines could carry out tasks considered uniquely human: reasoning and creativity

SNARC and Early Neural Initiatives (1951)

In 1951, Marvin Minsky and Dean Edmunds built SNARC—the Stochastic Neural Analog Reinforcement Calculator—an analog network simulating about 40 neurons, powered by vacuum tubes, to mimic animal learning.

Arthur Samuel’s Checkers Program (1952–1959)

Arthur Samuel’s checkers-playing program, originating in 1952, was among the earliest self-learning programs. By 1959, Samuel coined the term “machine learning,” using his program to demonstrate how machines could improve through experience.

5. Perceptron, Lisp, and the Rise of Symbolic AI

Perceptron (1957)

Frank Rosenblatt introduced the Perceptron, the first neural network model capable of recognizing patterns—a two-layer network that learned weights via training . It was a landmark development toward later neural methods.

Lisp (1958)

John McCarthy created Lisp—LISt Processing language—specifically for AI research. Its symbolic data handling made it a practical standard for early AI development

6. Language, Robotics, and Expert Systems

ELIZA (1966)

Joseph Weizenbaum’s ELIZA simulated a therapist through pattern matching and scripting. Though simplistic, it sparked ethical debates about human‑machine interaction when users ascribed personality to it .

SHRDLU (1970)

Built by Terry Winograd, SHRDLU was a natural language system that could understand and manipulate blocks in a virtual world—and interpret English commands in a limited domain.

DENDRAL (1965 onward)

Stanford researchers created DENDRAL, an expert system that interpreted mass spectrometry data to identify organic molecules. It demonstrated how AI could apply specialized knowledge to solve real scientific problems

MYCIN (1972)

Another early expert system, MYCIN, advised on antibiotic prescribing using rule-based logic. Though powerful, it was never clinically deployed due to legal and ethical hurdles

7. Robotics and Reasoning: Shakey (1966)

Shakey the Robot—developed at SRI from 1966 to 1972—became the first general-purpose mobile robot to reason about its actions, combining perception, planning, and action . It pioneered algorithms such as A* search and vision‑based mapping, influencing later autonomous systems.

8. AI Winters and Revivals

Despite early promises, progress slowed. In 1973, the Lighthill report criticized AI’s slow real-world impact, sparking the first “AI winter” as funding dried up. Another downturn occurred in the late 1980s as expert systems plateaued. Yet, each “winter” set the stage for future renaissances, such as the rise of neural networks and machine learning in the late 1980s and 1990s .

9. Deep Learning & Modern Revival

By the 2000s, affordable GPUs and vast datasets reignited neural network research—ushering in the era of deep learning. Breakthroughs included:

IBM’s Deep Blue defeating chess champion Garry Kasparov (1997).
Image classification using deep convolutional networks (2012).
Google’s AlphaGo beating Go champion Lee Sedol (2016).
Transformer architectures enabling large‑scale language models like GPT‑3/4 and ChatGPT (2020+).
10. Today’s Generative AI & Future Outlook

Generative AI, including ChatGPT and DALL·E, creates original text, images, and more from prompts. Built upon deep learning foundations, it has launched a new wave of public interest in AI . Nobel Prize winners John Hinton and John Hopfield were recently honored for their pioneering neural network research, highlighting AI’s scientific importance

Summary Timeline of AI’s Origins

Antiquity–19th Century: Philosophical and logical foundations (Aristotle, Leibniz, Boole).
1930s–1940s: Turing Machine, universal computation, neuron modeling (McCulloch & Pitts).
1950: Turing questions if machines can think; proposes Turing Test.
1951–1955: Early neural models and symbolic AI (SNARC, Checkers, Logic Theorist).
1956: Dartmouth Workshop marks AI field’s birth.
Late 1950s–1960s: Perceptron neural networks, Lisp, early language programs (ELIZA, SHRDLU).
1960s–70s: Expert systems (DENDRAL, MYCIN), robotics (Shakey).
1970s–1980s: AI winters focused research.
1980s–90s: Renewed interest in neural networks, machine learning.
1997–2012+: Public milestones in game AI, image recognition, and reinforcement learning.
2020s: Generative AI boom, deep learning ubiquity, ethical concerns, and global impact.

Closing Thoughts

Artificial intelligence didn’t suddenly appear; it evolved from ancient logic, mid-20th-century computing inventions, and decades of experimentation. From the Dartmouth workshop in 1956 to today’s generative AI revolution, it’s been a long journey of breakthroughs, setbacks, and renewed hope.

It’s fascinating to realize that today’s AI—like conversation agents, self-driving systems, and creative tools—have roots stretching back to the Logic Theorist, the Perceptron, and even Aristotle. As we forge ahead, both the promise and responsibility of AI remain as prominent as ever.

Let me know if you’d like me to expand on any of these eras or talk about especially recent developments!