- Foundations and Pre-History
- Thinkers explored the mechanization of reasoning long before computers existed.
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- Ancient Mythology: Myths such as the Greek Talos, a giant bronze automaton, reflected early human fascination with lifelike machines.
- Mathematical Logic: In the 1850s, George Boole published The Laws of Thought, formalizing logic into a mathematical calculus.
- Early Computing: Charles Babbage and Ada Lovelace worked on the Analytical Engine in the 19th century, which Lovelace envisioned as a “reasoning machine.”
- The Birth of the Field (1950–1956) [1]
- Modern AI emerged as a formal discipline following World War II.
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- Alan Turing (1950): Published “Computing Machinery and Intelligence,” posing the question “Can machines think?” and introducing the Turing Test.
- Dartmouth Workshop (1956): Organized by John McCarthy, this conference officially coined the term “artificial intelligence” and established it as a research field.
- Early Optimism and “AI Winters”
- The field initially saw rapid successes followed by periods of disillusionment when expectations outpaced technology. [1, 2, 3]
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- Successes (1950s–1960s): Programs like the Logic Theorist and ELIZA (the first chatbot) demonstrated that machines could solve theorems and simulate simple conversation.
- The AI Winters: Limited computing power and funding cuts led to two major “AI Winters” in the mid-1970s and late 1980s.
- Expert Systems (1980s): AI enjoyed a temporary resurgence with specialized “expert systems” designed to solve narrow problems in fields such as medicine and chemistry. [1, 2, 3, 4, 5,
- The Modern Era: Machine Learning and Deep Learning
- The late 1990s marked a shift from rule-based logic to data-driven learning.
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- Deep Blue (1997): IBM’s computer defeated world chess champion Garry Kasparov, a milestone for machine capability.
- The Deep Learning Revolution (2012): Alexander’s success in an image recognition contest demonstrated the power of neural networks fueled by massive datasets and modern GPUs.
- Generative AI (2020s): The release of models such as GPT-3 and DALL-E popularized generative AI, which can produce human-like text, images, and code.
| Era [1, 2, 3, 4, 5] | Key Characteristic | Notable Examples |
|---|---|---|
| 1950s–1970s | Symbolic AI / Logic | Logic Theorist, ELIZA, SHRDLU |
| 1980s | Expert Systems | MYCIN, XCON, R1 |
| 1990s–2010 | Machine Learning | Deep Blue, Dragon Systems, Roomba |
| 2012–Present | Deep Learning / GenAI | AlexNet, AlphaGo, ChatGPT, Gemini |
AI can make mistakes, so double-check responses
- General Electric
- RCA.
- Some examples of 1960s second-generation computers from those vendors are:
- the IBM 1401, the IBM 7090/7094, and the IBM System/360;
- the Burroughs 5000 series;
- the UNIVAC 1107;
- the NCR 315;
- the CDC 1604 and the CDC 3000 series;
- the Honeywell 200, Honeywell 400, and Honeywell 800;
- the GE-400 series and the GE-600 series;
- The RCA 301, 3301, 501, and the Spectra 70 series.
What is the history of artificial intelligence (AI)? – Tableau
What is the history of artificial intelligence (AI)? It may sometimes feel like AI is a recent development in technology.
History of artificial intelligence – Wikipedia
Precursors: Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and the bronze giant.
- The History of Artificial Intelligence – Swiss Cyber Institute
- 1950s: The early foundations. The story of AI begins with questions rather than technology. In the 1950s, researchers started exploring…
