Where Did AI Begin?
Artificial Intelligence feels like a modern phenomenon — something born in the age of smartphones and cloud computing. But the roots of AI stretch back further than most people realise, to a question posed by a British mathematician in 1950: “Can machines think?”
That mathematician was Alan Turing, and his landmark paper “Computing Machinery and Intelligence” laid the philosophical groundwork for everything that followed. The Turing Test — his proposed measure of machine intelligence — is still referenced in AI discussions today, 75 years later.
The Birth of a Field: 1956
The term “Artificial Intelligence” was officially coined at the Dartmouth Conference in 1956, where John McCarthy, Marvin Minsky, and others gathered with an ambitious belief: that every aspect of learning and intelligence could in principle be so precisely described that a machine could simulate it.
The optimism was enormous. Researchers predicted that human-level AI was just ten to twenty years away. They were wrong — but they weren’t entirely wrong about the destination, just the timeline.
Claude Shannon: The Man Who Gave AI Its Language
While Turing asked whether machines could think, another genius was quietly solving how they could communicate. Claude Elwood Shannon — mathematician, electrical engineer, and arguably the most important figure most people have never heard of — published his landmark paper “A Mathematical Theory of Communication” in 1948, just two years before Turing’s famous essay.
Shannon’s contribution was deceptively simple in concept but revolutionary in impact: he proved that all information — text, images, sound, data of any kind — could be encoded, transmitted, and decoded using a binary system of ones and zeros. He coined the term “bit” (binary digit), the fundamental unit of information that underpins every computer, every AI model, and every digital device ever built.
Why Shannon’s Work Was a Turning Point for AI
Before Shannon, information was a vague, intuitive concept. After Shannon, it was mathematics. That shift had three profound consequences for the direction of AI:
- It gave machines a universal language. If all information could be expressed in binary, then any data — a human voice, a photograph, a chess move — could in principle be processed by a machine. This was the theoretical bedrock that made neural networks and machine learning mathematically possible.
- It introduced the concept of noise and error correction. Shannon’s information theory dealt not just with transmitting data, but with doing so reliably despite interference. This thinking directly influenced how AI systems are trained to handle incomplete, ambiguous, or corrupted data — a core challenge in every modern AI application.
- It redefined intelligence as information processing. Shannon’s framework implied that thinking, reasoning, and learning were fundamentally about processing and transforming information — a philosophical shift that moved AI away from trying to replicate the human brain anatomically, and towards building systems that could replicate its function.
Shannon also built one of the earliest chess-playing programs and demonstrated that machines could be designed to solve problems through logical search — a direct precursor to the game-playing AI systems that would follow decades later.
His 1950 paper “Programming a Computer for Playing Chess” laid out principles of heuristic search and evaluation functions that influenced AI research for the next 40 years, right through to IBM’s Deep Blue defeating Garry Kasparov in 1997.
Shannon’s Legacy in Today’s AI
Every time a large language model like GPT-4 or Claude predicts the next word in a sentence, it is — at a fundamental level — doing exactly what Shannon described: calculating the most probable next symbol in an information sequence based on prior context. Shannon himself demonstrated this principle in the 1940s by showing that humans could predict missing letters in English text with remarkable accuracy.
The modern field of machine learning is, in many ways, applied information theory. Concepts like entropy, mutual information, and data compression — all Shannon originals — sit at the mathematical core of how AI systems learn from data.
Without Turing’s question, we might never have asked whether machines could think. Without Shannon’s mathematics, we would have had no idea how to build one that could.
The AI Winters: When Progress Stalled
Progress in AI has never been a straight line. The field experienced two major “AI Winters” — periods in the 1970s and late 1980s — when funding dried up, enthusiasm collapsed, and computers simply couldn’t deliver on the hype.
The problems were fundamental: not enough computing power, not enough data, and algorithms that couldn’t scale. Early rule-based “expert systems” showed promise but broke down outside their narrow domains.
The Deep Learning Revolution: 2012 Onwards
Everything changed in 2012 when a team from the University of Toronto, using a neural network called AlexNet, demolished the competition at a major image recognition contest — by a margin that shocked the entire field.
The ingredients were finally in place: massive datasets (thanks to the internet), powerful graphics processing units (GPUs), and refined algorithms. Deep learning — where neural networks with many layers learn directly from raw data — became the dominant paradigm almost overnight.
The Transformer Era: 2017 to Now
In 2017, Google researchers published a paper called “Attention Is All You Need,” introducing the Transformer architecture. This became the foundation for GPT, BERT, Claude, Gemini, and every large language model that followed.
By 2022, ChatGPT reached 100 million users in just two months — the fastest adoption of any technology in human history. In 2024, AI moved from novelty to necessity. In 2025, agentic AI systems began handling multi-step tasks autonomously.
Where Are We Now — and Where Does Ireland Stand?
Irish AI adoption jumped from 49% to 91% in a single year. That’s not a trend — that’s a transformation. But adoption and effective implementation are two very different things.
Most Irish SMEs are at Level 1 AI: using ChatGPT for one-off tasks, saving minutes instead of hours. The businesses that will lead the next decade are the ones moving to Level 2 (automation) and Level 3 (AI agents) — now, not later.
At Kinetic Rising AI, we help Irish and UK SMEs make that leap. With 30 years of real business experience and 6 years of hands-on AI implementation, we don’t just understand AI — we understand business.
Ready to find out where your business sits on the AI maturity scale? Book a free AI assessment today →
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