What is Artificial Intelligence?
Artificial intelligence is the field of computer science focused on building systems that can perform tasks that would normally require human intelligence. That includes things like recognising a face in a photo, understanding spoken words, translating between languages or deciding the fastest route through traffic.
The word "artificial" simply means man-made. The word "intelligence" here refers to the ability to learn, reason and solve problems. Put them together and you have machines that can do things we once thought only humans could do.
A Brief History
AI is not a new idea. The foundations go back further than most people realise.
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1950: Alan Turing asks the questionBritish mathematician Alan Turing published a paper asking "Can machines think?" He proposed the Turing Test: if a machine can hold a conversation that is indistinguishable from a human, it can be considered intelligent. This is widely regarded as the starting point of AI as a field.
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1956: The Dartmouth ConferenceA group of researchers in the United States coined the term "artificial intelligence" at a summer workshop at Dartmouth College. They believed that within a generation, machines would be capable of doing any work a human could do. They were optimistic: but not wrong, just early.
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1960sā1970s: Early optimism and first programsEarly AI programs could play chess, solve algebra problems and prove mathematical theorems. Researchers were confident that general machine intelligence was just around the corner. Funding flowed freely.
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1970sā1980s: The first AI winterProgress stalled. The problems turned out to be much harder than expected. Computers were too slow, data was too scarce and the approaches being used hit a ceiling. Government funding dried up. This period is called the "AI winter."
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1980s: Expert systemsAI made a comeback through "expert systems": programs that encoded the knowledge of human experts into rules. If the patient has a fever and a cough, suggest these tests. These systems worked well in narrow domains and were widely used in industry.
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Late 1980s: Second AI winterExpert systems were brittle. They could not handle anything outside their programmed rules. Maintaining them was expensive. Another period of reduced investment followed.
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1990sā2000s: Machine learning takes holdInstead of programming rules by hand, researchers started building systems that could learn patterns from data. This was a fundamental shift. The machine was no longer following instructions written by a human: it was finding its own patterns.
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2012: The deep learning momentA team from the University of Toronto entered an image recognition competition and won by a margin that shocked the research community. Their system, AlexNet, used a deep neural network trained on a large dataset of labelled images. This is widely considered the turning point that launched the modern AI era.
In 2017, Google researchers published a paper called "Attention is All You Need" introducing the Transformer architecture. This became the foundation for all modern large language models, including GPT, Claude and Gemini.
In 2022, ChatGPT launched in November and reached 100 million users in two months, making it the fastest-growing consumer application in history. AI moved from a research topic to something anyone with a browser could use.
What AI Can Do Today
Today's AI systems can:
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Visual recognitionRecognise objects, faces and scenes in images and video.
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Language understandingUnderstand and generate text in dozens of languages.
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Code assistanceWrite, debug and explain code.
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Creative generationCompose music, generate images and produce video.
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Medical diagnosticsDiagnose diseases from medical scans.
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Autonomous drivingDrive vehicles on public roads.
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Strategic gameplayBeat the world's best players at chess, Go and complex strategy games.
What AI Cannot Do (Yet)
For all its capability, today's AI has real limitations:
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No true understandingIt does not truly "understand" in the way humans do.
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HallucinationsIt can be confidently wrong (hallucinations).
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Physical common senseIt struggles with tasks that require physical common sense.
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Long-horizon planningIt cannot reliably plan over long time horizons.
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No genuine goalsIt has no genuine goals, values or desires of its own.
Key TakeawayAI is not magic and it is not a threat from science fiction: at least not yet. It is a set of techniques for building systems that learn from data and make decisions. Understanding what it is and how it works puts you ahead of the vast majority of people using it every day.
ReflectionWhich year is widely considered the turning point that launched the modern AI era and what happened that year?