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The Open Source Wars: When AI Went Free
The story of the open source AI movement — from Meta's LLaMA leak in March 2023 through DeepSeek-R1's January 2025 release that triggered a US stock market rout. The debate between open and closed AI, what open source AI has enabled, what risks it has introduced, and the specific governance question of whether the open source model is compatible with the safety requirements of frontier AI.
Sam Altman Returns: The Year That Made OpenAI
The sequel to P16 — Sam Altman's return to OpenAI after the November 2023 board crisis and the year that followed. The year of GPT-4o, the for-profit conversion, the $157 billion valuation, the Sutskever departure, the AGI rhetoric escalation, the Apple partnership, and the deeper question of what the year revealed about OpenAI's mission and Altman's position in the AI industry.
The Future of Creativity: Art, Music, and Writing in the AI Age
The story of what generative AI is doing to the creative professions — art, music, writing, and the deeper question of what creativity means when the boundary between the artist's idea and the machine's contribution becomes impossible to draw. The cultural and economic transformation that the technical breakthroughs of the previous decade have produced, and what it means for the relationship between humans and the work of making things.
The Reasoning Models: When AI Learned to Think Before Speaking
The story of the development of chain-of-thought reasoning and the emergence of 'thinking' models — AI systems that generate explicit intermediate reasoning steps before producing a final answer, dramatically improving performance on complex reasoning tasks. The technical development that began closing the gap between AI pattern-matching and genuine systematic reasoning.
Stuart Russell: The Philosopher of AI Safety
The Berkeley computer science professor who wrote the most widely used AI textbook, who was among the first mainstream AI researchers to engage seriously with the alignment problem, and who has developed the most coherent technical and philosophical framework for building AI systems that are genuinely safe. The intellectual architect of the cooperative AI vision.
The Rise of the Thinking Machine: Deep Learning Takes Over
The story of the deep learning revolution from the AlexNet moment through the development of the Transformer, told as a narrative of rapid, cascading discovery in which one breakthrough enabled the next. The fastest scientific revolution in the history of AI — and what it actually means that machines can now do what only humans could do before.
The Attention Economy: How the Transformer Changed Everything
The full intellectual story of the Transformer architecture — why self-attention was the right idea, how it enabled the pre-training revolution, and why scaling Transformer models produced capabilities that nobody predicted and nobody fully understands. The architecture at the heart of every large language model in existence, and what it reveals about the nature of language, intelligence, and learning at scale.
The Race to AGI: When Silicon Valley Decided to Change the World
The story of the companies, the investors, the research programmes, and the competitive dynamics that defined the AI industry from 2015 to 2025 — the people who believed they were building the most transformative and most dangerous technology in human history, and how that belief shaped what they built. The race to AGI, told from the inside.
The Alignment Problem: Can We Build AI That Wants What We Want?
The full intellectual story of AI alignment research — from Norbert Wiener's early warnings to the technical work of the current era, from the philosophical foundations to the specific technical approaches. The most important unsolved problem in AI, and the people trying to solve it before it matters most.
The Bias Problem: When AI Reflects Our Worst Selves
How AI systems trained on human-generated data encode and amplify the biases, inequities, and prejudices of the societies that generated that data — and the researchers who are trying to understand, measure, and mitigate these harms. The most practically urgent problem in deployed AI, the hardest to solve, and the one with the most direct consequences for the most people.
What AI Cannot Do: The Limits of the Possible
The honest account of what AI systems — including the most capable systems currently deployed — genuinely cannot do, and what those limits reveal about the nature of intelligence, understanding, and the distance remaining to truly general AI. The most important counterweight to the enthusiasm that surrounds every wave of AI progress.
The Labour Question: What Happens When AI Does the Work?
The full economic story of AI's impact on work — the productivity gains, the displaced jobs, the new roles created, the historical parallels, and the fundamental question of how societies should respond to a technology that can do more and more of what people used to be paid to do. The most socially consequential question raised by the AI revolution, examined without the optimism or the despair that usually distort it.
The Consciousness Question: Does AI Experience Anything?
The hardest philosophical problem raised by artificial intelligence — whether AI systems can have inner experience, whether there is something it is like to be a language model, and what the answer means for how we build and treat AI systems. The question that philosophy has been building toward for centuries and that AI has made impossible to defer any longer.
The Governance Gap: Can Humanity Govern What It Has Built?
The full account of the gap between the pace of AI capability development and the pace of governance development — the regulatory frameworks being built, the international institutions being assembled, the voluntary commitments being made, and the fundamental question of whether any of it will be adequate to the challenge of governing the most consequential technology in human history.
The Science of AI: What Research Still Needs to Answer
The most important open questions in AI research — about why deep learning works, about how to make AI systems more reliable, about the relationship between scale and capability, about alignment and interpretability. The scientific frontier of AI as seen from 2025: what we know, what we do not know, and why the unknown matters.
The Memory Machine: How AI Changes What We Know and How We Know It
The story of AI's transformation of the relationship between humans and knowledge — how AI assistants are changing how we search, remember, learn, and create, and what it means for human cognition, expertise, and culture when a tool this capable is available to anyone. The deepest long-term consequence of the AI revolution may be what it does to how we think.
The Transformer, 2017: Attention Is All You Need
The full story of the 'Attention Is All You Need' paper — how a Google Brain team developed a new architecture for sequence modelling that discarded recurrence entirely, why it worked better than everything that came before, and how it became the foundation for every large language model in existence. The paper that made GPT possible, and the elegant idea at its heart.
AlphaGo vs. Lee Sedol, 2016: The Game That Humbled Humanity
The full story of the five-game match between the world's greatest Go player and DeepMind's AI system in Seoul, March 2016 — the move 37 that changed the match, Lee Sedol's astonishing comeback in Game 4, the existential questions the match raised about human intuition and machine reasoning, and why one professional Go player retired because of what he witnessed.
AlphaFold, 2020: The Protein Folding Revolution
The full story of how DeepMind's AlphaFold 2 solved one of biology's grand challenges — predicting protein structures from amino acid sequences — and why this single AI achievement has already transformed drug discovery, biological research, and the relationship between AI and science. The most scientifically consequential AI result since the deep learning revolution began, and the event that earned AI its first Nobel Prize.
ChatGPT, 2022: When AI Became Everyone's Business
The full story of the ChatGPT launch — the five days to a million users, the hundred million users in two months, the panic in schools and universities, the excitement in offices, the existential dread in newsrooms, and what it meant that AI had crossed the threshold from specialised tool to mainstream reality. The moment AI became everyone's business.
The Great Pause: The Moment the AI World Held Its Breath
The story of the March 2023 open letter calling for a six-month pause in AI development — who signed, who didn't, what it meant, and what it revealed about the state of AI governance. The brief, charged moment when it seemed possible that humanity might collectively decide to slow down before building more powerful AI — and what happened instead.
The GPT-4 Moment: One Year That Changed Everything
The full story of the twelve months from March 2023 to March 2024 — the deployment of GPT-4, the multimodal revolution, the competitive response from Google and Anthropic, the new capabilities that seemed to emerge at scale, and the extraordinary pace at which AI went from impressive to indispensable for millions of people. The year that made AI real.
The Multimodal Moment: When AI Learned to See, Hear, and Speak
The full story of the multimodal revolution — from DALL-E to Stable Diffusion to GPT-4V to Sora — the integration of vision, audio, and language into unified AI systems that can engage with the full richness of human communication. How the barriers between modalities fell, what the integration enabled, and what it revealed about the nature of intelligence and representation.
The AI Election: When Synthetic Media Met Democracy
The story of the 2024 election cycle — the deepfakes, the voice clones, the AI-generated misinformation, the unprecedented challenge to democratic process posed by AI systems capable of generating compelling synthetic media of any candidate saying anything, and the responses — technical, legal, and institutional — that emerged. The first election in which AI was a central battleground.
The Agentic Turn: When AI Started Doing Things
The story of the transition from AI systems that answered questions to AI systems that took actions — the development of autonomous agents that could browse the web, write and execute code, manage files, and take sequences of actions in the world on behalf of users. The capability shift that changed the relationship between humans and AI systems, and the new alignment challenges it created.
The Scientific AI: When Machines Became Research Partners
The full story of AI's transformation of scientific research — from AlphaFold to AI-designed drugs to AI-generated mathematical proofs to AI models of climate and physics. The beginning of a new kind of science in which AI systems are genuine research partners, not just analytical tools, and what it means for the pace of discovery, the nature of scientific knowledge, and the future of human curiosity.
Fei-Fei Li: The Woman Who Taught Machines to See
The Stanford professor who assembled fourteen million labelled images when everyone told her the project was impossible, built the benchmark that made the deep learning revolution possible, went to Google as Chief AI Scientist, returned to academia to lead the movement for human-centered AI, and became one of the most important voices connecting technical capability to human values. The complete portrait of the creator of ImageNet.
Sam Altman & OpenAI: The Organisation That Changed Everything
The full story of OpenAI — the founding mission, the commercial pivot, the release of ChatGPT, the board crisis of 2023, and what it means that the organisation most explicitly committed to humanity's benefit has become the world's most commercially successful AI company. The most consequential organisation in the history of artificial intelligence, and the complicated man who leads it.
Demis Hassabis & DeepMind: Science as the Goal
The neuroscience-inspired AI lab founded in London, acquired by Google, responsible for AlphaGo and AlphaFold, and committed to a vision of AI as a tool for scientific discovery. The full story of DeepMind — the research culture, the extraordinary victories, the tensions of operating inside one of the world's most powerful companies, and the vision of one of the most intellectually ambitious people in the history of AI.
Dario & Daniela Amodei: Building AI You Can Trust
The siblings who left OpenAI to found Anthropic, the organisation that has put AI safety at the centre of its commercial strategy. The Constitutional AI approach, the interpretability research, the billion-dollar fundraising, and the genuinely difficult question of whether safety and commercialism can coexist at the frontier of AI.
Ilya Sutskever: The Mind That Saw the Future
The co-founder of OpenAI, the architect of GPT, the chief scientist who voted to fire Sam Altman and then reversed course, and the founder of Safe Superintelligence Inc. The most technically gifted and most philosophically serious of the deep learning generation's leaders — and the figure whose trajectory tracks the field's own ambivalence about what it is building.
Kate Crawford: The Woman Who Mapped the AI Atlas
The researcher who built the Atlas of AI — the most comprehensive account of the material, political, and social dimensions of AI development — and who has been arguing, against a field that preferred not to hear it, that AI is not a neutral technology but a product of specific choices made by specific people with specific interests. The most important critical voice in contemporary AI.
Timnit Gebru: The Researcher Who Wouldn't Back Down
The AI ethics researcher who was fired from Google for co-authoring a paper that criticised large language models — and who turned that firing into the founding of the Distributed AI Research Institute, one of the most important independent AI research organisations in the world. The full story of the event that crystallised the tension between AI safety rhetoric and AI research practice.
Geoffrey Hinton's Farewell: The Godfather Who Changed His Mind
The Nobel laureate who spent fifty years building the technology he now warns against — the full story of Hinton's departure from Google, his public warnings about AI risk, and what it means that the person who did more than anyone to make the deep learning revolution possible now believes that revolution may have been a mistake. The most consequential change of mind in the history of AI.
Yann LeCun: The Architect Who Disagrees
The Meta AI chief scientist who has spent his career doing foundational deep learning work, winning the Turing Award alongside Hinton and Bengio, and is now the most prominent voice disagreeing with the catastrophic AI risk narrative. Why the person who built so much of what AI is has such a different view of where it is going — and why his disagreement deserves as much serious attention as the warnings of those who fear AI most.
Yoshua Bengio: The Scientist Who Changed Sides
The third Godfather of Deep Learning, who spent his career building the mathematical foundations of neural network learning and who has, in the years since the ChatGPT moment, become one of the most active and most credible advocates for AI safety research and AI regulation. Why the person who built so much of what AI is now believes that the field's progress requires governance it has been reluctant to accept.
The First AI Winter: When the Dream Crashed
The dream of machine intelligence collided with reality in the early 1970s. Government funding collapsed. Research groups dissolved. Careers were redirected. This is the full narrative of AI's first great crisis — told from the inside, through the experiences of the people who built the dream and watched it crash — and the deeper questions about ambition, honesty, and institutional courage that the crisis forced.
Expert Systems: AI Learns to Be a Specialist
How the field that had been humbled by the first winter found a commercially viable path forward through the expert systems approach — going narrow, going deep, and going to work. The full story of AI's first commercial era, from DENDRAL and MYCIN to XCON and the billion-dollar industry that grew around knowledge engineering, and what it revealed about the nature of expertise itself.
Japan's Billion-Dollar Bet on AI
The full narrative of Japan's Fifth Generation Computer Project — the announcement that alarmed the world, the decade of work inside ICOT, the scientific contributions that were real, and the quiet failure that nobody wanted to acknowledge. The most audacious national AI programme in history, and what it revealed about the relationship between industrial policy, scientific vision, and the unpredictability of technological progress.
The Second AI Winter: Lightning Strikes Twice
The full narrative of AI's second great contraction — told from the inside, through the experiences of the researchers who built the boom and watched it collapse. The collapse of the expert systems industry, the dissolution of the LISP machine market, the reduction of DARPA funding, and the underground movement that kept the neural network approach alive through the cold.
The Godfathers Go Underground
The personal story of Hinton, LeCun, Bengio, and their collaborators during the years when neural network research was marginalised, underfunded, and dismissed. The decades of intellectual courage, the specific ideas that kept the research programme alive, and the slow accumulation of results that eventually made the field pay attention. How the most important research programme in modern AI survived by going underground.
The First AI Programs: Teaching Machines to Play Games
The exhilarating early years when Arthur Samuel's checkers program learned to beat its creator, when chess programs first challenged amateur players, and when every new demonstration felt like proof that general machine intelligence was just around the corner. What those early programs actually did, why they were so impressive, and why the gap between their performance and genuine intelligence was wider than it appeared.
ELIZA and the Illusion of Understanding
The deeper story behind the world's first chatbot — what ELIZA revealed about human psychology, the nature of language, and the gap between the appearance of understanding and its reality. We go beyond the famous chatbot story to the philosophical questions it raised, and why those questions matter more than ever in the age of large language models.
The Optimists: When AI Was Going to Solve Everything
The audacious, exhilarating, ultimately tragic story of the AI optimists of the 1960s — the people who genuinely believed that machine intelligence was years away, who made predictions that have made them famous for being wrong, and who were right about the destination even as they were spectacularly wrong about the distance.
Backpropagation Goes Mainstream, 1986: The Algorithm That Refused to Die
The most important algorithm in modern AI was discovered at least three times before anyone paid attention. The full story of backpropagation — its multiple independent inventions, the decades it spent in the shadows of the symbolic AI winter, and the specific confluence of people, ideas, and timing that produced the 1986 paper that changed everything. How an algorithm refused to die, and what happened when the world finally listened.
Deep Blue vs. Kasparov, 1997: The Match the World Watched
The full story of the chess match that divided history — the two games, the controversy, Kasparov's accusations that IBM had cheated, the cultural earthquake of a machine defeating the greatest chess player alive, and what it actually meant for AI, for human exceptionalism, and for the question of what it means to think. The most watched intellectual contest in history.
The Netflix Prize, 2006: The Moment the Crowd Beat the Experts
In 2006 Netflix offered a million-dollar prize to anyone who could improve its movie recommendation algorithm by 10%. What followed was the most consequential open machine learning competition in history — three years of innovation, collaboration, unexpected breakthroughs, and ultimately a winning solution that Netflix never actually deployed. The strange, productive, ultimately instructive story of science by competition.
The ImageNet Project, 2009: Teaching Machines to See
How Fei-Fei Li assembled fourteen million labelled images over three years of painstaking work, created the most important dataset in the history of AI, and built the benchmark that made the deep learning revolution possible. The full story of ImageNet — the years of labour, the unconventional methodology, the competition that changed everything in 2012, and why one woman's conviction that data was as important as algorithms turned out to be exactly right.
AlexNet, 2012: The Breakthrough Nobody Saw Coming
The full story of the ImageNet competition in 2012 — the weeks of training on two gaming GPUs, the submission that shocked the computer vision world, the researchers who remember exactly where they were when the results came in, and why a single paper changed the trajectory of artificial intelligence. The starting gun of the modern AI era.
The First AI Winter, 1974–1980: The Great Disillusionment
What the first AI winter actually felt like for the people who lived through it. The funding cuts, the disbanded research groups, the researchers who left the field, and the stubborn few who kept working in the cold. The story of how AI's first collapse shaped everything that came after.
The Rise of Expert Systems, 1980: AI Gets a Job
How MYCIN, XCON, and thousands of corporate AI systems made real money by going narrow — and how the field that had been humbled by the first AI winter found a commercially viable path forward. The rise of expert systems: AI's first commercial era, and the seeds of its second collapse that were planted in its very success.
Japan's Fifth Generation Project, 1982: The Billion-Dollar Gamble
In 1982, Japan announced the most audacious AI project in history: a ten-year programme to build a new generation of computers based on artificial intelligence. The announcement triggered global panic. The United States and Britain launched emergency responses. And then, quietly, over the next decade, the project failed. How national ambition, geopolitical anxiety, and genuine scientific vision combined to produce one of the great misadventures of the technology era.
The Second AI Winter, 1987–1993: Lightning Strikes Twice
The expert systems boom collapsed almost as quickly as it had risen. The LISP machine market imploded overnight. DARPA cut funding. And for the second time in AI's history, the field contracted, researchers left, and the most pessimistic observers began to wonder whether the project was fundamentally misguided. The full story of AI's second near-death experience — and the underground movement that kept the neural network flame alive through the cold.
Frank Rosenblatt: The Forgotten Father of Neural Networks
He built the Perceptron — the first machine that could learn. The press called it a thinking machine. Marvin Minsky called it a dead end. History called Minsky right for fifteen years and then spent the next fifty years proving Rosenblatt correct. The most underrated figure in the history of AI, whose ideas now run the world.
Geoffrey Hinton: The Stubborn Godfather
He kept working on neural networks when everyone told him it was a dead end. He developed backpropagation, built the research group that produced AlexNet, and triggered the deep learning revolution. Then he quit Google to warn the world that he was afraid of what he had built. The full story of the most important AI researcher of his generation — and the journey from stubborn optimist to frightened sage.
Yann LeCun: The Rebel with a Vision
He invented convolutional neural networks, built the first AI system to process real-world visual data at scale, and became the architect of Meta's AI empire. The most argumentative of the Godfathers of Deep Learning — and why his arguments, however combative the delivery, usually turn out to be right.
Yoshua Bengio: The Conscience of AI
The third Godfather of Deep Learning — his foundational contributions to recurrent networks, attention mechanisms, and generative models, his evolution from pure researcher to the field's most prominent voice for AI safety and ethics, and why the man who spent his career building increasingly powerful AI systems has become one of the most persuasive voices for ensuring those systems are safe.
Jürgen Schmidhuber: The Angry Genius
He invented LSTM — the architecture that enabled speech recognition, machine translation, and early language models. He has been arguing, loudly and persistently, that he deserves far more credit for modern AI than he receives. The case for why he might be right — and the complicated personality that has made it harder to make that case effectively.
John McCarthy: The Man Who Named AI
He organised the Dartmouth Conference, coined the term artificial intelligence, invented LISP, and founded the Stanford AI Laboratory. For half a century he was the field's most important institution-builder and its most stubborn defender of symbolic reasoning. The complicated legacy of the man who gave AI its name and its mission.
Marvin Minsky: The Brilliant Optimist Who Got It Wrong
His towering influence on early AI, his wildly overconfident predictions that a generation would see machine intelligence, the devastating critique of neural networks that set the field back a decade, and the late-career vision of the Society of Mind that remains one of the most original theories of intelligence ever proposed. The most complex figure in the history of AI.
Allen Newell & Herbert Simon: The Dynamic Duo of Early AI
The Logic Theorist, the General Problem Solver, the cognitive revolution, and a Nobel Prize. The most consequential scientific partnership in the history of AI — two men who were convinced they had found the key to human intelligence, built programs that demonstrated it, and spent the rest of their careers working out what they had actually discovered.
Joseph Weizenbaum: The Man Who Built ELIZA and Regretted It
He built the world's first chatbot as a demonstration of how shallow conversation could be — and watched in horror as people fell in love with it. The story of Joseph Weizenbaum: the man who understood, earlier and more clearly than almost anyone, what was dangerous about our relationship with intelligent machines, and who spent the rest of his life trying to make the world hear the warning.
Ada Lovelace & The First Algorithm
She wrote the world's first computer program in 1843 — but what did it actually say? We go deep into the mathematics of Ada Lovelace's Notes on the Analytical Engine, what the Bernoulli number algorithm actually did, and why the ideas buried in those footnotes were more radical than even most computer scientists realise.
Alan Turing: The Man Who Imagined Everything
A thematic synthesis of Alan Turing's entire intellectual journey — from the Turing Machine to Bletchley Park to the 1950 paper to morphogenesis — and why one mind, working across mathematics, philosophy, biology, and engineering, laid the conceptual foundations of an entire civilisation's technology.
The Summer That Named AI
The 1956 Dartmouth Conference in full narrative detail — who was in the room, what they argued about in the evenings, what they agreed on, what they were spectacularly wrong about, and how a single summer's conversation gave a field its name, its mission, and the overconfidence that would eventually send it into its first winter.
ELIZA, 1966: The Chatbot That Made People Cry
In 1966 a simple pattern-matching program became the world's first chatbot — and people fell in love with it, told it their secrets, and begged not to have it turned off. Its creator was horrified. The story of ELIZA, the DOCTOR persona, and what a program that understood nothing revealed about the human need to be heard.
The Lighthill Report, 1973: The Document That Killed AI
In 1973 a distinguished mathematician named James Lighthill wrote a review of AI research that was so devastating it caused governments across the world to pull their funding and sent the entire field into its first winter. How one document changed the course of AI history — and whether it was right.
Norbert Wiener: The Father of Cybernetics
He invented cybernetics, the science of communication and control in machines and animals. He warned the world about automation destroying human labour — in 1950. He predicted the ethical crises of AI before the field had a name. And almost nobody listened. The forgotten genius who saw everything coming.
Claude Shannon: The Man Who Invented Information
In 1948 a Bell Labs engineer published a paper that invented information theory and gave the entire digital age its mathematical foundation. He then spent his later years building juggling robots, riding a unicycle through the corridors, and playing chess against early computers. The remarkable, playful, essential life of Claude Shannon.
The Logic Theorist, 1956: The First AI Program
On a winter night in 1955, a program running on a primitive computer proved a mathematical theorem for the first time in history. Its creators believed they had cracked the secret of human intelligence. They were wrong about that — but right about something more important. The full story of the Logic Theorist: the first AI program ever built.
John von Neumann: The Man Who Designed the Modern Computer
He spoke eight languages, memorized entire books, and solved differential equations in his head for fun. He designed the architecture every computer in the world still uses today, helped build the atomic bomb, and died at fifty-three still dictating equations from his hospital bed. The astonishing, troubling, irreplaceable life of John von Neumann.
The Philosophers Who Asked 'Can Machines Think?'
Before the engineers came the philosophers. Leibniz dreamed of a calculus of thought. Pascal built the first mechanical calculator. Descartes asked whether mechanism had limits. The thinkers who laid the conceptual groundwork for everything that followed — and the questions they left unanswered that we are still wrestling with today.
The Turing Test, 1950: The Question That Still Has No Answer
In October 1950, a mathematician published a thirty-page paper in a philosophy journal that asked a deceptively simple question: can machines think? The paper proposed a test. The test sparked a debate. The debate has never ended. This is the story of the most important thought experiment in the history of AI.
Alan Turing: The Man Who Invented the Future
He broke the Nazi's unbreakable code, designed the architecture of the modern computer, asked whether machines could think, and was then chemically castrated by the government he had saved. The full, extraordinary, heartbreaking life of Alan Turing.
Clockwork Wonders: The Automata Era
Before computers, before electricity, before Ada Lovelace wrote the first program — craftsmen across Europe built mechanical marvels that walked, wrote, played music, and digested food. The extraordinary story of the automata era and the question it forced the world to ask.
The Dartmouth Conference, 1956: The Summer AI Was Born
In the summer of 1956, ten men gathered at a small New Hampshire college and gave a name to the dream of thinking machines. They were wildly overconfident, occasionally wrong, and completely right about the one thing that mattered most. This is the story of the week Artificial Intelligence was born.
Ada Lovelace: The First Programmer the World Forgot
She wrote the world's first computer program in 1843 — for a machine that didn't exist yet. Then history forgot her for a hundred years. The extraordinary life of Ada Lovelace, the woman who saw the future before anyone else.
The Ancient Dream of Artificial Life
Before there were computers, before there were circuits, before there was even electricity — there was the dream. The dream that one day, human hands might shape something that thinks, feels, and breathes. This is where the story of Artificial Intelligence really starts: not in 1956, not in 1950, but thousands of years ago, in the human imagination.