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⭕Key Developments: -Myths of automata (e.g., Talos in Greek mythology). -Mechanical inventions by Banū Mūsā (9th century). -Philosophers: Aristotle (logic), Ramon Llull (Ars Magna), Hobbes (reason as calculation). -Ada Lovelace (1843) anticipates programming potential, introduces “Lovelace’s Objection.” ⭕Significance: -Established logical, mechanical, and philosophical groundwork. -Shifted the question from myth to scientific possibility of artificial thought.
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⭕Key Developments: -Gödel’s incompleteness theorems (1931). -Turing’s machine (1936) and Turing Test (1950). -McCulloch Pitts’ model of neurons (1943). -First electronic computers (ENIAC, 1945). ⭕Significance: -Laid the theoretical foundations of computation and intelligence. -Brought AI from pure philosophy to formal mathematics and hardware.
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⭕Key Developments: -Dartmouth Conference (1956) defines AI as a field. -Logic Theorist (1956), Perceptron (1957), LISP (1958). -ELIZA (1966) shows early NLP. -Samuel’s Checkers Program (1959) demonstrates machine learning. ⭕Significance: -Marked the birth of AI as a scientific discipline. -Generated optimism and heavy funding, with expectations of rapid progress.
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⭕Key Developments: -Minsky Papert expose limits of perceptrons (1969). -Shakey robot shows reasoning + mobility (late 1960s). -Lighthill Report (1973) causes funding cuts. ⭕Significance: -Revealed technical and computational limits. -Triggered the First AI Winter, slowing research and reducing investment.
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⭕Key Developments: -Expert systems like XCON and MYCIN achieve practical success. -Japan’s Fifth Generation Project (1981). -Backpropagation rediscovered (1986). ⭕Significance: -Demonstrated commercial usefulness of narrow AI. -Sparked optimism and funding, but dependence on hand-coded rules made systems brittle.
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⭕Key Developments: -Collapse of expert systems and Lisp machine market. -New approaches: Bayesian networks (1988), CNNs (1989), LSTM (1995). -IBM’s Deep Blue defeats Kasparov (1997). ⭕Significance: -AI term loses credibility, but machine learning thrives quietly. -Foundations laid for future breakthroughs in data-driven learning.
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⭕Key Developments: -Hinton introduces “Deep Learning” (2006). -GPUs + ImageNet dataset (2009) accelerate training. -IBM Watson wins Jeopardy! (2011). ⭕Significance: -Marked the transition from symbolic AI to data-driven AI. -Set the stage for the deep learning revolution.
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⭕Key Developments: -AlexNet dominates ImageNet (2012). -GANs introduced (2014). -AlphaGo beats Lee Sedol (2016). -Transformers proposed (2017). ⭕Significance: -Sparked the deep learning boom across vision, NLP, and robotics. -Established neural networks as the dominant paradigm in AI research.
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⭕Key Developments: -BERT (2018), GPT-3 (2020), AlphaFold (2020). -Generative AI: DALL-E, MidJourney, Stable Diffusion, GitHub Copilot. -ChatGPT launches (2022), reaching millions of users quickly. ⭕Significance: -AI shifts into mainstream daily life, with global adoption. -Raises urgent debates on ethics, regulation, and the path to AGI.