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The Start of AI
The Start of AI
1950, Turning Test: Alan Turing's groundbreaking research paper and subsequent 'Turing Test' gauged a machine's ability to mimic human intelligence, pioneering the field of artificial intelligence.
1951-1970
1951-1970
1958, Neural Networks created: In an attempt to better understand the human brain, Frank Rosenblatt invented the perceptron, a foundational artificial neural network model, simulating the brain's processing to recognize patterns and make decisions.
1965, Moore’s Law discovered: Gordon Moore observed the doubling of transistors on chips every two years had a direct correlation with the rise in computing power, a trend (Moore's Law) vital for advancing AI development over the years.
1966, ELIZA Chatbot: ELIZA, created in 1966, was pivotal in AI for demonstrating early natural language processing and simulating human conversation, laying the groundwork for future chatbots and conversational AI.
1971-1990
1971-1990
1970, Back Propagation discovered: Researchers Paul Werbos, Seppo Linnainmaa, and others developed back propagation, a method enabling neural networks to adjust and learn from errors, revolutionizing AI learning.
1973, 1st AI Winter: AI funding was slashed following critical reports and growing skepticism about over-hyped expectations, leading to the first AI winter, a period of reduced interest and investment in AI research.
1986, Deep Learning discovered: Geoffrey Hinton and colleagues introduced deep learning, employing multi-layer neural networks for advanced pattern recognition, significantly advancing AI's capabilities in processing complex data.
1987, 2nd AI Winter: The second AI winter followed unmet high expectations, leading to a sharp decrease in funding and interest in AI research. This downturn mirrored the first AI winter, reflecting the cyclical pattern of hype and disappointment in the field of artificial intelligence.
1991-2010
1991-2010
1993, Internet Goes Public: The Internet's introduction in 1993 provided vast data resources and global connectivity, crucial for AI's advancements in learning, data processing, and collaborative research development.
1995, ALICE Chatbot: In 1995, the chatbot A.L.I.C.E. utilized natural language processing, advancing AI in human-like interaction and winning the Loebner Prize thrice for its conversational abilities.
1997, IBM’s Deep Blue: IBM's Deep Blue's 1997 victory over world chess champion Garry Kasparov marked a milestone, showcasing AI's ability to outperform humans in complex strategic tasks.
2002-05, Robotics & Autonomy: In 2005, the Roomba, and first autonomous vehicle demonstrated AI's practical applications in everyday life and transportation, showcasing AI's potential in robotics and autonomous navigation.
2011-Present
2011-Present
2011-16, Watson, Alexa, Siri: IBM Watson's Jeopardy victory showcased advanced natural language processing, Google's image recognition revolutionized visual AI, and Amazon Alexa popularized voice-activated AI, collectively driving significant advancements and commercial interest in diverse AI applications.
2016-18, AlphaGO & ChatGPT 1: AlphaGo's victory in Go highlighted AI's strategic prowess, while OpenAI's release of GPT in 2018 demonstrated advanced language understanding and generation, both crucial in showcasing AI's growing sophistication and versatility in complex tasks.
2022, ChatGPT 3 released publicly: The public release of ChatGPT and its record of a million users in five days marked a groundbreaking moment in AI, demonstrating unparalleled user engagement and interest in advanced natural language processing and conversational AI technologies.
2023, The Year of Artificial Intelligence: GPT-4's advancement, featuring enhanced language understanding and generation, combined with breakthroughs in multimodal AI capabilities, culminated in the U.S. Senate's first official hearing on AI, highlighting a year of unprecedented progress and recognition in the field of artificial intelligence.
2024 and Beyond: As AI evolves in 2024, opportunities in automation, personalized education, and healthcare emerge, while ethical considerations around privacy, bias, job displacement, and responsible AI usage demand careful attention and informed policy development.
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