Evolution of AI Explained: from Turing test to AI agents in 2025 - The real history, explained
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Evolution of AI Explained: from Turing test to AI agents in 2025 - The real history, explained
82 просмотра · 2 дн. назад
AlgoInsight
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82 просмотра · 2 дн. назад
The complete history of AI: from Turing's 1950 question to today's AI agents, with every major breakthrough explained simply.
This video answers a simple question: how did a 1950 thought experiment become ChatGPT and AI agents? You will see Alan Turing's imitation game, the 1956 Dartmouth workshop that named AI, rule-based expert systems like MYCIN and XCON, two AI winters, the shift to machine learning, Rosenblatt's perceptron and the XOR problem, backpropagation, Deep Blue beating Kasparov in 1997, AlexNet's 2012 ImageNet win, AlphaGo's 2016 move 37 against Lee Sedol, the 2017 Transformer paper 'Attention Is All You Need', and how GPT models and RLHF led to ChatGPT in November 2022 and today's agents.
What you'll learn:
Explain Turing's 1950 imitation game and why it became the Turing Test
Trace how the 1956 Dartmouth workshop gave the field the name AI
Compare rule-based expert systems like MYCIN and XCON with machine learning
Describe why Minsky's XOR problem froze neural networks for two decades
Explain how AlexNet's 15.3% error at ImageNet 2012 started deep learning
Explain Transformer attention using the trophy-and-bag sentence example
Describe how RLHF shaped GPT-3 into the ChatGPT that launched in 2022
Chapters:
0:00 The question
5:00 1950: Can a machine think?
9:10 1956: AI gets its name
11:11 1956 to 1970: The golden years
14:23 1958 to 1980: Neurons, and the first winter
17:55 The 1980s: Intelligence as rules
22:20 The 1990s: Learning from data
27:03 1997: Deep Blue
29:45 2006 to 2012: Deep learning
34:44 2012: Machines learn to see
38:40 Teaching machines language
42:53 2016: Move 37
45:10 2017: Attention is all you need
47:55 Large language models
51:15 November 2022: ChatGPT
54:15 2023 to 2025: The race
55:38 2025: Agents
57:09 The whole story
Key concepts: Turing Test, Dartmouth workshop, expert systems, perceptron and XOR problem, backpropagation, AlexNet and ImageNet, Transformer attention, large language models, RLHF, AI agents
Who this is for: Engineers and CS students who want the real history behind AI terms like transformers and LLMs before using them in interviews.
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