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AI Timeline

AI Timeline

Here's a concise timeline of AI (Artificial Intelligence) development, tracing key milestones from its origins through 2024:

AI Timeline: Major Milestones

Pre-1950s: Early Concepts

  • 1943: Warren McCulloch & Walter Pitts propose the first artificial neuron.
  • 1950: Alan Turing publishes “Computing Machinery and Intelligence” and proposes the Turing Test.

1956–1969: The Birth of AI

  • 1956: John McCarthy coins the term "Artificial Intelligence" at the Dartmouth Conference.
  • 1958: John McCarthy develops LISP, a key AI programming language.
  • 1960s: Early programs like ELIZA (Joseph Weizenbaum, 1966) can carry on limited conversations.

1970–1987: The First AI Boom & Winter

  • 1970s: Expert systems like MYCIN handle medical diagnoses.
  • 1974–1980: First "AI Winter"—funding declines due to lack of progress.

1987–1993: The Second Boom & Winter

  • Late 1980s: Rise of neural networks and backpropagation.
  • 1987–1993: Second "AI Winter" after another wave of disappointments.

1997–2010: Rise of Machine Learning

  • 1997: IBM's Deep Blue defeats chess champion Garry Kasparov.
  • 2002: Roomba, an autonomous vacuum cleaner, demonstrates practical AI.
  • 2006: Geoffrey Hinton popularizes "deep learning" and multi-layer neural networks.

2010–2018: Deep Learning Revolution

  • 2011: IBM Watson wins Jeopardy!
  • 2012: AlexNet wins ImageNet; deep learning outperforms previous approaches.
  • 2014: Generative Adversarial Networks (GANs) introduced.
  • 2016: AlphaGo (Google DeepMind) beats Go champion Lee Sedol.
  • 2018: BERT—Google’s model for natural language processing.

2019–2024: Foundation Models and AI Explosion

  • 2020: OpenAI releases GPT-3, a massive language model.
  • 2021: DALL-E and CLIP showcase multimodal AI capabilities.
  • 2022: ChatGPT brings large language models to the public.
  • 2022: AlphaFold achieves near-expert performance in protein folding.
  • 2023: Google’s Bard, Meta’s LLaMA, and other major LLMs compete publicly.
  • 2023–2024: Rapid advancement and deployment of AI in fields like image generation (Midjourney, Stable Diffusion); increasing AI regulation debates; widespread industry adoption.

What’s Next?

AI research is currently focused on:

  • More efficient and ethical models
  • General AI (AGI)
  • Human-AI collaboration
  • Explainability and trust in AI systems

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