A field gets its name.
A proposal for a summer research project put “artificial intelligence” on the page—and set an ambitious agenda for 1956.
Book a consultation THE HISTORY OF AI
The moments that moved AI forward.
What happened. Why it mattered. What came next.
A proposal for a summer research project put “artificial intelligence” on the page—and set an ambitious agenda for 1956.
IBM’s Deep Blue defeated Garry Kasparov in a six-game chess match, a striking demonstration of specialized machine capability.
NVIDIA unveiled CUDA, opening GPU parallel computing to a wider range of scientific and research workloads.
AlexNet showed how a deep neural network trained with GPUs could dramatically improve large-scale image classification.
Generative adversarial networks introduced a training framework in which a generator and a discriminator improve through competition.
AlphaGo’s 4–1 match victory over Lee Sedol demonstrated the power of combining learning with search.
The Transformer introduced a different architecture for working with language. A foundation for much of the AI that followed.
Google’s BERT research used bidirectional pre-training to learn language representations from surrounding context.
The GPT-3 paper explored how a large language model could perform many tasks from instructions and examples in its input.
DeepMind announced AlphaFold’s breakthrough results on the protein-folding challenge at CASP14.
DALL·E explored generating images from written descriptions, combining language with visual creation.
GitHub introduced Copilot as an AI pair programmer, bringing code suggestions directly into a developer’s workflow.
Stable Diffusion’s public release made model weights and code available for a broader range of image-making experiments.
ChatGPT’s public research preview gave people a simple way to explore language AI: ask a question, then keep talking.
Meta introduced LLaMA, a family of foundation models intended to broaden research access to large language models.
Anthropic introduced Claude more broadly through chat and an API, following testing with early partners.
GPT-4 introduced a large multimodal model that could accept image and text inputs and produce text outputs.
Google introduced Gemini, a family of models designed around multimodal capabilities.
OpenAI introduced GPT-4o with an emphasis on working across text, vision, and audio.
Claude’s Artifacts preview introduced a dedicated space to see and iterate on content alongside a conversation.
The Model Context Protocol proposed an open standard for connecting AI assistants to data and tools.
The DeepSeek-R1 paper described reinforcement-learning approaches for developing reasoning behavior in language models.
OpenAI introduced Codex as a cloud software-engineering agent that could work on tasks in isolated environments.
ChatGPT agent brought a virtual computer into the workflow, combining research and actions under user direction.
Anthropic announced Claude Fable 5 and Mythos 5, pairing new capabilities with different access safeguards.
GPT-6 Astra brought a new focus on computer use, software engineering, and complex professional workflows.
A curated collection, not an exhaustive history. Event dates and original references are listed on every story.
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