Learning to see takes a leap.
AlexNet showed how a deep neural network trained with GPUs could dramatically improve large-scale image classification.
Book a consultation THE HISTORY OF AI
The moments that moved AI forward.
What happened. Why it mattered. What came next.
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.
Meta introduced LLaMA, a family of foundation models intended to broaden research access to large language models.
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.
The DeepSeek-R1 paper described reinforcement-learning approaches for developing reasoning behavior in language models.
A curated collection, not an exhaustive history. Event dates and original references are listed on every story.
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