BERT sweeps the NLP benchmarks
Google's bidirectional encoder sets new records on 11 tasks and becomes the workhorse of applied NLP.
From one Google paper to models that do real work — every launch, breakthrough and mega-round that built the AI era. Scroll through it.
One paper rewires the field. Attention replaces recurrence, and scale starts to whisper.
Google's bidirectional encoder sets new records on 11 tasks and becomes the workhorse of applied NLP.
OpenAI demonstrates that unsupervised pre-training plus fine-tuning beats task-specific architectures.
Eight Google researchers introduce the Transformer. Every frontier model since is a descendant of this architecture.
OpenAI staggers the 1.5B-parameter release over misuse concerns — and starts the public conversation about AI safety.
Bigger is different. Language models become few-shot learners and images fall to diffusion.
DeepMind cracks a 50-year grand challenge in biology, later earning the 2024 Nobel Prize in Chemistry.
175B parameters. No fine-tuning needed — just describe the task in the prompt. The API economy of AI begins.
DeepMind shows most large models are undertrained — data, not just parameters, is the bottleneck.
Codex-powered completions land in the editor. Developers become the first profession transformed by LLMs.
Text-to-image arrives. An avocado armchair becomes the most famous chair on the internet.
Weights on the internet, image generation on consumer GPUs. An entire creative ecosystem ignites overnight.
AI meets everyone. A research demo becomes the fastest-growing product in history.
The cloud wars become AI wars: every hyperscaler now backs a frontier lab.
A French startup's first model beats Llama 2 13B — Europe enters the frontier race with €105M and seven weeks of training.
A “low-key research preview” becomes the fastest-growing consumer product ever — 100M users in two months.
Multimodal, top-decile on professional exams, and the new benchmark every lab chases for two years.
Anthropic's assistant launches, trained with explicit principles instead of pure human preference.
The largest AI investment to date locks Azure in as the compute backbone of the frontier.
Meta's weights leak within a week of release — accidentally founding the open-weights movement.
Google's answer to GPT-4 ships natively multimodal, in three sizes, across every Google product.
Models learn to see, hear, speak — and think before they answer.
Reasoning tokens arrive: test-time compute becomes the new scaling axis, and benchmark ceilings shatter.
Anthropic open-sources the Model Context Protocol — the “USB-C of AI” connecting models to tools and data.
One model, native across voice, vision and text — with sub-300ms conversational latency.
Claude Code, Codex and Cursor agents move from autocomplete to closing tickets end-to-end in the background.
Frontier-class reasoning, MIT-licensed, trained for a fraction of Western budgets. Nvidia sheds $600B in a day.
OpenAI's video model stuns with physics-plausible scenes and sets off the generative-video race.
From answering to acting. Models use computers, write software and do real work.
84.2% on SWE-bench. Long-horizon software work — plan, build, test, ship — increasingly runs unattended.
4M-token context and first-party desktop control. The model doesn't just advise on work — it does the work. This week.
Datacenter buildouts hit nation-state scale; compute, power and chips become geopolitics.
One model routes between fast answers and deep thinking — and agentic reliability becomes the headline metric.