Releases

Gen-1 established Runway's Gen family of generative video models.

Runway released Gen-1 for text- and image-guided video transformation.

The release of Eleven v1 established ElevenLabs' proprietary audio-model family.

ElevenLabs launched its first text-to-speech model and opened the platform in beta.

OpenAI released text-embedding-ada-002, replacing multiple earlier embedding models with a more capable and lower-cost model.

Meta withdrew the public Galactica demo after criticism over unreliable and misleading generated scientific content.

Meta AI released a research demo of Galactica, a large language model trained on scientific knowledge.

BigScience released BLOOMZ and mT0, instruction-finetuned multilingual models derived from BLOOM and the xP3 task collection.

Midjourney released V4 with major improvements in realism, artistic coherence, and detail.

Cohere released its second-generation multilingual embedding model for semantic search across more than 100 languages.

Cohere established its Rerank family of cross-encoder relevance models for search pipelines.

Meta released EnCodec, a neural audio codec for high-fidelity compression.

OpenAI released Whisper, a multilingual speech-recognition model, together with model weights and inference code.

Midjourney released V3 as the default image model.

The BigScience workshop released BLOOM, a 176-billion-parameter multilingual language model trained on the Jean Zay supercomputer.

Meta released NLLB-200, a machine-translation model covering 200 languages.

Meta released the Open Pretrained Transformer family and provided OPT-175B access to researchers.

OpenAI introduced DALL-E 2 with higher-resolution image generation and editing capabilities.

Midjourney released its second-generation image model.

Midjourney released its first public text-to-image model.

OpenAI introduced instruction-following models trained with human feedback.

Cohere introduced its Embed model family for semantic representation and retrieval.

Microsoft releases DeBERTa V3 with replaced-token detection and gradient-disentangled embedding sharing.

OpenAI introduced Codex, a descendant of GPT-3 that translates natural language into code.

DeepMind publishes AlphaFold 2's methodology and releases source code, expanding access to high-accuracy protein prediction.

Google introduces LaMDA as a conversational language model designed for open-ended dialogue.

OpenAI introduced CLIP, a neural network trained on image-text pairs for transferable visual recognition.

OpenAI introduced DALL-E, a model that generates images from text descriptions.

Facebook AI released M2M-100, a multilingual machine-translation model translating directly between 100 languages.

OpenAI published GPT-3, demonstrating few-shot learning at 175 billion parameters.

OpenAI released Jukebox, a neural network that generated music including rudimentary singing as raw audio.

Microsoft introduces Turing-NLG, a 17-billion-parameter generative language model and, at the time, the largest published language model.

OpenAI completed the staged GPT-2 release with the 1.5-billion-parameter model and accompanying research.

Hugging Face released DistilBERT, a smaller and faster distilled BERT model retaining most of the teacher model’s language-understanding performance.

OpenAI released the 774-million-parameter GPT-2 model and shared further analysis of misuse and detection.

OpenAI introduced MuseNet, a deep neural network for generating multi-instrument musical compositions.

Facebook open-sourced its Deep Learning Recommendation Model reference implementation.

Baidu released its first knowledge-enhanced language representation model.

OpenAI introduced GPT-2 and began a staged release process due to misuse concerns.

Google open-sources BERT, enabling bidirectional pre-training for language understanding and downstream NLP tasks.

OpenAI presented its five-agent Dota 2 system trained through large-scale self-play.

Baidu opened PaddlePaddle to the global developer community for deep learning, vision, speech and NLP.

Google releases TensorFlow as an open-source machine-learning framework, making internal deep-learning infrastructure broadly available.

IBM publicly demonstrated its DeepQA question-answering architecture through the Jeopardy! competition.