Google DeepMind releases Gemini 2.5 Pro
Google releases an intermediate upgrade to Gemini's native multimodal architecture with stronger benchmark performance.
ORGANIZATION
English reports linked to this entity will appear here as their translations are published.
Google releases an intermediate upgrade to Gemini's native multimodal architecture with stronger benchmark performance.
Google DeepMind releases a new Gemma family generation that adds native multimodal support to the lightweight open-model tier.
Google DeepMind releases a derivative of the 2.0 Flash architecture with explicit reasoning for logic-heavy tasks.
Google DeepMind releases a second-generation lightweight foundation model with faster responses and improved concurrent multitask handling.
Google DeepMind releases the second Gemma generation with architectural improvements that raise performance at the same parameter scale.
At Google I/O 2024, Google announces Imagen 3 with improvements in photorealism, complex prompt understanding, detail and text rendered in images.
Google DeepMind releases a lightweight open model derived from the Gemini technology architecture.
Google DeepMind releases a model with a new architecture and native support for context windows of up to 1 million tokens.
Google makes Gemini 1.0 Ultra available through Gemini Advanced, moving the largest Gemini 1.0 model from announcement to a consumer product.
Google introduces the natively multimodal Gemini 1.0 family—Ultra, Pro and Nano—with Pro and Nano entering products immediately and developer APIs following a week later.
PaLM 2 emphasized multilingual, reasoning, and coding capabilities and launched across Bard, Workspace, Google Cloud, and other products.
Google opened an AI Test Kitchen waitlist for MusicLM, allowing selected users to generate music from text descriptions and provide feedback.
Google instruction-tuned the T5 family on 1,836 tasks and released five checkpoints ranging from 80 million to 11 billion parameters.
Google Research announces Imagen, a text-to-image diffusion model focused on photorealism and language understanding; its code and public demo were not released at launch.
DeepMind introduced Flamingo, an 80-billion-parameter visual language model that used a few examples to handle image, video, and text tasks.
Google presents a 540B-parameter dense language model trained with Pathways; the initial release is a research disclosure, without public weights or a general-purpose API.
Google DeepMind releases a model and scaling-law result arguing that parameter count and training data should grow in proportion.
DeepMind introduced the code-generation system AlphaCode, which performed at about the median level of participants in Codeforces competitions.
The paper documents LaMDA as a dialogue-focused model family with up to 137B parameters and examines response quality, safety and factual grounding.
DeepMind introduced Gopher, a 280-billion-parameter language model, and published research on its capabilities, limitations, and risks without releasing public weights or an API.