We’re bringing more to Google One AI Premium by adding Gemini in Gmail, Docs, Slides & more — so you can get more done without tab hopping. From drafting a note in Gmail to writing an itinerary in Docs, Gemini can help. Coming to 150 countries in English. goo.gle/3PepQ2X

Excited for Gemini to be available to Workspace customers! For people who use a Workspace domain, you can enable access to Ultra 1.0 for your accounts of all sizes. Looking forward to your feedback!

We are aware that Gemini is offering inaccuracies in some historical image generation depictions, and we are working to fix this immediately. As part of our AI principles ai.google/responsibility/pri…, we design our image generation capabilities to reflect our global user base, and we take representation and bias seriously. We will continue to do this for open ended prompts (images of a person walking a dog are universal!) Historical contexts have more nuance to them and we will further tune to accommodate that. This is part of the alignment process - iteration on feedback. Thank you and keep it coming!

Wow. Google just released Gemma, the most powerful open LLM yet. Open for commercial use, it outperforms Mistral AI 7B and LLaMa 2 on Human Eval and MMLU. It's the first open LLM based on Gemini. Details: - Comes in two flavors: 2B and 7B. - Beats Mistral 7B, DeciLM 7B and Qwen1.5 7B - Instruction models in 2B and 7B variants. - 8192 Default context window. - MMLU score of 64.56, average leaderboard score 63.75 for 7B. -2B model compatible with mobile phones. Available on HuggingFace, Kaggle and Vertex AI.

We have a long history of supporting responsible open source & science, which can drive rapid research progress, so we’re proud to release Gemma: a set of lightweight open models, best-in-class for their size, inspired by the same tech used for Gemini blog.google/technology/devel…

👋 open models

Gemini has just received an update You can now execute and edit code in addition to generating it. Here's where to find these features and how to use them:

As we continue to improve the coding experience with Gemini Advanced, one thing we’ve heard is many people want to try to make a few mods run it first to ensure it works exactly as they want before merging into their workflow. Give it a shot and let us know what you think!

The Gemini 1.5 Pro model guide is live! With support of up to 1 million tokens context length, you may be wondering what's possible with Gemini 1.5 Pro. My overall impression after our first round of testing is that Gemini 1.5 Pro is among the most powerful long context LLMs available today. I've published a summary of Gemini 1.5 Pro's capabilities along with concrete examples in the prompting guide. These are just preliminary tests. I will continue to analyze and document the model's capabilities and limitations. Stay tuned! From preliminary experiments, Gemini 1.5 Pro shows impressive capabilities around multimodal reasoning, video understanding, long document question answering, code reasoning on entire codebases, and in-context learning. One insight from testing this model is that we will have different kinds of LLMs that support different types of use cases. Gemini 1.5 Pro is not meant to be a model to reign among all. The long context LLMs are not meant to cover every use case imaginable, they are meant to unlock complex use cases that were unimaginable before with LLMs. Link to guide below ↓

This is very meta! @Sam_Witteveen uses Gemini 1.5 Pro to analyze and ask questions about the video content of a lecture I gave last week about ML trends, including some discussion of Gemini.

Gemini 1.5 Pro and its 1M tokens context length show huge potential! I have been experimenting with Gemini 1.5 Pro (inside Google AI Studio) and find that its reasoning ability over long-form content is quite good. I am particularly interested in LLMs that can retrieve and reason over long contexts across different modalities. This is what unlocks all kinds of complex use cases. For now, my experiments are around scientific papers and the kind of complex analysis or questions the model can accurately answer. In the screenshot, we prompt the model with two papers as input. The model needs to analyze both papers before it can return an answer. What I found interesting in the response it gave me is that it even analyzed tables before it sent back a response. It's exciting to see this type of analysis on the fly without using a RAG system. Beyond this, we can ask for more concrete explanations of findings and experiments by giving it more context. You can also prompt the model to extend a survey paper based on recent papers or even generate your own based on a desired format. And a whole lot more. A full analysis and more examples of Gemini 1.5 Pro will be published in the promoting guide soon. Stay tuned!

Long-context reasoning at 10M scale is a colossal achievement but I don't think it renders RAG, which can operate over 100T tokens, obsolete. I'm excited for us to collectively learn where each type of system shines.

Been testing Gemini 1.5 pro and i'm really impressed so far Recall has been outstanding, and its really good at following instructions even with > 200k tokens. oh and agents just got a lot better. the only missing piece is really latency cost

really want to design a way with ai to give people the feeling of what learning to code in middle school was like reading o’reilly books you can’t afford in the back of a borders… thinking about it deeply on the ride home… then hacking away to see if you figured it out some of the best thinking comes with breaks to process!

Gemini (gemini.google.com) end of week update/recap: Blown away by all the people who have shared using Gemini to help them with things like iterating through a business strategy using their preferred framework, working through possible solutions to a gnarly coding challenge they were facing for days, and sharing they used it to navigate a tricky advocacy scenario for a loved one. Please keep your stories coming - they inspire our team deeply ❤️ On the product side, we have cut refusals in ~half since launch, which is hard to believe was 8 days ago. Working to continue to cut this down while still focusing on alignment. We updated the Android app to auto submit your voice inputs to bring it closer to Assistant behavior. Also fixed many sign in errors that were happening for folks who switched their device language to English for the app and then back to an unsupported language… thank you for going through hoops to install it 🙃 More users have access to mobile in iOS via Google app and Android via Assistant opt in. Still continuing to progressively roll it out. Supports English, Japanese and Korean in eligible countries: support.google.com/gemini/an… More improvements & requested features on the way… thank you again for participating in the dialog. We are also eager for feedback on 1.5 Pro! Private preview helps us understand how to best bring it to more products like Gemini - sign up here for access via AI Studio: aistudio.google.com/app/wait… ok… back to work…

Gemini 1.5 Pro - A highly capable multimodal model with a 10M token context length Today we are releasing the first demonstrations of the capabilities of the Gemini 1.5 series, with the Gemini 1.5 Pro model. One of the key differentiators of this model is its incredibly long context capabilities, supporting millions of tokens of multimodal input. The multimodal capabilities of the model means you can interact in sophisticated ways with entire books, very long document collections, codebases of hundreds of thousands of lines across hundreds of files, full movies, entire podcast series, and more. Gemini 1.5 was built by an amazing team of people from @GoogleDeepMind, @GoogleResearch, and elsewhere at @Google. @OriolVinyals (my co-technical lead for the project) and I are incredibly proud of the whole team, and we’re so excited to be sharing this work and what long context and in-context learning can mean for you today! There’s lots of material about this, some of which are linked to below. Main blog post: blog.google/technology/ai/go… Technical report: “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” goo.gle/GeminiV1-5 Videos of interactions with the model that highlight its long context abilities: Understanding the three.js codebase: youtube.com/watch?v=SSnsmqIj… Analyzing a 45 minute Buster Keaton movie: youtube.com/watch?v=wa0MT8Ow… Apollo 11 transcript interaction: youtube.com/watch?v=LHKL_210… Starting today, we’re offering a limited preview of 1.5 Pro to developers and enterprise customers via AI Studio and Vertex AI. Read more about this on these blogs: Google for Developers blog: developers.googleblog.com/20… Google Cloud blog: cloud.google.com/blog/produc… We’ll also introduce 1.5 Pro with a standard 128,000 token context window when the model is ready for a wider release. Coming soon, we plan to introduce pricing tiers that start at the standard 128,000 context window and scale up to 1 million tokens, as we improve the model. Early testers can try the 1 million token context window at no cost during the testing period. We’re excited to see what developer’s creativity unlocks with a very long context window. Let me walk you through the capabilities of the model and what I’m excited about!

Gemini 1.5 has arrived. Pro 1.5 with 1M tokens available as an experimental feature via AI Studio and Vertex AI in private preview. Then there’s this: In our research, we tested Gemini 1.5 on up to 2M tokens for audio, 2.8M tokens for video, and 🤯10M 🤯 tokens for text. From Shannon’s 1950s bi-gram models (2 tokens), and after being mesmerized by LSTMs many years ago able to model 200 tokens, it feels almost impossible that I would be talking about hundreds of thousands of tokens in context length, let alone millions. ♊️💙 Tech report: goo.gle/GeminiV1-5

“This means 1.5 Pro can process vast amounts of information in one go — including 1 hour of video, 11 hours of audio, codebases with over 30,000 lines of code or over 700,000 words. In our research, we’ve also successfully tested up to 10 million tokens.” Thanks @demishassabis & team for pushing AI forward. Available for developers via AI Studio and Vertex AI in private preview today, working to expand more!

Gemini (gemini.google.com) Wednesday update: 1) Refusal reduction: we eased refusals on many images with people in them. Gemini will extract the text and reason upon it. This will especially help on images with screenshots which mobile users are really putting to the task! We still won't allow things like take a selfie and superimpose yourself on a bodybuilder. No shortcuts! note: Gemini is great for building plans for working out or even just ideas on how to touch grass g.co/gemini/share/8ab417971c… We'll continue to work through responsibly aligning on refusals with both images and text. 2) Mobile: we've begun to roll out Gemini in Japanese and Korean on Android (first via Assistant opt in) and iOS (via Google app). This is in addition to the global rollout started in English yesterday and will take a few days to complete. Countries where mobile apps will be available can be found here: support.google.com/gemini/an… More features, countries & languages in progress. 3) omg THANK YOU for all of your feedback, shares, support, and DMs. Our team is inspired by it and we want to get to it all as quickly as we can. Please keep it coming. ok. back to work...

i ❤️ TPUs… so smooth