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Gemini – chat to supercharge your ideas. Supercharge your creativity and productivity. Chat to start writing, planning, learning and more with Google AI. Sign in. Bard is now Gemini. Get help...
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Gemini - chat to supercharge your ideas. Sign in. Bard is now Gemini. Get help with writing, planning, learning, and more from Google AI.
Gemini is a multimodal AI model that can reason across text, images, audio, video, and code. Learn about its features, benchmarks, safety, and applications in this web page.
- Sundar Pichai
- Introducing Gemini. By Demis Hassabis, CEO and Co-Founder of Google DeepMind, on behalf of the Gemini team. AI has been the focus of my life's work, as for many of my research colleagues.
- State-of-the-art performance. We've been rigorously testing our Gemini models and evaluating their performance on a wide variety of tasks. From natural image, audio and video understanding to mathematical reasoning, Gemini Ultra’s performance exceeds current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.
- Next-generation capabilities. Until now, the standard approach to creating multimodal models involved training separate components for different modalities and then stitching them together to roughly mimic some of this functionality.
- More reliable, scalable and efficient. We trained Gemini 1.0 at scale on our AI-optimized infrastructure using Google’s in-house designed Tensor Processing Units (TPUs) v4 and v5e.
Dec 6, 2023 · Gemini is Google's largest and most capable AI model, designed to understand and operate across different types of information, such as text, images, audio, video and code. Learn how Gemini works, what it can do, and how to access it in various products and services.
Feb 15, 2024 · Gemini 1.5 is a multimodal foundation model that delivers enhanced performance and longer context understanding. It can process up to 1 million tokens of text, video, audio, code and more, and perform complex reasoning tasks across modalities.
