What Is Gemini Google?
Google Gemini is a family of multimodal artificial intelligence (AI) large language models that have capabilities in language, audio, code, and video understanding.
Gemini 1.0 was announced on Dec. 6, 2023, and was built by Alphabet’s Google DeepMind business unit, which is focused on advanced AI research and development. Google co-founder Sergey Brin is credited with helping develop the Gemini large language models (LLMs), alongside other Google staff.
At its release, Gemini was the most advanced set of LLMs at Google, superseding the company’s Pathways Language Model (PaLM 2), which was released on May 10, 2023. As was the case with PaLM 2, Gemini is integrated into multiple Google technologies providing generative AI capabilities. Among the most visible user-facing examples of Gemini in action is the Google Bard AI chatbot, which was previously powered by PaLM 2.
Gemini integrates natural language processing capabilities, providing the ability to understand and process language, which is used to comprehend input queries, as well as data. It also has image understanding and recognition capabilities that enable the parsing of complex visuals, such as charts and figures, without the need for external optical character recognition (OCR).What Is Gemini Google? I hope its no more thought.
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What can Gemini do?
The Google Gemini models are capable of many tasks across multiple modalities, including text, image, audio and video understanding. The multimodal nature of Gemini also enables different modalities to be combined to understand and generate an output.
Tasks that Gemini can do include the following:
- Text summarization. Gemini models can summarize content from different types of data.
- Text generation. Gemini can generate text based on a user prompt. That text can also be driven by a Q&A-type chatbot interface.
- Text translation. The Gemini models have broad multilingual capabilities, enabling translation and understanding of more than 100 languages.
- Image understanding. Gemini can parse complex visuals, such as charts, figures, and diagrams, without external OCR tools. It can be used for image captioning and visual Q&A capabilities.
- Audio processing. Gemini has support for speech recognition across more than 100 languages and audio translation tasks.
- Video understanding. Gemini can process and understand video clip frames to answer questions and generate descriptions.
- Multimodal reasoning. A key strength of Gemini is multimodal reasoning, where different types of data can be mixed for a prompt to generate an output.
- Code analysis and generation. Gemini can understand, explain, and generate code in popular programming languages, including Python, Java, C++, and Go.
Introducing Gemini 1.5
By Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team
This is an exciting time for AI. New advances in the field have the potential to make AI more helpful for billions of people over the coming years. Since introducing Gemini 1.0, we’ve been testing, refining and enhancing its capabilities.
Today, we’re announcing our next-generation model: Gemini 1.5.
Gemini 1.5 delivers dramatically enhanced performance. It represents a step change in our approach, building upon research and engineering innovations across nearly every part of our foundation model development and infrastructure. This includes making Gemini 1.5 more efficient to train and serve, with a new Mixture-of-Experts (MoE) architecture.
The first Gemini 1.5 model we’re releasing for early testing is Gemini 1.5 Pro. It’s a mid-size multimodal model, optimized for scaling across a wide-range of tasks, and performs at a similar level to 1.0 Ultra, our largest model to date. It also introduces a breakthrough experimental feature in long-context understanding.
Gemini 1.5 Pro comes with a standard 128,000 token context window. But starting today, a limited group of developers and enterprise customers can try it with a context window of up to 1 million tokens via AI Studio and Vertex AI in private preview.
As we roll out the full 1 million token context window, we’re actively working on optimizations to improve latency, reduce computational requirements and enhance the user experience. We’re excited for people to try this breakthrough capability, and we share more details on future availability below.
These continued advances in our next-generation models will open up new possibilities for people, developers and enterprises to create, discover and build using AI. What Is Gemini Google? is now you know .
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