Our Gemini models are making Google Search more intelligent, agentic, and personalized, allowing users to ask longer and more complex queries.

The Gemini model is being leveraged to enhance AI capabilities in projects like Mariner, focusing on long context and memory.

The integration of native search capabilities in Gemini is critical, allowing the model to provide fresh and factual information in response to user queries.

The project Mariner leverages the Gemini model's multimodal capabilities to understand user tasks and execute them effectively.

Gemini 1.5 is notable for being a true mixture of experts, activating specific paths for different data types, making it more energy efficient compared to models that use modular approaches.

The investment in the Gemini program has focused on integrating various capabilities quickly, allowing the model to handle tasks like coding, text generation, and image generation all in one place.

The Gemini Developer API introduced many new features, including thought summaries directly in the API, native audio output for better voice control, and a URL context tool that allows models to analyze web links, enhancing the building blocks for new capabilities.

Gemini was built to be natively multimodal, meaning it can take in images, audio, video, and more, which is essential for progressing towards Artificial General Intelligence (AGI).

With Gemini, users can ask questions in their own language, and the model can find and translate content from other languages, breaking down barriers to information access.