Building transformative applications with Gemini on Google Cloud
TLDRThe session introduces Google's new Gemini model, a state-of-the-art multimodal AI model designed for developers and integrated into various Google products. The model offers advanced capabilities such as sophisticated reasoning and native multimodality, optimized for performance and choice. Safety and responsibility are core to its architecture. Developers can access Gemini through Google AI Studio and Vertex AI Studio, leveraging extensive resources, tools, and integrations for seamless application development. The session also highlights the model's potential across industries and its upcoming expansion into more Google Cloud services.
Takeaways
- 🚀 Google announces the Gemini model, a state-of-the-art natively multimodal AI model designed for transformative applications.
- 🌟 Gemini is a large-scale collaboration across various AI teams at Google, including Google DeepMind and Google Research.
- 🔍 Gemini is optimized for developers, offering performant, multimodal capabilities with a focus on choice and flexibility for different use cases.
- 🛡️ Built with responsibility and safety at its core, Gemini incorporates robust safety considerations and filters from the ground up.
- 📈 Gemini outperforms human experts in various benchmarks, showcasing its sophisticated reasoning and advanced capabilities.
- 🔧 Google Cloud customers can access Gemini through APIs on Cloud and experience it in consumer products like Google B and Pixel.
- 💡 Gemini is available in different models, from Gemini Ultra for complex tasks to Gemini Nano for smaller-scale applications on devices like Pixel 8.
- 🌐 The model is integrated into the vertex AI platform, providing developers with extensive resources, tools, and a diverse model garden.
- 🔗 Google Cloud Consulting and partner networks offer expertise and support for businesses leveraging Gemini for end-to-end services and innovation.
- 🎉 Gemini is currently in public preview and is available for free on Google Cloud until mid-January, encouraging developers to experiment and build applications.
Q & A
What is the Gemini model and why is it significant?
-The Gemini model is a state-of-the-art, natively multimodal model developed by Google. It is significant because it was trained from the ground up on multimodal inputs, designed to handle multimodal prompts and use cases. It represents the next chapter of AI innovation at Google, with capabilities that outperform human experts in various benchmarks.
How does Gemini model optimize for developers while preserving choice?
-Gemini model optimizes for developers by being performant for many use cases, multimodal, and running on Google's TPUs. It preserves choice by offering different versions of the model, from Gemini Ultra for complex tasks to Gemini Nano for smaller applications, allowing developers to select the best fit for their needs.
What safety considerations has Google taken into account when building the Gemini model?
-Google has built the Gemini model with responsibility and safety at its core. This includes a robust safety evaluation framework, diverse risk assessments, and collaboration with external experts. Safety is not an afterthought but an integral part of the model's development, influencing pre-training sets, safety filters, and commercial product development for the cloud.
What are some of the resources available for developers to get started with Gemini?
-Developers have access to a quick start library with code samples, free developer labs and training resources at Cloud Skills Boost, and integrations with popular third-party developer tools. Additionally, there are packages and extensions that support Google Cloud Foundation models in the Google app developer platform.
How can Gemini be integrated into current applications?
-Gemini can be integrated into current applications through APIs and SDKs that are cohesive with the Google developer ecosystem. This includes integrations with Firebase and Flutter, extensive documentation, code samples, and tools like Terraform for easier deployment and management.
What is the role of the Vertex AI platform in developing with Gemini?
-Vertex AI is the platform for AI and ML developers, providing a suite of tools and services to develop, deploy, and manage AI models. It offers a seamless environment for developers to build and scale applications using Gemini, with features like the AI Studio for rapid prototyping and the Vertex AI Workbench for more production-ready development.
How does Gemini handle multimodal inputs and what are some examples of its capabilities?
-Gemini is a natively multimodal model, meaning it can understand and generate responses across different modalities like text, images, and video. Examples include image recognition and description, generating brand names and slogans based on images, and extracting and translating content from poems or articles.
What are the different versions of the Gemini model and when will they be available?
-The Gemini model includes Gemini Pro, which is currently available for scaling a variety of use cases and has been since December 13th, 2023. Gemini Ultra, the largest and most capable model, is expected to be available in early 2024, with a limited private preview starting next year. Gemini Nano is a smaller model optimized for serving on hardware like Pixel 8.
How does the Gemini model handle safety ratings and content blocking?
-Gemini model includes safety settings that allow developers to configure the level of content blocking based on the probability of harm. Categories like harassment, hate speech, sexual explicit content, and dangerous content can be adjusted, and the model provides safety ratings for each output, enabling developers to limit or block certain types of information as needed.
What are some use cases for Gemini in different industries?
-Gemini's use cases span various industries, including travel and booking sites for content generation and image tagging, retail and e-commerce for product descriptions and inventory management, financial services for stock analysis and reporting, and more. Its multimodal capabilities make it a versatile tool for enhancing user experience and operational efficiency.
Outlines
🎤 Introduction to the Gemini Model and Agenda Overview
The session begins with an introduction to Ken McDow Donal, the product manager from Google Cloud, and Chrisen Vel as the lead for Google Cloud Consulting. They express excitement about the day's agenda, which includes a discussion on transformative applications with the new Gemini model. Ken provides a quick overview of the agenda, mentioning an in-depth look at the Gemini model, hands-on demos, resources for developers, and a Q&A session. The session aims to cover how developers can leverage the capabilities of the Gemini model for various applications.
🚀 Launch of the Gemini Model and its Unique Features
Ken discusses the launch of the Gemini model, highlighting its state-of-the-art natively multimodal capabilities. He explains that Gemini is a large-scale collaboration across different Google teams, including Google DeepMind and Google Research. The model is designed to handle multimodal inputs and optimized for developers while maintaining choice and flexibility. Ken emphasizes the importance of responsibility and safety in the development of Gemini, assuring that it is built with these considerations at its core.
🌟 Special Features of Gemini and its Advantages for Developers
The paragraph focuses on the special features of Gemini, such as its sophisticated reasoning and ability to handle complex prompting. Ken talks about the optimizations made for developers, including the performance of the model and its flexibility to support various use cases across industries. He also mentions the unique advantage of Google's own hardware, the TPU, designed specifically for serving large models like Gemini, offering scalability and efficiency.
🛠️ Tools and Resources for Developers Using Gemini
Ken outlines the tools and resources available for developers using Gemini. He mentions the extensive quick start library with code samples and jump starts, free developer labs and training resources, and robust integrations with popular third-party developer tools. Ken also highlights the support for Google Cloud Foundation models in Google app developer platforms, emphasizing the ease of activation and integration of these models into various platforms.
🌐 Exploring the Multimodal Capabilities of Gemini
Chrisen demonstrates the multimodal capabilities of Gemini by using Google AI Studio. He shows how the model can process both images and text to extract information and generate responses. Chrisen explains how developers can use the platform to build applications with Gemini, highlighting the ease of use and the potential for rapid prototyping and application development.
🔧 Utilizing the Vertex AI Workbench for Development
Chrisen discusses the Vertex AI Workbench, an enterprise-ready AI platform for developers looking to scale their applications into production. He explains how developers can use the workbench to customize, augment, deploy, and govern their models. The workbench offers a full end-to-end lifecycle for building generative AI systems, providing a range of tools for developers to work with.
📊 Multimodality and Prompt Engineering with Gemini
The focus of this paragraph is on the multimodality capabilities of Gemini and the technique of prompt engineering. Chrisen demonstrates how to use images and text prompts to guide the model's responses. He shows how Gemini can be used to generate content, translate text, and perform tasks like prompting with a series of images, highlighting the model's adaptability and the potential for various use cases.
🏦 Industry-Specific Applications and Use Cases of Gemini
Chrisen explores industry-specific applications and use cases of Gemini, such as travel booking, retail, and finance. He demonstrates how the model can be used to enhance user experience by generating descriptions, tagging images, and providing detailed analysis of financial data. The paragraph emphasizes the versatility of Gemini in adapting to different industries and its potential to streamline and improve various business processes.
📈 Financial Analysis and Video Data Extraction with Gemini
In this paragraph, Chrisen showcases Gemini's ability to analyze financial graphs and reports, as well as extract information from video data. He demonstrates how the model can identify stock prices, trends, and other financial metrics from images and videos. The session also highlights the potential of Gemini to assist in research and corporate advisory roles by speeding up internal processes and providing valuable insights.
🤖 Q&A and Resources for Developers Interested in Gemini
The session concludes with a Q&A segment where Ken and Chrisen answer questions about integrating Gemini into applications, frameworks for using Gemini, applying the model to industry domains, and its future integration with other Google Cloud products. They also provide resources for developers to explore and learn more about the capabilities of Gemini, encouraging experimentation during the public preview period.
Mindmap
Keywords
💡Gemini
💡Multimodal
💡Developers
💡AI
💡Cloud
💡Safety and Responsibility
💡Innovation
💡Public Preview
💡Integration
💡Consulting
Highlights
Ken McDow Donal, Google Cloud's product manager, and Chrisen Vel, lead for Google Cloud Consulting, discuss the new Gemini model for building transformative applications.
Gemini is a state-of-the-art natively multimodal model, representing the next chapter of AI innovation at Google.
The model was trained from the ground up on multimodal inputs, designed to handle multimodal prompts and use cases.
Gemini is optimized for developers while preserving choice, with performance and flexibility for various use cases across industries.
Google has integrated the model into consumer products like B and Pixel, and made it available for developers through Cloud APIs.
The model is built with responsibility and safety at its core, with a robust safety evaluation framework and collaboration with external experts.
Gemini outperforms human experts in a variety of benchmarks, showcasing its sophisticated reasoning and advanced capabilities.
Google has created a unique advantage with its Google gener at tpus, offering flexibility in model versions from Gemini Ultra to Gemini Nano.
The Gemini model is built on vertex AI, Google's platform for AI and ML developers, providing extensive quick start libraries and free developer labs.
Google Cloud customers can easily try Gemini through the AI studio's new multimodal space, prompting playground.
The entire vertex stack includes Gemini alongside 130 other first-party and third-party models, promoting developer flexibility and choice.
Chrisen demonstrates the multimodal capabilities of Gemini, including image and text inputs, and the model's ability to generate detailed and relevant responses.
Developers can leverage Google AI Studio and vertex AI studio to prototype, scale, and build end-to-end experiences with Gemini.
Google Cloud Consulting is available to help businesses leverage Google Cloud Foundation models and build transformative applications.
The session concludes with a Q&A, addressing integration of Gemini into current applications, framework for usage, and its permeation through various Google Cloud products.