Posted by Lauren Usui and Joe Fernandez
Artificial intelligence is a topic of kitchen table conversations around the world today, and as AI becomes more accessible for users and developers, we want to make it easier and more useful for everyone. This year at Google I/O, we highlighted how we are helping developers like you build with generative AI, use machine learning in spreadsheets and applications, create ML models from the ground up, and scale them up to serve millions of users.
While AI technology is advancing rapidly, we must continue to ensure it is used responsibly. So we also took some time to explain how Google is taking a principled approach to applying generative AI and how you can apply our guidelines and tools to make sure your AI-powered products and projects are built responsibly to serve all your users.
If you are new to AI and want to get a quick overview of the technology, check out the getting started video from Google’s AI advocate lead, Laurence Moroney.
Develop generative AI apps with PaLM 2
Everyone seems to be chatting with—or about—generative AI recently, and we want you to be able to use Google’s latest large language model, PaLM 2, to power new and helpful experiences for your users with the PaLM API. Our session on Generative AI reveals more about how you can easily prompt models with MakerSuite to quickly prototype generative AI applications. We demonstrate how you can use the PaLM API for prompting using examples, conversational chat interactions, and using embedding functionality to compress and compare text data in useful ways. We also showed off how to use the PaLM API in Google Colab notebooks with a simple, magical syntax. Check out this talk and sign up to request access to the PaLM API and MakerSuite!
Crunch numbers with AI-powered spreadsheets
Hundreds of millions of people use spreadsheets to organize, manage, and analyze data for everything from business transactions, to inventory accounting, to family budgets. We’re making it easy for everyone to bring the power of AI into spreadsheets with Simple ML for Sheets, a Google Sheets add-on. We recently updated this tool to include anomaly detection and forecasting features. Check out the demonstration of how to predict missing data values and forecast sales with the tool. No coding required!
Simplify on-device ML applications with MediaPipe
AI is finding its way into applications across multiple platforms and MediaPipe makes it easy to build, customize, and deploy on-device ML solutions. We upgraded MediaPipe Solutions this year, improving existing solutions and adding new ones, including interactive segmentation to blur the background behind a selected subject and face stylization to render that selfie in your favorite graphic style.
Do more with Web ML
Every week, hundreds of thousands of developers build AI-powered applications to run in the browser or Node.js using JavaScript and web technologies. Web ML has advanced in multiple areas, and we provide a round up of the top updates in this year’s I/O talk. We announced Visual Blocks for ML, an open JavaScript framework for quickly and interactively building custom ML pipelines. You can now run machine learning models even faster with improved WebGL performance and the release of WebGPU in Chrome. More tools and resources are also now available for web ML developers, including TensorFlow Decision Forest support, a visual debugger for models, JAX to JS conversion support, and a new Zero to Hero training course to grow your skills in Web ML.
Find pre-trained models fast with Kaggle Models
Building machine learning models can take a huge amount of time and effort: collecting data, training, evaluating, and optimizing. Kaggle is making it a whole lot easier for developers to discover and use pretrained models. With Kaggle Models, you can search thousands of open-licensed models from leading ML researchers for multiple ML platforms. Find the model you need quickly with filters for tasks, supported data types, model architecture, and more. Combine this new feature with Kaggle’s huge repository of over 200K datasets and accelerate your next ML project.
Apply ML to vision and text with Keras
Lots of developers are exploring AI technologies and many of you are interested in working on computer vision and natural language processing applications. Keras released new, easy-to-use libraries for computer vision and natural language processing with KerasCV and KerasNLP. Using just a few lines of code, you can apply the latest techniques and models for data augmentation, object detection, image and text generation, and text classification. These new libraries provide modular implementations that are easy to customize and are tightly integrated with the broader TensorFlow ecosystem including TensorFlow Lite, TPUs, and DTensor.
Build ML flexibly and scalably with TensorFlow
With one of the largest ML development communities in the world, the TensorFlow ecosystem helps hundreds of thousands of developers like you build, train, deploy, and manage machine learning models. ML technology is rapidly evolving, and we’re upgrading TensorFlow with new tools to give you more flexibility, scalability, and efficiency. If you’re using JAX, you can now bring your model components into the TensorFlow ecosystem with JAX2TF. We also improved DTensor support for model parallelization, allowing you to scale up execution of larger models by running portions of a single model, or shards, across multiple machines. We also announced a toolkit for applying quantization techniques to practically any TensorFlow model, helping you gain substantial efficiency improvements for your AI applications. The quantization toolkit will be available later this year.
Scale large language models with Google Cloud
When it’s time to deploy your AI-powered applications to your business, enterprise, or the world, you need reliable tools and services that scale with you. Google Cloud’s Vertex AI is an end-to-end ML platform that helps you develop ML models quickly and easily, and deploy them at any scale. To help you build generative AI technology for your product or business, we’ve introduced Model Garden and the Generative AI Studio as part of the Vertex AI platform. Model Garden gives you quick access to the latest foundation models such as Google PaLM 2, and many more to build AI-powered applications for text processing, imagery, and code. Generative AI Studio lets you quickly prototype generative AI applications right in your browser, and when you are ready to deploy, Vertex AI and Google Cloud services enable you to scale up to hundreds, thousands, or millions of users.
Explore new resources to build with Google AI
As tools, technology, and techniques for AI development rapidly advance, finding what you need to get started or take the next step with your project can be challenging. We’re making it easier to find the right resources to accelerate your AI development at Build with Google AI. This new site brings together tools, guidance, and community for building, deploying, and managing ML. Whether you are creating AI for on-device apps or deploying AI at scale, we help you navigate the options and find your path. Check out our latest toolkits on Building an LLM on Android and Text Classification with Keras.
Making Generative AI safe and responsible
AI is a powerful tool, and it’s up to all of us to ensure that it is used responsibly and for the benefit of all. We’re committed to ensuring Google’s AI systems are developed according to our AI principles. This year at Google I/O, we shared how we’ve created guidelines and tools for building generative AI safely and responsibly, and how you can apply those same guidelines and tools for your own projects.
Aaannnd that’s a wrap! Check out the full playlist of all the AI-related sessions we mentioned above. We are excited to share these new tools, resources, and technologies with you, and we can’t wait to see what you build with them!
Posted by Lauren Usui and Joe Fernandez
Artificial intelligence is a topic of kitchen table conversations around the world today, and as AI becomes more accessible for users and developers, we want to make it easier and more useful for everyone. This year at Google I/O, we highlighted how we are helping developers like you build with generative AI, use machine learning in spreadsheets and applications, create ML models from the ground up, and scale them up to serve millions of users.
While AI technology is advancing rapidly, we must continue to ensure it is used responsibly. So we also took some time to explain how Google is taking a principled approach to applying generative AI and how you can apply our guidelines and tools to make sure your AI-powered products and projects are built responsibly to serve all your users.
If you are new to AI and want to get a quick overview of the technology, check out the getting started video from Google’s AI advocate lead, Laurence Moroney.
Develop generative AI apps with PaLM 2
Everyone seems to be chatting with—or about—generative AI recently, and we want you to be able to use Google’s latest large language model, PaLM 2, to power new and helpful experiences for your users with the PaLM API. Our session on Generative AI reveals more about how you can easily prompt models with MakerSuite to quickly prototype generative AI applications. We demonstrate how you can use the PaLM API for prompting using examples, conversational chat interactions, and using embedding functionality to compress and compare text data in useful ways. We also showed off how to use the PaLM API in Google Colab notebooks with a simple, magical syntax. Check out this talk and sign up to request access to the PaLM API and MakerSuite!
Crunch numbers with AI-powered spreadsheets
Hundreds of millions of people use spreadsheets to organize, manage, and analyze data for everything from business transactions, to inventory accounting, to family budgets. We’re making it easy for everyone to bring the power of AI into spreadsheets with Simple ML for Sheets, a Google Sheets add-on. We recently updated this tool to include anomaly detection and forecasting features. Check out the demonstration of how to predict missing data values and forecast sales with the tool. No coding required!
Simplify on-device ML applications with MediaPipe
AI is finding its way into applications across multiple platforms and MediaPipe makes it easy to build, customize, and deploy on-device ML solutions. We upgraded MediaPipe Solutions this year, improving existing solutions and adding new ones, including interactive segmentation to blur the background behind a selected subject and face stylization to render that selfie in your favorite graphic style.
Do more with Web ML
Every week, hundreds of thousands of developers build AI-powered applications to run in the browser or Node.js using JavaScript and web technologies. Web ML has advanced in multiple areas, and we provide a round up of the top updates in this year’s I/O talk. We announced Visual Blocks for ML, an open JavaScript framework for quickly and interactively building custom ML pipelines. You can now run machine learning models even faster with improved WebGL performance and the release of WebGPU in Chrome. More tools and resources are also now available for web ML developers, including TensorFlow Decision Forest support, a visual debugger for models, JAX to JS conversion support, and a new Zero to Hero training course to grow your skills in Web ML.
Find pre-trained models fast with Kaggle Models
Building machine learning models can take a huge amount of time and effort: collecting data, training, evaluating, and optimizing. Kaggle is making it a whole lot easier for developers to discover and use pretrained models. With Kaggle Models, you can search thousands of open-licensed models from leading ML researchers for multiple ML platforms. Find the model you need quickly with filters for tasks, supported data types, model architecture, and more. Combine this new feature with Kaggle’s huge repository of over 200K datasets and accelerate your next ML project.
Apply ML to vision and text with Keras
Lots of developers are exploring AI technologies and many of you are interested in working on computer vision and natural language processing applications. Keras released new, easy-to-use libraries for computer vision and natural language processing with KerasCV and KerasNLP. Using just a few lines of code, you can apply the latest techniques and models for data augmentation, object detection, image and text generation, and text classification. These new libraries provide modular implementations that are easy to customize and are tightly integrated with the broader TensorFlow ecosystem including TensorFlow Lite, TPUs, and DTensor.
Build ML flexibly and scalably with TensorFlow
With one of the largest ML development communities in the world, the TensorFlow ecosystem helps hundreds of thousands of developers like you build, train, deploy, and manage machine learning models. ML technology is rapidly evolving, and we’re upgrading TensorFlow with new tools to give you more flexibility, scalability, and efficiency. If you’re using JAX, you can now bring your model components into the TensorFlow ecosystem with JAX2TF. We also improved DTensor support for model parallelization, allowing you to scale up execution of larger models by running portions of a single model, or shards, across multiple machines. We also announced a toolkit for applying quantization techniques to practically any TensorFlow model, helping you gain substantial efficiency improvements for your AI applications. The quantization toolkit will be available later this year.
Scale large language models with Google Cloud
When it’s time to deploy your AI-powered applications to your business, enterprise, or the world, you need reliable tools and services that scale with you. Google Cloud’s Vertex AI is an end-to-end ML platform that helps you develop ML models quickly and easily, and deploy them at any scale. To help you build generative AI technology for your product or business, we’ve introduced Model Garden and the Generative AI Studio as part of the Vertex AI platform. Model Garden gives you quick access to the latest foundation models such as Google PaLM 2, and many more to build AI-powered applications for text processing, imagery, and code. Generative AI Studio lets you quickly prototype generative AI applications right in your browser, and when you are ready to deploy, Vertex AI and Google Cloud services enable you to scale up to hundreds, thousands, or millions of users.
Explore new resources to build with Google AI
As tools, technology, and techniques for AI development rapidly advance, finding what you need to get started or take the next step with your project can be challenging. We’re making it easier to find the right resources to accelerate your AI development at Build with Google AI. This new site brings together tools, guidance, and community for building, deploying, and managing ML. Whether you are creating AI for on-device apps or deploying AI at scale, we help you navigate the options and find your path. Check out our latest toolkits on Building an LLM on Android and Text Classification with Keras.
Making Generative AI safe and responsible
AI is a powerful tool, and it’s up to all of us to ensure that it is used responsibly and for the benefit of all. We’re committed to ensuring Google’s AI systems are developed according to our AI principles. This year at Google I/O, we shared how we’ve created guidelines and tools for building generative AI safely and responsibly, and how you can apply those same guidelines and tools for your own projects.
Aaannnd that’s a wrap! Check out the full playlist of all the AI-related sessions we mentioned above. We are excited to share these new tools, resources, and technologies with you, and we can’t wait to see what you build with them! Read More Generative AI, Google AI, Google I/O 2023, Kaggle, Keras, large language models, MakerSuite, MediaPipe, PaLM API, Responsible AI, Simple ML for Sheets, Tensorflow, Vertex AI, Web ML