Today we’re releasing our “AI Sprinters” report, outlining ways for developing countries to take advantage of AI’s potential.
We’re sharing a few insights from a survey with nonprofits about how they’re using generative AI.
Learn about the nonprofits joining our newest accelerator focused on generative AI.
An overview of our paper in Nature on how we are using AI to help scale flood forecasting to vulnerable communities around the world.
We are off to another exciting year in Artificial Intelligence (AI) and it's time to build more applications with Google AI technology! The Build with Google AI video series is for developers looking to build helpful and practical applications with AI. We focus on useful code projects you can implement and extend in an afternoon to bring the power of artificial intelligence into your workflow or organization. Our first season received over 100,000 views in six weeks! We are glad to see that so many of you liked the series, and we are excited to bring you even more Google AI application projects.
Today, we are launching Season 2 of the Build with Google AI series, featuring projects built with Google's Gemini API technology. The launch of Gemini and the Gemini API has brought developers even more advanced AI capabilities, including advanced reasoning, content generation, information synthesis, and image interpretation. Our goal with this season is to help you put those capabilities to work for you and your organizations.
The Build with Google AI series features practical application code projects created for you to use and customize. However, we know that you are the best judge of what you or your organization needs to solve day-to-day problems and get work done. That's why each application we feature in this series is also meant to be used as an AI pattern. You can extend the applications immediately to solve problems and provide value for your business, and these applications show you a general coding pattern for getting value out of AI technology.
For this second season of this series, we show how you can leverage Google's Gemini AI model capabilities for applications. Here's what's coming up:
- AI Slides Reviewer with Google Workspace (3/20) - Image interpretation is one of the Gemini model's biggest new features. We show you how to make practical use of it with a presentation review app for Google Slides that you can customize with your organization's guidelines and recommendations.
- AI Flutter Code Agent with Gemini API (3/27) - Code generation was the most popular episode from last season, so we are digging deeper into this topic. Build a code generation extension to write Flutter code and explore user interface designs and looks with just a few words of description.
- AI Data Agent with Google Cloud (4/3) - Why write code to extract data when you can just ask for it? Build a web application that uses Gemini API's Function Calling feature to translate questions into code calls and data into plain language answers.
Season 1 upgraded to Gemini API: We've upgraded Season 1 tutorials and code projects to use the Gemini API so you can take advantage of the latest in generative AI technology from Google. Check them out!
Just like last season, we'll go back to the studio to talk with coders who built these projects so they can share what they learned along the way. How do you make the Gemini model review an entire presentation? What's the most effective way to generate code with AI? How do you get a database to answer questions with the Gemini API? Get insights into coding with AI to jump start your own development project.
Developers interested in Google's AI offerings now have a new home at ai.google.dev. There you'll find a wealth of resources for building with AI from Google, including the Build with Google AI tutorials. Stay tuned for much more content through the rest of the year.
We are excited to bring you the second season of Build with Google AI – check out Season 2 right now! Use those video comments to let us know what you think and tell us what you'd like to see in future episodes.
Keep learning! Keep building!
World-famous Go champion Lee Sae Dol reflects on his match against our AI system AlphaGo eight years ago.
The following post was originally published in October 2023. Today, we've updated the post to share how you can easily tune Gemini models in Google AI Studio or with the Gemini API.
Last year, we launched Gemini 1.0 Pro, our mid-sized multimodal model optimized for scaling across a wide range of tasks. And with 1.5 Pro this year, we demonstrated the possibilities of what large language models can do with an experimental 1M context window. Now, to quickly and easily customize the generally available Gemini 1.0 Pro model (text) for your specific needs, we’ve added Gemini Tuning to Google AI Studio and the Gemini API.
Developers often require higher quality output for custom use cases than what can be achieved through few-shot prompting. Tuning improves on this technique by further training the base model on many more task-specific examples—so many that they can’t all fit in the prompt.
You may have heard about classic “fine-tuning” of models. This is where a pre-trained model is adapted to a particular task by training it on a smaller set of task-specific labeled data. But with today’s LLMs and their huge number of parameters, fine-tuning is complex: it requires machine learning expertise, lots of data, and lots of compute.
Tuning in Google AI Studio uses a technique called Parameter Efficient Tuning (PET) to produce higher-quality customized models with lower latency compared to few-shot prompting and without the additional costs and complexity of traditional fine-tuning. In addition, PET produces high quality models with as little as a few hundred data points, reducing the burden of data collection for the developer.
Tuning enables you to customize Gemini models with your own data to perform better for niche tasks while also reducing the context size of prompts and latency of the response. Developers can use tuning for a variety of use cases including but not limited to:
- Classification: Run natural language tasks like classifying your data into predefined categories, without needing tons of manual work or tools.
- Information extraction: Extract structured information from unstructured data sources to support downstream tasks within your product.
- Structured output generation: Generate structured data, such as tables, quickly and easily.
- Critique Models: Use tuning to create critique models to evaluate output from other models.
It’s easy to tune models in Google AI Studio. This removes any need for engineering expertise to build custom models. Start by selecting “New tuned model” in the menu bar on the left.
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You can tune your model from an existing structured prompt or import data from Google Sheets or a CSV file. You can get started with as few as 20 examples and to get the best performance, we recommend providing a dataset of at least 100 examples.
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View your tuning progress in your library. Once the model has finished tuning, you can view the details by clicking on your model. Start running your tuned model through a structured or freeform prompt.
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You can also access your newly tuned model by creating a new structured or freeform prompt and selecting your tuned model from the list of available models.
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Google AI Studio is the fastest and easiest way to start tuning Gemini models. You can also access the feature via the Gemini API by passing the training data in the API request when creating a tuned model. Learn more about how to get started here.
We’re excited about the possibilities that tuning opens up for developers and can’t wait to see what you build with the feature. If you’ve got some ideas or use cases brewing, share them with us on X (formerly known as Twitter) or Linkedin.
Hellmann's introduces Meal Reveal, a new tool powered by Google Cloud AI that analyses fridge contents and suggests matching recipes.
Google I/O is arriving this year on May 14th and you’re invited to join us online! I/O offers something for everyone, whether you are developing a new application, modernizing an existing one, or transforming it into a business.
The Gemini era unlocks new possibilities for developers to build creative and productive AI-enabled applications. I/O is where you’ll hear how you can get from idea to production AI applications faster. We’re excited to share what’s new for mobile, web, and multiplatform development, and how to scale your applications in the cloud. You will be able to dive deeper into topics that interest you with over 100 sessions, workshops, codelabs, and demos.
Visit the Google I/O site and register to stay informed about I/O and other related events coming soon. The livestreamed keynotes start May 14 at 10am PT, so mark your calendar.
If you haven’t already, go try out our newest Google I/O puzzle and head to @googlefordevs on Instagram if you need a hint.
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| Gema Parreño Piqueras, AI/ML GDE, Madrid, Spain |
Gema's dedication to the GDE program makes her a true leader within the Google Developers community, and her work in Artificial Intelligence and Machine Learning pushes the boundaries of Google's technological capabilities.
Gema is a force to be reckoned with in the world of data science. As a data scientist at Izertis and a GDE, she's not only making significant contributions to the field of AI/ML but also blazing a trail for women in tech. Her unique background in architecture and her passion for problem-solving led her to an impressive career in AI/ML and development of her extraordinary project – helping NASA track asteroids! Learn more about her projects incorporating AI:
Gema's architectural skills proved invaluable when she turned her attention to AI. In 2016, she created the program Deep Asteroid for NASA's International Space Apps Challenge. This innovative program assists scientists in detecting, tracking, and classifying asteroids, potentially protecting our planet from future threats.
Intrigued by the potential of AI, Gema embarked on a journey that merged her architectural background with cutting-edge technology. Her experience with 3D modeling translated seamlessly into the world of machine learning, giving her a fresh perspective. Over the past seven years, she's overcome challenges and established herself as a true expert.
As a Google Developer Expert, Gema has found a vibrant community that has fueled her growth. She has attended numerous GDE events throughout Europe and had the opportunity to collaborate with Google teams. This experience was instrumental in the development of Deep Asteroid, demonstrating the power of community and access to advanced technology.
Gema’s advice for women aspiring to enter the field is simple and powerful: "Don't be afraid to experiment, fail, and learn from those failures. Persistence and a willingness to dive into the unknown are what will set you apart." Gema encourages women to find supportive communities, like the GDE program, where they can network, learn, and grow.
You can find Gema on LinkedIn, GitHub and X (formerly known as twitter).
The Google Developer Experts (GDE) program is a global network of highly experienced technology experts, influencers, and thought leaders who actively support developers, companies, and tech communities by speaking at events and publishing content.
Posted by Joe Fernandez – Google AI Developer Relations
Posted by Cher Hu, Product Manager and Saravanan Ganesh, Software Engineer for Gemini API
Posted by Jeanine Banks – VP & General Manager, Developer X, and Head of Developer Relations
Posted by Justyna Politanska-Pyszko – Program Manager, Google Developer Experts