Tag Archives: AI

Android Studio uses Gemini Pro to make Android development faster and easier

Posted by Sandhya Mohan – Product Manager, Android Studio

As part of the next chapter of our Gemini era, we announced we were bringing Gemini to more products. Today we’re excited to announce that Android Studio is using the Gemini 1.0 Pro model to make Android development faster and easier, and we’ve seen significant improvements in response quality over the last several months through our internal testing. In addition, we are making this transition more apparent by announcing that Studio Bot is now called Gemini in Android Studio.

Gemini in Android Studio is an AI-powered coding assistant which can be accessed directly in the IDE. It can accelerate your ability to develop high-quality Android apps faster by helping generate code for your app, providing complex code completions, answering your questions, finding relevant resources, adding code comments and more — all without ever having to leave Android Studio. It is available in 180+ countries and territories in Android Studio Jellyfish.

If you were already using Studio Bot in the canary channel, you’ll continue experiencing the same helpful and powerful features, but you’ll notice improved quality in responses compared to earlier versions.

Ask Gemini your Android development questions

Gemini in Android Studio can understand natural language, so you can ask development questions in your own words. You can enter your questions in the chat window ranging from very simple and open-ended ones to specific problems that you need help with.

Here are some examples of the types of queries it can answer:

    • How do I add camera support to my app?
    • Using Compose, I need a login screen with the following: a username field, a password field, a 'Sign In' button, a 'Forgot Password?' link. I want the password field to obscure the input.
    • What's the best way to get location on Android?
    • I have an 'orders' table with columns like 'order_id', 'customer_id', 'product_id', 'price', and 'order_date'. Can you help me write a query that calculates the average order value per customer over the last month?
Moving image demonstrating a conversation in Android Studio

Gemini in Android Studio remembers the context of the conversation, so you can also ask follow-up questions, such as “Can you give me the code for this in Kotlin?” or “Can you show me how to do it in Compose?”

Code faster with AI powered Code Completions

Gemini in Android Studio can help you be more productive by providing you with powerful AI code completions. You can receive suggestions of multi-line code completions, suggestions for how to do comments for your code, or how to add documentation to your code.

Moving image demonstrating code completion in Android Studio

Designed with privacy in mind

Gemini in Android Studio was designed with privacy in mind. Gemini is only available after you log in and enable it. You don’t need to send your code context to take advantage of most features. By default, Gemini in Android Studio’s chat responses are purely based on conversation history, and you control whether you want to share additional context for customized responses. You can update this anytime in Android Studio > Settings at a granular project level. We also have a custom way for you to opt out certain files and folders through an .aiexclude file. Much like our work on other AI projects, we stick to a set of AI Principles that hold us accountable. Learn more here.

image of share settings in Android Studio

Build a Generative AI app using the Gemini API starter template

Not only does Android Studio use Gemini to help you be more productive, it can also help you take advantage of Gemini models to create AI-powered features in your applications. Get started in minutes using the Gemini API starter template available in the canary release – channel for Android Studio – under File > New Project > Gemini API Starter. You can also use the code sample available at File > Import Sample > Google Generative AI sample.

The Gemini API is multimodal, meaning it can support image and text inputs. For example, it can support conversational chat, summarization, translation, caption generation etc. using both text and image inputs.

image of starter templates in Android Studio

Try Gemini in Android Studio

Gemini in Android Studio is still in preview, but we have added many feature improvements — and now a major model update — since we released the experience in May 2023. It is currently no-cost for developers to try out. Now is a great time to test it and let us know what you think, before we release this experience to stable.


Stay updated on the latest by following us on LinkedIn, Medium, YouTube, or X. Let's build the future of Android apps together!

ML Olympiad 2024: Globally Distributed ML Competitions by Google ML Community

Posted by Bitnoori Keum – DevRel Community Manager

The ML Olympiad consists of Kaggle Community Competitions organized by ML GDE, TFUG, and other ML communities, aiming to provide developers with opportunities to learn and practice machine learning. Following successful rounds in 2022 and 2023, the third round has now launched with support from Google for Developers for each competition host. Over the last two rounds, 605 teams participated in 32 competitions, generating 105 discussions and 170 notebooks. We encourage you to join this round to gain hands-on experience with machine learning and tackle real-world challenges.


ML Olympiad Community Competitions

Over 20 ML Olympiad community competitions are currently open. Visit the ML Olympiad page to participate.

Smoking Detection in Patients

Predict smoking status with bio-signal ML models
Host: Rishiraj Acharya (AI/ML GDE) / TFUG Kolkata

TurtleVision Challenge

Develop a classification model to distinguish between jellyfish and plastic pollution in ocean imagery
Host: Anas Lahdhiri / MLAct

Detect hallucinations in LLMs

Detect which answers provided by a Mistral 7B instruct model are most likely hallucinations
Host: Luca Massaron (AI/ML GDE)

ZeroWasteEats

Find ML solutions to reduce food wastage
Host: Anushka Raj / TFUG Hajipur

Predicting Wellness

Predict the percentage of body fat in men using multiple regression methods
Host: Ankit Kumar Verma / TFUG Prayagraj

Offbeats Edition

Build a regression model to predict the age of the crab
Host: Ayush Morbar / Offbeats Byte Labs

Nashik Weather

Predict the condition of weather in Nashik, India
Host: TFUG Nashik

Predicting Earthquake Damage

Predict the level of damage to buildings caused by earthquake based on aspects of building location and construction
Host: Usha Rengaraju

Forecasting Bangladesh's Weather

Predict the rainy day; amount of rainfall, and average temperature for a particular day.
Host: TFUG Bangladesh (Dhaka)

CO2 Emissions Prediction Challenge

Predict CO2 emissions per capita for 2030 using global development indicators
Host: Md Shahriar Azad Evan, Shuvro Pal / TFUG North Bengal

AI & ML Malaysia

Predict loan approval status
Host: Kuan Hoong (AI/ML GDE) / Artificial Intelligence & Machine Learning Malaysia User Group

Sustainable Urban Living

Predict the habitability score of properties
Host: Ashwin Raj / BeyondML

Toxic Language (PTBR) Detection

(in local language)
Classify Brazilian Portuguese tweets in one of the two classes: toxics or non toxics.
Host: Mikaeri Ohana, Pedro Gengo, Vinicius F. Caridá (AI/ML GDE)

Improving disaster response

Predict the humanitarian aid contributions as a response to disasters occurs in the world
Host: Yara Armel Desire / TFUG Abidjan

Urban Traffic Density

Develop predictive models to estimate the traffic density in urban areas
Host: Kartikey Rawat / TFUG Durg

Know Your Customer Opinion

Classify each customer opinion into several Likert scale
Host: TFUG Surabaya

Forecasting India's Weather

Predict the temperature of the particular month
Host: Mohammed Moinuddin / TFUG Hyderabad

Classification Champ

Develop classification models to predict tumor malignancy
Host: TFUG Bhopal

AI-Powered Job Description Generator

Build a system that employs Generative AI and a chatbot interface to automatically generate job descriptions
Host: Akaash Tripathi / TFUG Ghaziabad

Machine Translation French-Wolof

Develop robust algorithms or models capable of accurately translating French sentences into Wolof.
Host: GalsenAI

Water Mapping using Satellite Imagery

Water mapping using satellite imagery and deep learning for dam drought detection
Host: Taha Bouhsine / ML Nomads


Navigating ML Olympiad

To see all the community competitions around the ML Olympiad, search "ML Olympiad" on Kaggle and look for further related posts on social media using #MLOlympiad. Browse through the available competitions and participate in those that interest you!

Build with Google AI video series, Season 2: more AI patterns

Posted by Joe Fernandez – Google AI Developer Relations

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.


AI app patterns

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!


Learn from the developers

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.


New home for AI developer content

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!

Tune Gemini Pro in Google AI Studio or with the Gemini API

Posted by Cher Hu, Product Manager and Saravanan Ganesh, Software Engineer for Gemini API

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.


What is tuning?

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.


Fine-tuning vs. Parameter Efficient Tuning

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.


Why tuning?

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.

Get started quickly with Google AI Studio


1. Create a tuned model

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.

moving image showing how to create a tuned model in Google AI Studio by opening 'New Tuned Model' from the menu

2. Select data for tuning

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.

moving image showing how to select data for tuning in Google AI Studio by importing data

3. View your tuned model

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.

moving image showing how to view your tuned model in Google AI Studio by importing data

4. Run your tuned model anytime

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.

moving image demonstrating what it looks like to run your tuned model in Google AI Studio after importing data

Tuning with the Gemini API

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.