Tag Archives: GenerativeAI

PyTorch machine learning models on Android

Posted by Paul Ruiz – Senior Developer Relations Engineer

Earlier this year we launched Google AI Edge, a suite of tools with easy access to ready-to-use ML tasks, frameworks that enable you to build ML pipelines, and run popular LLMs and custom models – all on-device. For AI on Android Spotlight Week, the Google team is highlighting various ways that Android developers can use machine learning to help improve their applications.

In this post, we'll dive into Google AI Edge Torch, which enables you to convert PyTorch models to run locally on Android and other platforms, using the Google AI Edge LiteRT (formerly TensorFlow Lite) and MediaPipe Tasks libraries. For insights on other powerful tools, be sure to explore the rest of the AI on Android Spotlight Week content.

To get started with Google AI Edge easier, we've provided samples available on GitHub as an executable codelab. They demonstrate how to convert the MobileViT model for image classification (compatible with MediaPipe Tasks) and the DIS model for segmentation (compatible with LiteRT).

a red Android figurine is shown next to a black and white silhouette of the same figure, labeled 'Original Image' and 'PT Mask' respectively, demonstrating image segmentation.
DIS model output

This blog guides you through how to use the MobileViT model with MediaPipe Tasks. Keep in mind that the LiteRT runtime provides similar capabilities, enabling you to build custom pipelines and features.

Convert MobileViT model for image classification compatible with MediaPipe Tasks

Once you've installed the necessary dependencies and utilities for your app, the first step is to retrieve the PyTorch model you wish to convert, along with any other MobileViT components you might need (such as an image processor for testing).

from transformers import MobileViTImageProcessor, MobileViTForImageClassification

hf_model_path = 'apple/mobilevit-small'
processor = MobileViTImageProcessor.from_pretrained(hf_model_path)
pt_model = MobileViTForImageClassification.from_pretrained(hf_model_path)

Since the end result of this tutorial should work with MediaPipe Tasks, take an extra step to match the expected input and output shapes for image classification to what is used by the MediaPipe image classification Task.

class HF2MP_ImageClassificationModelWrapper(nn.Module):

  def __init__(self, hf_image_classification_model, hf_processor):
    super().__init__()
    self.model = hf_image_classification_model
    if hf_processor.do_rescale:
      self.rescale_factor = hf_processor.rescale_factor
    else:
      self.rescale_factor = 1.0

  def forward(self, image: torch.Tensor):
    # BHWC -> BCHW.
    image = image.permute(0, 3, 1, 2)
    # RGB -> BGR.
    image = image.flip(dims=(1,))
    # Scale [0, 255] -> [0, 1].
    image = image * self.rescale_factor
    logits = self.model(pixel_values=image).logits  # [B, 1000] float32.
    # Softmax is required for MediaPipe classification model.
    logits = torch.nn.functional.softmax(logits, dim=-1)

    return logits


hf_model_path = 'apple/mobilevit-small'
hf_mobile_vit_processor = MobileViTImageProcessor.from_pretrained(hf_model_path)
hf_mobile_vit_model = MobileViTForImageClassification.from_pretrained(hf_model_path)
wrapped_pt_model = HF2MP_ImageClassificationModelWrapper(
hf_mobile_vit_model, hf_mobile_vit_processor).eval()

Whether you plan to use the converted MobileViT model with MediaPipe Tasks or LiteRT, the next step is to convert the model to the .tflite format.

First, match the input shape. In this example, the input shape is 1, 256, 256, 3 for a 256x256 pixel three-channel RGB image.

Then, call AI Edge Torch's convert function to complete the conversion process.

import ai_edge_torch

sample_args = (torch.rand((1, 256, 256, 3)),)
edge_model = ai_edge_torch.convert(wrapped_pt_model, sample_args)

After converting the model, you can further refine it by incorporating metadata for the image classification labels. MediaPipe Tasks will utilize this metadata to display or return pertinent information after classification.

from mediapipe.tasks.python.metadata.metadata_writers import image_classifier
from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
from mediapipe.tasks.python.vision.image_classifier import ImageClassifier
from pathlib import Path

flatbuffer_file = Path('hf_mobile_vit_mp_image_classification_raw.tflite')
edge_model.export(flatbuffer_file)
tflite_model_buffer = flatbuffer_file.read_bytes()

//Extract the image classification labels from the HF models for later integration into the TFLite model.
labels = list(hf_mobile_vit_model.config.id2label.values())

writer = image_classifier.MetadataWriter.create(
    tflite_model_buffer,
    input_norm_mean=[0.0], #  Normalization is not needed for this model.
    input_norm_std=[1.0],
    labels=metadata_writer.Labels().add(labels),
)
tflite_model_buffer, _ = writer.populate()

With all of that completed, it's time to integrate your model into an Android app. If you're following the official Colab notebook, this involves saving the model locally. For an example of image classification with MediaPipe Tasks, explore the GitHub repository. You can find more information in the official Google AI Edge documentation.

moving image of Newly converted ViT model with MediaPipe Tasks
Newly converted ViT model with MediaPipe Tasks

After understanding how to convert a simple image classification model, you can use the same techniques to adapt various PyTorch models for Google AI Edge LiteRT or MediaPipe Tasks tooling on Android.

For further model optimization, consider methods like quantizing during conversion. Check out the GitHub example to learn more about how to convert a PyTorch image segmentation model to LiteRT and quantize it.

What's Next

To keep up to date on Google AI Edge developments, look for announcements on the Google for Developers YouTube channel and blog.

We look forward to hearing about how you're using these features in your projects. Use #AndroidAI hashtag to share your feedback or what you've built in social media and check out other content in AI on Android Spotlight Week!

How to bring your AI Model to Android devices

Posted by Kateryna Semenova – Senior Developer Relations Engineer and Mark Sherwood – Senior Product Manager

During AI on Android Spotlight Week, we're diving into how you can bring your own AI model to Android-powered devices such as phones, tablets, and beyond. By leveraging the tools and technologies available from Google and other sources, you can run sophisticated AI models directly on these devices, opening up exciting possibilities for better performance, privacy, and usability.

Understanding on-device AI

On-device AI involves deploying and executing machine learning or generative AI models directly on hardware devices, instead of relying on cloud-based servers. This approach offers several advantages, such as reduced latency, enhanced privacy, cost saving and less dependence on internet connectivity.

For generative text use cases, explore Gemini Nano that is now available in experimental access through its SDK. For many on-device AI use cases, you might want to package your own models in your app. Today we will walk through how to do so on Android.

Key resources for on-device AI

The Google AI Edge platform provides a comprehensive ecosystem for building and deploying AI models on edge devices. It supports various frameworks and tools, enabling developers to integrate AI capabilities seamlessly into their applications. The Google AI Edge platforms consists of:

    • MediaPipe Tasks - Cross-platform low-code APIs to tackle common generative AI, vision, text, and audio tasks
    • LiteRT (formerly known as TensorFlow Lite) - Lightweight runtime for deploying custom machine learning models on Android
    • MediaPipe Framework - Pipeline framework for chaining multiple ML models along with pre and post processing logic


Google AI Edge Logo

How to build custom AI features on Android

    1. Define your use case: Before diving into technical details, it's crucial to clearly define what you want your AI feature to achieve. Whether you're aiming for image classification, natural language processing, or another application, having a well-defined goal will guide your development process.

    2. Choose the right tools and frameworks: Depending on your use case, you might be able to use an out of the box solution or you might need to create or source your own model. Look through MediaPipe Tasks for common solutions such as gesture recognition, image segmentation or face landmark detection. If you find a solution that aligns with your needs, you can proceed directly to the testing and deployment step.


Google AI Edge Logo

    If you need to create or source a custom model for your use case, you will need an on-device ML framework such as LiteRT (formerly TensorFlow Lite). LiteRT is designed specifically for mobile and edge devices and provides a lightweight runtime for deploying machine learning models. Simply follow these substeps:

        a. Develop and train your model: Develop your AI model using your chosen framework. Training can be performed on a powerful machine or cloud environment, but the model should be optimized for deployment on a device. Techniques like quantization and pruning can help reduce the model size and improve inference speed. Model Explorer can help understand and explore your model as you're working with it.

        b. Convert and optimize the model: Once your model is trained, convert it to a format suitable for on-device deployment. LiteRT, for example, requires conversion to its specific format. Optimization tools can help reduce the model’s footprint and enhance performance. AI Edge Torch allows you to convert PyTorch models to run locally on Android and other platforms, using Google AI Edge LiteRT and MediaPipe Tasks libraries.

        c. Accelerate your model: You can speed up model inference on Android by using GPU and NPU. LiteRT’s GPU delegate allows you to run your model on GPU today. We’re working hard on building the next generation of GPU and NPU delegates that will make your models run even faster, and enable more models to run on GPU and NPU. We’d like to invite you to participate in our early access program to try out this new GPU and NPU infrastructure. We will select participants out on a rolling basis so don’t wait to reach out.

    3. Test and deploy: To ensure that your model delivers the expected performance across various devices, rigorous testing is crucial. Deploy your app to users after completing the testing phase, offering them a seamless and efficient AI experience. We're working on bringing the benefits of Google Play and Android App Bundles to delivering custom ML models for on-device AI features. Play for On-device AI takes the complexity out of launching, targeting, versioning, downloading, and updating on-device models so that you can offer your users a better user experience without compromising your app's size and at no additional cost. Complete this form to express interest in joining the Play for On-device AI early access program.

Build trust in AI through privacy and transparency

With the growing role of AI in everyday life, ensuring models run as intended on devices is crucial. We're emphasizing a "zero trust" approach, providing developers with tools to verify device integrity and user control over their data. In the zero trust approach, developers need the ability to make informed decisions about the device's trustworthiness.

The Play Integrity API is recommended for developers looking to verify their app, server requests, and the device environment (and, soon, the recency of security updates on the device). You can call the API at important moments before your app’s backend decides to download and run your models. You can also consider turning on integrity checks for installing your app to reduce your app’s distribution to unknown and untrusted environments.

Play Integrity API makes use of Android Platform Key Attestation to verify hardware components and generate integrity verdicts across the fleet, eliminating the need for most developers to directly integrate different attestation tools and reducing device ecosystem complexity. Developers can use one or both of these tools to assess device security and software integrity before deciding whether to trust a device to run AI models.

Conclusion

Bringing your own AI model to a device involves several steps, from defining your use case to deploying and testing the model. With resources like Google AI Edge, developers have access to powerful tools and insights to make this process smoother and more effective. As on-device AI continues to evolve, leveraging these resources will enable you to create cutting-edge applications that offer enhanced performance, privacy, and user experience. We are currently seeking early access partners to try out some of our latest tools and APIs at Google AI Edge. Simply fill in this form to connect and explore how we can work together to make your vision a reality.

Dive into these resources and start exploring the potential of on-device AI—your next big innovation could be just a model away!

Use #AndroidAI hashtag to share your feedback or what you've built on social media and catch up with the rest of the updates being shared during Spotlight Week: AI on Android.

An introduction to privacy and safety for Gemini Nano

Posted by Terence Zhang – Developer Relations Engineer, and Adrien Couque – Software Engineer

AI can enhance the user experience and productivity of Android apps. If you're looking to build GenAI features that benefit from additional data privacy or offline inference, on-device GenAI is a good choice as it processes prompts directly on your device without any server calls.

Gemini Nano is the most efficient model in Google's Gemini family, and Android’s foundational model for running on-device GenAI. It's supported by AICore, a system service that works behind the scenes to centralize the model’s runtime, ensure its safe execution, and protect your privacy. With Gemini Nano, apps can offer more personalized and reliable AI experiences without sending your data off the device.

In this blog post, we'll provide an introductory look into how Gemini Nano and AICore work together to deliver powerful on-device AI capabilities while prioritizing users’ privacy and safety.

Private Compute Core (PCC) compliance

At Google I/O 2021, we introduced Private Compute Core (PCC), a secure environment designed to keep your data private. At I/O in 2024, we shared that AICore is PCC compliant, meaning that it operates under strict privacy rules. It can only interact with a limited set of other system packages that are also PCC compliant, and it cannot directly access the internet. Any requests to download models or other information are routed through a separate, open-source companion APK called Private Compute Services.

This framework helps protect your privacy while still allowing apps to benefit from the power of Gemini Nano. Consider a keyboard application using Gemini Nano for a reply suggestion feature. Without PCC, the keyboard would require direct access to the conversation context. With PCC, the code that has access to the conversation runs in a secure sandbox and interacts directly with Gemini Nano to generate suggestions on behalf of the keyboard. This allows the keyboard app to benefit from Gemini Nano's capabilities without directly accessing or storing sensitive conversation data. You can find out more about how this works in the PCC Whitepaper.

Protecting your privacy through data isolation

AICore is built to isolate each request to protect your privacy. This prevents apps from accessing data that does not belong to them. Requests are handled independently and processed from a single app at a time to mitigate the risk of data being exposed to other apps.

Additionally, AICore doesn't store any record of the input data or the resulting outputs after processing each request. This design, combined with the fact that Gemini Nano’s inference happens directly on your device, helps ensure your app’s data stays private and secure.

Prioritizing Safety in Gemini Nano

A flow chart illustrating the architecture of an AI system, highlighting the flow of data and processing steps from the 'Client app' to the 'Service' component, including 'Input safety signals', 'Output safety signals', 'Weights' and 'Runtime'

We're committed to building AI responsibly, and that includes making sure Gemini Nano is safe. We've implemented multiple layers of protection to limit harmful or unintended results:

    • Native model safety: All Gemini models, including Gemini Nano, are trained to be safety-aware out of the box. This means safety considerations are built into the core of the model, not just added as an afterthought.
    • Safety aware fine-tuning: We use a LoRA fine-tuning block to adapt Gemini Nano for the needs of specific apps. When we train the LoRA block, we incorporate safety data specific to the app’s use case to preserve and even enhance the model's safety features during fine-tuning where applicable.
    • Safety filters on input and output: As a final safeguard, both the input prompt and results generated by the Gemini Nano runtime are evaluated against our safety filters before providing the results to the app. This helps prevent unsafe content from slipping through, without any loss in quality.

These layers of protection work together to ensure that Gemini Nano provides a safe and helpful experience for everyone.


Get started

Learn more about Gemini Nano for app development, and try it out in your own app!

Be sure to check out the other amazing AI on Android Spotlight week content!

Gemini Nano is now available on Android via experimental access

Posted by Taj Darra – Product Manager

Gemini, introduced last year, is Google’s most capable family of models yet; designed for flexibility, it can run on everything from data centers to mobile devices. Since announcing Gemini Nano, our most efficient model built for on-device tasks, we've been working with a limited set of partners to support a range of use cases for their apps.

Today, we’re opening up access to experiment with Gemini Nano to all Android developers with the AI Edge SDK via AICore. Developers will initially have access to experiment with text-to-text prompts on Pixel 9 series devices. Support for more devices and modalities will be added in the future. Check out our documentation and video to get started. Note that experimental access is for development purposes, and is not for production usage at this time.


Fast, private and cost-effective on-device AI

On-device generative AI processes prompts directly on your device without server calls. It offers many benefits: sensitive user data is processed locally on the device, full functionality without internet connectivity, and no additional monetary cost for each inference.

Since on-device generative AI models run on devices with less computational power than cloud servers, they are significantly smaller and less generalized than their cloud-based equivalents. As a result, the model works best for tasks where the requests can be clearly specified rather than open-ended use cases such as chatbots. Here are some use cases you can try:

    • Rephrasing - Rephrasing and rewriting text to change the tone to be more casual or formal.
    • Smart reply - Given several chat messages in a thread, suggest the next likely response.
    • Proofreading - Removing spelling or grammatical errors from text.
    • Summarization - Generating a summary of a long document, either as a paragraph or as bullet points.

Check out our prompting strategies to achieve best results when experimenting with the above use-cases. If you want to test your own use case, you can download our sample app for an easy way to start experimenting with Gemini Nano.


Gemini Nano performance and usage

Compared to its predecessor, the model being made available to developers today (referred to in the academic paper as “Nano 2”) delivers a substantial improvement in quality. At nearly twice the size of the predecessor (“Nano 1”), it excels in both academic benchmarks and real-world applications, offering capabilities that rival much larger models.

MMLU (5-shot)*

MATH (4-shot)*

Paraphrasing**

Smart Reply**
Nano 1

46%

14%

44%

44%
Nano 2

56%

23%

90%

82%
* As reported in Gemini: A Family of Highly Capable Multimodal Models. Note that both these models are a part of our Gemini 1.0 series.
** Percentage of good answers measured on public datasets via an autorater powered by Gemini 1.5 Pro.

Gemini Nano is already in use by Google apps. Pixel Screenshots, Talkback, Recorder and many more have leveraged Gemini Nano’s text and image understanding to deliver new experiences:

    • Talkback - Android’s accessibility app leverages Gemini Nano’s multimodal capabilities to improve image descriptions for blind and low vision users.
    moving image of Talkback app UI highlighting improved image descriptions with multimodality model for users with low vision

    • Pixel Recorder - Gemini Nano with Multimodality model enables support for longer recordings and higher quality summaries.
moving image of Talkback app UI highlighting improved image descriptions with multimodality model for users with low vision

Seamless model integration with AI Edge SDK using AICore

Integrating generative AI models directly into mobile apps is challenging due to the significant computational resources and storage space they require. To address this challenge, we developed AICore, a new system service in Android. AICore allows you to benefit from AI running directly on the device without needing to distribute runtimes, models and other components yourself.

To run inference with Gemini Nano in AICore, you use the AI Edge SDK. The AI Edge SDK enables developers to customize prompts and inference parameters to their specific needs, enabling greater control over each inference.

To experiment with the AI Edge SDK, add the following to your apps’ dependency:

implementation("com.google.ai.edge.aicore:aicore:0.0.1-exp01")

The AI Edge SDK allows you to customize inference parameters. Some of the more commonly-used parameters include:

    • Temperature, which controls randomness. Higher values increase diversity and creativity of output.
    • Top K, which specifies how many tokens from the highest-ranking ones are to be considered.
    • Candidate count, which describes the maximum number of responses to return.
    • Max output tokens, which is the length of the desired response.

When you are ready to run the inference with your model, the AI Edge SDK offers an easy way to pass in multiple strings as input to accommodate long inference data.

Here’s an example:

scope.launch {
    // Single string input prompt
    val input = "I want you to act as an English proofreader. I will 
    provide you texts, and I would like you to review them for any 
    spelling, grammar, or punctuation errors. Once you have finished 
    reviewing the text, provide me with any necessary corrections or 
    suggestions for improving the text: 
    These arent the droids your looking for."
    val response = generativeModel.generateContent(input)
    print(response.text)

    // Or multiple strings as input
    val response = generativeModel.generateContent(
        content {
            text("I want you to act as an English proofreader.I will 
            provide you texts and I would like you to review them for 
            any spelling, grammar, or punctuation errors.")
            text("Once you have finished reviewing the text, 
            provide me with any necessary corrections or suggestions 
            for improving the text:")
            text("These arent the droids your looking for.")
        }
    )
    print(response.text)
}

Our integration guide has more information on the AI Edge SDK as well as detailed instructions to start your experimentation with Gemini Nano. To learn more about prompting, check out the Gemini prompting strategies.


Get Started

Learn more about Gemini Nano for app development by watching our video walkthrough, and try out Gemini Nano experimental access in your own app today.

We are excited to see what you build and welcome your input as you evaluate this new technology for your use cases! Post your creations on social media and include the hashtag #AndroidAI to share what you build. To share your ideas and feedback for on-device GenAI and help shape our APIs, you can file a ticket.

There’s a lot more that we’re covering this week for you to build great AI experiences on Android so be sure to check out the rest of the AI on Android Spotlight Week content!

Welcome to AI on Android Spotlight Week

Posted by Joseph Lewis – Technical Writer, Android AI

AI on Android Spotlight Week this year runs September 30th to October 4th! As part of the Android “Spotlight Weeks” series, this week’s content and updates are your gateway to understanding how to integrate cutting-edge AI into your Android apps. Whether you're a seasoned Android developer, an AI enthusiast, or just starting out on your development journey, get ready for a week filled with insightful sessions, practical demos, and inspiring success stories that'll help you build intuitive and powerful AI integrations.

Throughout the week, we'll dive into the core technologies driving AI experiences on Android. This blog will be updated throughout the week with links to new announcements and resources, so check back here daily for updates!


Monday: Getting started with AI

September 30, 2024

Learn how to begin with AI on Android development. Understand which AI models and versions you can work with. Learn about developer tools to help you start building features empowered with AI.

We'll guide you through the differences between traditional programming and machine learning, and contrast traditional machine learning with generative AI. The post explains large language models (LLMs), the transformer architecture, and key concepts like context windows and embeddings. It also touches on fine-tuning and the future of LLMs on Android.

Read the blog post: A quick introduction to large language models for Android Developers

We'll then provide a look behind the scenes at our work improving developer productivity with Gemini in Android Studio. We'll discuss Studio's new AI code completion feature, how we've been working to improve the accuracy and quality of suggested code, and how this feature can benefit your workflow.

Read the blog post: Gemini in Android Studio: Code Completion gains powerful model improvements


Tuesday: On-device AI capabilities with Gemini Nano

October 1, 2024

Discover how Gemini Nano empowers Android developers to unlock the full potential of generative AI, offering personalization and privacy benefits for next-generation apps. We'll share how you can begin integrating your Android apps with on-device LLMs. Look for more information and announcements here on Tuesday!


Wednesday: On-device AI with custom models

October 2, 2024

On Wednesday, we'll help you understand how to bring your own AI model to Android devices, and how you can integrate tools and technologies from Google and other sources. The ability to run sophisticated AI models directly on devices – whether it's a smartphone, tablet, or embedded system – opens up exciting possibilities for better performance, privacy, usability, and cost efficiency.

We'll also give you a detailed walkthrough of how Android developers can leverage Google AI Edge Torch to convert PyTorch machine learning models for on-device execution, using the LiteRT and MediaPipe Tasks libraries. This walkthrough includes code examples and explanations for converting a MobileViT model for image classification and a DIS model for segmentation, and highlights the steps involved in preparing these models for seamless integration into Android applications. By following this guide, developers can harness PyTorch models to enhance their Android apps with advanced machine learning capabilities.


Thursday: Access cloud models with Android SDKs

October 3, 2024

Tap into the boundless potential of Gemini 1.5 Pro and Gemini 1.5 Flash, the revolutionary generative AI models that are redefining the capabilities of Android apps. With Gemini 1.5 Pro and 1.5 Flash, you'll have the tools you need to create apps that are truly intelligent and interactive.

On Thursday, we'll give you a codelab that'll help you understand how to integrate the Gemini API capabilities into your Android projects. We'll guide you through crafting effective prompts and integrate Vertex AI in Firebase. By the end of this hands-on tutorial, you'll be able to implement features like text summarization in your own app all powered by the cutting-edge Gemini models.

Next we'll publish a blog post exploring the potential of the Gemini API with case studies. We'll delve into how Android developers are leveraging generative AI capabilities in innovative ways, showcasing real-world examples of apps that have successfully integrated the Gemini API. From meal planning to journaling and personalized user experiences, the article highlights examples of how Android developers are already taking advantage of Gemini transformative capabilities in their apps.

We'll also share with you examples of advanced features of the Gemini API to go beyond simple text prompting. You'll learn how system instructions can shape the model behavior, how JSON support streamlines development, and how multimodal capabilities and function calling can unlock exciting new use cases for your apps.


Friday: Build with AI on Android and beyond

October 4, 2024

As the capstone for AI on Android Spotlight Week, we'll host a discussion with Kateryna Semenova, Oli Gaymond, Miguel Ramos, and Khanh LeViet to talk about building with AI on Android. We'll explore the latest AI advancements tailored for Android engineers, showcasing how these technologies can elevate your app development game. Through engaging discussions and real-world examples, we will unveil the potential of AI, from fast, private on-device solutions using Gemini Nano to the powerful capabilities of Gemini 1.5 Flash and Pro. We'll discuss building generative AI solutions rapidly using Vertex AI in Firebase. And we'll dive into harnessing the power of AI with safety and privacy in mind.


Work with Gemini beyond Android

As we wrap things up for AI on Android Spotlight Week, know that we're striving to provide comprehensive AI solutions for cross-platform Gemini development. The AI capabilities showcased during Android AI Week can extend to other platforms, such as built-in AI in Chrome. Web developers can leverage similar tools and techniques to create web experiences enhanced by AI. Developers can run Gemini Pro in the cloud for natural language processing and other complex user journeys. Or, you explore the benefits of performing AI inferenceclient-side, with Gemini Nano in Chrome.

Build with usability and privacy in mind

As you embark on your AI development journey, we want you to keep in mind a few important considerations:

    • Privacy: Prioritize user privacy and data security when implementing AI features, especially when handling sensitive user information. When it becomes available, opt for on-device AI solutions like Gemini Nano whenever possible to minimize data exposure.
    • Seamless user experience: Ensure that AI features seamlessly integrate into your app's overall user experience. AI should enhance the user experience, not disrupt it.
    • Ethical considerations: AI technologies are developed and deployed in a way that benefits society while minimizing potential harm. By considering fairness, transparency, privacy, accountability, and societal impact, developers can play a vital role in creating a future where AI serves humanity's best interests. Be mindful of the ethical implications of AI, such as potential biases in your AI models. Strive to create AI-powered features that are fair and inclusive.

AI on Android Spotlight Week is an opportunity to explore the latest in AI and its potential for Android app development. We encourage you to delve into the wealth of resources shared during the week and begin experimenting with AI in your own projects. The future of Android is rooted in AI and machine learning, and with the tools and knowledge shared during Android AI Week, developers are well-equipped to build the next generation of AI-powered apps.


What's next

Come back to this blog post for updates; we’ll add links to blog and video content and more throughout the week. Follow Android Developers on X and Android Developers at LinkedIn, and remember to use the hashtag #AndroidAI to share your AI-powered Android creations, and join the vibrant community of developers pushing the boundaries of mobile AI.

AI on Android Spotlight Week begins September 30th

Posted by Joseph Lewis – Technical Writer, Android AI

AI on Android Spotlight Week is our latest installment of the Spotlight Weeks series. We'll have a full week of investigation into the latest advancements in AI for Android developers. We’ll feature a variety of exciting activities, including an AMA with Google AI experts, technical talks, early access to our new tools and API, and demos of the latest Android generative AI technologies. AI on Android Spotlight Week kicks off next week on September 30th through October 4th, and will feature information and activities for developers, researchers, and enthusiasts interested in the future of generative AI app development on Android-powered devices.

Get the latest on Android AI developer strategies

During our Spotlight Week: AI on Android, we’ll feature a number of new and exciting opportunities to learn more about how to work with generative AI and machine learning for Android app development, including:

    • Conversations about on-device and cloud based GenAI solutions with Gemini Nano, Vertex AI in Firebase, and LiteRT (formerly known as TensorFlow Lite)
    • Partner demos and deep dives into the latest AI technologies and how to integrate them in Android apps
    • Discussions around model capabilities, developer tools and integration strategies from web to mobile
    • Answers to top questions from dev community about AI on Android

How to participate

Our Spotlight Week: AI on Android will happen entirely online, across Android Developer’s channels - YouTube, X, LinkedIn, and on d.android.com: check the Android AI developer page on Monday, September 30, 2024 to read our next blog post with full details!

Follow @AndroidDev on X for the latest updates, and help spread the word about AI on Android Spotlight Week, and use #AndroidAI on your favorite social media platforms to ask questions and share your AI projects with the community. We’re excited for you to join us!

The Recorder app on Pixel sees a 24% boost in engagement with Gemini Nano-powered feature

Posted by Terence Zhang – Developer Relations Engineer and Kristi Bradford - Product Manager

Google Pixel’s Recorder app allows people to record, transcribe, save, and share audio. To make it easier for users to manage and revisit their recordings, Recorder’s developers turned to Gemini Nano, a powerful on-device large language model (LLM). This integration introduces an AI-powered audio summarization feature to help users more easily find the right recordings and quickly grasp key points.

Earlier this month, Gemini Nano got a power boost with the introduction of the new Gemini Nano with Multimodality model. The Recorder app is already leveraging this upgrade to summarize longer voice recordings, with improved processing for grammar and nuance.

Meeting user needs with on-device AI

Recorder developers initially experimented with a cloud-based solution, achieving impressive levels of performance and quality. However, to prioritize accessibility and privacy for their users, they sought an on-device solution. The development of Gemini Nano presented a perfect opportunity to build the concise audio summaries users were looking for, all while keeping data processing on the device.

Gemini Nano is Google’s most efficient model for on-device tasks. “Having the LLM on-device is beneficial to users because it provides them with more privacy, less latency, and it works wherever they need since there’s no internet required,” said Kristi Bradford, the product manager for Pixel’s essential apps.

To achieve better results, Recorder also fine-tuned the model using data that matches its use case. This is done using low order rank adaptation (LoRA), which enables Gemini Nano to consistently output three-bullet point descriptions of the transcript that include any speaker names, key takeaways, and themes.

AICore, an Android system service that centralizes runtime, delivery, and critical safety components for LLMs, significantly streamlined Recorder's adoption of Gemini Nano. The availability of a developer SDK for running GenAI workloads allowed the team to build the transcription summary feature in just four months, with only four developers. This efficiency was achieved by eliminating the need for maintaining in-house models.

Since its release, Recorder users have been using the new AI-powered summarization feature averaging 2 to 5 times daily, and the number of overall saved recordings increased by 24%. This feature has contributed to a significant increase in app engagement and user retention overall. The Recorder team also noted that feedback about the new feature has been positive, with many users citing the time the new AI-powered summarization feature saves them.

“We were surprised by how truly capable the model was… before and after LoRA tuning.” — Kristi Bradford, product manager for Pixel’s essential apps

The next big evolution: Gemini Nano with multimodality

Recorder developers also implemented the latest Gemini Nano model, known as Gemini Nano with multimodality, to further improve its summarization feature on Pixel 9 devices. The new model is significantly larger than the previous one on Pixel 8 devices, and it’s more capable, accurate, and scalable. The new model also has expanded token support that lets Recorder summarize much longer transcripts than before. Gemini Nano with multimodality is currently only available on Pixel 9 devices.

Integrating Gemini Nano with multimodality required another round of fine-tuning. However, Recorder developers were able to use the original Gemini Nano model's fine-tuning dataset as a foundation, streamlining the development process.

To fully leverage the new model's capabilities, Recorder developers expanded their dataset with support for longer voice recordings, implemented refined evaluation methods, and established launch criteria metrics focused on grammar and nuance. The inclusion of grammar as a new metric for assessing inference quality was made possible solely by the enhanced capabilities of Gemini Nano with Multimodality.

UI example

Doing more with on-device AI

“Given the novelty of GenAI, the whole team had fun learning how to use it,” said Kristi. “Now, we’re empowered to push the boundaries of what we can accomplish while meeting emerging user needs and opportunities. It’s truly brought a new level of creativity to problem-solving and experimentation. We’ve already demoed at least two more GenAI features that help people get time back internally for early feedback, and we’re excited about the possibilities ahead.”

Get started

Learn more about how to bring the benefits of on-device AI with Gemini Nano to your apps.

Top 3 Updates for Building with AI on Android at Google I/O ‘24

Posted by Terence Zhang – Developer Relations Engineer

At Google I/O, we unveiled a vision of Android reimagined with AI at its core. As Android developers, you're at the forefront of this exciting shift. By embracing generative AI (Gen AI), you'll craft a new breed of Android apps that offer your users unparalleled experiences and delightful features.

Gemini models are powering new generative AI apps both over the cloud and directly on-device. You can now build with Gen AI using our most capable models over the Cloud with the Google AI client SDK or Vertex AI for Firebase in your Android apps. For on-device, Gemini Nano is our recommended model. We have also integrated Gen AI into developer tools - Gemini in Android Studio supercharges your developer productivity.

Let’s walk through the major announcements for AI on Android from this year's I/O sessions in more detail!

#1: Build AI apps leveraging cloud-based Gemini models

To kickstart your Gen AI journey, design the prompts for your use case with Google AI Studio. Once you are satisfied with your prompts, leverage the Gemini API directly into your app to access Google’s latest models such as Gemini 1.5 Pro and 1.5 Flash, both with one million token context windows (with two million available via waitlist for Gemini 1.5 Pro).

If you want to learn more about and experiment with the Gemini API, the Google AI SDK for Android is a great starting point. For integrating Gemini into your production app, consider using Vertex AI for Firebase (currently in Preview, with a full release planned for Fall 2024). This platform offers a streamlined way to build and deploy generative AI features.

We are also launching the first Gemini API Developer competition (terms and conditions apply). Now is the best time to build an app integrating the Gemini API and win incredible prizes! A custom Delorean, anyone?


#2: Use Gemini Nano for on-device Gen AI

While cloud-based models are highly capable, on-device inference enables offline inference, low latency responses, and ensures that data won’t leave the device.

At I/O, we announced that Gemini Nano will be getting multimodal capabilities, enabling devices to understand context beyond text – like sights, sounds, and spoken language. This will help power experiences like Talkback, helping people who are blind or have low vision interact with their devices via touch and spoken feedback. Gemini Nano with Multimodality will be available later this year, starting with Google Pixel devices.

We also shared more about AICore, a system service managing on-device foundation models, enabling Gemini Nano to run on-device inference. AICore provides developers with a streamlined API for running Gen AI workloads with almost no impact on the binary size while centralizing runtime, delivery, and critical safety components for Gemini Nano. This frees developers from having to maintain their own models, and allows many applications to share access to Gemini Nano on the same device.

Gemini Nano is already transforming key Google apps, including Messages and Recorder to enable Smart Compose and recording summarization capabilities respectively. Outside of Google apps, we're actively collaborating with developers who have compelling on-device Gen AI use cases and signed up for our Early Access Program (EAP), including Patreon, Grammarly, and Adobe.

Moving image of Gemini Nano operating in Adobe

Adobe is one of these trailblazers, and they are exploring Gemini Nano to enable on-device processing for part of its AI assistant in Acrobat, providing one-click summaries and allowing users to converse with documents. By strategically combining on-device and cloud-based Gen AI models, Adobe optimizes for performance, cost, and accessibility. Simpler tasks like summarization and suggesting initial questions are handled on-device, enabling offline access and cost savings. More complex tasks such as answering user queries are processed in the cloud, ensuring an efficient and seamless user experience.

This is just the beginning - later this year, we'll be investing heavily to enable and aim to launch with even more developers.

To learn more about building with Gen AI, check out the I/O talks Android on-device GenAI under the hood and Add Generative AI to your Android app with the Gemini API, along with our new documentation.


#3: Use Gemini in Android Studio to help you be more productive

Besides powering features directly in your app, we’ve also integrated Gemini into developer tools. Gemini in Android Studio is your Android coding companion, bringing the power of Gemini to your developer workflow. Thanks to your feedback since its preview as Studio Bot at last year’s Google I/O, we’ve evolved our models, expanded to over 200 countries and territories, and now include this experience in stable builds of Android Studio.

At Google I/O, we previewed a number of features available to try in the Android Studio Koala preview release, like natural-language code suggestions and AI-assisted analysis for App Quality Insights. We also shared an early preview of multimodal input using Gemini 1.5 Pro, allowing you to upload images as part of your AI queries — enabling Gemini to help you build fully functional compose UIs from a wireframe sketch.


You can read more about the updates here, and make sure to check out What’s new in Android development tools.

Get ready for Google I/O: Program lineup revealed

Posted by Timothy Jordan – Director, Developer Relations and Open Source

Developers, get ready! Google I/O is just around the corner, kicking off live from Mountain View with the Google keynote on Tuesday, May 14 at 10 am PT, followed by the Developer keynote at 1:30 pm PT.

But the learning doesn’t stop there. Mark your calendars for May 16 at 8 am PT when we’ll be releasing over 150 technical deep dives, demos, codelabs, and more on-demand. If you register online, you can start building your 'My I/O' agenda today.

Here's a sneak peek at some of the exciting highlights from the I/O program preview:

Unlocking the power of AI: The Gemini era unlocks a new frontier for developers. We'll showcase the newest features in the Gemini API, Google AI Studio, and Gemma. Discover cutting-edge pre-trained models from Kaggle, and delve into Google's open-source libraries like Keras and JAX.

Android: A developer's playground: Get the latest updates on everything Android! We'll cover groundbreaking advancements in generative AI, the highly anticipated Android 15, innovative form factors, and the latest tools and libraries in the Jetpack and Compose ecosystem. Plus, discover how to optimize performance and streamline your development workflow.

Building beautiful and functional web experiences: We’ll cover Baseline updates, a revolutionary tool that empowers developers with a clear understanding of web features and API interoperability. With Baseline, you'll have access to real-time information on popular developer resource sites like MDN, Can I Use, and web.dev.

The future of ChromeOS: Get a glimpse into the exciting future of ChromeOS. We'll discuss the developer-centric investments we're making in distribution, app capabilities, and operating system integrations. Discover how our partners are shaping the future of Chromebooks and delivering world-class user experiences.

This is just a taste of what's in store at Google I/O. Stay tuned for more updates, and get ready to be a part of the future.

Don't forget to mark your calendars and register for Google I/O today!

Gemini 1.5 Pro Now Available in 180+ Countries; With Native Audio Understanding, System Instructions, JSON Mode and More

Posted by Jaclyn Konzelmann and Megan Li - Google Labs

Grab an API key in Google AI Studio, and get started with the Gemini API Cookbook

Less than two months ago, we made our next-generation Gemini 1.5 Pro model available in Google AI Studio for developers to try out. We’ve been amazed by what the community has been able to debug, create and learn using our groundbreaking 1 million context window.

Today, we’re making Gemini 1.5 Pro available in 180+ countries via the Gemini API in public preview, with a first-ever native audio (speech) understanding capability and a new File API to make it easy to handle files. We’re also launching new features like system instructions and JSON mode to give developers more control over the model’s output. Lastly, we’re releasing our next generation text embedding model that outperforms comparable models. Go to Google AI Studio to create or access your API key, and start building.


Unlock new use cases with audio and video modalities

We’re expanding the input modalities for Gemini 1.5 Pro to include audio (speech) understanding in both the Gemini API and Google AI Studio. Additionally, Gemini 1.5 Pro is now able to reason across both image (frames) and audio (speech) for videos uploaded in Google AI Studio, and we look forward to adding API support for this soon.


screen grab of a clooege professor using Gemini 1.5 Pro to create a quiz based on their latest lecture video in Google AI Studio
You can upload a recording of a lecture, like this 117,000+ token lecture from Jeff Dean, and Gemini 1.5 Pro can turn it into a quiz with an answer key. Video sped up for demo purposes.

Gemini API Improvements

Today, we’re addressing a number of top developer requests:

1. System instructions: Guide the model’s responses with system instructions, now available in Google AI Studio and the Gemini API. Define roles, formats, goals, and rules to steer the model's behavior for your specific use case.

image showing where System Instructions is located in Google AI Studio
Set System Instructions easily in Google AI Studio

2. JSON mode: Instruct the model to only output JSON objects. This mode enables structured data extraction from text or images. You can get started with cURL, and Python SDK support is coming soon.

3. Improvements to function calling: You can now select modes to limit the model’s outputs, improving reliability. Choose text, function call, or just the function itself.


A new embedding model with improved performance

Starting today, developers will be able to access our next generation text embedding model via the Gemini API. The new model, text-embedding-004, (text-embedding-preview-0409 in Vertex AI), achieves a stronger retrieval performance and outperforms existing models with comparable dimensions, on the MTEB benchmarks.

table showing Gecko: Versativel Text Embeddings Distilled from Large Language Models
'Text-embedding-004' (aka Gecko) using 256 dims output outperforms all larger 768 dim output models on MTEB benchmarks

These are just the first of many improvements coming to the Gemini API and Google AI Studio in the next few weeks. We’re continuing to work on making Google AI Studio and the Gemini API the easiest way to build with Gemini. Get started today in Google AI Studio with Gemini 1.5 Pro, explore code examples and quickstarts in our new Gemini API Cookbook, and join our community channel on Discord.