Samsung android

I watched few videos from this year's Google I/O sessions 2024, and it seems there is a significant emphasis on artificial intelligence, or AI-related topics. As an Android developer, I found the discussion on Gemini Nano particularly relevant. It appears to be a valuable tool for us to develop AI-specific applications.
I've spent some time on the topic, and here are my findings so far, which I would like to share with all of you.
On Android, now we can deliver rich generative AI experiences without needing a network connection or sending data to the cloud. On-device AI is a great solution for use-cases where low latency, low cost, and privacy safeguards are the primary concerns.
Imagine an educational apps, we can create interactive learning experiences with on-device question answering or personalized tutoring functionalities. During the demo in Google IO session, they shown the recording app and how it was using Gemini Nano features for text interpretation. So, the possibilities are wide and wild.
If we need to develop a sentiment detection or mood analysis app that handles private and sensitive data without relying on the Internet, it’s essential to choose a solution that can perform complex AI tasks locally on the device. And this a use case which Gemini Nano seems to be addressing.
Let's understand the Gemini Nano's architecture in more details,

• To use Gemini Nano in your app, you need Google AI Edge SDK. This software development kit from Google provides the tools and APIs needed for your Android app to interact with AlCore and run Gemini Nano on the device.
• Gemini Nano runs in Android's AICore system service, which leverages device hardware to enable low inference latency and keeps the model up to date. Android AICore is a new system service in Android 14 that provides easy access to Gemini Nano. AICore handles model management, runtimes, safety features and more, simplifying the work for you to incorporate AI into your apps.
• LORA (Low-Rank Adaptation): This is an optional block that you can use to fine-tune the performance of Gemini Nano for your specific application's needs. It essentially tailors the model to your app's unique use case.
• Safety Features: Built into AlCore are safety features designed to evaluate Gemini Nano's outputs against Google's safety filters. This helps mitigate potential risks associated with using AI models.
• TPU/NPU Accelerator: This refers to Tensor Processing Unit or Neural Processing Unit hardware that can be present on some Android devices. These specialized processors can accelerate the performance of AI tasks handled by Gemini Nano, making them run faster and more efficiently.

https://preview.redd.it/xfl05nlrqx0d1.png?width=1233&format=png&auto=webp&s=9ffeef61d9ebdca3bc7bc50da6ab668184fad94a
The diagram depicts how your Android app can interact with AlCore through the Google AI Edge SDK to leverage Gemini Nano's on-device AI capabilities.
LORA allows for fine-tuning the model, and safety features ensure responsible AI use.
The excitement was palpable, so I decided to experiment with Gemini Nano on my Android phone through Android Studio. I planned to test some quick use cases such as text summarization and sentiment analysis utilizing Gemini Nano's capabilities, but there was a catch.
Unfortunately, Gemini Nano isn't quite ready for every Android device or emulator just yet. As of May 2024, Gemini Nano and the required AICore system service are only available on specific devices: Google Pixel 8 Pro and Samsung S24 Series. While wider support is planned, it hasn't rolled out yet. Currently, there's no way to run Gemini Nano directly on an Android emulator to the best my knowledge.
But hopefully in a coming days and weeks, we should be getting more support on other Android devices.

I'm actually afraid of green line issue.

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