News from the AI & ML world
DeeperML
Ashutosh Singh@The Tech Portal
//
Google has launched the 'AI Edge Gallery' app for Android, with plans to extend it to iOS soon. This innovative app enables users to run a variety of AI models locally on their devices, eliminating the need for an internet connection. The AI Edge Gallery integrates models from Hugging Face, a popular AI repository, allowing for on-device execution. This approach not only enhances privacy by keeping data on the device but also offers faster processing speeds and offline functionality, which is particularly useful in areas with limited connectivity.
The app uses Google’s AI Edge platform, which includes tools like MediaPipe and TensorFlow Lite, to optimize model performance on mobile devices. A key model utilized is Gemma 31B, a compact language model designed for mobile platforms that can process data rapidly. The AI Edge Gallery features an interface with categories like ‘AI Chat’ and ‘Ask Image’ to help users find the right tools. Additionally, a ‘Prompt Lab’ is available for users to experiment with and refine prompts.
Google is emphasizing that the AI Edge Gallery is currently an experimental Alpha release and is encouraging user feedback. The app is open-source under the Apache 2.0 license, allowing for free use, including for commercial purposes. However, the performance of the app may vary based on the device's hardware capabilities. While newer phones with advanced processors can run models smoothly, older devices might experience lag, particularly with larger models.
In related news, Google Cloud has introduced advancements to BigLake, its storage engine designed to create open data lakehouses on Google Cloud that are compatible with Apache Iceberg. These enhancements aim to eliminate the need to sacrifice open-format flexibility for high-performance, enterprise-grade storage management. These updates include Open interoperability across analytical and transactional systems:
The BigLake metastore provides the foundation for interoperability, allowing you to access all your Cloud Storage and BigQuery storage data across multiple runtimes including BigQuery, AlloyDB (preview), and open-source, Iceberg-compatible engines such as Spark and Flink.New, high-performance Iceberg-native Cloud Storage:
We are simplifying lakehouse management with automatic table maintenance (including compaction and garbage collection) and integration with Google Cloud Storage management tools, including auto-class tiering and encryption.
ImgSrc: thetechportal.c
References :
The app uses Google’s AI Edge platform, which includes tools like MediaPipe and TensorFlow Lite, to optimize model performance on mobile devices. A key model utilized is Gemma 31B, a compact language model designed for mobile platforms that can process data rapidly. The AI Edge Gallery features an interface with categories like ‘AI Chat’ and ‘Ask Image’ to help users find the right tools. Additionally, a ‘Prompt Lab’ is available for users to experiment with and refine prompts.
Google is emphasizing that the AI Edge Gallery is currently an experimental Alpha release and is encouraging user feedback. The app is open-source under the Apache 2.0 license, allowing for free use, including for commercial purposes. However, the performance of the app may vary based on the device's hardware capabilities. While newer phones with advanced processors can run models smoothly, older devices might experience lag, particularly with larger models.
In related news, Google Cloud has introduced advancements to BigLake, its storage engine designed to create open data lakehouses on Google Cloud that are compatible with Apache Iceberg. These enhancements aim to eliminate the need to sacrifice open-format flexibility for high-performance, enterprise-grade storage management. These updates include Open interoperability across analytical and transactional systems:
The BigLake metastore provides the foundation for interoperability, allowing you to access all your Cloud Storage and BigQuery storage data across multiple runtimes including BigQuery, AlloyDB (preview), and open-source, Iceberg-compatible engines such as Spark and Flink.New, high-performance Iceberg-native Cloud Storage:
We are simplifying lakehouse management with automatic table maintenance (including compaction and garbage collection) and integration with Google Cloud Storage management tools, including auto-class tiering and encryption.
ImgSrc: thetechportal.c
Share:
References :
- Data Analytics: BigLake evolved: Build open, high-performance, enterprise Iceberg-native lakehouses
- The Tech Portal: Google rolls out ‘AI Edge Gallery’ app for Android that lets you run AI models locally on device
- www.infoworld.com: Google Cloud’s BigLake-driven lakehouse updates aim to optimize performance, costs
- TechCrunch: Last week, Google quietly released an app that lets users run a range of openly available AI models from the AI dev platform Hugging Face on their phones.
- Neowin: Google's new AI Edge Gallery app brings offline AI to your Android (and soon iOS) device
- www.infoworld.com: Google’s AI Edge Gallery will let developers deploy offline AI models — here’s how it works
- www.zdnet.com: This new Google app lets you use AI on your phone without the internet - here's how
- developers.googleblog.com: The 529MB Gemma 3 1B model delivers up to 2,585 tokens per second during on mobile GPUs, enabling sub-second tasks like text generation and image analysis.
- Techzine Global: New Google app runs AI offline on smartphones
- venturebeat.com: Google quietly launches AI Edge Gallery, letting Android phones run AI without the cloud
- Dataconomy: Google released the Google AI Edge Gallery app last week, enabling users to download and run AI models from Hugging Face on their phones.
Classification:
- HashTags: #AI #GoogleAI #EdgeAI
- Company: Google
- Target: Enterprises
- Product: AI Edge Gallery
- Feature: AI Edge Computing
- Type: AI
- Severity: Informative