AI model

AppWizard
May 16, 2025
Google is preparing to unveil new mobile AI tools at the upcoming I/O event, including a suite of APIs that will enable developers to use Gemini Nano for on-device AI applications. The ML Kit SDK will be updated to support on-device generative AI functionalities through Gemini Nano, which integrates with existing models and offers predefined features for ease of implementation. The ML Kit’s GenAI APIs will allow applications to perform tasks such as summarization, proofreading, rewriting, and image description without cloud data transmission. However, Gemini Nano's capabilities are limited compared to cloud-based options, with summaries restricted to three bullet points and image descriptions available only in English. The standard version, Gemini Nano XS, requires about 100MB of storage, while the smaller Gemini Nano XXS is text-only and occupies a quarter of that size. ML Kit is compatible with devices beyond Google's Pixel lineup, including the OnePlus 13, Samsung Galaxy S25, and Xiaomi 15, providing developers opportunities to enhance applications with generative AI features.
AppWizard
May 16, 2025
Google is expanding its Gemini Nano AI model by introducing new ML Kit GenAI APIs, expected to be unveiled at the I/O 2025 event. These APIs will allow developers to integrate features such as text summarization, proofreading, rewriting, and image description generation into their applications. Gemini Nano operates on devices, enhancing privacy by processing data locally. The ML Kit GenAI APIs will support various languages and functionalities, including generating concise summaries, correcting grammar, transforming chat messages, and providing image descriptions. Unlike the experimental AI Edge SDK, the GenAI APIs will be in beta, allowing for broader device compatibility beyond the Pixel 9 series, including other Android devices. Public documentation for the ML Kit GenAI APIs is now available for developers.
Tech Optimizer
May 15, 2025
Databricks intends to acquire Neon, a serverless Postgres startup, for approximately USD 1 billion. Neon specializes in a modern database service based on PostgreSQL, offering features such as near-instantaneous database provisioning, elastic scaling, and powerful branching capabilities. This acquisition aims to enhance Databricks' offerings for AI tools, particularly AI Agents. Databricks has a history of expanding through acquisitions, including the purchase of MosaicML for USD 1.3 billion in 2023 and Tabular for over USD 1 billion in 2024. The company has a valuation of USD 62 billion and projected annualized revenue of USD 2.4 billion by mid-year. The acquisition awaits regulatory approvals.
AppWizard
April 22, 2025
Google has launched the Gemini 2.5 Flash, an updated version of its Gemini AI model featuring hybrid reasoning capabilities that allow developers to adjust the model's 'thinking' process. This new model is designed to be faster, more cost-effective, and outperforms competing AI models in benchmark tests. The Gemini 2.5 Flash is available in preview through the Gemini API via Google AI Studio and Vertex AI. It builds on the foundation of the earlier Gemini 2.0 Flash models.
AppWizard
April 9, 2025
Google has introduced a new version of Gemini for Android Studio, specifically designed for businesses. This version ensures that company code is not saved by Google and is not used for AI model training. It includes IP protection against claims related to AI-generated code. The offering, available through Gemini Code Assist Standard or Enterprise subscriptions, enhances existing features with security and IP protections, including tools like build and sync error support and App Quality Insights. Google emphasizes its commitment to security with certifications such as SOC 1/2/3 and ISO/IEC 27001. Additionally, businesses benefit from IP indemnification against copyright infringement claims related to AI-generated code. The enterprise-grade version can be accessed via the Android Studio Narwhal build on the Canary release channel with an eligible Gemini Code Assist license.
Winsage
April 1, 2025
Generative AI is being integrated into modern technology, with Microsoft incorporating its Copilot AI into Windows 11. Marc Andreessen revealed that a small Llama AI model from Meta operated on a Windows 98 PC with 128MB of RAM. Although the specific Meta AI model was not disclosed, it suggests older technology could have supported generative AI capabilities. Andreessen noted that running Llama AI on a 26-year-old Dell PC could have enabled human-like interactions with computers decades ago. An experiment by Exo Labs successfully ran a modified version of Meta's Llama 2 on a Pentium II-based Windows 98 PC, overcoming challenges related to sourcing compatible peripherals and transferring files. The team used Borland C++ 5.02 for compiling modern code but eventually switched to an older version of the C programming language due to compatibility issues. The project required developing a streamlined version of the AI model to function within the hardware limitations of the legacy PC. This illustrates that even older PCs had the potential to support generative AI.
AppWizard
March 29, 2025
Google is introducing a new feature in its Maps application called the "Ask about place" chip, powered by its AI model, Gemini. This feature allows users to ask questions about a selected location, providing information such as directions and travel details. The feature is currently being tested and may not function perfectly at all times. Additionally, Google is working on another update to improve user-friendliness, which will display essential information like parking availability and estimated time of arrival (ETA) upon setting a destination.
Tech Optimizer
March 19, 2025
Researchers have developed a new artificial intelligence model that enhances data processing through advanced deep learning techniques. Key features include enhanced learning algorithms for rapid learning from large datasets, improved accuracy with reduced error rates, scalability for handling increasing data volumes, and a user-friendly interface. During testing, the model processed complex datasets in real-time, benefiting industries such as finance, healthcare, and logistics by improving decision-making and operational efficiency. The research team plans to refine the model and explore integration with technologies like blockchain and the Internet of Things (IoT).
Search