News from the AI & ML world
DeeperML - #google
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@google.github.io
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Google Cloud has announced the public preview of Vertex AI Agent Engine Memory Bank, a significant advancement for developers building conversational AI agents. This new managed service is designed to empower agents with long-term memory, enabling them to maintain context, personalize interactions, and remember user preferences across multiple sessions. This addresses a critical limitation in current AI agent development, where agents often "forget" previous interactions, leading to repetitive conversations and a less engaging user experience. Memory Bank aims to eliminate this by providing a persistent and up-to-date information store for agents.
The integration of Memory Bank with the Google Agent Development Kit (ADK) and support for popular frameworks like LangGraph and CrewAI are key features of this announcement. Developers can now leverage Memory Bank to create more sophisticated and stateful agents that can recall past conversations and user details, leading to more natural and efficient interactions. The service utilizes Google's powerful Gemini models to extract and manage these memories, ensuring that agents have access to relevant and accurate information. This move by Google Cloud is set to streamline the development of truly personalized and context-aware AI assistants. This release marks a crucial step forward in making AI agents more helpful and human-like. By moving beyond the limitations of solely relying on an LLM's context window, which can be expensive and inefficient, Memory Bank offers a robust solution for managing an agent's knowledge. This capability is essential for building production-ready AI agents that can handle complex user needs and provide consistent, high-quality assistance over time. The public preview availability signifies Google Cloud's commitment to providing developers with the tools needed to innovate in the rapidly evolving field of generative AI. Recommended read:
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Robby Payne@chromeunboxed.com
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Google is significantly enhancing its Gemini AI integration across its product ecosystem, signaling a major push to make AI a more seamless part of users' daily digital experiences. The Gemini app has received a visual refresh with a new, colorful icon that aligns it with Google's core branding, appearing on both Android and iPhone devices. This updated branding signifies Gemini's growing importance within Google's suite of services.
In addition to the visual update, Google is rolling out a more functional Android widget for Gemini. This widget is designed to offer users quicker and more intuitive access to Gemini's AI capabilities directly from their homescreen. These improvements highlight Google's commitment to deepening AI integration, making Gemini more accessible and useful across its platforms. Furthermore, Gemini's capabilities are expanding to Wear OS, with support beginning to roll out to smartwatches. Beyond app and device integrations, Google continues to advance Gemini's features. The company has introduced a new photo-to-video feature powered by its Veo 3 AI model, allowing users to transform static images into short video clips with AI-generated sound. This feature, now available through the Gemini app, expands creative possibilities. Google is also making strides in professional applications, with advancements in Google Meet's AI note-taking for smarter summaries and enhanced host controls, and the Vertex AI Agent Engine offering Memory Bank for persistent agent conversations, further solidifying Gemini's role as a versatile AI assistant. Recommended read:
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Ellie Ramirez-Camara@Data Phoenix
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Google's Gemini app is now offering a powerful new photo-to-video feature, allowing AI Pro and Ultra subscribers to transform still images into dynamic eight-second videos complete with AI-generated sound. This enhancement, powered by Google's advanced Veo 3 AI model, has already seen significant user engagement, with over 40 million videos generated since the model's launch. Users can simply upload a photo, provide a text prompt describing the desired motion and any audio cues, and Gemini brings the image to life with remarkable realism. The results have been described as cinematic and surprisingly coherent, with Gemini demonstrating an understanding of objects, depth, and context to create subtle camera pans, rippling water, or drifting clouds while maintaining image stability. This feature, previously available in Google's AI filmmaking tool Flow, is now rolling out more broadly across the Gemini app and web.
In parallel with these advancements in creative AI, Google Cloud is enabling companies like Jina AI to build robust and scalable systems. Google Cloud Run is empowering Jina AI to construct a secure and reliable web scraping system, specifically optimizing container lifecycle management for browser automation. This allows Jina AI to efficiently execute large models, such as a 1.5-billion-parameter model, directly on Cloud Run GPUs. This integration highlights Google Cloud's role in providing the infrastructure necessary for cutting-edge AI development and deployment, ensuring that organizations can handle complex tasks with enhanced efficiency and scalability. Furthermore, the broader impact of AI on the technology industry is being underscored by the opening of the 2025 DORA survey. DORA research indicates that AI is fundamentally transforming every stage of the software development lifecycle, with a significant 76% of technologists relying on AI in their daily work. The survey aims to provide valuable insights into team practices and identify opportunities for growth, building on previous findings that show AI positively impacts developer well-being and job satisfaction when organizations adopt transparent AI strategies and governance policies. The survey encourages participation from technologists worldwide, offering a chance to contribute to a global snapshot of the AI landscape in technology teams. Recommended read:
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M.G. Siegler@Spyglass
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In a significant development in the AI landscape, Google DeepMind has successfully recruited Windsurf's CEO, Varun Mohan, and key members of his R&D team. This strategic move follows the collapse of OpenAI's rumored $3 billion acquisition deal for the AI coding startup Windsurf. The unexpected twist saw Google swooping in to license Windsurf's technology for $2.4 billion and securing top talent for its own advanced projects. This development signals a highly competitive environment for AI innovation, with major players actively seeking to bolster their capabilities.
Google's acquisition of Windsurf's leadership and technology is primarily aimed at strengthening its DeepMind division, particularly for agentic coding projects and the enhancement of its Gemini model. Varun Mohan and co-founder Douglas Chen are expected to spearhead efforts in developing AI agents capable of writing test code, refactoring projects, and automating developer workflows. This integration is poised to boost Google's position in the AI coding sector, directly countering OpenAI's attempts to enhance its expertise in this critical area. The financial details of Google's non-exclusive license for Windsurf's technology have been kept confidential, but the substantial sum indicates the high value placed on Windsurf's innovations. The fallout from the failed OpenAI deal has left Windsurf in a precarious position. While the company remains independent and will continue to license its technology, it has lost its founding leadership and a portion of its technical advantage. Jeff Wang has stepped up as interim CEO to guide the company, with the majority of its 250 employees remaining. The situation highlights the intense competition and the fluid nature of talent acquisition in the rapidly evolving AI industry, where startups like Windsurf can become caught between tech giants vying for dominance. Recommended read:
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@github.com
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Data Analytics
, www.artificialintelligence-new
Google is enhancing SQL with a new pipe syntax in BigQuery and Cloud Logging. This extension to GoogleSQL introduces a linear, top-down approach to writing queries, using the pipe operator to chain operations such as filtering, aggregating, and joining. This method simplifies data transformations and makes queries more intuitive, readable, and maintainable, contrasting with the rigid clause structures of traditional SQL which often require complex subqueries. Since its general availability in April, users have embraced pipe syntax for streamlining data transformations, building insightful reports, and efficiently analyzing logs.
The key benefit of pipe syntax lies in its ability to create a more natural flow of data transformations. Operations can be applied in any order, reducing the need for cumbersome subqueries or Common Table Expressions (CTEs). For example, building a recommendation report to determine the most cost-effective pricing model for long-term data storage in BigQuery can be simplified using pipe syntax compared to the more verbose standard SQL. This improved readability and flexibility allow developers and data analysts to work more efficiently. Beyond SQL enhancements, Google is also advancing healthcare through AI with the release of open MedGemma AI models. Unlike previous models locked behind expensive APIs, these tools are made available to healthcare developers, allowing them to be downloaded, modified, and run as needed. MedGemma models, including the flagship MedGemma 27B Multimodal, can process medical text and analyze medical images such as X-rays and pathology slides. These models have shown impressive performance on medical knowledge benchmarks, offering a cost-effective solution for healthcare systems and potentially transforming patient care. Recommended read:
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@www.infoq.com
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Google has launched Gemini CLI, a new open-source AI command-line interface that brings the full capabilities of its Gemini 2.5 Pro model directly into developers' terminals. Designed for flexibility, transparency, and developer-first workflows, Gemini CLI provides high-performance, natural language AI assistance through a lightweight, locally accessible interface. Last Week in AI #314 also mentioned Gemini CLI, placing it alongside other significant AI developments. Google aims to empower developers by providing a tool that enhances productivity and streamlines AI workflows.
This move has potentially major implications for the AI coding assistant market, especially for developers who previously relied on costly tools. An article on Towards AI highlights that Gemini CLI could effectively eliminate the need for $200/month AI coding tools. This is because it will match or beat expensive tools for $0. The open-source nature of Gemini CLI fosters community-driven development and transparency, enabling developers to customize and extend the tool to suit their specific needs. Google is also integrating Gemini with other development tools to create a more robust AI development ecosystem. Build Smarter AI Workflows with Gemini + AutoGen + Semantic Kernel suggests that Gemini CLI can be combined with other frameworks to enhance AI workflow. This is a new step to provide developers with a complete suite of tools. Google's launch of Gemini CLI not only underscores its commitment to open-source AI development but also democratizes access to advanced AI capabilities, making them available to a wider range of developers. Recommended read:
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Alexey Shabanov@TestingCatalog
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Google is aggressively integrating its Gemini AI model across a multitude of platforms, signaling a significant push towards embedding AI into everyday technologies. The initiatives span from enhancing user experiences in applications like Google Photos to enabling advanced capabilities in robotics and providing developers with powerful coding tools via the Gemini CLI. This widespread integration highlights Google's vision for a future where AI is a seamless and integral part of various technological ecosystems.
The integration of Gemini into Google Photos is designed to improve search functionality, allowing users to find specific images more efficiently using natural language queries. Similarly, the development of on-device Gemini models for robotics addresses critical concerns around privacy and latency, ensuring that robots can operate effectively even without a constant internet connection. This is particularly crucial for tasks requiring real-time decision-making, where delays could pose significant risks. Furthermore, Google's release of the Gemini CLI provides developers with an open-source AI agent directly accessible from their terminal. This tool supports various coding and debugging tasks, streamlining the development process. Additionally, Gemini models are being optimized for edge deployment, allowing for AI functionality in environments with limited or no cloud connectivity, further demonstrating Google's commitment to making AI accessible and versatile across diverse applications. Recommended read:
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@siliconangle.com
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Google LLC, in collaboration with Google Research and DeepMind, has unveiled an artificial intelligence model designed to significantly enhance tropical cyclone forecasting. This new AI system, accessible through the newly launched Weather Lab platform, demonstrates the potential to predict both the path and intensity of tropical cyclones days in advance. The AI model's development addresses limitations found in traditional physics-based weather prediction models, which often struggle to accurately forecast both a cyclone's track and intensity simultaneously due to the different atmospheric factors governing these aspects.
The AI model was trained using extensive datasets detailing the historical paths and intensities of nearly 5,000 cyclones over the past 45 years, alongside millions of observations about past weather conditions. During internal tests, the algorithm showcased its capabilities by accurately predicting the paths of recent cyclones, in some cases almost a week in advance. The system can generate 50 possible scenarios, extending forecasts up to 15 days out, providing a broader and more detailed outlook compared to traditional models. This improved accuracy and extended forecasting range represents a substantial advancement in hurricane prediction capabilities, marking what some are calling a major breakthrough. Google DeepMind has also announced a partnership with the U.S. National Hurricane Center, which will incorporate the AI's experimental predictions into their operational forecasting workflow. The Weather Lab platform, now available to researchers, provides access to the AI model and two years of historical forecasts, allowing for comparisons with traditional methods. By providing more accurate and timely insights into cyclone behavior, this AI-driven approach promises to improve disaster preparedness and response efforts for communities in hurricane-prone regions, potentially mitigating economic losses and saving lives. Recommended read:
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@www.analyticsvidhya.com
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DEVCLASS
, Pivot to AI
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Google has launched Gemini CLI (command line interface), a terminal-based version of its AI assistant. This new tool allows users to interact with Gemini through a command line, offering a generous free tier of up to 60 model requests per minute and 1,000 per day. The Gemini CLI is designed to cater to developers and other users who prefer a command-line interface for coding assistance, debugging, project management, and querying documentation. It supports various operating systems, including Mac, Linux (including ChromeOS), and Windows, with a native Windows version that doesn't require WSL.
Google’s Ryan Salva highlighted the "unmatched usage limits" of Gemini CLI, which includes a 1 million token context window and use of the Gemini 2.5 Pro LLM. The CLI also integrates with the gcloud CLI, suggesting Google's intent to encourage developers to deploy applications to Google Cloud. While there is a free tier, a paid option that uses an AI Studio or Vertex API key exists. It unlocks additional features such as policy and governance capabilities, choice of models, and the ability to run agents in parallel, while removing the requirement to use Gemini activity to improve Google’s products. The tool is open source on GitHub under the Apache 2.0 license. Verizon has integrated a Google Gemini-based chatbot into its My Verizon app to provide 24/7 customer service. The company claims to be seeing accuracy "north of 90 percent" with the bot, however this means up to 10% of responses are not accurate. David Gerard mentions an example of Personal Shopper, where random items are added to bills. Verizon's CEO, Sowmyanarayan Sampath, stated that AI is the answer to customer churn after a price increase in the first quarter of 2025. Recommended read:
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@blog.google
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edu.google.com
, Google Workspace Updates
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Google is expanding access to its Gemini AI app to all Google Workspace for Education users, marking a significant step in integrating AI into educational settings. This rollout, announced on June 20, 2025, provides educators and students with a range of AI-powered tools. These tools include real-time support for learning, assistance in creating lesson plans, and capabilities for providing feedback on student work, all designed to enhance the learning experience and promote AI literacy. The Gemini app is covered under the Google Workspace for Education Terms of Service, ensuring enterprise-grade data protection and compliance with regulations like FERPA, COPPA, FedRamp, and HIPAA.
A key aspect of this expansion is the implementation of stricter content policies for users under 18. These policies are designed to prevent potentially inappropriate or harmful responses, creating a safer online environment for younger learners. Additionally, Google is introducing a youth onboarding experience with AI literacy resources, endorsed by ConnectSafely and the Family Online Safety Institute, to guide students in using AI responsibly. The first time a user asks a fact-based question, a "double-check response" feature, powered by Google Search, will automatically run to validate the answer. Gemini incorporates LearnLM, Google’s family of models fine-tuned for learning and built with experts in education, making it a leading model for educational purposes. To ensure responsible use, Google provides resources for educators, including a Google teacher center offering guidance on incorporating Gemini into lesson plans and teaching responsible AI practices. Administrators can manage user access to the Gemini app through the Google Workspace Admin Help Center, allowing them to set up groups or organizational units to control access within their domain and tailor the AI experience to specific educational needs. Recommended read:
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@colab.research.google.com
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Google's Magenta project has unveiled Magenta RealTime (Magenta RT), an open-weights live music model designed for interactive music creation, control, and performance. This innovative model builds upon Google DeepMind's research in real-time generative music, providing opportunities for unprecedented live music exploration. Magenta RT is a significant advancement in AI-driven music technology, offering capabilities for both skill-gap accessibility and enhancement of existing musical practices. As an open-weights model, Magenta RT is targeted towards eventually running locally on consumer hardware, showcasing Google's commitment to democratizing AI music creation tools.
Magenta RT, an 800 million parameter autoregressive transformer model, was trained on approximately 190,000 hours of instrumental stock music. It leverages SpectroStream for high-fidelity audio (48kHz stereo) and a newly developed MusicCoCa embedding model, inspired by MuLan and CoCa. This combination allows users to dynamically shape and morph music styles in real-time by manipulating style embeddings, effectively blending various musical styles, instruments, and attributes. The model code is available on Github and the weights are available on Google Cloud Storage and Hugging Face under permissive licenses with some additional bespoke terms. Magenta RT operates by generating music in sequential chunks, conditioned on both previous audio output and style embeddings. This approach enables the creation of interactive soundscapes for performances and virtual spaces. Impressively, the model achieves a real-time factor of 1.6 on a Colab free-tier TPU (v2-8 TPU), generating two seconds of audio in just 1.25 seconds. This technology unlocks the potential to explore entirely new musical landscapes, experiment with never-before-heard instrument combinations, and craft unique sonic textures, ultimately fostering innovative forms of musical expression and performance. Recommended read:
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Ellie Ramirez-Camara@Data Phoenix
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Google has recently launched an experimental feature that leverages its Gemini models to create short audio overviews for certain search queries. This new feature aims to provide users with an audio format option for grasping the basics of unfamiliar topics, particularly beneficial for multitasking or those who prefer auditory learning. Users who participate in the experiment will see the option to generate an audio overview on the search results page, which Google determines would benefit from this format.
When an audio overview is ready, it will be presented to the user with an audio player that offers basic controls such as volume, playback speed, and play/pause buttons. Significantly, the audio player also displays relevant web pages, allowing users to easily access more in-depth information on the topic being discussed in the overview. This feature builds upon Google's earlier work with audio overviews in NotebookLM and Gemini, where it allowed for the creation of podcast-style discussions and audio summaries from provided sources. Google is also experimenting with a new feature called Search Live, which enables users to have real-time verbal conversations with Google’s Search tools, providing interactive responses. This Gemini-powered AI simulates a friendly and knowledgeable human, inviting users to literally talk to their search bar. The AI doesn't stop listening after just one question but rather engages in a full dialogue, functioning in the background even when the user leaves the app. Google refers to this system as “query fan-out,” which means that instead of just answering your question, it also quietly considers related queries, drawing in more diverse sources and perspectives. Additionally, Gemini on Android can now identify songs, similar to the functionality previously offered by Google Assistant. Users can ask Gemini, “What song is this?” and the chatbot will trigger Google’s Song Search interface, which can recognize music from the environment, a playlist, or even if the user hums the tune. However, unlike the seamless integration of Google Assistant’s Now Playing feature, this song identification process is not fully native to Gemini. When initiated, it launches a full-screen listening interface from the Google app, which feels a bit clunky and doesn't stay within Gemini Live’s conversational experience. Recommended read:
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@www.marktechpost.com
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Google has unveiled a new AI model designed to forecast tropical cyclones with improved accuracy. Developed through a collaboration between Google Research and DeepMind, the model is accessible via a newly launched website called Weather Lab. The AI aims to predict both the path and intensity of cyclones days in advance, overcoming limitations present in traditional physics-based weather prediction models. Google claims its algorithm achieves "state-of-the-art accuracy" in forecasting cyclone track and intensity, as well as details like formation, size, and shape.
The AI model was trained using two extensive datasets: one describing the characteristics of nearly 5,000 cyclones from the past 45 years, and another containing millions of weather observations. Internal testing demonstrated the algorithm's ability to accurately predict the paths of recent cyclones, in some cases up to a week in advance. The model can generate 50 possible scenarios, extending forecast capabilities up to 15 days. This breakthrough has already seen adoption by the U.S. National Hurricane Center, which is now using these experimental AI predictions alongside traditional forecasting models in its operational workflow. Google's AI's ability to forecast up to 15 days in advance marks a significant improvement over current models, which typically provide 3-5 day forecasts. Google made the AI accessible through a new website called Weather Lab. The model is available alongside two years' worth of historical forecasts, as well as data from traditional physics-based weather prediction algorithms. According to Google, this could help weather agencies and emergency service experts better anticipate a cyclone’s path and intensity. Recommended read:
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@cloud.google.com
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AI & Machine Learning
, google.github.io
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Google Cloud is offering Financial Services Institutions (FSIs) a powerful solution to streamline and enhance their Know Your Customer (KYC) processes by leveraging the Agent Development Kit (ADK) in combination with Gemini models and Search Grounding. KYC processes are critical for regulatory compliance and risk mitigation, involving the verification of customer identities and the assessment of associated risks. Traditional KYC methods are often manual, time-consuming, and prone to errors, which can be challenging in today's environment where customers expect instant approvals. The Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
The ADK simplifies the creation and orchestration of agents, handling agent definition, tool integration, state management, and inter-agent communication. These agents are powered by Gemini models hosted on Vertex AI, providing core reasoning, instruction-following, and language understanding capabilities. Gemini's multimodal analysis, including image processing from IDs and documents, and multilingual support further enhances the KYC process for diverse customer bases. By incorporating Search Grounding, the system connects Gemini responses to real-time information from Google Search, reducing hallucinations and increasing the reliability of the information provided. Furthermore, integration with BigQuery allows secure interaction with internal datasets, ensuring comprehensive data access while maintaining data security. The multi-agent architecture offers several key benefits for FSIs including improved efficiency through the automation of large portions of the KYC workflow, reducing manual effort and turnaround times. AI is leveraged for consistent document analysis and comprehensive external checks, leading to enhanced accuracy. The solution also strengthens compliance by improving auditability through clear reporting and source attribution via grounding. Google Cloud provides resources to get started, including $300 in free credit for new customers to build and test proof of concepts, along with free monthly usage of over 20 AI-related products and APIs. The combination of ADK, Gemini models, Search Grounding, and BigQuery integration represents a significant advancement in KYC processes, offering FSIs a robust and efficient solution to meet regulatory requirements and improve customer experience. Recommended read:
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Michael Nuñez@AI News | VentureBeat
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Google has recently rolled out its latest Gemini 2.5 Flash and Pro models on Vertex AI, bringing advanced AI capabilities to enterprises. The release includes the general availability of Gemini 2.5 Flash and Pro, along with a new Flash-Lite model available for testing. These updates aim to provide organizations with the tools needed to build sophisticated and efficient AI solutions.
The Gemini 2.5 Flash model is designed for speed and efficiency, making it suitable for tasks such as large-scale summarization, responsive chat applications, and data extraction. Gemini 2.5 Pro handles complex reasoning, advanced code generation, and multimodal understanding. Additionally, the new Flash-Lite model offers cost-efficient performance for high-volume tasks. These models are now production-ready within Vertex AI, offering the stability and scalability needed for mission-critical applications. Google CEO Sundar Pichai has highlighted the improved performance of the Gemini 2.5 Pro update, particularly in coding, reasoning, science, and math. The update also incorporates feedback to improve the style and structure of responses. The company is also offering Supervised Fine-Tuning (SFT) for Gemini 2.5 Flash, enabling enterprises to tailor the model to their unique data and needs. A new updated Live API with native audio is also in public preview, designed to streamline the development of complex, real-time audio AI systems. Recommended read:
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Unknown (noreply@blogger.com)@Google Workspace Updates
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Google is significantly expanding its AI capabilities across its platforms, impacting software development, advertising, and search functionalities. A major upgrade to Gemini Code Assist now features the Gemini 2.5 Pro model. This enhances the code assistance tool with a 1 million-token context window and custom commands, improving its capacity for more comprehensive code generation, deeper refactoring, and more thorough pull-request reviews. This upgrade allows developers to tailor the assistant's behavior to adhere to internal conventions and reuse prompts, significantly boosting coding task completion rates. Internal studies indicate that teams using the tool are 2.5 times more likely to finish typical coding tasks, with early community benchmarks showing higher accuracy than Copilot on context-heavy queries.
Google is also innovating in the realm of search and content delivery, testing a feature that transforms some search results into AI-generated podcasts. This experimental feature, part of Google Labs, aims to provide users with "quick, conversational audio overviews" of search queries. The feature leverages Google’s Gemini model to research search queries, analyze various third-party websites, and generate a transcript that is then read aloud by two AI-generated hosts. While this new feature offers a convenient way for users to absorb information while multitasking, it raises concerns about potentially diverting traffic away from the original sources of information. In a groundbreaking move, an AI-generated advertisement created with Google's Veo3 aired during the NBA Finals. This marks a significant milestone in AI-driven content creation, demonstrating the potential for drastic cost reductions in advertising production. The advertisement for the event-betting platform Kalshi was created by AI Filmmaker PJ Accetturo in just three days, resulting in an estimated 95% cost reduction compared to traditional commercial production methods. This showcases a shift towards smaller, more agile creative teams leveraging AI to produce high-volume, brand-relevant content quickly and affordably while highlighting the importance of human skills such as comedy writing and directorial experience in the age of AI. Recommended read:
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Michael Kan@PCMag Middle East ai
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SiliconANGLE
, THE DECODER
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Google is pushing forward with advancements in artificial intelligence across a range of its services. Google DeepMind has developed an AI model that can forecast tropical cyclones with state-of-the-art accuracy, predicting their path and intensity up to 15 days in advance. This model is now being used by the U.S. National Hurricane Center in its official forecasting workflow, marking a significant shift in how these storms are predicted. The AI system learns from decades of historical storm data and can generate 50 different hurricane scenarios, offering a 1.5-day improvement in prediction accuracy compared to traditional models. Google has launched a Weather Lab website to make this AI accessible to researchers, providing historical forecasts and data for comparison.
Google is also experimenting with AI-generated search results in audio format, launching "Audio Overviews" in its Search Labs. Powered by the Gemini language model, this feature delivers quick, conversational summaries for certain search queries. Users can opt into the test and, when available, a play button will appear in Google Search, providing an audio summary alongside relevant websites. The AI researches the query and generates a transcript, read out loud by AI-generated voices, citing its sources. This feature aims to provide a hands-free way to absorb information, particularly for users who are multitasking or prefer audio content. The introduction of AI-powered features comes amid ongoing debate about the impact on traffic to third-party websites. There are concerns that Google’s AI-driven search results may prioritize its own content over linking to external sources. Some users have also noted instances of Google's AI search summaries spreading incorrect information. Google says it's seen an over 10% increase in usage of Google for the types of queries that show AI Overviews. Recommended read:
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Matt G.@Search Engine Journal
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Google has launched Audio Overviews in Search Labs, introducing a new way for users to consume information hands-free and on-the-go. This experimental feature utilizes Google's Gemini AI models to generate spoken summaries of search results. US users can opt in via Search Labs and, when available, will see an option to create a short audio overview directly on the search results page. The technology aims to provide a convenient method for understanding new topics or multitasking, turning search results into conversational AI podcasts.
Once a user clicks the button to generate the summary, the AI processes information from the Search Engine Results Page (SERP) to create an audio snippet. According to Google, this feature is designed to help users "get a lay of the land" when researching unfamiliar topics. The audio player includes standard controls like play/pause, volume adjustment, and playback speed options. Critically, the audio player also displays links to the websites used in generating the overview, allowing users to delve deeper into specific sources if desired. While Google emphasizes that Audio Overviews provide links to original sources, concerns remain about the potential impact on website traffic. Some publishers fear that AI-generated summaries might satisfy user intent without them needing to visit the original articles. Google acknowledges the experimental nature of the AI, warning of potential inaccuracies and audio glitches. Users can provide feedback via thumbs-up or thumbs-down ratings, which Google intends to use to refine the feature before broader release. The feature currently works only in English and only for users in the United States. Recommended read:
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Sana Hassan@MarkTechPost
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siliconangle.com
, Maginative
Google has recently unveiled significant advancements in artificial intelligence, showcasing its continued leadership in the tech sector. One notable development is an AI model designed for forecasting tropical cyclones. This model, developed through a collaboration between Google Research and DeepMind, is available via the newly launched Weather Lab website. It can predict the path and intensity of hurricanes up to 15 days in advance. The AI system learns from decades of historical storm data, reconstructing past weather conditions from millions of observations and utilizing a specialized database containing key information about storm tracks and intensity.
The tech giant's Weather Lab marks the first time the National Hurricane Center will use experimental AI predictions in its official forecasting workflow. The announcement comes at an opportune time, coinciding with forecasters predicting an above-average Atlantic hurricane season in 2025. This AI model can generate 50 different hurricane scenarios, offering a more comprehensive prediction range than current models, which typically provide forecasts for only 3-5 days. The AI has achieved a 1.5-day improvement in prediction accuracy, equivalent to about a decade's worth of traditional forecasting progress. Furthermore, Google is experiencing exponential growth in AI usage. Google DeepMind noted that Google's AI usage grew 50 times in one year, reaching 500 trillion tokens per month. Logan Kilpatrick from Google DeepMind discussed Google's transformation from a "sleeping giant" to an AI powerhouse, citing superior compute infrastructure, advanced models like Gemini 2.5 Pro, and a deep talent pool in AI research. Recommended read:
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@www.analyticsvidhya.com
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Google has made significant strides in the realm of artificial intelligence, introducing several key advancements. One notable development is the launch of Edge Gallery, an application designed to enable the execution of Large Language Models (LLMs) directly on smartphones. This groundbreaking app eliminates the need for cloud dependency, offering users free access to powerful AI processing capabilities on their personal devices. By shifting processing to the edge, Edge Gallery empowers users with greater control over their data and ensures enhanced privacy and security.
The company has also quietly upgraded Gemini 2.5 Pro, Google's flagship LLM, to boost its performance across coding, reasoning, and response quality. This upgrade addresses prior feedback regarding the model's tone, resulting in more structured and creative outputs. In addition to enhancing its core AI models, Google is expanding access to Project Mariner, an AI-driven browser assistant, to more Ultra subscribers. Project Mariner is designed to interact with the user’s active Chrome tabs via a dedicated extension, enabling it to query and manipulate information from any open webpage. Furthermore, Google has introduced an open-source full-stack AI agent stack powered by Gemini 2.5 and LangGraph, designed for multi-step web search, reflection, and synthesis. This research agent stack allows AI agents to perform autonomous web searches, validate results, and refine responses, effectively mimicking a human research assistant. This initiative underscores Google's commitment to fostering innovation and collaboration within the AI community by making advanced tools and resources freely available. Recommended read:
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