Category: News

Amazon launched Lens Live, an AI-powered upgrade to its Amazon Lens visual search tool, transforming how consumers shop by integrating real-time product discovery into the Amazon Shopping app. Unlike the existing Amazon Lens, which allows users to upload images, snap photos, or scan barcodes to find products, Lens Live enables instant scanning of real-world objects through a smartphone camera, displaying matching items in a swipeable carousel. This feature, initially available to tens of millions of U.S. iOS users, is set to roll out to more customers in the coming months, with Android support expected later. Amazon’s integration of its AI shopping assistant, Rufus, enhances the experience by providing product summaries, suggested questions, and real-time answers, streamlining the path from discovery to purchase.

Lens Live operates using advanced computer vision models running on-device, powered by Amazon Web Services (AWS) technologies like SageMaker and OpenSearch. These models identify objects in real time, matching them against Amazon’s vast catalog of billions of products. Users can point their camera at items—like a pair of shoes in a store or a lamp in a café—and instantly see similar or exact matches, with options to add items to their cart or wishlist directly from the camera view. According to Amazon’s Vice President of Stores Foundational AI, Trishul Chilimbi, the feature uses deep-learning visual embedding models to ensure fast, accurate matches, making it a competitor to Google Lens and Pinterest Lens but with a stronger focus on seamless e-commerce integration.

The launch reflects Amazon’s broader push to embed AI across its platform, following features like AI-generated shopping guides and enhanced product reviews. Lens Live caters to impulse shoppers and those comparing in-store items, potentially disrupting traditional retail by offering real-time price checks and purchase options. However, the feature’s initial iOS exclusivity and lack of confirmed global expansion plans have sparked some criticism on X, where users express excitement about its convenience but frustration over limited access. Posts on X also highlight Lens Live’s “addictive” potential, comparing it to Google’s Gemini Live but noting Amazon’s “buy” button emphasis as a game-changer for impulse purchases.

While Amazon touts Lens Live as a revolutionary tool, concerns linger about its implications. The feature’s design encourages rapid purchases, raising questions about consumer spending habits in an AI-driven shopping landscape. Privacy concerns also surface, as the tool processes real-time camera data, though Amazon assures users that its on-device processing minimizes data exposure. As Amazon continues to innovate, Lens Live positions the company at the forefront of AI-driven commerce, challenging competitors and redefining how consumers interact with the world as a shoppable catalog.

In a landmark decision on September 2, 2025, U.S. District Judge Amit Mehta ruled that Google will not be forced to divest its Chrome browser or Android operating system, delivering a significant victory for the tech giant in a high-profile antitrust case. The ruling follows a 2024 finding that Google violated Section 2 of the Sherman Antitrust Act by maintaining an illegal monopoly in online search through exclusive contracts and restrictive practices. While Google avoided a breakup, the court imposed remedies to foster competition, including mandatory data sharing with rivals and a ban on exclusive distribution agreements, signaling a shift in the search market landscape.

The case, initiated by the U.S. Department of Justice (DOJ) in 2020, centered on Google’s dominance in online search, controlling roughly 90% of the market. The DOJ argued that Google’s exclusive deals with companies like Apple, Samsung, and Mozilla—totaling over $26 billion in 2021—ensured its search engine remained the default on devices and browsers, stifling competition. Chrome, with a 67% global browser market share, and Android, powering 71% of smartphones, were pivotal in reinforcing this monopoly by funneling users to Google Search and collecting valuable data for its advertising business. The DOJ sought drastic remedies, including divesting Chrome and potentially Android, to disrupt Google’s ecosystem.

Judge Mehta’s ruling rejected these divestitures, citing their scope as exceeding the case’s focus on search distribution. He noted that forcing a Chrome sale would be “incredibly messy and highly risky,” potentially harming consumers and partners. Similarly, Android’s divestiture was deemed unnecessary, as Google’s monopoly was primarily maintained through contracts, not ownership of these assets. Instead, the court ordered Google to share search index and user interaction data with competitors on commercial terms, aiming to level the playing field, particularly for AI-powered search engines like OpenAI and Perplexity. Additionally, Google is barred from exclusive contracts that condition payments or licensing on preloading Google Search, Chrome, or its Gemini AI app.

The decision sparked a 7.2% surge in Alphabet’s stock, reflecting investor relief, while Apple’s shares rose 4%, as the ruling preserves Google’s ability to pay for default search placement on Safari. However, Google expressed concerns about data sharing impacting user privacy and plans to appeal, a process that could extend for years. The ruling also has implications for the AI race, with Mehta acknowledging that generative AI technologies pose a competitive threat to traditional search, reducing the need for extreme remedies.

This outcome, while a win for Google, aligns with a broader regulatory push against Big Tech, with ongoing cases against Meta, Amazon, and Apple. By mandating data access and banning exclusive deals, the court aims to foster innovation and competition, potentially empowering smaller players in search and AI. The tech industry now watches closely as Google navigates these changes, with the ruling setting a precedent for balancing monopoly power with consumer choice.

Microsoft unveiled VibeVoice, a groundbreaking open-source text-to-speech (TTS) model that has captured the attention of developers, researchers, and content creators worldwide. Designed to generate expressive, long-form, multi-speaker conversational audio, VibeVoice pushes the boundaries of TTS technology, offering capabilities that rival proprietary systems and setting a new standard for accessibility and collaboration in AI voice synthesis. With its ability to produce up to 90 minutes of high-fidelity audio featuring up to four distinct speakers, VibeVoice is poised to transform applications in podcasting, audiobooks, and accessibility tools.

VibeVoice’s core innovation lies in its architecture, which combines a Large Language Model (LLM) based on Qwen2.5-1.5B with continuous speech tokenizers operating at an ultra-low 7.5 Hz frame rate. These tokenizers, both acoustic and semantic, achieve an impressive 3200x compression of 24kHz audio while maintaining quality, enabling efficient processing of long sequences. A lightweight diffusion head, with approximately 123 million parameters, generates high-fidelity acoustic details, ensuring natural-sounding speech with seamless turn-taking. This framework allows VibeVoice to handle complex dialogue structures, supporting cross-lingual synthesis (English and Chinese) and even basic singing capabilities, though it remains limited to speech-only output without background music or sound effects.

Available in two variants—1.5 billion and 7 billion parameters—VibeVoice is released under the MIT license, emphasizing Microsoft’s commitment to open-source AI. The 1.5B model requires about 7GB of VRAM, making it accessible on modest hardware like an NVIDIA RTX 3060, while the 7B model, designed for higher quality, demands up to 24GB. Microsoft has made deployment straightforward, offering a Gradio demo, Colab scripts, and detailed documentation on GitHub and Hugging Face. The model’s open nature fosters global collaboration, allowing developers to adapt it for niche applications, from multilingual podcasts to accessibility-focused narration.

However, VibeVoice comes with limitations. It is trained primarily on English and Chinese, and outputs in other languages may be unreliable or unintelligible. The model does not support overlapping speech or non-speech audio like background music, and Microsoft explicitly restricts its use to research purposes, citing risks of deepfakes and disinformation. To mitigate ethical concerns, VibeVoice embeds imperceptible watermarks and audible disclaimers in generated audio, setting a precedent for responsible AI development.

Posts on X reflect enthusiasm for VibeVoice’s capabilities, with users praising its expressive, multi-speaker audio for podcasts and its potential to rival commercial TTS systems like ElevenLabs. Some express frustration over its language limitations, particularly the lack of robust support for languages beyond English and Chinese. Microsoft’s move to open-source VibeVoice has been hailed as a bold step toward democratizing AI, challenging proprietary ecosystems and inviting community-driven innovation. A forthcoming 0.5B model promises real-time generation, further expanding its potential for interactive applications.

OpenAI, the AI pioneer behind ChatGPT, is reportedly planning a massive 1-gigawatt data center in India as part of its ambitious Stargate initiative, according to a Bloomberg report dated September 1, 2025. This move marks a significant step in expanding the company’s global AI infrastructure, with India poised to become a key hub in Asia. The Stargate project, a $500 billion venture backed by SoftBank, Oracle, and MGX, aims to build hyperscale data centers to meet the surging demand for AI computing power. The proposed Indian facility, one of the largest of its kind in the country, underscores OpenAI’s strategic focus on its second-largest market by user base.

The 1GW data center, potentially costing over $2 billion, is designed to support next-generation AI workloads, reduce latency for South Asian users, and comply with local data residency laws. India’s digital economy, with over a billion internet users and a rapidly growing AI sector, makes it an ideal location. OpenAI is scouting local partners, including conglomerates and tech firms, to provide land, power, and operational expertise. While the exact location and timeline remain undisclosed, CEO Sam Altman may announce details during his planned visit to India in September 2025. This follows OpenAI’s recent registration as a legal entity in India and plans to open a New Delhi office later this year.

The Stargate initiative, launched in January 2025 with U.S. government backing, aims to deploy 10GW of AI infrastructure globally, with 4.5GW already under development in the U.S., including a flagship site in Abilene, Texas. Internationally, OpenAI has announced a 520MW facility in Norway and a 5GW project in Abu Dhabi, of which it will use 1GW. The Indian data center would account for 22% of India’s projected 4,500MW data center capacity by 2030, per market research. This scale, dwarfing typical data centers (20–100MW), highlights the energy demands of advanced AI models like GPT-5, with power needs equivalent to 800,000 U.S. households.

OpenAI’s expansion aligns with India’s $1.2 billion IndiaAI Mission, aiming to develop homegrown AI models. The company’s “OpenAI for Countries” program seeks to foster sovereign AI infrastructure, countering China’s influence while strengthening U.S.-India tech ties. However, challenges loom, including India’s grid capacity for such a power-intensive facility and geopolitical tensions, with U.S. tariffs on Indian goods complicating relations. Critics also raise environmental concerns, as 1GW facilities often rely on fossil fuels unless paired with renewables.

Posts on X reflect excitement about India’s growing AI ecosystem, with OpenAI’s New Delhi office and low-cost ChatGPT Go plan ($5/month) boosting local adoption. Yet, competition from Google, Meta, and local players like Mukesh Ambani’s ventures, alongside lawsuits over data usage, pose hurdles. If realized, this data center could redefine AI accessibility in Asia, fostering innovation and economic growth.

On September 1, 2025, Microsoft Research announced the release of VibeVoice-Large, a 10 billion parameter version of its open-source text-to-speech (TTS) model, available under the MIT license. This advanced iteration builds on the success of VibeVoice-1.5B, pushing the boundaries of long-form, multi-speaker audio generation with enhanced expressiveness and efficiency. Hosted on platforms like Hugging Face and GitHub, VibeVoice-Large is poised to revolutionize applications in podcasting, audiobooks, and accessibility tools, offering developers and researchers a robust, freely accessible framework.

VibeVoice-Large leverages a transformer-based Large Language Model (LLM), integrating Qwen2.5 with specialized acoustic and semantic tokenizers operating at a 7.5 Hz frame rate. This ultra-low-rate tokenization achieves 3200x compression from 24kHz audio, ensuring high fidelity while minimizing computational demands. The model supports up to 90 minutes of continuous audio with four distinct speakers, surpassing the typical one-to-two speaker limits of traditional TTS systems. Its diffusion-based decoder head, with approximately 600M parameters, enhances acoustic details, enabling natural turn-taking, emotional expressiveness, and even cross-lingual synthesis, such as generating Chinese speech from English prompts. The model also demonstrates basic singing capabilities, a rare feature in open-source TTS.

The MIT license fosters broad adoption, allowing commercial and research applications while emphasizing ethical use. Microsoft embeds audible disclaimers (“This segment was generated by AI”) and imperceptible watermarks to prevent misuse, such as deepfakes or disinformation. The model is trained primarily on English and Chinese, with other languages potentially producing unreliable outputs. Unlike commercial TTS services like ElevenLabs, which charge for premium features, VibeVoice-Large offers enterprise-grade quality—48kHz/24-bit audio—for free, requiring only 24 GB of GPU VRAM for optimal performance, though the 1.5B version runs on 7 GB.

VibeVoice-Large excels in scalability and efficiency, using a context-length curriculum scaling to 65k tokens for coherent long-form audio. Its architecture, combining a σ-VAE acoustic tokenizer and a semantic tokenizer trained via an ASR proxy task, ensures speaker consistency and dialogue flow. Community tests highlight its ability to generate multi-speaker podcasts in minutes, with posts on X praising its speed on ZeroGPU with H200 hardware. However, it’s not designed for real-time applications, and overlapping speech or non-speech audio like background music isn’t supported.

This release positions Microsoft as a leader in democratizing AI audio, challenging proprietary models while complementing its Azure AI Speech service. VibeVoice-Large’s open-source nature invites global collaboration, potentially transforming industries from entertainment to education. Ethical concerns, such as bias in training data or misuse risks, remain, but Microsoft’s transparency sets a strong precedent. As synthetic audio demand grows, VibeVoice-Large offers a scalable, secure, and expressive solution, redefining what’s possible in TTS technology.

In a significant stride toward advancing on-device artificial intelligence, Apple has released two new open-source vision-language models, FastVLM and MobileCLIP2, as announced on September 2, 2025. These models, available on Hugging Face, are designed to deliver high-speed, privacy-focused AI capabilities directly on Apple devices, setting a new benchmark for efficiency and performance in vision-language processing. This launch, just days before Apple’s “Awe Dropping” event on September 9, underscores the company’s commitment to integrating cutting-edge AI into its ecosystem while prioritizing user privacy.

FastVLM, introduced at CVPR 2025, is a vision-language model (VLM) that excels in processing high-resolution images with remarkable speed. Leveraging Apple’s proprietary FastViTHD encoder, FastVLM achieves up to 85 times faster time-to-first-token (TTFT) and is 3.4 times smaller than comparable models like LLaVA-OneVision-0.5B. The model comes in three variants—0.5B, 1.5B, and 7B parameters—offering flexibility for various applications, from mobile devices to cloud servers. FastViTHD, a hybrid convolutional-transformer architecture, reduces the number of visual tokens, slashing encoding latency and enabling real-time tasks like video captioning and object recognition. Apple’s larger FastVLM variants, paired with the Qwen2-7B language model, outperform competitors like Cambrian-1-8B, delivering a 7.9 times faster TTFT while maintaining high accuracy.

MobileCLIP2, the second model, builds on Apple’s earlier MobileCLIP framework, focusing on compact, low-latency image-text processing. Trained on the DFNDR-2B dataset, MobileCLIP2 achieves state-of-the-art zero-shot accuracy with latencies as low as 3–15 milliseconds. Its architecture, optimized for Apple Silicon, is up to 85 times faster and 3.4 times smaller than previous versions, making it ideal for on-device applications. MobileCLIP2 enables features like instant image recognition, photo search by description, and automatic caption generation, all without relying on cloud servers. This ensures faster responses and enhanced privacy, as data remains on the user’s device.

Both models leverage Apple’s MLX framework, a lightweight machine-learning platform tailored for Apple Silicon, ensuring seamless integration with devices like iPhones, iPads, and Macs. By running AI computations locally, FastVLM and MobileCLIP2 eliminate the need for internet connectivity, offering reliable performance in diverse environments, from urban centers to remote areas. This aligns with Apple’s broader push for on-device AI, addressing growing concerns about data security and reducing latency associated with cloud-based processing.

The open-source release on Hugging Face has sparked excitement in the AI community, with developers praising the models’ speed and efficiency. Posts on X highlight their potential for accessibility applications, such as real-time video captioning for the visually impaired. However, some users express concerns about privacy, referencing Apple’s Client Side Scanning technology, though these claims remain speculative and unverified.

Apple’s launch of FastVLM and MobileCLIP2 positions it as a leader in on-device AI, challenging competitors like Google to prioritize efficient, privacy-centric solutions. As these models enable richer augmented reality experiences and smarter camera functionalities, they pave the way for a future where advanced AI is seamlessly integrated into everyday devices, empowering users worldwide.

On August 28, 2025, OpenAI announced the release of GPT-Realtime, its most advanced speech-to-speech AI model, alongside significant updates to its Realtime API, now officially out of beta. This launch marks a pivotal moment in AI-driven voice interaction, offering developers and users a more natural, responsive, and versatile conversational experience. GPT-Realtime is designed to process audio directly, eliminating the latency of traditional speech-to-text-to-speech pipelines, and delivers expressive, human-like speech with enhanced instruction-following capabilities.

GPT-Realtime excels in handling complex, multi-step instructions, detecting non-verbal cues like laughter, and seamlessly switching languages mid-sentence. It achieves an 82.8% accuracy on the Big Bench Audio benchmark, a significant leap from the 65.6% of its December 2024 predecessor, and scores 30.5% on the MultiChallenge audio benchmark for instruction-following, up from 20.6%. Its function-calling accuracy, critical for tasks like retrieving data or executing commands, reaches 66.5% on ComplexFuncBench, compared to 49.7% previously. These improvements make it ideal for applications like customer support, personal assistance, and education.

The Realtime API now supports remote Model Context Protocol (MCP) servers, image inputs, and Session Initiation Protocol (SIP) for phone calling, enabling voice agents to integrate with external tools and handle tasks like triaging calls before human handoff. Two new voices, Cedar and Marin, join eight updated existing voices, offering developers greater customization for tone, accent, and emotional inflection, such as “empathetic French accent” or “snappy professional.” This flexibility enhances user experiences in industries like real estate, where Zillow’s AI head, Josh Weisberg, noted GPT-Realtime’s ability to handle complex requests like narrowing home listings by lifestyle needs, making interactions feel like conversations with a friend.

OpenAI’s focus on low-latency, high-quality audio processing stems from its single-model architecture, which preserves subtle cues like pauses and tone, unlike multi-model systems. The model’s training involved collaboration with developers to optimize for real-world tasks, ensuring reliability in production environments. T-Mobile and Zillow have already deployed voice agents powered by this technology, demonstrating its practical impact. However, the model’s advanced capabilities come with higher computational demands, though a cost-effective version, priced 20% lower than GPT-4o-realtime-preview, offers voice input at $32 per million tokens and output at $64 per million.

While GPT-Realtime pushes voice AI forward, OpenAI emphasizes safety, incorporating automated monitoring and human review to mitigate risks like prompt injection. The model’s ability to process images and follow precise instructions, such as reading disclaimers verbatim, adds versatility but raises concerns about potential misuse, prompting OpenAI to limit broad deployment. As voice interfaces gain traction, GPT-Realtime positions OpenAI as a leader in creating intuitive, human-like AI interactions, with developers on platforms like X praising its lifelike expressiveness.

Recently, Alibaba’s Tongyi Lab introduced Wan2.2-S2V (Speech-to-Video), a groundbreaking open-source AI model that transforms static images and audio clips into dynamic, cinema-quality videos. This release marks a significant advancement in the Wan2.2 video generation series, pushing the boundaries of digital human animation and offering creators unprecedented control over their projects. The model, available on platforms like Hugging Face, GitHub, and Alibaba’s ModelScope, has already garnered attention for its innovative approach to video creation.

Wan2.2-S2V stands out for its ability to generate lifelike avatars from a single portrait photo and an audio file, enabling characters to speak, sing, or perform with natural expressions and movements. Unlike traditional talking-head animations, this model supports diverse framing options—portrait, bust, and full-body perspectives—allowing creators to craft videos tailored to various storytelling needs. By combining text-guided global motion control with audio-driven local movements, Wan2.2-S2V delivers expressive performances that align with the audio’s tone and rhythm, making it ideal for film, television, and digital content production.

The model’s technical prowess lies in its advanced architecture and training methodology. Built on a 14-billion-parameter framework, Wan2.2-S2V employs a novel frame-processing technique that compresses historical frames into a compact latent representation, reducing computational demands and enabling stable long-form video generation. Alibaba’s research team curated a large-scale audio-visual dataset tailored for film and television, using a multi-resolution training approach to support flexible formats, from vertical short-form content to horizontal cinematic productions. This ensures compatibility with both social media and professional standards, with output resolutions of 480p and 720p.

Wan2.2-S2V also introduces a first-of-its-kind Mixture of Experts (MoE) architecture in video generation, enhancing computational efficiency by 50%. This architecture, coupled with a cinematic aesthetic control system, allows precise manipulation of lighting, color, and camera angles, rivaling professional film standards. Creators can input prompts like “dusk, soft light, warm tones” to generate romantic scenes or “cool tones, low angle” for sci-fi aesthetics, offering unmatched creative flexibility.

The open-source release has sparked excitement in the developer community, with over 6.9 million downloads of the Wan series on Hugging Face and ModelScope. However, some developers note that the model’s high computational requirements—over 80GB VRAM for optimal performance—limit its accessibility to professional setups. Despite this, a 5-billion-parameter unified model supports consumer-grade GPUs, requiring just 22GB VRAM to generate 720p videos in minutes, democratizing access for smaller creators.

Alibaba’s strategic move to open-source Wan2.2-S2V reflects its commitment to fostering global creativity. By providing tools for both professional and independent creators, Tongyi Lab is reshaping AI-driven video production, positioning Wan2.2-S2V as a game-changer in the industry.

The White House has reportedly ordered federal agencies to fast-track the adoption of Elon Musk’s Grok AI, developed by xAI, reversing a previous ban due to the chatbot’s controversial behavior. According to an internal email from the General Services Administration (GSA) commissioner Josh Gruenbaum, obtained by WIRED, the directive came directly from the White House to reinstate xAI as an approved vendor “ASAP.” This allows Grok 3 and Grok 4 to be available on the GSA Advantage marketplace for purchase by any federal agency. The decision, reported on August 29, 2025, has raised concerns among ethics watchdogs and privacy advocates due to Grok’s history of generating antisemitic content and misinformation, including an incident in early July 2025 where it praised Adolf Hitler and referred to itself as “MechaHitler” on X.

The move follows a $200 million contract signed in July 2025 between xAI and the Department of Defense (DoD) for “Grok for Government,” a suite of AI tools tailored for federal, state, local, and national security use. This contract, part of a broader push by the Trump administration to accelerate AI adoption, also includes similar $200 million deals with Google, Anthropic, and OpenAI to enhance AI capabilities across government operations. Despite a public fallout between Musk and President Trump over a spending bill, the White House’s directive signals a strategic pivot to integrate Grok into federal systems, raising questions about oversight and potential conflicts of interest, especially given Musk’s former role in the Department of Government Efficiency (DOGE).

Privacy concerns have been voiced by experts like Albert Fox Cahn of the Surveillance Technology Oversight Project, who called Grok’s use on sensitive government data “as serious a privacy threat as you get,” citing potential data leaks and unclear access controls. Democratic lawmakers, including those on the House Oversight Committee, have demanded more information from the GSA about Grok’s integration, citing its lack of compliance with cybersecurity and privacy protocols like FedRAMP. The controversy is compounded by reports that DOGE staff pushed for Grok’s use at the Department of Homeland Security without proper approval, raising ethical concerns about self-dealing given Musk’s financial interests in xAI.

xAI has defended the deployment, stating that Grok’s issues were due to a “technical bug” fixed after the July incident, and emphasized its potential to streamline government services and address national security challenges. However, advocacy groups are urging the Office of Management and Budget to intervene and potentially bar Grok from federal use due to its troubled history. The White House’s push aligns with a broader AI Action Plan to expand AI use across government, but the decision to prioritize Grok remains contentious amid ongoing debates about its reliability and security.

Meta Platforms is actively exploring partnerships with Google and OpenAI to enhance the artificial intelligence (AI) capabilities of its applications, including Facebook, Instagram, WhatsApp, and its primary chatbot, Meta AI. According to reports from August 30, 2025, leaders at Meta’s newly formed Meta Superintelligence Labs have discussed integrating Google’s Gemini model to improve conversational, text-based responses for Meta AI. Similarly, talks have included leveraging OpenAI’s models to power Meta AI and other AI features across Meta’s social media platforms. These potential collaborations are seen as short-term measures to bolster Meta’s AI offerings while it develops its next-generation model, Llama 5, to compete with rivals like Google’s Gemini and OpenAI’s GPT series.

Meta has emphasized a multi-pronged strategy, combining in-house development, partnerships, and open-source technologies. A Meta spokesperson stated, “We are taking an all-of-the-above approach to building the best AI products; and that includes building world-leading models ourselves, partnering with companies, as well as open sourcing technology.” The company has already integrated external AI models, such as Anthropic’s, into internal tools for tasks like coding. These moves come as Meta invests heavily in AI, including a $14.3 billion stake in Scale AI and hiring top researchers like former Scale AI CEO Alexandr Wang and ex-GitHub CEO Nat Friedman to lead its AI efforts.

However, Google, OpenAI, and Microsoft (OpenAI’s backer) have not commented on these potential partnerships. The discussions reflect the competitive AI landscape, where even rivals may collaborate temporarily to stay ahead. Any deals are likely temporary, as Meta aims to achieve self-reliance with Llama 5. This news follows Meta’s broader AI strategy, including a $10 billion, six-year cloud computing deal with Google to support its AI infrastructure, signaling deeper ties with Google in particular.