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GIGABYTE and TAICCA partner to address the growing demand for efficient and high-quality immersive content creation
Published: March 11, 2025
Rory Greener
This week, at the 2025 SXSW festival, alongside arts and technology, immersive technology innovator GIGABYTE announced a partnership with the Taiwan Creative Content Agency (TAICCA) to promote the deployment of an AI-powered XR content creation platform. 
The event is famed for showcasing the latest in arts and technology, and this year at the SXSW 2025 XR Lounge, the partnership underlined the event’s reputation.  
As part of the GIGABYTE/TAICCA partnership, the firms are developing AI computing technology, which will enable a new editing workflow alternative for creating XR content.  
The platform supports 360 VR video editing with AI-powered enhancements, up to 8K Topaz video AI upscaling, Adobe Premiere Pro integrations, and immersive 360 VR experience storytelling features. 
Moreover, GIGABYTE announced VS AI Street Fighting Arcade, a text-to-image prompt-based service for creating gaming and immersive experiences.  
GIGABYTE also announced a 4K 360 VR Video Editing Workflow feature that is ready for VR professionals and promises optimized AI-powered video production.  
The partnership showcases the ongoing relationship between AI and XR, with the surging AI hardware and software market helping to supercharge interest in and the ability of AR/VR/MR solutions.
Notably, the GIGABYTE announcement is supported by the firm’s deep AI hardware portfolio, combining XR content creation workflows with the aforementioned AI software optimizations and hardware innovations.
GIGABYTE AI PCs support the VR content creation workflows. These PCs include integrated hardware such as a Z790 AORUS MASTER motherboard and GeForce RTX 40 Series GPUs to help drive forward GIGABYTE’s innovation solution.
Moreover, the TRX50 AERO D motherboard and GeForce RTX 4090 GAMING OC support the GIGABYTE 8 K VR video editing workflow service.
GIGABYTE says the combined hardware/software product offering demonstrates the “next evolution” of VR content creation workflows. Therefore, it boosts the abilities of XR content creators at a time when immersive media consumption is on the rise.
For example, marketing and advertising campaigns increasingly leverage XR media to provide an interactive and memorable customer outreach program to engage with new and old customers ready for a new generation of media.
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Google is aware of how some devices, including Pixel, on Android 16 and 16 QPR1 are failing to install Play system updates.
This issue was acknowledged today on the Pixel and Android Community forums. It involves a “Failed to update” error when going to Settings > Security & privacy > System & updates > Google Play system update. Tapping “Try again” fails, with this emerging as a problem in recent weeks.
This impacts “some devices running Android 16 and Android 16 beta.” In our example, a Pixel phone is still on the May 1, 2025 Google Play system update in July. That said, most of our devices are not seeing any issues. 
Google says “there is no need to factory reset your device.” It has “identified a fix” and will “roll it out to users in an upcoming Google Play system update.”
As far as we can tell, this Google Play system update gap has not resulted in any missing features.
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These companies are fine-tuning their marketing efforts with machine learning.
The promise of using machine learning in marketing is this: Data, properly harnessed, can help marketers and advertisers better understand customers and automate finely tuned, targeted brand campaigns with unsurpassed consumer personalization.
To give you a better sense of how this promise is being kept, we rounded up examples of machine learning in marketing.
 
 
Snap, the technology company behind the popular Snapchat app, is advancing its machine learning models to enhance advertiser performance and drive conversions for its partners. For example, its 7-0 Pixel Purchase optimization model allows advertisers to bid for attributed seven-day clickthrough conversions, which it says led to a more than 75 percent year-over-year increase in purchase-related conversions. By refining its ML models, Snap aims to improve return on advertising spend, or ROAS, and significantly reduce cost per action for its partners.
 
This digital marketing solution from System1 is a closed-loop customer acquisition platform that uses proprietary algorithms, intent data and auctioning technology to help advertisers acquire customers at scale. The technology integrates with major digital ad networks and monetization channels such as search, display, video, commerce, lead generation and subscription.
 
Klaviyo makes a marketing automation platform for businesses in multiple industries, including retail and e-commerce. Powered by AI and machine learning, its software informs and drives email and SMS marketing. The technology uses customer data to support automated marketing campaigns that engage customers with personalized messaging across channels.
 
JPMorgan Chase equipped its marketing team with an AI copywriter that produces more customer-friendly content. The financial giant showcases a partnership with Persado, which uses machine learning and AI technology to write ad copy that catches consumers’ attention. By enlisting the capabilities of this software startup, JPMorgan Chase has enjoyed higher click rates with its revamped copywriting.
 
Gong employs artificial intelligence (including machine learning) to help B2B sales teams close more deals by automatically recording, transcribing and analyzing the content of all sales-oriented calls, web conferences and emails. Its revenue intelligence technology integrates well with platforms like Salesforce, Office 365 and Slack, creating a seamless experience for sales teams looking to refine their operations.
 
Netflix uses machine learning in its recommender system, as well as for research efforts and A/B testing. The company also uses machine learning to inform its decisions on which content to add to its streaming platform and to optimize production of original TV series and movies through its film studio.
 
IBM offers a suite of AI tools that helps businesses streamline their marketing strategies and customer interactions. Its AI assistant Watson taps into the possibilities of machine learning to conduct audience analytics, personalize one-on-one conversations and connect with audiences through their preferred channels.
 
E-commerce companies can stay one step ahead of customers with Bluecore’s platform, which personalizes interactions for online shoppers. AI and machine learning guide one-on-one conversations and recommend products to customers across a range of channels. Predictive automated intelligence also gathers info on the ideal ways to reach out to customers, so teams can adapt marketing activities to audiences’ preferences.
 
Airbnb hosts several open-source products, including Aerosolve, a machine learning library. The library is ideal for running algorithms with easily interpretable features like searching for keywords and filters for vacation rentals. Aerosolve is the same library that Airbnb uses to predict booking probability and suggest pricing to its hosts.
 
Ada gives companies the ability to deliver consistent, high-quality customer service with a brand interaction platform that displays conversational AI features. Machine learning models enable Ada’s platform to analyze text in over 100 languages and then predict the needs of customers. With this proactive technology, businesses can reduce the time it takes to resolve issues and provide customers with the answers they’re looking for.
More on Marketing20 Big Data Companies Redefining Marketing
 
As part of its partnership with Appen, GumGum has leveraged AI and machine learning to determine ideal web pages and digital spaces for posting ads. Verity serves as the company’s contextual intelligence platform, which scans videos, audio clips, images, text and other online elements. With this tool, businesses can place ads on web pages and platforms without accidentally associating their brands with irrelevant or controversial content.
 
Strong Analytics makes it easier for marketers to develop personalized content and campaigns with a machine learning platform. A combination of AI, analytics and machine learning tools enables teams to compile data on customer behavior, predict future needs and tailor marketing efforts to meet those needs. In addition, marketers can rely on Strong Analytics’ platform to help them quickly deploy machine learning applications and streamline their operations.
 
People.ai’s sales automation tools use machine learning algorithms, freeing up time for the sales and marketing professionals who use them. With the company’s data platform, teams are able to compile information from various interactions to determine who needs to be contacted to increase revenue streams moving forward. The company’s platform also sends out timely AI-driven alerts, so teams know which accounts to focus on to close deals.
 
Applecart’s marketing platform locates promising leads — and their connections — leading to more efficient marketing campaigns. After collecting data from public sources, the company’s Social Graph Platform uses machine learning algorithms to determine professional and personal relationships for each individual. Companies can quickly map out the social networks of leads and tailor their content to these audiences.
 
Working behind the scenes, Brandfolder makes it easier for teams to find marketing assets with its Brand Intelligence platform. This platform uses AI, natural language processing and machine learning algorithms to tag creative assets, understand how they’re organized and recommend ways for classifying them. Teams and stakeholders can then save time and energy locating assets within a cleaner marketing ecosystem.
 
A digital marketing technology platform for real estate agents, Ylopo incorporates a variety of ingredients — including social media marketing, targeted demographic and psychographic advertising, big data and AI — into its Total Digital Marketing Solution product. Its AI agent RAIYA blends natural language processing and machine learning to carry conversations with customers and guide them along their property search.
 
Drift helps businesses forge bonds with customers and prospects through its AI-based platform. Equipped with machine learning, Drift’s Conversational Cloud platform engages with site visitors and potential buyers to answer questions, close deals and encourage future visits. Chatbots also record insights during each conversation, so companies can understand customer needs and better serve returning visitors.
 
Acquia provides marketers with the data they need to better understand audiences and tailor content to specific target groups. Machine learning features build models and classify customers based on certain behaviors, so marketing teams can form accurate insights and buyer personas. As a result, businesses can adapt their products to customers’ preferences and create unique marketing experiences.
 
Dynamic Yield employs advanced machine learning to help marketers increase revenue through single-platform personalization, recommendations, automatic optimization and one-on-one messaging. Once businesses link their marketing campaigns with Dynamic Yield’s platform, they can sort through rich data sets and select content that caters to target audiences’ interests.
 
Affinitiv helps car dealerships and OEMs improve customer service and long-term loyalty with digital services. The company’s Atlas DX platform features rich demographic and behavioral data, so dealerships can match their marketing content to the interests of prospective and current customers. As a result, teams can determine the best channels through which to contact audiences and convert them into buyers.
 
To optimize marketing campaigns, Bliss Point Media has designed an application that reveals where marketers should focus their attention. Machine learning tools compile real-time data, analyze it and determine sources that deliver the most revenue. Marketers can shape strategies around their target audiences while still diversifying their channels and outreach methods.
 
Built on an AI platform that blends machine learning with natural language processing and natural language generation, Conversica’s AI assistant automatically contacts, engages, qualifies and follows up with leads using natural two-way communication. It also fills in lead contact information, keeping a company’s CRM up to date.
 
Swayable allows companies to gauge audience opinions through emotional analyses. A mix of machine learning and computer vision technology captures consumers’ reactions to products and tracks wider opinions after gathering data. Businesses use Swayable’s causal AI to sharpen their marketing campaigns right from the beginning.
More on Machine Learning16 Machine Learning Examples You Should Know
Instapage’s landing page builder helps marketers design their website with conversion in mind. It uses AI and machine learning to analyze ad campaigns and match the language to landing pages for more successful conversion rates. This AI-generated landing page content saves marketers time and money that would otherwise be spent on rewriting and A/B testing.
 
An applied data science company, Dstillery employs machine learning to produce actionable customer insights from its sprawling database of constantly updated online and offline behavioral profiles. Marketers use Dstillery’s Custom AI Audiences tool to build customer profiles with first-party data, pinpointing relevant leads.
 
DoorDash is coming up with smarter ways to spend its marketing budget by employing machine learning to study previous campaigns. Machine learning algorithms review historical data to separate successful campaigns from less successful ones. With a clearer picture of what works and what doesn’t, DoorDash can adjust its marketing strategies and invest in content that resonates more with customers’ needs and interests.
 
Liftoff builds technology to help mobile businesses grow. The company’s Accelerate product offers machine learning capabilities to power user acquisition. Its solutions enable re-engagement campaigns that target inactive app users, for example, and drive efforts to draw app users to mobile web pages.
 
NextRoll has two products powered by machine learning and artificial intelligence. RollWorks identifies anonymous purchasers, analyzes them and guides interaction with those most likely to make a purchase. AdRoll is a programmatic advertising platform that offers full-funnel and multi-channel advertising campaigns, AI-powered audience targeting, analytics, insights and cross-channel attribution. 
 
Sojern provides travel and hospitality businesses with marketing solutions. It aims to help client companies align their digital marketing strategies across channels and platforms. The company’s AI-powered software platform includes tools and insights to efficiently engage targeted travel audiences and optimize campaign performance. 
 
Rose Velazquez, Margo Steines, Sara B.T. Thiel, Ana Gore and Ashley Bowden contributed reporting to this story.

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Google has confirmed that its Android Earthquake Alerts system experienced issues in the wake of the 2023 earthquake that rocked Turkey and nearby regions.
Google’s Android Earthquake Alerts system leverages the sensors in Android phones to detect the early signs of an earthquake, providing alert to users when major quakes are coming before their effects are felt. While the system can usually only provide a few seconds of warning, that can be incredibly valuable in major quakes.
In early 2023, though, the system seemingly didn’t kick in during the major quake that rocked Turkey. It was reported in the months following the quake that users never saw these alerts, despite the region being comprised of nearly 80% Android users. Google, at the time, said that it was “confident” the system had worked correctly. That 7.8 magnitude quake led to the deaths of tens of thousands of people, largely due to collapsing buildings.
Now, Google is confirming that its Android Earthquake Alerts system failed to properly alert users of the Turkey quake.
Speaking to the BBC, Google says that its system partially failed. Earthquake alerts have two different forms. For quakes with lower impact, a notification is sent alongside other warnings. But for major quakes, the system sends a fullscreen notification with audible alerts and overrides user settings such as Do Not Disturb. In the Turkey earthquake, Android’s system sent lower-level “BeAware” warnings to around half a million users, and the full “TakeAction” alerts to a mere 469 people. The report estimates that the system could have sent alerts to “10 million people within 98 miles of the epicenter” of the earthquake.
What went wrong? Google apparently cites “limitations to the detection algorithms,” with the company also confirming that the system did work properly on later aftershocks. Later the same day, the system sent “8,158” high level alerts to users, with “just under four million” users. In a later simulation of the initial quake, the system would have sent high-level alerts to 10 million users, and 67 million lower level alerts to those further from the epicenter.
Google recently published a deeper study regarding the Android Earthquake Alerts system, noting the “challenge of estimating an earthquake’s power.” That study, along with a further paper published to Science, doesn’t address the failure in Turkey.
In the study, though, Google says that it continues to learn from user feedback as the system continues to function, with some “key takeaways” including:
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