• NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR

    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognitionconference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop.
    This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR.
    The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehiclesimulation framework.
    The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs.
    Generating Safe and Adaptive Driving Trajectories
    Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically.
    Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research.
    The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoringmethod, which generates a variety of trajectories and progressively filters out the best one.
    GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories.
    GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories.
    This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions.

    NVIDIA Automotive Research at CVPR 
    More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more.
    In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+.
    The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs:

    Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion ModelsFoundationStereo: Zero-Shot Stereo MatchingZero-Shot Monocular Scene Flow Estimation in the WildDifix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting
    Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
    Zero-Shot 4D Lidar Panoptic Segmentation
    NVILA: Efficient Frontier Visual Language Models
    RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models
    OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning

    Explore automotive workshops and tutorials at CVPR, including:

    Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA
    Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA
    Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe
    Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA
    Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe
    Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone
    LatinX in CV Workshop, featuring Leal-Taixe
    Workshop on Exploring the Next Generation of Data, featuring Alvarez
    Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA
    Continuous Data Cycle via Foundation Models, led by NVIDIA
    Distillation of Foundation Models for Autonomous Driving, led by NVIDIA

    Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang.
    Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.
    The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.
    #nvidia #scores #consecutive #win #endtoend
    NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR
    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognitionconference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehiclesimulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoringmethod, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion ModelsFoundationStereo: Zero-Shot Stereo MatchingZero-Shot Monocular Scene Flow Estimation in the WildDifix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model. #nvidia #scores #consecutive #win #endtoend
    BLOGS.NVIDIA.COM
    NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR
    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehicle (AV) simulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoring (GTRS) method, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion Models (Read more in this blog.) FoundationStereo: Zero-Shot Stereo Matching (Best Paper nominee) Zero-Shot Monocular Scene Flow Estimation in the Wild (Best Paper nominee) Difix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models (Best Paper nominee) 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.
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  • Retail Reboot: Major Global Brands Transform End-to-End Operations With NVIDIA

    AI is packing and shipping efficiency for the retail and consumer packaged goodsindustries, with a majority of surveyed companies in the space reporting the technology is increasing revenue and reducing operational costs.
    Global brands are reimagining every facet of their businesses with AI, from how products are designed and manufactured to how they’re marketed, shipped and experienced in-store and online.
    At NVIDIA GTC Paris at VivaTech, industry leaders including L’Oréal, LVMH and Nestlé shared how they’re using tools like AI agents and physical AI — powered by NVIDIA AI and simulation technologies — across every step of the product lifecycle to enhance operations and experiences for partners, customers and employees.
    3D Digital Twins and AI Transform Marketing, Advertising and Product Design
    The meeting of generative AI and 3D product digital twins results in unlimited creative potential.
    Nestlé, the world’s largest food and beverage company, today announced a collaboration with NVIDIA and Accenture to launch a new, AI-powered in-house service that will create high-quality product content at scale for e-commerce and digital media channels.
    The new content service, based on digital twins powered by the NVIDIA Omniverse platform, creates exact 3D virtual replicas of physical products. Product packaging can be adjusted or localized digitally, enabling seamless integration into various environments, such as seasonal campaigns or channel-specific formats. This means that new creative content can be generated without having to constantly reshoot from scratch.
    Image courtesy of Nestlé
    The service is developed in partnership with Accenture Song, using Accenture AI Refinery built on NVIDIA Omniverse for advanced digital twin creation. It uses NVIDIA AI Enterprise for generative AI, hosted on Microsoft Azure for robust cloud infrastructure.
    Nestlé already has a baseline of 4,000 3D digital products — mainly for global brands — with the ambition to convert a total of 10,000 products into digital twins in the next two years across global and local brands.
    LVMH, the world’s leading luxury goods company, home to 75 distinguished maisons, is bringing 3D digital twins to its content production processes through its wine and spirits division, Moët Hennessy.
    The group partnered with content configuration engine Grip to develop a solution using the NVIDIA Omniverse platform, which enables the creation of 3D digital twins that power content variation production. With Grip’s solution, Moët Hennessy teams can quickly generate digital marketing assets and experiences to promote luxury products at scale.
    The initiative, led by Capucine Lafarge and Chloé Fournier, has been recognized by LVMH as a leading approach to scaling content creation.
    Image courtesy of Grip
    L’Oréal Gives Marketing and Online Shopping an AI Makeover
    Innovation starts at the drawing board. Today, that board is digital — and it’s powered by AI.
    L’Oréal Groupe, the world’s leading beauty player, announced its collaboration with NVIDIA today. Through this collaboration, L’Oréal and its partner ecosystem will leverage the NVIDIA AI Enterprise platform to transform its consumer beauty experiences, marketing and advertising content pipelines.
    “AI doesn’t think with the same constraints as a human being. That opens new avenues for creativity,” said Anne Machet, global head of content and entertainment at L’Oréal. “Generative AI enables our teams and partner agencies to explore creative possibilities.”
    CreAItech, L’Oréal’s generative AI content platform, is augmenting the creativity of marketing and content teams. Combining a modular ecosystem of models, expertise, technologies and partners — including NVIDIA — CreAltech empowers marketers to generate thousands of unique, on-brand images, videos and lines of text for diverse platforms and global audiences.
    The solution empowers L’Oréal’s marketing teams to quickly iterate on campaigns that improve consumer engagement across social media, e-commerce content and influencer marketing — driving higher conversion rates.

    Noli.com, the first AI-powered multi-brand marketplace startup founded and backed by the  L’Oréal Groupe, is reinventing how people discover and shop for beauty products.
    Noli’s AI Beauty Matchmaker experience uses L’Oréal Groupe’s century-long expertise in beauty, including its extensive knowledge of beauty science, beauty tech and consumer insights, built from over 1 million skin data points and analysis of thousands of product formulations. It gives users a BeautyDNA profile with expert-level guidance and personalized product recommendations for skincare and haircare.
    “Beauty shoppers are often overwhelmed by choice and struggling to find the products that are right for them,” said Amos Susskind, founder and CEO of Noli. “By applying the latest AI models accelerated by NVIDIA and Accenture to the unparalleled knowledge base and expertise of the L’Oréal Groupe, we can provide hyper-personalized, explainable recommendations to our users.” 

    The Accenture AI Refinery, powered by NVIDIA AI Enterprise, will provide the platform for Noli to experiment and scale. Noli’s new agent models will use NVIDIA NIM and NVIDIA NeMo microservices, including NeMo Retriever, running on Microsoft Azure.
    Rapid Innovation With the NVIDIA Partner Ecosystem
    NVIDIA’s ecosystem of solution provider partners empowers retail and CPG companies to innovate faster, personalize customer experiences, and optimize operations with NVIDIA accelerated computing and AI.
    Global digital agency Monks is reshaping the landscape of AI-driven marketing, creative production and enterprise transformation. At the heart of their innovation lies the Monks.Flow platform that enhances both the speed and sophistication of creative workflows through NVIDIA Omniverse, NVIDIA NIM microservices and Triton Inference Server for lightning-fast inference.
    AI image solutions provider Bria is helping retail giants like Lidl and L’Oreal to enhance marketing asset creation. Bria AI transforms static product images into compelling, dynamic advertisements that can be quickly scaled for use across any marketing need.
    The company’s generative AI platform uses NVIDIA Triton Inference Server software and the NVIDIA TensorRT software development kit for accelerated inference, as well as NVIDIA NIM and NeMo microservices for quick image generation at scale.
    Physical AI Brings Acceleration to Supply Chain and Logistics
    AI’s impact extends far beyond the digital world. Physical AI-powered warehousing robots, for example, are helping maximize efficiency in retail supply chain operations. Four in five retail companies have reported that AI has helped reduce supply chain operational costs, with 25% reporting cost reductions of at least 10%.
    Technology providers Lyric, KoiReader Technologies and Exotec are tackling the challenges of integrating AI into complex warehouse environments.
    Lyric is using the NVIDIA cuOpt GPU-accelerated solver for warehouse network planning and route optimization, and is collaborating with NVIDIA to apply the technology to broader supply chain decision-making problems. KoiReader Technologies is tapping the NVIDIA Metropolis stack for its computer vision solutions within logistics, supply chain and manufacturing environments using the KoiVision Platform. And Exotec is using NVIDIA CUDA libraries and the NVIDIA JetPack software development kit for embedded robotic systems in warehouse and distribution centers.
    From real-time robotics orchestration to predictive maintenance, these solutions are delivering impact on uptime, throughput and cost savings for supply chain operations.
    Learn more by joining a follow-up discussion on digital twins and AI-powered creativity with Microsoft, Nestlé, Accenture and NVIDIA at Cannes Lions on Monday, June 16.
    Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.
    #retail #reboot #major #global #brands
    Retail Reboot: Major Global Brands Transform End-to-End Operations With NVIDIA
    AI is packing and shipping efficiency for the retail and consumer packaged goodsindustries, with a majority of surveyed companies in the space reporting the technology is increasing revenue and reducing operational costs. Global brands are reimagining every facet of their businesses with AI, from how products are designed and manufactured to how they’re marketed, shipped and experienced in-store and online. At NVIDIA GTC Paris at VivaTech, industry leaders including L’Oréal, LVMH and Nestlé shared how they’re using tools like AI agents and physical AI — powered by NVIDIA AI and simulation technologies — across every step of the product lifecycle to enhance operations and experiences for partners, customers and employees. 3D Digital Twins and AI Transform Marketing, Advertising and Product Design The meeting of generative AI and 3D product digital twins results in unlimited creative potential. Nestlé, the world’s largest food and beverage company, today announced a collaboration with NVIDIA and Accenture to launch a new, AI-powered in-house service that will create high-quality product content at scale for e-commerce and digital media channels. The new content service, based on digital twins powered by the NVIDIA Omniverse platform, creates exact 3D virtual replicas of physical products. Product packaging can be adjusted or localized digitally, enabling seamless integration into various environments, such as seasonal campaigns or channel-specific formats. This means that new creative content can be generated without having to constantly reshoot from scratch. Image courtesy of Nestlé The service is developed in partnership with Accenture Song, using Accenture AI Refinery built on NVIDIA Omniverse for advanced digital twin creation. It uses NVIDIA AI Enterprise for generative AI, hosted on Microsoft Azure for robust cloud infrastructure. Nestlé already has a baseline of 4,000 3D digital products — mainly for global brands — with the ambition to convert a total of 10,000 products into digital twins in the next two years across global and local brands. LVMH, the world’s leading luxury goods company, home to 75 distinguished maisons, is bringing 3D digital twins to its content production processes through its wine and spirits division, Moët Hennessy. The group partnered with content configuration engine Grip to develop a solution using the NVIDIA Omniverse platform, which enables the creation of 3D digital twins that power content variation production. With Grip’s solution, Moët Hennessy teams can quickly generate digital marketing assets and experiences to promote luxury products at scale. The initiative, led by Capucine Lafarge and Chloé Fournier, has been recognized by LVMH as a leading approach to scaling content creation. Image courtesy of Grip L’Oréal Gives Marketing and Online Shopping an AI Makeover Innovation starts at the drawing board. Today, that board is digital — and it’s powered by AI. L’Oréal Groupe, the world’s leading beauty player, announced its collaboration with NVIDIA today. Through this collaboration, L’Oréal and its partner ecosystem will leverage the NVIDIA AI Enterprise platform to transform its consumer beauty experiences, marketing and advertising content pipelines. “AI doesn’t think with the same constraints as a human being. That opens new avenues for creativity,” said Anne Machet, global head of content and entertainment at L’Oréal. “Generative AI enables our teams and partner agencies to explore creative possibilities.” CreAItech, L’Oréal’s generative AI content platform, is augmenting the creativity of marketing and content teams. Combining a modular ecosystem of models, expertise, technologies and partners — including NVIDIA — CreAltech empowers marketers to generate thousands of unique, on-brand images, videos and lines of text for diverse platforms and global audiences. The solution empowers L’Oréal’s marketing teams to quickly iterate on campaigns that improve consumer engagement across social media, e-commerce content and influencer marketing — driving higher conversion rates. Noli.com, the first AI-powered multi-brand marketplace startup founded and backed by the  L’Oréal Groupe, is reinventing how people discover and shop for beauty products. Noli’s AI Beauty Matchmaker experience uses L’Oréal Groupe’s century-long expertise in beauty, including its extensive knowledge of beauty science, beauty tech and consumer insights, built from over 1 million skin data points and analysis of thousands of product formulations. It gives users a BeautyDNA profile with expert-level guidance and personalized product recommendations for skincare and haircare. “Beauty shoppers are often overwhelmed by choice and struggling to find the products that are right for them,” said Amos Susskind, founder and CEO of Noli. “By applying the latest AI models accelerated by NVIDIA and Accenture to the unparalleled knowledge base and expertise of the L’Oréal Groupe, we can provide hyper-personalized, explainable recommendations to our users.”  The Accenture AI Refinery, powered by NVIDIA AI Enterprise, will provide the platform for Noli to experiment and scale. Noli’s new agent models will use NVIDIA NIM and NVIDIA NeMo microservices, including NeMo Retriever, running on Microsoft Azure. Rapid Innovation With the NVIDIA Partner Ecosystem NVIDIA’s ecosystem of solution provider partners empowers retail and CPG companies to innovate faster, personalize customer experiences, and optimize operations with NVIDIA accelerated computing and AI. Global digital agency Monks is reshaping the landscape of AI-driven marketing, creative production and enterprise transformation. At the heart of their innovation lies the Monks.Flow platform that enhances both the speed and sophistication of creative workflows through NVIDIA Omniverse, NVIDIA NIM microservices and Triton Inference Server for lightning-fast inference. AI image solutions provider Bria is helping retail giants like Lidl and L’Oreal to enhance marketing asset creation. Bria AI transforms static product images into compelling, dynamic advertisements that can be quickly scaled for use across any marketing need. The company’s generative AI platform uses NVIDIA Triton Inference Server software and the NVIDIA TensorRT software development kit for accelerated inference, as well as NVIDIA NIM and NeMo microservices for quick image generation at scale. Physical AI Brings Acceleration to Supply Chain and Logistics AI’s impact extends far beyond the digital world. Physical AI-powered warehousing robots, for example, are helping maximize efficiency in retail supply chain operations. Four in five retail companies have reported that AI has helped reduce supply chain operational costs, with 25% reporting cost reductions of at least 10%. Technology providers Lyric, KoiReader Technologies and Exotec are tackling the challenges of integrating AI into complex warehouse environments. Lyric is using the NVIDIA cuOpt GPU-accelerated solver for warehouse network planning and route optimization, and is collaborating with NVIDIA to apply the technology to broader supply chain decision-making problems. KoiReader Technologies is tapping the NVIDIA Metropolis stack for its computer vision solutions within logistics, supply chain and manufacturing environments using the KoiVision Platform. And Exotec is using NVIDIA CUDA libraries and the NVIDIA JetPack software development kit for embedded robotic systems in warehouse and distribution centers. From real-time robotics orchestration to predictive maintenance, these solutions are delivering impact on uptime, throughput and cost savings for supply chain operations. Learn more by joining a follow-up discussion on digital twins and AI-powered creativity with Microsoft, Nestlé, Accenture and NVIDIA at Cannes Lions on Monday, June 16. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions. #retail #reboot #major #global #brands
    BLOGS.NVIDIA.COM
    Retail Reboot: Major Global Brands Transform End-to-End Operations With NVIDIA
    AI is packing and shipping efficiency for the retail and consumer packaged goods (CPG) industries, with a majority of surveyed companies in the space reporting the technology is increasing revenue and reducing operational costs. Global brands are reimagining every facet of their businesses with AI, from how products are designed and manufactured to how they’re marketed, shipped and experienced in-store and online. At NVIDIA GTC Paris at VivaTech, industry leaders including L’Oréal, LVMH and Nestlé shared how they’re using tools like AI agents and physical AI — powered by NVIDIA AI and simulation technologies — across every step of the product lifecycle to enhance operations and experiences for partners, customers and employees. 3D Digital Twins and AI Transform Marketing, Advertising and Product Design The meeting of generative AI and 3D product digital twins results in unlimited creative potential. Nestlé, the world’s largest food and beverage company, today announced a collaboration with NVIDIA and Accenture to launch a new, AI-powered in-house service that will create high-quality product content at scale for e-commerce and digital media channels. The new content service, based on digital twins powered by the NVIDIA Omniverse platform, creates exact 3D virtual replicas of physical products. Product packaging can be adjusted or localized digitally, enabling seamless integration into various environments, such as seasonal campaigns or channel-specific formats. This means that new creative content can be generated without having to constantly reshoot from scratch. Image courtesy of Nestlé The service is developed in partnership with Accenture Song, using Accenture AI Refinery built on NVIDIA Omniverse for advanced digital twin creation. It uses NVIDIA AI Enterprise for generative AI, hosted on Microsoft Azure for robust cloud infrastructure. Nestlé already has a baseline of 4,000 3D digital products — mainly for global brands — with the ambition to convert a total of 10,000 products into digital twins in the next two years across global and local brands. LVMH, the world’s leading luxury goods company, home to 75 distinguished maisons, is bringing 3D digital twins to its content production processes through its wine and spirits division, Moët Hennessy. The group partnered with content configuration engine Grip to develop a solution using the NVIDIA Omniverse platform, which enables the creation of 3D digital twins that power content variation production. With Grip’s solution, Moët Hennessy teams can quickly generate digital marketing assets and experiences to promote luxury products at scale. The initiative, led by Capucine Lafarge and Chloé Fournier, has been recognized by LVMH as a leading approach to scaling content creation. Image courtesy of Grip L’Oréal Gives Marketing and Online Shopping an AI Makeover Innovation starts at the drawing board. Today, that board is digital — and it’s powered by AI. L’Oréal Groupe, the world’s leading beauty player, announced its collaboration with NVIDIA today. Through this collaboration, L’Oréal and its partner ecosystem will leverage the NVIDIA AI Enterprise platform to transform its consumer beauty experiences, marketing and advertising content pipelines. “AI doesn’t think with the same constraints as a human being. That opens new avenues for creativity,” said Anne Machet, global head of content and entertainment at L’Oréal. “Generative AI enables our teams and partner agencies to explore creative possibilities.” CreAItech, L’Oréal’s generative AI content platform, is augmenting the creativity of marketing and content teams. Combining a modular ecosystem of models, expertise, technologies and partners — including NVIDIA — CreAltech empowers marketers to generate thousands of unique, on-brand images, videos and lines of text for diverse platforms and global audiences. The solution empowers L’Oréal’s marketing teams to quickly iterate on campaigns that improve consumer engagement across social media, e-commerce content and influencer marketing — driving higher conversion rates. Noli.com, the first AI-powered multi-brand marketplace startup founded and backed by the  L’Oréal Groupe, is reinventing how people discover and shop for beauty products. Noli’s AI Beauty Matchmaker experience uses L’Oréal Groupe’s century-long expertise in beauty, including its extensive knowledge of beauty science, beauty tech and consumer insights, built from over 1 million skin data points and analysis of thousands of product formulations. It gives users a BeautyDNA profile with expert-level guidance and personalized product recommendations for skincare and haircare. “Beauty shoppers are often overwhelmed by choice and struggling to find the products that are right for them,” said Amos Susskind, founder and CEO of Noli. “By applying the latest AI models accelerated by NVIDIA and Accenture to the unparalleled knowledge base and expertise of the L’Oréal Groupe, we can provide hyper-personalized, explainable recommendations to our users.”  https://blogs.nvidia.com/wp-content/uploads/2025/06/Noli_Demo.mp4 The Accenture AI Refinery, powered by NVIDIA AI Enterprise, will provide the platform for Noli to experiment and scale. Noli’s new agent models will use NVIDIA NIM and NVIDIA NeMo microservices, including NeMo Retriever, running on Microsoft Azure. Rapid Innovation With the NVIDIA Partner Ecosystem NVIDIA’s ecosystem of solution provider partners empowers retail and CPG companies to innovate faster, personalize customer experiences, and optimize operations with NVIDIA accelerated computing and AI. Global digital agency Monks is reshaping the landscape of AI-driven marketing, creative production and enterprise transformation. At the heart of their innovation lies the Monks.Flow platform that enhances both the speed and sophistication of creative workflows through NVIDIA Omniverse, NVIDIA NIM microservices and Triton Inference Server for lightning-fast inference. AI image solutions provider Bria is helping retail giants like Lidl and L’Oreal to enhance marketing asset creation. Bria AI transforms static product images into compelling, dynamic advertisements that can be quickly scaled for use across any marketing need. The company’s generative AI platform uses NVIDIA Triton Inference Server software and the NVIDIA TensorRT software development kit for accelerated inference, as well as NVIDIA NIM and NeMo microservices for quick image generation at scale. Physical AI Brings Acceleration to Supply Chain and Logistics AI’s impact extends far beyond the digital world. Physical AI-powered warehousing robots, for example, are helping maximize efficiency in retail supply chain operations. Four in five retail companies have reported that AI has helped reduce supply chain operational costs, with 25% reporting cost reductions of at least 10%. Technology providers Lyric, KoiReader Technologies and Exotec are tackling the challenges of integrating AI into complex warehouse environments. Lyric is using the NVIDIA cuOpt GPU-accelerated solver for warehouse network planning and route optimization, and is collaborating with NVIDIA to apply the technology to broader supply chain decision-making problems. KoiReader Technologies is tapping the NVIDIA Metropolis stack for its computer vision solutions within logistics, supply chain and manufacturing environments using the KoiVision Platform. And Exotec is using NVIDIA CUDA libraries and the NVIDIA JetPack software development kit for embedded robotic systems in warehouse and distribution centers. From real-time robotics orchestration to predictive maintenance, these solutions are delivering impact on uptime, throughput and cost savings for supply chain operations. Learn more by joining a follow-up discussion on digital twins and AI-powered creativity with Microsoft, Nestlé, Accenture and NVIDIA at Cannes Lions on Monday, June 16. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.
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  • HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift

    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas.
    The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers, to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI.
    The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market.
    The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster.
    This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs.
    These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows.
    HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October.
    In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption.
    The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center.
    To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis.
    HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity.
    Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments.

    Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay.
    Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page.
    #hpe #nvidia #debut #factory #stack
    HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift
    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas. The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers, to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI. The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market. The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster. This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows. HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October. In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption. The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center. To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis. HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity. Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments. Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay. Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page. #hpe #nvidia #debut #factory #stack
    BLOGS.NVIDIA.COM
    HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift
    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas. The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers (HPE ProLiant Compute DL380a Gen12), to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI. The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market. The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster. This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows. HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October. In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption. The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center. To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis. HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity. Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments. Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay. Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page.
    0 Yorumlar 0 hisse senetleri
  • Tech billionaires are making a risky bet with humanity’s future

    “The best way to predict the future is to invent it,” the famed computer scientist Alan Kay once said. Uttered more out of exasperation than as inspiration, his remark has nevertheless attained gospel-like status among Silicon Valley entrepreneurs, in particular a handful of tech billionaires who fancy themselves the chief architects of humanity’s future. 

    Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals and ambitions in the near term, but their grand visions for the next decade and beyond are remarkably similar. Framed less as technological objectives and more as existential imperatives, they include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality; establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos.

    While there’s a sprawling patchwork of ideas and philosophies powering these visions, three features play a central role, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits. In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker calls this triumvirate of beliefs the “ideology of technological salvation” and warns that tech titans are using it to steer humanity in a dangerous direction. 

    “In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress.”

    “The credence that tech billionaires give to these specific science-fictional futures validates their pursuit of more—to portray the growth of their businesses as a moral imperative, to reduce the complex problems of the world to simple questions of technology,to justify nearly any action they might want to take,” he writes. Becker argues that the only way to break free of these visions is to see them for what they are: a convenient excuse to continue destroying the environment, skirt regulations, amass more power and control, and dismiss the very real problems of today to focus on the imagined ones of tomorrow. 

    A lot of critics, academics, and journalists have tried to define or distill the Silicon Valley ethos over the years. There was the “Californian Ideology” in the mid-’90s, the “Move fast and break things” era of the early 2000s, and more recently the “Libertarianism for me, feudalism for thee”  or “techno-­authoritarian” views. How do you see the “ideology of technological salvation” fitting in? 

    I’d say it’s very much of a piece with those earlier attempts to describe the Silicon Valley mindset. I mean, you can draw a pretty straight line from Max More’s principles of transhumanism in the ’90s to the Californian Ideologyand through to what I call the ideology of technological salvation. The fact is, many of the ideas that define or animate Silicon Valley thinking have never been much of a ­mystery—libertarianism, an antipathy toward the government and regulation, the boundless faith in technology, the obsession with optimization. 

    What can be difficult is to parse where all these ideas come from and how they fit together—or if they fit together at all. I came up with the ideology of technological salvation as a way to name and give shape to a group of interrelated concepts and philosophies that can seem sprawling and ill-defined at first, but that actually sit at the center of a worldview shared by venture capitalists, executives, and other thought leaders in the tech industry. 

    Readers will likely be familiar with the tech billionaires featured in your book and at least some of their ambitions. I’m guessing they’ll be less familiar with the various “isms” that you argue have influenced or guided their thinking. Effective altruism, rationalism, long­termism, extropianism, effective accelerationism, futurism, singularitarianism, ­transhumanism—there are a lot of them. Is there something that they all share? 

    They’re definitely connected. In a sense, you could say they’re all versions or instantiations of the ideology of technological salvation, but there are also some very deep historical connections between the people in these groups and their aims and beliefs. The Extropians in the late ’80s believed in self-­transformation through technology and freedom from limitations of any kind—ideas that Ray Kurzweil eventually helped popularize and legitimize for a larger audience with the Singularity. 

    In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress. I should say that AI researcher Timnit Gebru and philosopher Émile Torres have also done a lot of great work linking these ideologies to one another and showing how they all have ties to racism, misogyny, and eugenics.

    You argue that the Singularity is the purest expression of the ideology of technological salvation. How so?

    Well, for one thing, it’s just this very simple, straightforward idea—the Singularity is coming and will occur when we merge our brains with the cloud and expand our intelligence a millionfold. This will then deepen our awareness and consciousness and everything will be amazing. In many ways, it’s a fantastical vision of a perfect technological utopia. We’re all going to live as long as we want in an eternal paradise, watched over by machines of loving grace, and everything will just get exponentially better forever. The end.

    The other isms I talk about in the book have a little more … heft isn’t the right word—they just have more stuff going on. There’s more to them, right? The rationalists and the effective altruists and the longtermists—they think that something like a singularity will happen, or could happen, but that there’s this really big danger between where we are now and that potential event. We have to address the fact that an all-powerful AI might destroy humanity—the so-called alignment problem—before any singularity can happen. 

    Then you’ve got the effective accelerationists, who are more like Kurzweil, but they’ve got more of a tech-bro spin on things. They’ve taken some of the older transhumanist ideas from the Singularity and updated them for startup culture. Marc Andreessen’s “Techno-Optimist Manifesto”is a good example. You could argue that all of these other philosophies that have gained purchase in Silicon Valley are just twists on Kurzweil’s Singularity, each one building on top of the core ideas of transcendence, techno­-optimism, and exponential growth. 

    Early on in the book you take aim at that idea of exponential growth—specifically, Kurzweil’s “Law of Accelerating Returns.” Could you explain what that is and why you think it’s flawed?

    Kurzweil thinks there’s this immutable “Law of Accelerating Returns” at work in the affairs of the universe, especially when it comes to technology. It’s the idea that technological progress isn’t linear but exponential. Advancements in one technology fuel even more rapid advancements in the future, which in turn lead to greater complexity and greater technological power, and on and on. This is just a mistake. Kurzweil uses the Law of Accelerating Returns to explain why the Singularity is inevitable, but to be clear, he’s far from the only one who believes in this so-called law.

    “I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear.”

    My sense is that it’s an idea that comes from staring at Moore’s Law for too long. Moore’s Law is of course the famous prediction that the number of transistors on a chip will double roughly every two years, with a minimal increase in cost. Now, that has in fact happened for the last 50 years or so, but not because of some fundamental law in the universe. It’s because the tech industry made a choice and some very sizable investments to make it happen. Moore’s Law was ultimately this really interesting observation or projection of a historical trend, but even Gordon Mooreknew that it wouldn’t and couldn’t last forever. In fact, some think it’s already over. 

    These ideologies take inspiration from some pretty unsavory characters. Transhumanism, you say, was first popularized by the eugenicist Julian Huxley in a speech in 1951. Marc Andreessen’s “Techno-Optimist Manifesto” name-checks the noted fascist Filippo Tommaso Marinetti and his futurist manifesto. Did you get the sense while researching the book that the tech titans who champion these ideas understand their dangerous origins?

    You’re assuming in the framing of that question that there’s any rigorous thought going on here at all. As I say in the book, Andreessen’s manifesto runs almost entirely on vibes, not logic. I think someone may have told him about the futurist manifesto at some point, and he just sort of liked the general vibe, which is why he paraphrases a part of it. Maybe he learned something about Marinetti and forgot it. Maybe he didn’t care. 

    I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear. For many of these billionaires, the vibes of fascism, authoritarianism, and colonialism are attractive because they’re fundamentally about creating a fantasy of control. 

    You argue that these visions of the future are being used to hasten environmental destruction, increase authoritarianism, and exacerbate inequalities. You also admit that they appeal to lots of people who aren’t billionaires. Why do you think that is? 

    I think a lot of us are also attracted to these ideas for the same reasons the tech billionaires are—they offer this fantasy of knowing what the future holds, of transcending death, and a sense that someone or something out there is in control. It’s hard to overstate how comforting a simple, coherent narrative can be in an increasingly complex and fast-moving world. This is of course what religion offers for many of us, and I don’t think it’s an accident that a sizable number of people in the rationalist and effective altruist communities are actually ex-evangelicals.

    More than any one specific technology, it seems like the most consequential thing these billionaires have invented is a sense of inevitability—that their visions for the future are somehow predestined. How does one fight against that?

    It’s a difficult question. For me, the answer was to write this book. I guess I’d also say this: Silicon Valley enjoyed well over a decade with little to no pushback on anything. That’s definitely a big part of how we ended up in this mess. There was no regulation, very little critical coverage in the press, and a lot of self-mythologizing going on. Things have started to change, especially as the social and environmental damage that tech companies and industry leaders have helped facilitate has become more clear. That understanding is an essential part of deflating the power of these tech billionaires and breaking free of their visions. When we understand that these dreams of the future are actually nightmares for the rest of us, I think you’ll see that senseof inevitability vanish pretty fast. 

    This interview was edited for length and clarity.

    Bryan Gardiner is a writer based in Oakland, California. 
    #tech #billionaires #are #making #risky
    Tech billionaires are making a risky bet with humanity’s future
    “The best way to predict the future is to invent it,” the famed computer scientist Alan Kay once said. Uttered more out of exasperation than as inspiration, his remark has nevertheless attained gospel-like status among Silicon Valley entrepreneurs, in particular a handful of tech billionaires who fancy themselves the chief architects of humanity’s future.  Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals and ambitions in the near term, but their grand visions for the next decade and beyond are remarkably similar. Framed less as technological objectives and more as existential imperatives, they include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality; establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos. While there’s a sprawling patchwork of ideas and philosophies powering these visions, three features play a central role, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits. In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker calls this triumvirate of beliefs the “ideology of technological salvation” and warns that tech titans are using it to steer humanity in a dangerous direction.  “In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress.” “The credence that tech billionaires give to these specific science-fictional futures validates their pursuit of more—to portray the growth of their businesses as a moral imperative, to reduce the complex problems of the world to simple questions of technology,to justify nearly any action they might want to take,” he writes. Becker argues that the only way to break free of these visions is to see them for what they are: a convenient excuse to continue destroying the environment, skirt regulations, amass more power and control, and dismiss the very real problems of today to focus on the imagined ones of tomorrow.  A lot of critics, academics, and journalists have tried to define or distill the Silicon Valley ethos over the years. There was the “Californian Ideology” in the mid-’90s, the “Move fast and break things” era of the early 2000s, and more recently the “Libertarianism for me, feudalism for thee”  or “techno-­authoritarian” views. How do you see the “ideology of technological salvation” fitting in?  I’d say it’s very much of a piece with those earlier attempts to describe the Silicon Valley mindset. I mean, you can draw a pretty straight line from Max More’s principles of transhumanism in the ’90s to the Californian Ideologyand through to what I call the ideology of technological salvation. The fact is, many of the ideas that define or animate Silicon Valley thinking have never been much of a ­mystery—libertarianism, an antipathy toward the government and regulation, the boundless faith in technology, the obsession with optimization.  What can be difficult is to parse where all these ideas come from and how they fit together—or if they fit together at all. I came up with the ideology of technological salvation as a way to name and give shape to a group of interrelated concepts and philosophies that can seem sprawling and ill-defined at first, but that actually sit at the center of a worldview shared by venture capitalists, executives, and other thought leaders in the tech industry.  Readers will likely be familiar with the tech billionaires featured in your book and at least some of their ambitions. I’m guessing they’ll be less familiar with the various “isms” that you argue have influenced or guided their thinking. Effective altruism, rationalism, long­termism, extropianism, effective accelerationism, futurism, singularitarianism, ­transhumanism—there are a lot of them. Is there something that they all share?  They’re definitely connected. In a sense, you could say they’re all versions or instantiations of the ideology of technological salvation, but there are also some very deep historical connections between the people in these groups and their aims and beliefs. The Extropians in the late ’80s believed in self-­transformation through technology and freedom from limitations of any kind—ideas that Ray Kurzweil eventually helped popularize and legitimize for a larger audience with the Singularity.  In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress. I should say that AI researcher Timnit Gebru and philosopher Émile Torres have also done a lot of great work linking these ideologies to one another and showing how they all have ties to racism, misogyny, and eugenics. You argue that the Singularity is the purest expression of the ideology of technological salvation. How so? Well, for one thing, it’s just this very simple, straightforward idea—the Singularity is coming and will occur when we merge our brains with the cloud and expand our intelligence a millionfold. This will then deepen our awareness and consciousness and everything will be amazing. In many ways, it’s a fantastical vision of a perfect technological utopia. We’re all going to live as long as we want in an eternal paradise, watched over by machines of loving grace, and everything will just get exponentially better forever. The end. The other isms I talk about in the book have a little more … heft isn’t the right word—they just have more stuff going on. There’s more to them, right? The rationalists and the effective altruists and the longtermists—they think that something like a singularity will happen, or could happen, but that there’s this really big danger between where we are now and that potential event. We have to address the fact that an all-powerful AI might destroy humanity—the so-called alignment problem—before any singularity can happen.  Then you’ve got the effective accelerationists, who are more like Kurzweil, but they’ve got more of a tech-bro spin on things. They’ve taken some of the older transhumanist ideas from the Singularity and updated them for startup culture. Marc Andreessen’s “Techno-Optimist Manifesto”is a good example. You could argue that all of these other philosophies that have gained purchase in Silicon Valley are just twists on Kurzweil’s Singularity, each one building on top of the core ideas of transcendence, techno­-optimism, and exponential growth.  Early on in the book you take aim at that idea of exponential growth—specifically, Kurzweil’s “Law of Accelerating Returns.” Could you explain what that is and why you think it’s flawed? Kurzweil thinks there’s this immutable “Law of Accelerating Returns” at work in the affairs of the universe, especially when it comes to technology. It’s the idea that technological progress isn’t linear but exponential. Advancements in one technology fuel even more rapid advancements in the future, which in turn lead to greater complexity and greater technological power, and on and on. This is just a mistake. Kurzweil uses the Law of Accelerating Returns to explain why the Singularity is inevitable, but to be clear, he’s far from the only one who believes in this so-called law. “I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear.” My sense is that it’s an idea that comes from staring at Moore’s Law for too long. Moore’s Law is of course the famous prediction that the number of transistors on a chip will double roughly every two years, with a minimal increase in cost. Now, that has in fact happened for the last 50 years or so, but not because of some fundamental law in the universe. It’s because the tech industry made a choice and some very sizable investments to make it happen. Moore’s Law was ultimately this really interesting observation or projection of a historical trend, but even Gordon Mooreknew that it wouldn’t and couldn’t last forever. In fact, some think it’s already over.  These ideologies take inspiration from some pretty unsavory characters. Transhumanism, you say, was first popularized by the eugenicist Julian Huxley in a speech in 1951. Marc Andreessen’s “Techno-Optimist Manifesto” name-checks the noted fascist Filippo Tommaso Marinetti and his futurist manifesto. Did you get the sense while researching the book that the tech titans who champion these ideas understand their dangerous origins? You’re assuming in the framing of that question that there’s any rigorous thought going on here at all. As I say in the book, Andreessen’s manifesto runs almost entirely on vibes, not logic. I think someone may have told him about the futurist manifesto at some point, and he just sort of liked the general vibe, which is why he paraphrases a part of it. Maybe he learned something about Marinetti and forgot it. Maybe he didn’t care.  I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear. For many of these billionaires, the vibes of fascism, authoritarianism, and colonialism are attractive because they’re fundamentally about creating a fantasy of control.  You argue that these visions of the future are being used to hasten environmental destruction, increase authoritarianism, and exacerbate inequalities. You also admit that they appeal to lots of people who aren’t billionaires. Why do you think that is?  I think a lot of us are also attracted to these ideas for the same reasons the tech billionaires are—they offer this fantasy of knowing what the future holds, of transcending death, and a sense that someone or something out there is in control. It’s hard to overstate how comforting a simple, coherent narrative can be in an increasingly complex and fast-moving world. This is of course what religion offers for many of us, and I don’t think it’s an accident that a sizable number of people in the rationalist and effective altruist communities are actually ex-evangelicals. More than any one specific technology, it seems like the most consequential thing these billionaires have invented is a sense of inevitability—that their visions for the future are somehow predestined. How does one fight against that? It’s a difficult question. For me, the answer was to write this book. I guess I’d also say this: Silicon Valley enjoyed well over a decade with little to no pushback on anything. That’s definitely a big part of how we ended up in this mess. There was no regulation, very little critical coverage in the press, and a lot of self-mythologizing going on. Things have started to change, especially as the social and environmental damage that tech companies and industry leaders have helped facilitate has become more clear. That understanding is an essential part of deflating the power of these tech billionaires and breaking free of their visions. When we understand that these dreams of the future are actually nightmares for the rest of us, I think you’ll see that senseof inevitability vanish pretty fast.  This interview was edited for length and clarity. Bryan Gardiner is a writer based in Oakland, California.  #tech #billionaires #are #making #risky
    WWW.TECHNOLOGYREVIEW.COM
    Tech billionaires are making a risky bet with humanity’s future
    “The best way to predict the future is to invent it,” the famed computer scientist Alan Kay once said. Uttered more out of exasperation than as inspiration, his remark has nevertheless attained gospel-like status among Silicon Valley entrepreneurs, in particular a handful of tech billionaires who fancy themselves the chief architects of humanity’s future.  Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals and ambitions in the near term, but their grand visions for the next decade and beyond are remarkably similar. Framed less as technological objectives and more as existential imperatives, they include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality (or something close to it); establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos. While there’s a sprawling patchwork of ideas and philosophies powering these visions, three features play a central role, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits. In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker calls this triumvirate of beliefs the “ideology of technological salvation” and warns that tech titans are using it to steer humanity in a dangerous direction.  “In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress.” “The credence that tech billionaires give to these specific science-fictional futures validates their pursuit of more—to portray the growth of their businesses as a moral imperative, to reduce the complex problems of the world to simple questions of technology, [and] to justify nearly any action they might want to take,” he writes. Becker argues that the only way to break free of these visions is to see them for what they are: a convenient excuse to continue destroying the environment, skirt regulations, amass more power and control, and dismiss the very real problems of today to focus on the imagined ones of tomorrow.  A lot of critics, academics, and journalists have tried to define or distill the Silicon Valley ethos over the years. There was the “Californian Ideology” in the mid-’90s, the “Move fast and break things” era of the early 2000s, and more recently the “Libertarianism for me, feudalism for thee”  or “techno-­authoritarian” views. How do you see the “ideology of technological salvation” fitting in?  I’d say it’s very much of a piece with those earlier attempts to describe the Silicon Valley mindset. I mean, you can draw a pretty straight line from Max More’s principles of transhumanism in the ’90s to the Californian Ideology [a mashup of countercultural, libertarian, and neoliberal values] and through to what I call the ideology of technological salvation. The fact is, many of the ideas that define or animate Silicon Valley thinking have never been much of a ­mystery—libertarianism, an antipathy toward the government and regulation, the boundless faith in technology, the obsession with optimization.  What can be difficult is to parse where all these ideas come from and how they fit together—or if they fit together at all. I came up with the ideology of technological salvation as a way to name and give shape to a group of interrelated concepts and philosophies that can seem sprawling and ill-defined at first, but that actually sit at the center of a worldview shared by venture capitalists, executives, and other thought leaders in the tech industry.  Readers will likely be familiar with the tech billionaires featured in your book and at least some of their ambitions. I’m guessing they’ll be less familiar with the various “isms” that you argue have influenced or guided their thinking. Effective altruism, rationalism, long­termism, extropianism, effective accelerationism, futurism, singularitarianism, ­transhumanism—there are a lot of them. Is there something that they all share?  They’re definitely connected. In a sense, you could say they’re all versions or instantiations of the ideology of technological salvation, but there are also some very deep historical connections between the people in these groups and their aims and beliefs. The Extropians in the late ’80s believed in self-­transformation through technology and freedom from limitations of any kind—ideas that Ray Kurzweil eventually helped popularize and legitimize for a larger audience with the Singularity.  In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress. I should say that AI researcher Timnit Gebru and philosopher Émile Torres have also done a lot of great work linking these ideologies to one another and showing how they all have ties to racism, misogyny, and eugenics. You argue that the Singularity is the purest expression of the ideology of technological salvation. How so? Well, for one thing, it’s just this very simple, straightforward idea—the Singularity is coming and will occur when we merge our brains with the cloud and expand our intelligence a millionfold. This will then deepen our awareness and consciousness and everything will be amazing. In many ways, it’s a fantastical vision of a perfect technological utopia. We’re all going to live as long as we want in an eternal paradise, watched over by machines of loving grace, and everything will just get exponentially better forever. The end. The other isms I talk about in the book have a little more … heft isn’t the right word—they just have more stuff going on. There’s more to them, right? The rationalists and the effective altruists and the longtermists—they think that something like a singularity will happen, or could happen, but that there’s this really big danger between where we are now and that potential event. We have to address the fact that an all-powerful AI might destroy humanity—the so-called alignment problem—before any singularity can happen.  Then you’ve got the effective accelerationists, who are more like Kurzweil, but they’ve got more of a tech-bro spin on things. They’ve taken some of the older transhumanist ideas from the Singularity and updated them for startup culture. Marc Andreessen’s “Techno-Optimist Manifesto” [from 2023] is a good example. You could argue that all of these other philosophies that have gained purchase in Silicon Valley are just twists on Kurzweil’s Singularity, each one building on top of the core ideas of transcendence, techno­-optimism, and exponential growth.  Early on in the book you take aim at that idea of exponential growth—specifically, Kurzweil’s “Law of Accelerating Returns.” Could you explain what that is and why you think it’s flawed? Kurzweil thinks there’s this immutable “Law of Accelerating Returns” at work in the affairs of the universe, especially when it comes to technology. It’s the idea that technological progress isn’t linear but exponential. Advancements in one technology fuel even more rapid advancements in the future, which in turn lead to greater complexity and greater technological power, and on and on. This is just a mistake. Kurzweil uses the Law of Accelerating Returns to explain why the Singularity is inevitable, but to be clear, he’s far from the only one who believes in this so-called law. “I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear.” My sense is that it’s an idea that comes from staring at Moore’s Law for too long. Moore’s Law is of course the famous prediction that the number of transistors on a chip will double roughly every two years, with a minimal increase in cost. Now, that has in fact happened for the last 50 years or so, but not because of some fundamental law in the universe. It’s because the tech industry made a choice and some very sizable investments to make it happen. Moore’s Law was ultimately this really interesting observation or projection of a historical trend, but even Gordon Moore [who first articulated it] knew that it wouldn’t and couldn’t last forever. In fact, some think it’s already over.  These ideologies take inspiration from some pretty unsavory characters. Transhumanism, you say, was first popularized by the eugenicist Julian Huxley in a speech in 1951. Marc Andreessen’s “Techno-Optimist Manifesto” name-checks the noted fascist Filippo Tommaso Marinetti and his futurist manifesto. Did you get the sense while researching the book that the tech titans who champion these ideas understand their dangerous origins? You’re assuming in the framing of that question that there’s any rigorous thought going on here at all. As I say in the book, Andreessen’s manifesto runs almost entirely on vibes, not logic. I think someone may have told him about the futurist manifesto at some point, and he just sort of liked the general vibe, which is why he paraphrases a part of it. Maybe he learned something about Marinetti and forgot it. Maybe he didn’t care.  I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear. For many of these billionaires, the vibes of fascism, authoritarianism, and colonialism are attractive because they’re fundamentally about creating a fantasy of control.  You argue that these visions of the future are being used to hasten environmental destruction, increase authoritarianism, and exacerbate inequalities. You also admit that they appeal to lots of people who aren’t billionaires. Why do you think that is?  I think a lot of us are also attracted to these ideas for the same reasons the tech billionaires are—they offer this fantasy of knowing what the future holds, of transcending death, and a sense that someone or something out there is in control. It’s hard to overstate how comforting a simple, coherent narrative can be in an increasingly complex and fast-moving world. This is of course what religion offers for many of us, and I don’t think it’s an accident that a sizable number of people in the rationalist and effective altruist communities are actually ex-evangelicals. More than any one specific technology, it seems like the most consequential thing these billionaires have invented is a sense of inevitability—that their visions for the future are somehow predestined. How does one fight against that? It’s a difficult question. For me, the answer was to write this book. I guess I’d also say this: Silicon Valley enjoyed well over a decade with little to no pushback on anything. That’s definitely a big part of how we ended up in this mess. There was no regulation, very little critical coverage in the press, and a lot of self-mythologizing going on. Things have started to change, especially as the social and environmental damage that tech companies and industry leaders have helped facilitate has become more clear. That understanding is an essential part of deflating the power of these tech billionaires and breaking free of their visions. When we understand that these dreams of the future are actually nightmares for the rest of us, I think you’ll see that senseof inevitability vanish pretty fast.  This interview was edited for length and clarity. Bryan Gardiner is a writer based in Oakland, California. 
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  • NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs

    Generative AI has reshaped how people create, imagine and interact with digital content.
    As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well.
    By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4.
    NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance.
    In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers.
    RTX-Accelerated AI
    NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs.
    Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution.
    To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one.
    SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs.
    FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup.
    Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch.
    The optimized models are now available on Stability AI’s Hugging Face page.
    NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July.
    TensorRT for RTX SDK Released
    Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers.
    Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time.
    With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature.
    The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview.
    For more details, read this NVIDIA technical blog and this Microsoft Build recap.
    Join NVIDIA at GTC Paris
    At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay.
    GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event.
    Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. 
    Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.
    Follow NVIDIA Workstation on LinkedIn and X. 
    See notice regarding software product information.
    #nvidia #tensorrt #boosts #stable #diffusion
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information. #nvidia #tensorrt #boosts #stable #diffusion
    BLOGS.NVIDIA.COM
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion (SD) 3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kit (SDK) double performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time (JIT), on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8 (right) generates images in half the time with similar quality as FP16 (left). Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.
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  • Can Sonic Racing: CrossWorlds Outrun Mario Kart World?

    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts, a Gadget that improved his speed while trailing an opponent, and a Gadget that improved acceleration. There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track. The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku, Joker, Ichiban Kasuga, and Steve. However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year.
    #can #sonic #racing #crossworlds #outrun
    Can Sonic Racing: CrossWorlds Outrun Mario Kart World?
    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts, a Gadget that improved his speed while trailing an opponent, and a Gadget that improved acceleration. There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track. The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku, Joker, Ichiban Kasuga, and Steve. However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year. #can #sonic #racing #crossworlds #outrun
    ME.PCMAG.COM
    Can Sonic Racing: CrossWorlds Outrun Mario Kart World?
    Mario Kart World is one of the year's hottest games, but its pivot to an open world setting, while peeling back kart customization options, opened a massive rift for Sonic Racing: CrossWorlds to drift into. And Sega is determined to do everything possible to make its kart racer the one to beat by including numerous guest characters and cross-platform, multiplayer contests. I took Sonic Racing: CrossWorlds for a test drive at the Summer Game Fest, and it's a strong contender racing game of the year.Sonic Racing: CrossWorlds' Deep Kart CustomizationThe biggest difference between Sonic Racing: CrossWorlds and Mario Kart World is that Sega's title focuses on kart customization. I'm not just talking about colors and tires; CrossWorlds introduces Gadgets, add-ons that augment your car, giving your whip helpful abilities to bring into the race. (Credit: Sega)Each ride has a license plate with six slots where you can slot your chosen Gadgets. A Gadget can take up one, two, or three slots, so the idea is to find a mix that pairs well with character traits. There's a surprising amount of depth for people who want to min/max their favorite anthropomorphic animal.I chose Sonic, a speed character, and added a Gadget that started him with two boosts (one slot), a Gadget that improved his speed while trailing an opponent (two slots), and a Gadget that improved acceleration (three slots). There were so many Gadgets that I could have easily spent my entire demo session building a car to match my playstyle. I envision people happily getting lost in the weeds before participating in their first race.(Credit: Sega)Gameplay: This Ain't Mario Kart WorldAlthough it's not an open world like Mario Kart World, Sonic Racing: CrossWorlds injects a unique spin on traditional kart racing. The familiar trappings are all here, such as rings to boost your top speed. Each Grand Prix consists of three maps, but the gimmick at play is stage transitions. Recommended by Our EditorsAbout a third of the way down a course, a giant ring-portal opens, presenting a new world and track (hence the name "CrossWorlds"). The shift in tone and terrain keeps the races fast-paced and unpredictable. I particularly liked how whoever is in first place can sometimes choose which CrossWorlds track to go down, controlling the tempo. With every race completion, you earn credits based on your performance that you can cash in for new car parts.In a stark contrast to Mario Kart World, Sonic Racing: CrossWorlds is far more aggressive, even on lower difficulties. At the start of each grand prix, the game assigns you a rival—this is the character to beat, and the one who taunts you all match. Beat them all, and you can race high-powered Super variants.Just about everything caused you to lose rings: bumping into other players, the walls, and, of course, getting hit by items. The series' trademark rubberband AI is still in place, too. Even in the press demo, I wasn't safe from taking four items back to back and being knocked off the stage mere feet away from the finish line.(Credit: Sega)The demo didn't include the new characters that debuted at the Summer Game Fest, but I studied the character screen to see who else could be coming to the game. Including the 12 Sonic characters available in the demo, I counted a whopping 64 character slots. They include Hatsune Miku (the ultra-popular Vocaloid), Joker (from Persona 5), Ichiban Kasuga (from Like a Dragon), and Steve (from Minecraft). However, I hope to see other classic Sega IPs like in previous Sonic Racing titles.Platforms and Release DateWill Sega do what Nintendon't? I had an exhilarating time playing Sonic Racing: CrossWorld, and I can't wait to see more wild track compositions. Sonic Racing: CrossWorlds will be available on Nintendo Switch, PC, PlayStation 4, PlayStation 5, Xbox One, and Xbox Series X/S on Sept. 25, 2025. A Nintendo Switch 2 version is planned for later in the year.
    0 Yorumlar 0 hisse senetleri
  • From Networks to Business Models, AI Is Rewiring Telecom

    Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services.
    As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry.
    Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental.
    AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint.
    AI Is Reshaping Wireless Networks Already
    Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access, and intelligent automation across the stack.
    AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models.
    Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time.
    AI Acceleration Will Outpace Past Tech Shifts
    Many may underestimate the speed and magnitude of AI-driven change.
    The shift from traditional voice and data systems to AI-driven network intelligence is already underway.
    Although predictions abound, the true scope remains unclear.
    It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise.

    Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board.
    Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined.
    History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries.
    Technological shifts bring both new opportunities and complex trade-offs.
    AI Disruption Will Move Faster Than Ever
    The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed.
    Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway.
    As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other.
    Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward.
    AI Will Reshape All Sectors and Companies
    This shift will unfold faster than most organizations or individuals are prepared to handle.
    Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries.
    Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption.
    Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage.
    As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name.

    It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries.
    SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption.
    The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers.
    Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives.
    No Industry Is Immune From AI Disruption
    AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp.
    New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent.
    Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets.
    The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow.
    #networks #business #models #rewiring #telecom
    From Networks to Business Models, AI Is Rewiring Telecom
    Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services. As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry. Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental. AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint. AI Is Reshaping Wireless Networks Already Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access, and intelligent automation across the stack. AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models. Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time. AI Acceleration Will Outpace Past Tech Shifts Many may underestimate the speed and magnitude of AI-driven change. The shift from traditional voice and data systems to AI-driven network intelligence is already underway. Although predictions abound, the true scope remains unclear. It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise. Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board. Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined. History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries. Technological shifts bring both new opportunities and complex trade-offs. AI Disruption Will Move Faster Than Ever The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed. Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway. As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other. Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward. AI Will Reshape All Sectors and Companies This shift will unfold faster than most organizations or individuals are prepared to handle. Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries. Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption. Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage. As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name. It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries. SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption. The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers. Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives. No Industry Is Immune From AI Disruption AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp. New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent. Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets. The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow. #networks #business #models #rewiring #telecom
    From Networks to Business Models, AI Is Rewiring Telecom
    Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services. As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry. Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental. AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint. AI Is Reshaping Wireless Networks Already Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access (FWA), and intelligent automation across the stack. AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models. Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time. AI Acceleration Will Outpace Past Tech Shifts Many may underestimate the speed and magnitude of AI-driven change. The shift from traditional voice and data systems to AI-driven network intelligence is already underway. Although predictions abound, the true scope remains unclear. It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise. Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board. Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined. History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries. Technological shifts bring both new opportunities and complex trade-offs. AI Disruption Will Move Faster Than Ever The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed. Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway. As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other. Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward. AI Will Reshape All Sectors and Companies This shift will unfold faster than most organizations or individuals are prepared to handle. Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries. Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption. Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage. As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name. It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries. SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption. The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers. Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives. No Industry Is Immune From AI Disruption AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp. New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent. Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets. The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow.
    0 Yorumlar 0 hisse senetleri
  • Why Companies Need to Reimagine Their AI Approach

    Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI, have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment, leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Mostreport adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need forspeed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like
    #why #companies #need #reimagine #their
    Why Companies Need to Reimagine Their AI Approach
    Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI, have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment, leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Mostreport adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need forspeed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #why #companies #need #reimagine #their
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    Why Companies Need to Reimagine Their AI Approach
    Ivy Grant, SVP of Strategy & Operations, Twilio June 13, 20255 Min Readpeshkova via alamy stockAsk technologists and enterprise leaders what they hope AI will deliver, and most will land on some iteration of the "T" word: transformation. No surprise, AI and its “cooler than you” cousin, generative AI (GenAI), have been hyped nonstop for the past 24 months. But therein lies the problem. Many organizations are rushing to implement AI without a grasp on the return on investment (ROI), leading to high spend and low impact. Without anchoring AI to clear friction points and acceleration opportunities, companies invite fatigue, anxiety and competitive risk. Two-thirds of C-suite execs say GenAI has created tension and division within their organizations; nearly half say it’s “tearing their company apart.” Most (71%) report adoption challenges; more than a third call it a massive disappointment. While AI's potential is irrefutable, companies need to reject the narrative of AI as a standalone strategy or transformational savior. Its true power is as a catalyst to amplify what already works and surface what could. Here are three principles to make that happen. 1. Start with friction, not function Many enterprises struggle with where to start when integrating AI. My advice: Start where the pain is greatest. Identify the processes that create the most friction and work backward from there. AI is a tool, not a solution. By mapping real pain points to AI use cases, you can hone investments to the ripest fruit rather than simply where it hangs at the lowest. Related:For example, one of our top sources of customer pain was troubleshooting undeliverable messages, which forced users to sift through error code documentation. To solve this, an AI assistant was introduced to detect anomalies, explain causes in natural language, and guide customers toward resolution. We achieved a 97% real-time resolution rate through a blend of conversational AI and live support. Most companies have long-standing friction points that support teams routinely explain. Or that you’ve developed organizational calluses over; problems considered “just the cost of doing business.” GenAI allows leaders to revisit these areas and reimagine what’s possible. 2. The need for (dual) speed We hear stories of leaders pushing an “all or nothing” version of AI transformation: Use AI to cut functional headcount or die. Rather than leading with a “stick” through wholesale transformation mandates or threats to budgets, we must recognize AI implementation as a fundamental culture change. Just as you wouldn't expect to transform your company culture overnight by edict, it's unreasonable to expect something different from your AI transformation. Related:Some leaders have a tendency to move faster than the innovation ability or comfort level of their people. Most functional leads aren’t obstinate in their slow adoption of AI tools, their long-held beliefs to run a process or to assess risks. We hired these leaders for their decades of experience in “what good looks like” and deep expertise in incremental improvements; then we expect them to suddenly define a futuristic vision that challenges their own beliefs. As executive leaders, we must give grace, space and plenty of “carrots” -- incentives, training, and support resources -- to help them reimagine complex workflows with AI. And, we must recognize that AI has the ability to make progress in ways that may not immediately create cost efficiencies, such as for operational improvements that require data cleansing, deep analytics, forecasting, dynamic pricing, and signal sensing. These aren’t the sexy parts of AI, but they’re the types of issues that require superhuman intelligence and complex problem-solving that AI was made for. 3. A flywheel of acceleration The other transformation that AI should support is creating faster and broader “test and learn” cycles. AI implementation is not a linear process with start here and end there. Organizations that want to leverage AI as a competitive advantage should establish use cases where AI can break down company silos and act as a catalyst to identify the next opportunity. That identifies the next as a flywheel of acceleration. This flywheel builds on accumulated learnings, making small successes into larger wins while avoiding costly AI disasters from rushed implementation. Related:For example, at Twilio we are building a customer intelligence platform that analyzes thousands of conversations to identify patterns and drive insights. If we see multiple customers mention a competitor's pricing, it could signal a take-out campaign. What once took weeks to recognize and escalate can now be done in near real-time and used for highly coordinated activations across marketing, product, sales, and other teams. With every AI acceleration win, we uncover more places to improve hand-offs, activation speed, and business decision-making. That flywheel of innovation is how true AI transformation begins to drive impactful business outcomes. Ideas to Fuel Your AI Strategy Organizations can accelerate their AI implementations through these simple shifts in approach: Revisit your long-standing friction points, both customer-facing and internal, across your organization -- particularly explore the ones you thought were “the cost of doing business” Don’t just look for where AI can reduce manual processes, but find the highly complex problems and start experimenting Support your functional experts with AI-driven training, resources, tools, and incentives to help them challenge their long-held beliefs about what works for the future Treat AI implementation as a cultural change that requires time, experimentation, learning, and carrots (not just sticks) Recognize that transformation starts with a flywheel of acceleration, where each new experiment can lead to the next big discovery The most impactful AI implementations don’t rush transformation; they strategically accelerate core capabilities and unlock new ones to drive measurable change. About the AuthorIvy GrantSVP of Strategy & Operations, Twilio Ivy Grant is Senior Vice President of Strategy & Operations at Twilio where she leads strategic planning, enterprise analytics, M&A Integration and is responsible for driving transformational initiatives that enable Twilio to continuously improve its operations. Prior to Twilio, Ivy’s career has balanced senior roles in strategy consulting at McKinsey & Company, Edelman and PwC with customer-centric operational roles at Walmart, Polo Ralph Lauren and tech startup Eversight Labs. She loves solo international travel, hugging exotic animals and boxing. Ivy has an MBA from NYU’s Stern School of Business and a BS in Applied Economics from Cornell University. See more from Ivy GrantReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like
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