Generative AI is transforming PC software into breakthrough experiences — from digital humans to writing assistants, intelligent agents and creative tools. NVIDIA RTX AI PCs are powering this transformation with technology that makes it simpler to get started experimenting with generative AI and unlock greater performance on Windows 11. NVIDIA TensorRT has been reimagined for RTX AI PCs, combining industry-leading TensorRT 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. Announced at Microsoft Build, TensorRT for RTX is natively supported by Windows ML — a new inference stack that provides app developers with both broad hardware compatibility and state-of-the-art performance. For developers looking for AI features ready to integrate, NVIDIA software development kitsoffer a wide array of options, from NVIDIA DLSS to multimedia enhancements like NVIDIA RTX Video. This month, top software applications from Autodesk, Bilibili, Chaos, LM Studio and Topaz Labs are releasing updates to unlock RTX AI features and acceleration. AI enthusiasts and developers can easily get started with AI using NVIDIA NIM — prepackaged, optimized AI models that can run in popular apps like AnythingLLM, Microsoft VS Code and ComfyUI. Releasing this week, the FLUX.1-schnell image generation model will be available as a NIM microservice, and the popular FLUX.1-dev NIM microservice has been updated to support more RTX GPUs. Those looking for a simple, no-code way to dive into AI development can tap into Project G-Assist — the RTX PC AI assistant in the NVIDIA app — to build plug-ins to control PC apps and peripherals using natural language AI. New community plug-ins are now available, including Google Gemini web search, Spotify, Twitch, IFTTT and SignalRGB. Accelerated AI Inference With TensorRT for RTX Today’s AI PC software stack requires developers to compromise on performance or invest in custom optimizations for specific hardware. Windows ML was built to solve these challenges. Windows ML is powered by ONNX Runtime and seamlessly connects to an optimized AI execution layer provided and maintained by each hardware manufacturer. For GeForce RTX GPUs, Windows ML automatically uses the TensorRT for RTX inference library for high performance and rapid deployment. Compared with DirectML, TensorRT delivers over 50% faster performance for AI workloads on PCs. TensorRT delivers over 50% faster performance for AI workloads on PCs than DirectML. Performance measured on GeForce RTX 5090. Windows ML also delivers quality-of-life benefits for developers. It can automatically select the right hardware — GPU, CPU or NPU — to run each AI feature, and download the execution provider for that hardware, removing the need to package those files into the app. This allows for the latest TensorRT performance optimizations to be delivered to users as soon as they’re ready. TensorRT performance optimizations are delivered to users as soon as they’re ready. TensorRT, a library originally built for data centers, has been redesigned for RTX AI PCs. Instead of pre-generating TensorRT engines and packaging them with the app, TensorRT for RTX uses just-in-time, on-device engine building to optimize how the AI model is run for the user’s specific RTX GPU in mere seconds. And the library’s packaging has been streamlined, reducing its file size significantly by 8x. TensorRT for RTX is available to developers through the Windows ML preview today, and will be available as a standalone SDK at NVIDIA Developer in June. Developers can learn more in the TensorRT for RTX launch blog or Microsoft’s Windows ML blog. Expanding the AI Ecosystem on Windows 11 PCs Developers looking to add AI features or boost app performance can tap into a broad range of NVIDIA SDKs. These include NVIDIA CUDA and TensorRT for GPU acceleration; NVIDIA DLSS and Optix for 3D graphics; NVIDIA RTX Video and Maxine for multimedia; and NVIDIA Riva and ACE for generative AI. Top applications are releasing updates this month to enable unique features using these NVIDIA SDKs, including: LM Studio, which released an update to its app to upgrade to the latest CUDA version, increasing performance by over 30%. Topaz Labs, which is releasing a generative AI video model to enhance video quality, accelerated by CUDA. Chaos Enscape and Autodesk VRED, which are adding DLSS 4 for faster performance and better image quality. Bilibili, which is integrating NVIDIA Broadcast features such as Virtual Background to enhance the quality of livestreams. NVIDIA looks forward to continuing to work with Microsoft and top AI app developers to help them accelerate their AI features on RTX-powered machines through the Windows ML and TensorRT integration. Local AI Made Easy With NIM Microservices and AI Blueprints Getting started with developing AI on PCs can be daunting. AI developers and enthusiasts have to select from over 1.2 million AI models on Hugging Face, quantize it into a format that runs well on PC, find and install all the dependencies to run it, and more. NVIDIA NIM makes it easy to get started by providing a curated list of AI models, prepackaged with all the files needed to run them and optimized to achieve full performance on RTX GPUs. And since they’re containerized, the same NIM microservice can be run seamlessly across PCs or the cloud. NVIDIA NIM microservices are available to download through build.nvidia.com or through top AI apps like Anything LLM, ComfyUI and AI Toolkit for Visual Studio Code. During COMPUTEX, NVIDIA will release the FLUX.1-schnell NIM microservice — an image generation model from Black Forest Labs for fast image generation — and update the FLUX.1-dev NIM microservice to add compatibility for a wide range of GeForce RTX 50 and 40 Series GPUs. These NIM microservices enable faster performance with TensorRT and quantized models. On NVIDIA Blackwell GPUs, they run over twice as fast as running them natively, thanks to FP4 and RTX optimizations. The FLUX.1-schnell NIM microservice runs over twice as fast as on NVIDIA Blackwell GPUs with FP4 and RTX optimizations. AI developers can also jumpstart their work with NVIDIA AI Blueprints — sample workflows and projects using NIM microservices. NVIDIA last month released the NVIDIA AI Blueprint for 3D-guided generative AI, a powerful way to control composition and camera angles of generated images by using a 3D scene as a reference. Developers can modify the open-source blueprint for their needs or extend it with additional functionality. New Project G-Assist Plug-Ins and Sample Projects Now Available NVIDIA recently released Project G-Assist as an experimental AI assistant integrated into the NVIDIA app. G-Assist enables users to control their GeForce RTX system using simple voice and text commands, offering a more convenient interface compared to manual controls spread across numerous legacy control panels. Developers can also use Project G-Assist to easily build plug-ins, test assistant use cases and publish them through NVIDIA’s Discord and GitHub. The Project G-Assist Plug-in Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy to start creating plug-ins. These lightweight, community-driven add-ons use straightforward JSON definitions and Python logic. New open-source plug-in samples are available now on GitHub, showcasing diverse ways on-device AI can enhance PC and gaming workflows. They include: Gemini: The existing Gemini plug-in that uses Google’s cloud-based free-to-use large language model has been updated to include real-time web search capabilities. IFTTT: A plug-in that lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights or smart shades, or pushing the latest gaming news to a mobile device. Discord: A plug-in that enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. Explore the GitHub repository for more examples — including hands-free music control via Spotify, livestream status checks with Twitch, and more. Companies are adopting AI as the new PC interface. For example, SignalRGB is developing a G-Assist plug-in that enables unified lighting control across multiple manufacturers. Users will soon be able to install this plug-in directly from the SignalRGB app. SignalRGB’s G-Assist plug-in will soon enable unified lighting control across multiple manufacturers. Starting this week, the AI community will also be able to use G-Assist as a custom component in Langflow — enabling users to integrate function-calling capabilities in low-code or no-code workflows, AI applications and agentic flows. The G-Assist custom component in Langflow will soon enable users to integrate function-calling capabilities. Enthusiasts interested in developing and experimenting with Project G-Assist plug-ins are invited to join the NVIDIA Developer Discord channel to collaborate, share creations and gain support. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about 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 #microsoft #advance #development #rtx

Artificial intelligence: it’s not just for tech experts anymore. Instead, a heaping helping of free online resources has emerged. These classes are specifically designed to welcome beginners into the world of AI, even if they possess little or no prior technical background. I selected these Coursera courses for their beginner-friendly approach, high ratings, and comprehensive coverage of foundational concepts and key AI domains. AI For Everyone If you’re taking your very first steps into AI, “AI For Everyone” on Coursera is a great starting point. The course requires no prior experience in AI or programming, making it truly accessible to everyone, and it’s got a reasonable completion time of around six hours. The curriculum is structured into four modules: What is AI?, Building AI Projects, Building AI in Your Company, and AI and Society. Google AI Essentials Another good starting point for your AI journey is the “Google AI Essentials” course. It offers a unique perspective on Google’s AI philosophy and features hands-on activities and real-world scenarios. Similar to “AI For Everyone” mentioned above, “Google AI Essentials” is designed to be accessible to individuals of all skill levels. The six-hour course is structured into five modules: Introduction to AI, Maximize Productivity With AI Tools, Discover the Art of Prompting, Use AI Responsibly, and Stay Ahead of the AI Curve. Introduction to Artificial Intelligence For a slightly more structured and in-depth introduction to the foundational concepts of AI, the “Introduction to Artificial Intelligence” course offered by IBM on Coursera is an excellent option. This 12-hour course aims to equip beginners with a solid understanding of core AI concepts, and incorporates videos, readings, assignments, and even hands-on labs. The curriculum is divided into four modules that cover a range of essential topics: Introduction and Applications of AI; AI Concepts, Terminology, and Application Domains; Business and Career Transformation Through AI; and Issues, Concerns, and Ethical Considerations. Introduction to Generative AI Don’t have 6–12 hours to get up to speed with the aforementioned courses? Skip right to the good stuff with the “Introduction to Generative AI” course, which offers an overview of . . . well, what most people are referring to when they mention AI nowadays. The course defines generative AI, explains its underlying mechanisms, describes the different types of generative AI models, and discusses how the technology is used in the real world. It’s worth noting that this course is part of a larger “Introduction to Generative AI Learning Path Specialization,” so if you find the topic particularly engaging, you’ll be able to keep the good times rolling with additional courses. #four #free #coursera #courses #jumpstart

Microsoft is cutting about 7,000 jobs, or 3% of its workforce.The move isn’t about poor performance or falling revenue. It’s a clear shift in strategy—fewer layers, more engineers, and more investment in artificial intelligence.The layoffs affect staff across divisions and global offices. But the bulk of those let go are in middle management and non-technical roles, a pattern showing up across tech. The message: reduce overhead, speed up product cycles, and make room for bigger AI spending.The numbers behind the shiftMicrosoft ended its latest quarter with billion in revenue. That beat Wall Street estimates and shows strong business health, and the company plans to spend as much as billion this fiscal year—mainly on data centres designed for training and running AI models.That’s a big leap in infrastructure spending but it also explains why Microsoft is trimming elsewhere.AI models are compute-heavy and demand new types of hardware. Storage, cooling, and power need to scale: Building that capacity takes money, time, and fewer internal delays, and Microsoft appears to be cutting anything that slows the push.Management in the firing lineMost cuts hit middle managers and support staff. These are roles that help coordinate, review, and report—but don’t directly write code or design systems. While these positions have long helped large companies function, they’re now being seen as blockers to fast action.Sources told Business Insider that Microsoft wants a higher ratio of technical staff to managers. This isn’t just about saving costs, it’s about reducing the number of people between engineers and final decisions.Analyst Rishi Jaluria told the Financial Times that tech giants like Microsoft have “too many layers.” He said companies are trying to strip back bureaucracy as they chase AI leadership.Microsoft has not publicly broken down which departments were most affected. But reports suggest LinkedIn, a Microsoft subsidiary, saw job cuts as part of this broader shift.Aligning with a broader industry trendMicrosoft isn’t the only company trimming management, as Amazon, Google, and Meta have all done similarly. They’re removing layers and pushing more decisions closer to those building the product.For Microsoft, the changes come after several earlier rounds of cuts. In early 2024, the company laid off around 2,000 workers in performance-based trims. This new wave is different as it targets structure, not staff output.billion on AI infrastructureMicrosoft’s investment plan puts AI at the centre of its growth. According to Reuters, the company wants to spend up to billion in fiscal 2025, much of it going toward AI-enabled data centres.These centres power large language models, natural language tools, and enterprise AI systems. Without them, even the best models won’t run at scale.The company’s move shows how serious it is about owning the AI backbone. This is about more than software updates, it’s about physical hardware, cloud capacity, and tight control over how AI gets built and used.Microsoft’s early partnership with OpenAI gave it a jumpstart, but Google, Meta, Amazon, and Apple are all making big AI moves. Microsoft appears to be betting that first-mover advantage is only as strong as the infrastructure behind it.Employee reactions reflect mixed sentimentAs with most layoffs, employee reactions vary. Some posts on social media reflect understanding, others voice concern about job security and team stability.Several ex-employees described the mood as “tense but expected.” Many said they had been preparing for changes since Microsoft’s 2024 performance cuts.Some worry that too much focus on AI will weaken support roles, and others believe cutting managers will create confusion rather than clarity.Still, public sentiment shows a growing acceptance that AI is changing what jobs look like—even at the biggest firms.What this means for the industryMicrosoft’s restructuring sets a tone: Strong revenue no longer guarantees job security, and growth in AI now drives org charts, not the other way around.Middle management is no longer safe, and non-technical roles must prove direct value to AI goals. Even product teams may face more pressure to automate or streamline. For employees, the message is clear. Learn how AI fits your job—or risk being cut from the plan.For other tech firms, Microsoft’s strategy may serve as a roadmap. Spending more on AI means spending less elsewhere. and many companies will likely follow that playbook to stay competitive.Long-term questions remainThe short-term logic is clear. Microsoft is cutting structure to fund AI growth. But over time, companies will need to balance innovation with internal support.Removing middle managers may speed up some work, but it can also reduce mentorship, training, and context—things that help teams stay aligned.AI may need more data and compute. But people still build the tools, ask the right questions, and set the goals. How companies treat those people now will shape how well they compete later.See also: Alarming rise in AI-powered scams: Microsoft reveals B in thwarted fraudWant to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here. #why #microsoft #cutting #roles #despite

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The importance of databases to modern enterprise AI operations cannot be overstated. Data helps to train and ground AI, and multiple research reports show that without proper data, AI efforts tend to fail. With trends like vibe coding and agentic AI, it’s also increasingly important to have database technology that can be spun up as needed in a serverless approach to modern development efforts. In that environment, it should come as no surprise that databases are a particularly valuable commodity.  This week, that fact was on display with Databricks‘ acquisition of privately held serverless PostgreSQL startup Neon, which was founded in 2022. The deal is reportedly valued at a staggering billion, which is shocking given that barely two years ago, the company raised million in a series B round of funding. What is also particularly interesting is that Databricks itself is a data vendor, with its data lakehouse platform. At various points in the company’s history, it has positioned itself as an alternative to traditional databases, providing a data lake structure where users can make queries. So what was missing, and why did Databricks need to spend a billion dollars? What does it mean and say about what enterprise AI really needs? Functionally, it’s all about meeting developers’ needs to build agentic AI applications. According to Neon, over 80% of the databases created on its platform were created by AI agents. What is serverless PostgreSQL and why does it matter? While Neon is a startup, the core database technology that it’s based on is not new. PostgreSQL is one of the oldest and most established open-source database platforms, dating back to 1996. It’s a relational database technology, meaning it has tables and rows alongside extremely stable features that organizations have trusted for decades. The core open-source PostgreSQL database is now updated in a yearly release cadence. The most recent stable update was PostgreSQL 17, which debuted in Sept. 2024. As an open-source technology, PostgreSQL has enjoyed broad adoption and contributions. At one point, it was often compared to other proprietary relational database options, including Oracle as an alternative option. In 2025, though, PostgreSQL stands on its own. DB-Engines currently ranks PostgreSQL as the fourth most popular database in use today, behind Microsoft SQL Server, MySQL and Oracle. The state of PostgreSQL 2024 report from Timescale identifies the open-source database’s rising prominence as the database of choice for AI applications. The database’s well-established and understood nature and broad availability are among the numerous factors that make it attractive. PostgreSQL on its own is just the database, though. Running it as a serverless offering is an operational and deployment activity. The promise of any serverless database is ease of operations. Rather than requiring a dedicated database deployment that continually runs with dedicated resources, serverless is spun up on demand as needed. It’s a deployment option that is particularly attractive to developers as a way to build applications quickly. AI-based development is even more appealing as databases can be built and deployed programmatically. The serverless PostgreSQL landscape has a lot of vendors Every cloud hyperscaler has some form of PostgreSQL service and has for years.  Google has multiple offerings, including AlloyDB, Microsoft has Azure Database for PostgreSQL, while AWS has Amazon RDS for PostgreSQL and Amazon Aurora. Each of them also has some flavor of serverless offering, that is, a database available on demand. Numerous smaller vendors exist, including Timescale, EDB and NetApp Instaclustr. In fact, nearly two years ago, Databricks acquired serverless PostgreSQL vendor bit.io, which was also an early rival of Neon. As it turns out, the goals and capabilities of bit.io are quite different from Neon. “Together with the Neon team, we look forward to building the most developer and AI-agent-friendly database platform,” Phil Shin, senior director of corporate development and ventures at Databricks, told VentureBeat. “In contrast, the bit.io acquisition was not actually about Postgres but targeting developer experiences, especially in the trials and self-service process.” Shin added that the bit.io acquisition had a big impact on Databricks’ seamless signup experience.  How serverless PostgreSQL fits into the enterprise database landscape While Neon has only been around for a few years with its serverless PostgreSQL implementation, commercial vendor EDB has been in business since 2004. EDB has a series of its own commercially supported PostgreSQL offerings. Matt Yonkovit, VP of Product for EDB, told VentureBeat that the acquisition of Neon is a strong vote of confidence in Postgres as a foundational technology for AI and analytics.  “It reinforces what we’ve long believed: Postgres is increasingly central to modern data stacks,”  Yonkovit said. “Serverless is a great fit for dev/test environments and for quickly jumpstarting AI projects—but as those use cases scale, enterprises need the performance, compliance, and control of a sovereign platform.” Yonkovit noted that serverless is well-suited for short bursts and smaller workloads. It can scale up and down quickly or shut off entirely when idle, which significantly reduces costs associated with compute, power and storage. However, in his view, there are tradeoffs. “A significant challenge with serverless is that sovereign data management can become messy because you can’t control where the data is processed unless you have a well-restricted pool of resources,” Yonkovit said. The power of serverless PostgreSQL for agentic AI Neon’s serverless PostgreSQL approach separates storage and compute, making it developer-friendly and AI-native. It also enables automated scaling as well as branching in an approach that is similar to how the Git version control system works for code. Amalgam Insights CEO and Chief Analyst Hyoun Park noted that Databricks has been a pioneer in deploying and scaling AI projects.  “One of the big challenges in AI is managing the storage and compute associated with the data,” Park told VentureBeat. “Each of these needs will be increasingly hard to predict in an agentic world where end-user prompts and requests may quickly lead to unexpected demands in storage or compute to solve the problem. Park explained that Neon’s serverless autoscaling approach to PostgreSQL is important for AI because it allows agents and AI projects to grow as needed without artificially coupling storage and compute needs together. He added that for Databricks, this is useful both for agentic use cases and for supporting the custom models they have built over the last couple of years after its Mosaic AI acquisition.  What it means for enterprise AI leaders For enterprises looking to lead the way in AI, this acquisition signals a shift in infrastructure requirements for successful AI implementation. Data is critical for AI; that’s not a surprise. What is particularly insightful, though, is that the ability to rapidly spin up databases is essential for agentic AI success. The deal validates that even advanced data companies need specialized serverless database capabilities to support AI agents that create and manage databases programmatically.  Organizations should recognize that traditional database approaches may limit their AI initiatives, while flexible, instantly scalable serverless solutions enable the dynamic resource allocation that modern AI applications demand.  For companies still planning their AI roadmap, this acquisition signals that database infrastructure decisions should prioritize serverless capabilities that can adapt quickly to unpredictable AI workloads. This would transform database strategy from a technical consideration to a competitive advantage in delivering responsive, efficient AI solutions. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured. #billion #database #bet #what #databricks

We believe that the world is a better place with more creators in it. We make tools and services that help creators succeed, from individuals building their first games to professional studios working on the next great franchise.That’s why we continue to be excited by the promise of AI- and ML-driven techniques to reduce complexity, speed up creation, and, most importantly, unlock new ideas. Simply put, we think that this technology’s accessibility will help more people to become creators.We’ve worked for years, both internally and with partners, to explore how AI can be used in simulation, content creation, and game optimization. We see the present moment’s Cambrian explosion of generative AI as an opportunity to go even further.Unity is uniquely positioned to help you succeed while adopting generative AI because of the Unity Editor, runtime, data, and the Unity Network.More people use the Unity Editor to create games and other real-time 3D (RT3D) experiences than any other workflow in the world. Over the last 18 years, the Unity Editor has helped to democratize game development while contributing to a massive proliferation of new games across countless devices.Today, we strongly believe that the power of generative AI will enable Unity creators to be much more productive while ushering in scores of new creators who will face lower barriers to building RT3D games and experiences. We think that these AI tools will complement rather than replace existing tools and workflows. They offer the promise to help creators do more for and by themselves by filling the gaps in skill sets and resources so they can achieve what scarcely seems possible today.Just as a student might use a generative pre-trained transformer (GPT) tool to jumpstart research or even create a first draft before refining and finalizing a paper in Microsoft Word or Google Docs, Unity creators will be able to use natural-language generative tools together with deterministic, non-AI tools to create code, animations, physical effects, or other real-time content. Creators will move back and forth from rough approximations and text to fine-grained controls and code to iterate and refine the experience they envision.What’s better, we’re building the technology in the Unity Editor to better define what AI draws from. This not only means using appropriate and licensable datasets for generating content but also integrating AI techniques that are customized to their specific content (for example, by using Low-Rank Adaptation, or LoRA, language models during asset builds to deliver new content that’s trained on their existing work).The Unity runtime powers the most real-time applications in the world, with billions of downloads on billions of devices every month, in well over 100 countries. This means that Unity is the predominant way that content created with AI tools will come alive for consumers and users, since the output of any generative AI creation tools made available in the Unity Editor get delivered via the Unity runtime. The Unity runtime makes 3D content interactive and available on almost any device, ensuring that it responds to user input, as well as simulating effects like lighting or physics.But we see an even bigger opportunity. We believe that AI is not just the domain of creation tools, but that it offers the opportunity for new forms of interaction by moving inference – the process of feeding data through a machine learning model – to runtime.We’ve been working on this technology – code-named “Barracuda” – for more than five years. What will it mean when designers can build game loops that rely on inference on devices from mobile to console to web and PC? What happens when that AI capability is fast, efficient, scalable, and does not require expensive cloud compute?We have some ideas – NPCs that come to life, diffusion content as a gameplay mechanism, boundaryless user-generated content – but we know that our creators will do far more with this technology than we could ever even dream.Most of the digital content in the world today is 2D and linear – think sprites, photos, a set of film frames, a rendering of a building floor plan, or source code. AI data models train on this information to learn and, in the case of generative AI, to create content.Unity enables the real-time training of models based on unique datasets produced in the creation and operation of RT3D experiences. Through this training, we can build ever-richer services on top of Unity and provide extraordinary capabilities for our partners to leverage Unity as a data creation, simulation, and training engine for their own needs. Natural-language AI models incorporated into the Unity Editor and runtime train on real code and images. That real-usage training data is abstracted from its initial use (it’s not captured or recorded as-is), however this learning enables Unity’s customers to substantially increase their productivity.The Unity Network, whichconsists of our analytics tools, ad networks, publishing systems, and cloud services, reach a combined total of more than 4B users each month. Each of these service fields yields data that we can use to help our customers massively improve how they attract new users, increase engagement, or drive greater revenue from that base. Unity has been using the power of neural networks to help continuously optimize systems to support user acquisition, engagement and monetization for over three years.Generative AI has been used in some form or another for much of the history of video games, and it has tremendous potential as a tool to help developers achieve more with fewer resources. We’ll be sharing more over the coming months about our vision for AI at Unity, what we’re working on, and how this technology can help you achieve your vision.Stay tuned to the blog for more about Unity and AI, and, if you haven’t already, sign up for the AI Beta Program to be the first to hear about new tools and services. Source: https://unity.com/blog/news/why-we-are-excited-about-ai #why #were #excited #about #unity