NVIDIA Corporation NVIDIA Brings Generative AI to Worlds Enterprises With Cloud Services for Creating Large Language and Visual Models

As AI models become increasingly complex and computationally intensive, it’s essential for developers to have cost-effective tools that enable them to scale up quickly and efficiently. AI Workbench provides a single platform for managing data, models, and compute resources, for seamless collaboration and deployment across machines and environments. With this platform, developers of all skill levels can quickly create and deploy cost-effective, scalable generative AI models. With AI Workbench users can quickly create or clone existing generative AI projects to get started. Developers can go from early exploration on local machines, all the way up to model tuning on workstations and push into scalable resources in the cloud and data center for large-scale training. Developing custom generative AI models and applications is a journey, not a destination.

It may also be possible to train the model on various types of 3D shapes at once, rather than having to focus on one object category at a given time. Design-oriented enterprises can use visual datasets and generative AI to assist their work across many fronts. This has already been achieved with coding tools such as GitHub Copilot — trained on billions of lines of code — and similarly promises to help compress lengthy design timelines. Much like when early iPhone app developers began using GPS, accelerometers and other sensors to create mobile applications, AI developers now can tap foundation models to build new experiences and capabilities. Beyond the automotive product lifecycle, generative AI is also enabling new breakthroughs in autonomous vehicle (AV) development. Such research areas include the use of neural radiance field (NeRF) technology to turn recorded sensor data into fully interactive 3D simulations.

This post details a two-fold approach to improve spear phishing detection by boosting the signals of intent using NVIDIA Morpheus to run data processing and inferencing. Generative AI-based models can not only learn and understand natural languages — they can learn the very language of nature itself, presenting new possibilities for scientific research. The automotive industry now has an opportunity to use generative AI to instantly Yakov Livshits transform 2D sketches into NURBS models for leaps in productivity. These tools will not replace designers, but enable them to explore a wide range of options faster. Generative AI can help tie different data streams together, not just text to text, or text to image, but also with inputs and outputs like video or 3D. Using this powerful new computing model, a text prompt could return a physically accurate layout of an assembly plant.

NVIDIA-Certified Next-Generation Computing Platforms for AI, Video, and Data Analytics Performance

NVIDIA’s pretrained foundation models offer a simplified approach to building and running customized generative AI solutions for unique business use cases. NVIDIA and Getty Images, a global visual-content creator and marketplace, are collaborating to train responsible generative text-to-image and text-to-video foundation models. The models will allow the creation of images and video using simple text prompts and will be trained on Getty Images’ fully licensed assets. Getty Images will provide royalties to artists on any revenues generated from the models. Separately, NVIDIA today also announced new models for the NVIDIA BioNeMo™ cloud service for biology.

The following example outlines the steps that our team took when creating a Toy Jensen image. Follow NVIDIA Studio on Instagram, Twitter and Facebook and access tutorials — including on Omniverse — on the Studio YouTube channel. Get the latest Studio updates directly in your inbox by subscribing to the NVIDIA Studio newsletter. In a special Yakov Livshits address at CES, NVIDIA announced these features, as well as Omniverse preinstallation on NVIDIA Studio laptops and thousands of new, free USD assets to help accelerate adoption of 3D workflows. To meet growing data needs across aging infrastructure and new government compliance regulations, energy operators are looking to generative AI.

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Shutterstock is partnering with NVIDIA to develop models to generate 3D assets trained on fully licensed content from Shutterstock. These models can be used to generate high-fidelity 3D assets from simple text prompts which can in turn be used in game development, animation, and other 3D workflows. Discover the power of NVIDIA AI Foundations—cloud services for customizing and operating text, visual media, and biology-based generative AI models for your business.

The NVIDIA-powered AI infrastructure is the foundation of the new frontier into AI for Reliance Jio Infocomm, Reliance Industries’ telecom arm. To serve India’s vast potential in AI, Reliance will create AI applications and services for their 450 million Jio customers and provide energy-efficient AI infrastructure to scientists, developers and startups across India. Generative AI models have billions of parameters and require an efficient data training pipeline.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Generative AI will touch every aspect of the metaverse and it is already being leveraged for use cases like bringing AI avatars to life with Omniverse ACE. Many of these projects, like Audio2Face and Audio2Gesture, which generate animations from audio, have turned into widely loved tools in the Omniverse community. Temporal layers and novel video denoiser generates high-fidelity videos with temporal consistency.

The combination of FastPitch and HiFiGAN delivers end-to-end speech synthesis, where the FastPitch model produces a mel spectrogram from raw text, and HiFiGAN can generate audio from a mel spectrogram. Collectively, these pretrained models are ideal for a wide range of text-to-speech (TTS) applications such as audiobooks, voice cloning, and music generation. We’ve been optimizing every part of our hardware and software architecture for many years for AI, including fourth-generation Tensor Cores — dedicated AI hardware on RTX GPUs. Join Arash Vahdat, senior NVIDIA Researcher,  to learn about the path from GANs to diffusion models. In this talk, he’ll share his thoughts on how these fundamental technologies drove various applications such as text-to-2D images, video, and 3D content generation. The AI model is able to imitate specific styles prompted through given images or through a text prompt.

NVIDIA Maxine Elevates Video Conferencing in the Cloud

AI virtual assistants and chatbots powered by LLMs can instantly deliver relevant information to people online, taking the burden off of overstretched staff who work phone banks at agencies like the Treasury Department, IRS and DMV. In the energy industry, AI is powering predictive maintenance and asset optimization, smart grid management, renewable energy forecasting, grid security and more. Generative AI applications on handheld devices can support field technicians by scanning equipment and generating virtual tutorials to guide them through repairs. Virtual guides can then be enhanced with augmented reality, enabling technicians to analyze equipment in a 3D immersive environment or call on a remote expert for support.

These advancements have opened up new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research. Sustained Category LeadershipThe best Generative AI companies can generate a sustainable competitive advantage by executing relentlessly on the flywheel between user engagement/data and model performance. They will likely go into specific problem spaces (e.g., code, design, gaming) rather than trying to be everything to everyone. They will likely first integrate deeply into applications for leverage and distribution and later attempt to replace the incumbent applications with AI-native workflows.

Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications. And just as the inflection point of mobile created a market opening for a handful of killer apps a decade ago, we expect killer apps to emerge for Generative AI. The AI infrastructure will be hosted in AI-ready computing data centers that will eventually expand to 2,000 MW.

• EG3D, short for Efficient Geometry-Aware 3D, is a generative adversarial network-based pretrained model that produces high-quality 3D geometry in complex environments with improved computational efficiency.
• Train a state-of-the-art Edify model with your enterprise data for a proprietary foundation model for image, video, and 3D, and run inference through APIs.
• The Search-based Interest Model (SIM) is a system that predicts user behavior based on sequences of previous interactions.
• As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks.

These models are based on a combination of transformer and CNN architecture and achieve high accuracies on speech benchmarks. The pretrained models span 10+ languages like German, Italian, Japanese, Kinyarwanda, and more, making it ideal for customized speech applications for live captioning, digital human services, voice assistance, and more. EG3D, short for Efficient Geometry-Aware 3D, is a generative adversarial network-based pretrained model that produces high-quality 3D geometry in complex environments with improved computational efficiency. EG3D is a powerful tool for developers seeking to generate multi-view-consistent images in real time and 3D geometry for creative AI applications. Enterprises need a computing infrastructure that provides the performance, reliability, and scalability to deliver cutting-edge products and services while increasing operational efficiencies.

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Overjet, a member of NVIDIA Inception, is moving fast to bring AI to dentists’ offices. DENZA, BYD’s joint venture with Mercedes-Benz, is relying on WPP to build and deploy the first of its kind car configurators with Omniverse Cloud. Toyota, one of the world’s largest automakers, has developed a generative AI technique to ensure that early design sketches incorporate engineering parameters. To learn more about AI Workbench, or to sign up to be notified about the availability of early access, visit the AI Workbench page. AI Workbench makes it easy to ‌accomplish the entire process by cloning a Workbench project from GitHub.