Train Your AI Model Once and Deploy on Any Cloud with NVIDIA and Run:ai

Started a first new and realistic application.

Train Your AI Model Once and Deploy on Any Cloud with NVIDIA and Run:ai

Jul 07, 2023,  By Guy Salton and Abhishek Sawarkar

Organizations are increasingly adopting hybrid and multi-cloud strategies to access the latest compute resources, consistently support worldwide customers, and optimize cost. However, a major challenge that engineering teams face is operationalizing AI applications across different platforms as the stack changes. This requires MLOps teams to familiarize themselves with different environments and developers to customize applications to run across target platforms.

NVIDIA offers a consistent, full stack to develop on a GPU-powered on-premises or on-cloud instance. You can then deploy that AI application on any GPU-powered platform without code changes.

Introducing the latest NVIDIA Virtual Machine Image

The NVIDIA Cloud Native Stack Virtual Machine Image (VMI) is GPU-accelerated. It comes pre-installed with Cloud Native Stack, which is a reference architecture that includes upstream Kubernetes and the NVIDIA GPU Operator. NVIDIA Cloud Native Stack VMI enables you to build, test, and run GPU-accelerated containerized applications orchestrated by Kubernetes.

The NVIDIA GPU Operator automates the lifecycle management of the software required to expose GPUs on Kubernetes. It enables advanced functionality, including better GPU performance, utilization, and telemetry. Certified and validated for compatibility with industry-leading Kubernetes solutions, GPU Operator enables organizations to focus on building applications, rather than managing Kubernetes infrastructure.

NVIDIA Cloud Native Stack VMI is available on AWS, Azure, and GCP. ... ' 

AI Solutions from NVIDIA

Some intriguing work by NVIDIA

NVIDIA | VMWARE

https://youtu.be/Mu14EBJCI0I

AI Solutions, Your path to an integrated AI experience begins here.

GET STARTED:, Launch AI

Investigation, Start your AI path, and then

explore AI solutions and successes.

Explore AI, Solutions

Learn about the AI-Ready Enterprise

Platform for accelerated workloads.

Discover AI, Successes

See how NVIDIA & VMWare helps

enterprises handle critical AI workloads.

Optimizing Ethernet-Based AI Management Fabrics with MLAG

 And more improving training.

Optimizing Ethernet-Based AI Management Fabrics with MLAG

Jun 21, 2023,  By Davinder Singh

For HPC clusters purposely built for AI training, such as the NVIDIA DGX BasePOD and NVIDIA DGX SuperPOD, fine-tuning the cluster is critical to increasing and optimizing the overall performance of the cluster. This includes fine-tuning the overall performance of the management fabric (based on Ethernet), storage fabric (Ethernet or InfiniBand), and the compute fabric (Ethernet or InfiniBand). 

This post discusses how to maximize the overall throughput of the management fabric with Multi-Chassis Link Aggregation (MLAG), available on NVIDIA Cumulus Linux. MLAG enables two separate switches to advertise the same LACP system ID to downstream hosts. As a result, the downstream hosts see the uplinks as if they are connected to a single LACP partner.

One benefit of using MLAG is physical switch-level redundancy. If either of the two uplink switches experiences a failure, downstream host traffic will not be impacted. A second benefit is that the uplinks of the aggregated bond are all used at the same time. Finally, MLAG technology provides gateway-level redundancy, using technologies such as VRR/VRRP.   ... ' 

Speech AI Spotlight

 Would expand potential capabilities for AR ... makes me broaden they potential usefulness.

Speech AI Spotlight: Visualizing Spoken Language and Sounds on AR Glasses

Jun 23, 2023

By Sirisha Rella

Audio can include a wide range of sounds, from human speech to non-speech sounds like barking dogs and sirens. When designing accessible applications for people with hearing difficulties, the application should be able to recognize sounds and understand speech.

Such technology would help deaf or hard-of-hearing individuals with visualizing speech, like human conversations and non-speech sounds. Combining speech and sound AI together, you can overlay the visualizations onto AR glasses, making it possible for users to see and interpret sounds that they wouldn’t be able to hear otherwise. 

According to the World Health Organization, about 1.5B people (nearly 20% of the global population) live with hearing loss. This number could rise to 2.5B by 2050.

Cochl, an NVIDIA partner based in San Jose, is a deep-tech startup that uses sound AI technology to understand any type of audio. They are also a member of the NVIDIA Inception Program, which helps startups build their solutions faster by providing access to cutting-edge technology and NVIDIA experts.

The platform can recognize 37 environmental sounds, and the company went one step further by adding cutting-edge speech-to-text technology. This gives a truly complete understanding of the world of sound.

AR glasses to visualize any sound

AR glasses have the potential to greatly improve the lives of people with hearing loss as an accessible tool to visualize sounds. This technology can help enhance their communication abilities and make it easier for them to navigate and participate in the world around them.  ... ' 

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Generative Workflow

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Built on ServiceNow Platform With NVIDIA AI Software and DGX Infrastructure, Custom Large Language Models to Bring Intelligent Workflow Automation to Enterprises

May 17, 2023

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Knowledge 2023—ServiceNow and NVIDIA today announced a partnership to develop powerful, enterprise-grade generative AI capabilities that can transform business processes with faster, more intelligent workflow automation.

Using NVIDIA software, services and accelerated infrastructure, ServiceNow is developing custom large language models trained on data specifically for its ServiceNow Platform, the intelligent platform for end-to-end digital transformation. 

This will expand ServiceNow’s already extensive AI functionality with new uses for generative AI across the enterprise — including for IT departments, customer service teams, employees and developers — to strengthen workflow automation and rapidly increase productivity. 

ServiceNow is also helping NVIDIA streamline its IT operations with these generative AI tools, using NVIDIA data to customize NVIDIA® NeMo™ foundation models running on hybrid-cloud infrastructure consisting of NVIDIA DGX™ Cloud and on-premises NVIDIA DGX SuperPOD™ AI supercomputers.

“IT is the nervous system of every modern enterprise in every industry,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration to build super-specialized generative AI for enterprises will boost the capability and productivity of IT professionals worldwide using the ServiceNow platform.”

“As adoption of generative AI continues to accelerate, organizations are turning to trusted vendors with battle-tested, secure AI capabilities to boost productivity, gain a competitive edge, and keep data and IP secure,” said CJ Desai, president and chief operating officer of ServiceNow. “Together, NVIDIA and ServiceNow will help drive new levels of automation to fuel productivity and maximize business impact." 

Harnessing Generative AI to Reshape Digital Business 

ServiceNow and NVIDIA are exploring a number of generative AI use cases to simplify and improve productivity across the enterprise by providing high accuracy and higher value in IT. 

This includes developing intelligent virtual assistants and agents to help quickly resolve a broad range of user questions and support requests with purpose-built AI chatbots that use large language models and focus on defined IT tasks. 

To simplify the user experience, enterprises can customize chatbots with proprietary data to create a central generative AI resource that stays on topic while resolving many different requests.

These generative AI use cases are also applicable to customer service agents, allowing for case prioritization with greater accuracy, saving time and improving outcomes. Customer service teams can use generative AI for automatic issue resolution, knowledge-base article generation based on customer case summaries, and chat summarization for faster hand-off, resolution and wrap-up. 

In addition, generative AI can improve the employee experience by helping identify growth opportunities. For example, delivering customized learning and development recommendations, like courses and mentors, based on natural language queries and information from an employee’s profile. 

Full-Stack NVIDIA Generative AI Software and Infrastructure Fuel Rapid Development

In its generative AI research and development, ServiceNow is using NVIDIA AI Foundations cloud services and the NVIDIA AI Enterprise software platform, which includes the NVIDIA NeMo framework. 

Included in NeMo are prompt tuning, supervised fine-tuning and knowledge retrieval tools to help developers build, customize and deploy language models for enterprise use cases. NeMo Guardrails software is also included and enables developers to easily add topical, safety and security features for AI chatbots.

NeuralAngelo from NVIDIA via Neural Networks

Impressive, high detail transformation.   Could have used this in several past applications.  See videos at link. Architectural and other 3D vision apps.

 https://www.youtube.com/watch?v=PQMNCXR-WF8

Digital Renaissance: Neuralangelo by NVIDIA Research Reconstructs 3D

60,044 views  Jun 1, 2023

Neuralangelo, a new AI model by NVIDIA Research for 3D reconstruction using neural networks, turns 2D video clips into detailed 3D structures — generating lifelike virtual replicas of buildings, sculptures and other real-world objects. 

Like Michelangelo sculpting stunning, life-like visions from blocks of marble, Neuralangelo generates 3D structures with intricate details and textures.

Neuralangelo’s ability to translate the textures of complex materials — including roof shingles, panes of glass and smooth marble — from 2D videos to 3D assets significantly surpasses prior methods. The high fidelity makes its 3D reconstructions easier for developers and creative professionals to rapidly create usable virtual objects for their projects using footage captured by smartphones. 

Neuralangelo is one of nearly 30 projects by NVIDIA Research to be presented at the Conference on Computer Vision and Pattern Recognition (CVPR), taking place June 18-22 in Vancouver. The papers span topics including pose estimation, 3D reconstruction and video generation.

Read more: https://nvda.ws/3oF87HA

Learn more about NVIDIA Research at CVPR: https://www.nvidia.com/en-us/events/c... 

NVIDIA Research: https://www.nvidia.com/en-us/research/ 

Join the NVIDIA Developer Program: https://nvda.ws/3OhiXfl 

Read and subscribe to the NVIDIA Technical Blog: https://nvda.ws/3XHae9F

3D, Artificial Intelligence, AI, NVIDIA Research, Graphics, CVPR, CVPR2023, Neural Networks, Computer Vision  ... '

NVIDIA and ServiceNow

Good direction.

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise 

Built on ServiceNow Platform With NVIDIA AI Software and DGX Infrastructure, Custom Large Language Models to Bring Intelligent Workflow Automation to Enterprises

May 17, 2023

 Knowledge 2023—ServiceNow and NVIDIA today announced a partnership to develop powerful, enterprise-grade generative AI capabilities that can transform business processes with faster, more intelligent workflow automation.

Using NVIDIA software, services and accelerated infrastructure, ServiceNow is developing custom large language models trained on data specifically for its ServiceNow Platform, the intelligent platform for end-to-end digital transformation. 

This will expand ServiceNow’s already extensive AI functionality with new uses for generative AI across the enterprise — including for IT departments, customer service teams, employees and developers — to strengthen workflow automation and rapidly increase productivity. 

ServiceNow is also helping NVIDIA streamline its IT operations with these generative AI tools, using NVIDIA data to customize NVIDIA® NeMo™ foundation models running on hybrid-cloud infrastructure consisting of NVIDIA DGX™ Cloud and on-premises NVIDIA DGX SuperPOD™ AI supercomputers.

“IT is the nervous system of every modern enterprise in every industry,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration to build super-specialized generative AI for enterprises will boost the capability and productivity of IT professionals worldwide using the ServiceNow platform.”

“As adoption of generative AI continues to accelerate, organizations are turning to trusted vendors with battle-tested, secure AI capabilities to boost productivity, gain a competitive edge, and keep data and IP secure,” said CJ Desai, president and chief operating officer of ServiceNow. “Together, NVIDIA and ServiceNow will help drive new levels of automation to fuel productivity and maximize business impact." 

Harnessing Generative AI to Reshape Digital Business 

ServiceNow and NVIDIA are exploring a number of generative AI use cases to simplify and improve productivity across the enterprise by providing high accuracy and higher value in IT. 

This includes developing intelligent virtual assistants and agents to help quickly resolve a broad range of user questions and support requests with purpose-built AI chatbots that use large language modls and focus on defined IT tasks. 

NVIDIA Text to Video

 https://www.youtube.com/watch?v=30epFM6Vb0s

147,333 views  Apr 19, 2023  #chatgpt #AI #Robotics

NVIDIA's NEW AI 'Text To Video Takes the Industry By STORM! (NOW UNVEILED!)

https://research.nvidia.com/labs/toronto-ai/VideoLDM/

Welcome to our channel where we bring you the latest breakthroughs in AI. From deep learning to robotics, we cover it all. Our videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on our latest videos.

Was there anything we missed?

(For Business Enquiries)  contact@theaigrid.com

#LLM #Largelanguagemodel #chatgpt

#AI

A Challenge of Supercomputers

A Challenge: 

Google Says Its AI Supercomputer with TPU v4 Chips Outperforms Nvidia's A100 in Speed  By The Tech Portal (India)  from  CACM

April 7, 2023   Google CEO Sundar Pichai introducing the fourth generation of its Tensor Processing Unit.

In the paper released earlier this week, Google explained how it connected over 4,000 TPUs to create a supercomputer.

Google claims the supercomputers used for training its artificial intelligence (AI) models are faster and more energy-efficient than those employed by multinational technology firm Nvidia.

Google researchers detailed how they created a supercomputer from more than 4,000 fourth-generation Tensor Processing Units (TPUs), as well as custom optical switches to link individual machines.

The AI models are segmented across thousands of chips, which must collaboratively train the models for weeks or more.

Google's Norm Jouppi and David Patterson explained, "Circuit switching makes it easy to route around failed components. This flexibility even allows us to change the topology of the supercomputer interconnect to accelerate the performance of an ML (machine learning) model."

Google says its new supercomputer is up to 1.7 times faster and 1.9 times "greener" than a system based on Nvidia's A100 chip.

From The Tech Portal (India)

View Full Article    

Nicely Done 25 Page NVIDIA Free Intro E Book on Large Language Models

E-Book  by NVIDIA

Nicely done 25-age EBook on an Enterprise Guide to Large Language Models

Everything an enterprise needs to know about LLMs.

What’s Included In This eBook?

A comprehensive background on what LLMs are, how they work, and how to evaluate them, paired with use case examples and real-world case studies on the impact LLMs have had for the enterprise.

What Are Large Language Models and How Do They Work?  

Learn about the evolution of LLMs, the role of foundation models, and how the underlying technologies have come together to unlock the power of LLMs for the enterprise.

What Are Large Language Model Examples and Case Studies?

Dive into the LLM applications that are driving the most transformation for enterprises. Examine real-world case studies of companies that adopted LLM-based applications and analyze the impact it had on their business.

How to Build and Evaluate Large Language Models?

Learn the steps to take when building LLMs and how to evaluate whether an LLM is well-suited for your intended use cases.  ... ' 

NVIDIA GPU Accellerated Applications

From NVIDIA< GPU Accellerated Applications.    -      Quite a considerable library  Examining.

Industries

Financial Services

Consumer Internet

Healthcare

Higher Education

Retail

Public Sector

All Industries >

Solutions

Data Analytics

Machine Learning

Deep Learning Training

AI Inference

Conversational AI

Prediction and Forecasting

Large Language Models

Software

NGC Catalog

NVIDIA NGC

RAPIDS

Apache Spark

Inference Serving - Triton

Recommender Systems - Merlin

Open Source Portal

AI Enterprise Suite

NVIDIA Workbench

Products

PC

Laptops & Workstations

Data Center

Cloud

Resources

Professional Services

Technical Training

Startups

AI Accelerator Program

Content Library  NVIDIA Research  Developer Blog......  . And many more.

NVIDIA GTC Developer Conference: Metaverse

NVIDIA GTC  Developer Conference March 20-23, 2023

Keynote March 21, Metaverse/AI Developer sessions.

Here are short developer descriptions of developer sessions,  this is useful because if shows the breadth and kind of metaverse developments are  expected.  I am particularly interested in how these will be integrated with AI development. This appears informative, I may attend.  ... 

NVIDIA Event to Push Avatars

Making a point of 'uncanny valley', aspect of Avatars.  Not sure that would be an issue at a conference, but for typical human interactions, perhaps.  Has this been cleansed by more people getting into gaming use?     Though I still think there is a feeling among many people that an avatar interaction is not genuine, and is less trust-able.   Will it weaken security?   Can you sue an avatar? 

Developer Conference March 20-23, 2023

Keynote, March 21

AI Fundamentals for Building Intelligent, Interactive Digital Humans [S51676]

What does it take to bring a 3D character to life? How can you create a convincing interactive avatar that can see, perceive, converse intelligently, and provide recommendations to enhance the user’s experience? Join NVIDIA as they discuss the foundational AI building blocks required to create a convincing, lifelike avatar. They’ll explore the technical and design challenges of designing, animating, and connecting intelligence to these 3D virtual characters and share the latest best practices and solutions for overcoming the challenge of the “uncanny valley.”  ... '

Turn 2D Images into Immersive 3D Scenes with NVIDIA Instant NeRF in VR

Preparing content for 3-D VR and more.  

Turn 2D Images into Immersive 3D Scenes with NVIDIA Instant NeRF in VR   By Thomas Müller

Tags: Image Processing, NeRF, News, NVIDIA Research

Thousands of developers and content creators have built stunning 3D visuals with NVIDIA Instant NeRF, a rendering tool that turns a set of static images into a realistic 3D scene. Now, it is also possible to navigate Instant NeRF in VR and step into 3D creations with the latest Instant NeRF software update.

Named by TIME Magazine as a top invention of 2022, Instant NeRF provides a glimpse into the future of photography, 3D graphics, and virtual worlds.

With Instant NeRF in VR, users can rapidly create virtual scenes using 2D images. Using advanced rendering techniques including foveation, dynamic scaling, NVIDIA DLSS, and, optionally, a second GPU, creators achieve desired frame rates and resolution targets across a wide range of budgets.

Create and share immersive virtual scenes

Traditionally, creating 3D and virtual scenes is a time-consuming, costly process requiring specialized equipment and expertise. But Instant NeRF enables users to create scenes in minutes with just a few 2D photos. The tool processes static images into rendered 3D scenes using an AI-based technology called neural radiance fields (NeRF).

Since Instant NeRF was released last year, developers and content creators around the world have downloaded the source code to create striking and realistic 3D scenes.

Now with the addition of VR, users can render these scenes from static images and virtually step inside the environment, moving freely inside the 3D space. Creators can use almost any image—whether from the real world or rendered—as source content. NeRFs can be created using images and video captured on smartphones, or rendered images and screenshots captured from games, design applications, and more. 

Instant NeRF in VR comes with the capability to clean up scenes easily in VR, making the creation of high-quality NeRF more intuitive than ever before. With precompiled executables now available, the creation of your own NeRF has become accessible to even more people.  ... ' 

Dreaming of Driverless: Levels of Vehicle Autononomy

Examination of driverless vehicle Specs.

Dreaming of Driverless: What’s the Difference Between Level 2 and Level 5 Autonomy?

January 25, 2018 by Jeff Davis in NVidia

Cars with some autonomous capabilities are already here: here’s now to understand what’s coming next.

Self-driving cars are no longer science fiction. Today, you can already buy a car that steers itself through rush-hour traffic on the freeway and parallel parks. However, for a car that can do the same job as a chauffeur, you’ll have to wait a few years.

The “driverless” revolution is rolling out in stages, much like the first cars developed from slow, steam-powered contraptions to gasoline-powered automobiles to now fully electric vehicles. The key to making the leap forward from the prototypes packed with racks of servers already roaming California’s freeways to vehicles you can drive off the lot: putting more computing power into less space.

That’s coming, thanks to breakthroughs such as NVIDIA’s Xavier System-on-a-Chip. Detailed at this month’s Consumer Electronics Show, in Las Vegas, Xavier is the most complex system on a chip, or SoC, ever created, with more than 9 billion transistors and representing an investment of $2 billion in research and development.

So what kind of capabilities can this kind of computing horsepower unleash? To define the path to fully realized autonomy, the Society of Automotive Engineers (better known as SAE International) detailed six categories of autonomous capability to establish clear benchmarks in a field of technology that has so many different elements in play. (These guidelines supersede NHTSA’s earlier standards.)

Here’s a Cliffs Notes version of what those are, and when we’re likely to see them on the streets:

Level 0 – Your grandparents’ 1970s station wagon

Consumer vehicle introduction: 1900-present

At its minimum, level 0 is essentially a seat and steering wheel, with zero automation. (Sorry, automatic transmissions don’t count.) That covers a broad range, from Clark Griswold’s vinyl-sided station wagon in National Lampoon’s Vacation to much more modern vehicles.

Contemporary cars with driver assistance systems that issue visual and audible alerts such as Volvo’s Lane Departure Warning or Nissan’s Moving Object Detection fit here, too. But beeps and flashes are the limit — the car remains totally dependent on human drivers for steering and speed.  ...   (more at the link)   ,,,   (More below follows at link above)  .... 

NVIDIA Goes Virtual Assistants

To me an unexpected direction.   Apparently a prototype early access effort for partners.   

Develop Intelligent Virtual Assistants with NVIDIA Omniverse ACE Early Access

By Stephanie Rubenstein and Alex Qi    NVIDIA

NVIDIA just announced at CES 2023 that early access is now available for NVIDIA Omniverse Avatar Cloud Engine (ACE). Developers and teams building avatars and virtual assistants can register to join the program, which includes access to the Omniverse ACE suite of cloud-native AI microservices for faster, easier development of interactive avatars.

Early partners include Ready Player Me, whose avatar was showcased in the CES demo. With this early access program, NVIDIA is looking for developers and partners to provide feedback on the microservices, collaborate on product improvement, and help push the limits of what’s possible with lifelike, interactive, digital humans. Applicants are reviewed and approved on a rolling basis as the program continues to expand.   .... ' 

What is the Omniverse ACE early access program?

Methods for developing avatars often require expertise, specialized equipment, and manually intensive workflows.

To ease avatar creation, Omniverse ACE enables the seamless integration of NVIDIA AI technologies—including pre-built models, tool sets, and domain-specific reference applications. The available avatar applications are built on most engines and deployed on public or private clouds.

Open Robotics and Open Source Robotics Foundation

 Open Robotics Acquired

Alphabet’s Intrinsic Acquires Majority of Open Robotics ROS and Gazebo will stay with an independent Open Source Robotics Foundation   By Evan  Ackerman

Today, Open Robotics, which is the organization that includes the nonprofit Open Source Robotics Foundation (OSRF) as well as the for-profit Open Source Robotics Corporation (OSRC), is announcing that OSRC is being acquired by Intrinsic, a standalone company within Alphabet that’s developing software to make industrial robots intuitive and accessible.

Open Robotics is of course the organization that spun off from Willow Garage in 2012 to provide some independent structure and guidance for ROS, the Robot Operating System. Over the past dozen-ish years, ROS has expanded from specialized software for robotics nerds into a powerful platform for research and industry, supported by an enthusiastic and highly engaged open source community. Open Robotics, meanwhile, branched out in 2016 from a strict non-profit to also take on some high-profile projects for the likes of the Toyota Research Institute and NVIDIA. It has supported itself commercially by leveraging its experience and expertise in ROS development. Open Robotics currently employs more than three dozen engineers, most of whom are part of the for-profit corporation. ... ' 

Rapidly Generate 3D Assets for Virtual Words

 Seems odd, but can make sense, especially if you have to do many variants.  And, of course they can be just starting points for editing in more complex details.  Note mention of 'Digital Twins'.

Rapidly Generate 3D Assets for Virtual Worlds with Generative AI  By Gavriel State in NVIDIA

Tags: Digital Twin & Metaverse, News, Omniverse, synthetic data

To accelerate the development of 3D worlds and the metaverse, NVIDIA has launched numerous AI research projects to help creators across industries unlock new possibilities with generative AI.

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.

At CES, NVIDIA unveiled new generative AI technologies coming to Omniverse to help create virtual worlds faster and easier than ever. These models are available as both third-party Connectors from NVIDIA partners and internal AI projects published by the NVIDIA research team as extensions in AI ToyBox.

All generative AI research projects and Connectors available in NVIDIA Omniverse use legally licensed and purchased datasets.

Accelerating 3D content creation with generative AI Connectors and extensions

NVIDIA Omniverse is bringing in the latest and greatest generative AI technologies with Connectors and extensions for third-party technologies.

Move.ai generates animations from body movements. Move.ai enables you to capture human motion anywhere and export directly into Omniverse.

Video 1. An example of an individual boxing in NVIDIA Omniverse, with Move.ai motion data powering the character.

Lumirithmic generates 3D mesh for heads from facial scans. The connector enables you to easily create movie-grade avatars from facial scans and enables 3D scanning at scale for any industry. “We’re excited to be working with NVIDIA to bring our 3D facial scanning products through Omniverse to creators, gamers, and developers, democratizing access to the highest quality 3D faces for gaming, adtech, metaverse, and other applications,” said Gaurav Chawla, co-founder and CEO of Lumirithmic.

Imagining the AI Cockpit

Something we also imagined for running a large enterprise, rather than just a vehicle.  Show all the most useful data and forward simulations.  

Driving the Future: What Is an AI Cockpit?

Intelligent interiors are transforming transportation.

July 20, 2020 by Katie Burke

From Knight Rider’s KITT to Ironman’s JARVIS, intelligent copilots have been a staple of forward-looking pop culture.

Advancements in AI and high-performance processors are turning these sci-fi concepts into reality. But what, exactly, is an AI cockpit, and how will it change the way we move?

AI is enabling a range of new software-defined, in-vehicle capabilities across the transportation industry. With centralized, high-performance compute, automakers can now build vehicles that become smarter over time.

A vehicle’s cockpit typically requires a collection of electronic control units and switches to perform basic functions, such as powering entertainment or adjusting temperature. Consolidating these components with an AI platform such as NVIDIA DRIVE AGX simplifies the architecture while creating more compute headroom to add new features. In addition, NVIDIA DRIVE IX provides an open and extensible software framework for a software-defined cockpit experience.

Mercedes-Benz released the first such intelligent cockpit, the MBUX AI system, powered by NVIDIA technology, in 2018. The system is currently in more than 20 Mercedes-Benz models, with the second generation debuting in the upcoming S-Class.  ... ' 

On Ray and Path Tracing

Representing 3D Objects.

What Is Path Tracing?

March 23, 2022 by Brian Caulfield

Turn on your TV. Fire up your favorite streaming service. Grab a Coke. A demo of the most important visual technology of our time is as close as your living room couch. Propelled by an explosion in computing power over the past decade and a half, path tracing has swept through visual media. It brings big effects to the biggest blockbusters, casts subtle light and shadow on the most immersive melodramas and has propelled the art of animation to new levels.

Path tracing is going real time, unleashing interactive, photorealistic 3D environments filled with dynamic light and shadow, reflections and refractions.

So what is path tracing? The big idea behind it is seductively simple, connecting innovators in the arts and sciences over the span half a millennium.

What’s the Difference Between Rasterization and Ray Tracing?

First, let’s  define some terms, and how they’re used today to create interactive graphics — graphics that can react in real time to input from a user, such as in video games.

The first, rasterization, is a technique that produces an image as seen from a single viewpoint. It’s been at the heart of GPUs from the start. Modern NVIDIA GPUs can generate over 100 billion rasterized pixels per second. That’s made rasterization ideal for real-time graphics, like gaming.

Ray tracing is a more powerful technique than rasterization. Rather than being constrained to finding out what is visible from a single point, it can determine what is visible from many different points, in many different directions. Starting with the NVIDIA Turing architecture, NVIDIA GPUs have provided specialized RTX hardware to accelerate this difficult computation. Today, a single GPU can trace billions of rays per second.

Being able to trace all of those rays makes it possible to simulate how light scatters in the real world much more accurately than is possible with rasterization. However, we still must answer the questions, how will we simulate light and how will we bring that simulation to the GPU?

What’s Ray Tracing? Just Follow the String

To better answer that question, it helps to understand how we got here.

David Luebke, NVIDIA vice president of graphics research, likes to begin the story in the 16th century with Albrecht Dürer — one of the most important figures of the Northern European Renaissance — who used string and weights to replicate a 3D image on a 2D surface.

Dürer made it his life’s work to bring classical and contemporary mathematics together with the arts, achieving breakthroughs in expressiveness and realism.

The string’s the thing: Albrecht Dürer was the first to describe what’s now known as “ray tracing,” a technique for creating accurate representations of 3D objects on a 2D surfaces in Underweysung der Messung (Nuremberg, 1525),f15

In 1525 with Treatise on Measurement, Dürer was the first to describe the idea of ray tracing. Seeing how Dürer described the idea is the easiest way to get your head around the concept.

Just think about how light illuminates the world we see around us. Now imagine tracing those rays of light backward from the eye with a piece of string like the one Dürer used, to the objects that light interacts with. That’s ray tracing.

Ray Tracing for Computer Graphics    ....