Posts in category: nVidia AI

Generative AI is taking root at national and corporate labs, accelerating high-performance computing for business and science.

Researchers at Sandia National Laboratories aim to automatically generate code in Kokkos, a parallel programming language designed for use across many of the world’s largest supercomputers.

It’s an ambitious effort. The specialized language, developed by researchers from several national labs, handles the nuances of running tasks across tens of thousands of processors.

Sandia is employing retrieval-augmented generation (RAG) to create and link a Kokkos database with AI models. As researchers experiment with different RAG approaches, initial tests show promising results.

Cloud-based services like NeMo Retriever are among the RAG options the scientists will evaluate.

“NVIDIA provides a rich set of tools to help us significantly accelerate the work of our HPC software developers,” said Robert Hoekstra, a senior manager of extreme scale computing at Sandia.

Building copilots via model tuning and RAG is just a start. Researchers eventually aim to employ foundation models trained with scientific data from fields such as climate, biology and material science.

Getting Ahead of the Storm

Researchers and companies in weather forecasting are embracing CorrDiff, a generative AI model that’s part of NVIDIA Earth-2, a set of services and software for weather and climate research.

CorrDiff can scale the 25km resolution of traditional atmosphere models down to 2 kilometers and expand by more than 100x the number of forecasts that can be combined to improve confidence in predictions.

“It’s a promising innovation … We plan to leverage such models in our global and regional AI forecasts for richer insights,” said Tom Gowan, machine learning and modeling lead for Spire, a company in Vienna, Va., that collects data from its own network of tiny satellites.

Generative AI enables faster, more accurate forecasts, he said in a recent interview.

“It really feels like a big jump in meteorology,” he added. “And by partnering with NVIDIA, we have access to the world’s best GPUs that are the most reliable, fastest and most efficient ones for both training and inference.”

Switzerland-based Meteomatics recently announced it also plans to use NVIDIA’s generative AI platform for its weather forecasting business.

“Our work with NVIDIA will help energy companies maximize their renewable energy operations and increase their profitability with quick and accurate insight into weather fluctuations,” said Martin Fengler, founder and CEO of Meteomatics.

Generating Genes to Improve Healthcare

At Argonne National Laboratory, scientists are using the technology to generate gene sequences that help them better understand the virus behind COVID-19. Their award-winning models, called GenSLMs, spawned simulations that closely resemble real-world variants of SARS-CoV-2.

“Understanding how different parts of the genome are co-evolving gives us clues about how the virus may develop new vulnerabilities or new forms of resistance,” Arvind Ramanathan, a lead researcher, said in a blog.

GenSLMs were trained on more than 110 million genome sequences with NVIDIA A100 Tensor Core GPU-powered supercomputers, including Argonne’s Polaris system, the U.S. Department of Energy’s Perlmutter and NVIDIA’s Selene.

Microsoft Proposes Novel Materials

Microsoft Research showed how generative AI can accelerate work in materials science.

Their MatterGen model generates novel, stable materials that exhibit desired properties. The approach enables specifying chemical, magnetic, electronic, mechanical and other desired properties.

“We believe MatterGen is an important step forward in AI for materials design,” the Microsoft Research team wrote of the model they trained on Azure AI infrastructure with NVIDIA A100 GPUs.

Companies such as Carbon3D are already finding opportunities, applying generative AI to materials science in commercial 3D printing operations.

It’s just the beginning of what researchers will be able to do for HPC and science with generative AI. The NVIDIA H200 Tensor Core GPUs available now and the upcoming NVIDIA Blackwell Architecture GPUs will take their work to new levels.

Learn more about tools like NVIDIA Modulus, a key component in the Earth-2 platform for building AI models that obey the laws of physics, and NVIDIA Megatron-Core, a NeMo library to tune and train large language models.

Read More

Data centers need an upgraded dashboard to guide their journey to greater energy efficiency, one that shows progress running real-world applications.

The formula for energy efficiency is simple: work done divided by energy used. Applying it to data centers calls for unpacking some details.

Today’s most widely used gauge — power usage effectiveness (PUE)  — compares the total energy a facility consumes to the amount its computing infrastructure uses. Over the last 17 years, PUE has driven the most efficient operators closer to an ideal where almost no energy is wasted on processes like power conversion and cooling.

Finding the Next Metrics

PUE served data centers well during the rise of cloud computing, and it will continue to be useful. But it’s insufficient in today’s generative AI era, when workloads and the systems running them have changed dramatically.

That’s because PUE doesn’t measure the useful output of a data center, only the energy that it consumes. That’d be like measuring the amount of gas an engine uses without noticing how far the car has gone.

Many standards exist for data center efficiency. A 2017 paper lists nearly three dozen of them, several focused on specific targets such as cooling, water use, security and cost.

Understanding What’s Watts

When it comes to energy efficiency, the computer industry has a long and somewhat unfortunate history of describing systems and the processors they use in terms of power, typically in watts. It’s a worthwhile metric, but many fail to realize that watts only measure input power at a point in time, not the actual energy computers use or how efficiently they use it.

So, when modern systems and processors report rising input power levels in watts, that doesn’t mean they’re less energy efficient. In fact, they’re often much more efficient in the amount of work they do with the amount of energy they use.

Modern data center metrics should focus on energy, what the engineering community knows as kilowatt-hours or joules. The key is how much useful work they do with this energy.

Reworking What We Call Work

Here again, the industry has a practice of measuring in abstract terms, like processor instructions or math calculations. So, MIPS (millions of instructions per second) and FLOPS (floating point operations per second) are widely quoted.

Only computer scientists care how many of these low-level jobs their system can handle. Users would prefer to know how much real work their systems put out, but defining useful work is somewhat subjective.

Data centers focused on AI may rely on the MLPerf benchmarks. Supercomputing centers tackling scientific research typically use additional measures of work. Commercial data centers focused on streaming media may want others.

The resulting suite of applications must be allowed to evolve over time to reflect the state of the art and the most relevant use cases. For example, the last MLPerf round added tests using two generative AI models that didn’t even exist five years ago.

A Gauge for Accelerated Computing

Ideally, any new benchmarks should measure advances in accelerated computing. This combination of parallel processing hardware, software and methods is running applications dramatically faster and more efficiently than CPUs across many modern workloads.

For example, on scientific applications, the Perlmutter supercomputer at the National Energy Research Scientific Computing Center demonstrated an average of 5x gains in energy efficiency using accelerated computing. That’s why it’s among the 39 of the top 50 supercomputers — including the No. 1 system — on the Green500 list that use NVIDIA GPUs.

Companies across many industries share similar results. For example, PayPal improved real-time fraud detection by 10% and lowered server energy consumption nearly 8x with accelerated computing.

The gains are growing with each new generation of GPU hardware and software.

In a recent report, Stanford University’s Human-Centered AI group estimated GPU performance “has increased roughly 7,000 times” since 2003, and price per performance is “5,600 times greater.”

Two Experts Weigh In

Experts see the need for a new energy-efficiency metric, too.

With today’s data centers achieving scores around 1.2 PUE, the metric “has run its course,” said Christian Belady, a data center engineer who had the original idea for PUE. “It improved data center efficiency when things were bad, but two decades later, they’re better, and we need to focus on other metrics more relevant to today’s problems.”

Looking forward, “the holy grail is a performance metric. You can’t compare different workloads directly, but if you segment by workloads, I think there is a better likelihood for success,” said Belady, who continues to work on initiatives driving data center sustainability.

Jonathan Koomey, a researcher and author on computer efficiency and sustainability, agreed.

“To make good decisions about efficiency, data center operators need a suite of benchmarks that measure the energy implications of today’s most widely used AI workloads,” said Koomey.

“Tokens per joule is a great example of what one element of such a suite might be,” Koomey added. “Companies will need to engage in open discussions, share information on the nuances of their own workloads and experiments, and agree to realistic test procedures to ensure these metrics accurately characterize energy use for hardware running real-world applications.”

“Finally, we need an open public forum to conduct this important work,” he said.

It Takes a Village

Thanks to metrics like PUE and rankings like the Green500, data centers and supercomputing centers have made enormous progress in energy efficiency.

More can and must be done to extend efficiency advances in the age of generative AI. Metrics of energy consumed doing useful work on today’s top applications can take supercomputing and data centers to a new level of energy efficiency.

To learn more about available energy-efficiency solutions, explore NVIDIA sustainable computing.

Read More

Meet Media.Monks’ Wormhole, an alien-like, conversational robot with a quirky personality and the ability to offer keen marketing expertise. Lewis Smithingham, senior vice president of innovation and special ops at Media.Monks, a global marketing and advertising company, discusses the creation of Wormhole and AI’s potential to enhance media and entertainment with host Noah Kravitz in this AI Podcast episode recorded live at the NVIDIA GTC global AI conference. Wormhole was designed to showcase Monks.Flow, an AI-powered platform that streamlines marketing and content creation workflows. Smithingham delves into Media.Monks’ platforms for media, entertainment and advertising and speaks to its vision for a future where AI enhances creativity and allows for more personalized, scalable content creation.

Stay tuned for more episodes recorded live from GTC, and hear more from Smithingham in this GTC interview.

Time Stamps

1:45: What is Media.Monks?
6:23: Description of Wormhole
8:49: Possible use cases for Wormhole
10:21: Takeaways from developing Wormhole
12:02: What is Monks.Flow?
16:54: Response from creatives on using AI in their work
21:23: Smithingham’s outlook on hyperpersonalized content
34:24: What’s next for the future of AI-powered media?

You Might Also Like…

Exploring Filmmaking With Cuebric’s AI: Insights From Pinar Seyhan Demirdag – Ep. 214

In today’s episode of NVIDIA’s AI Podcast, host Noah Kravitz talks with Pinar Seyhan Demirdag, co-founder and CEO of Cuebric. Cuebric is on a mission to offer new solutions in filmmaking and content creation through immersive, two-and-a-half-dimensional cinematic environments.

Deepdub’s Ofir Krakowski on Redefining Dubbing From Hollywood to Bollywood – Ep. 202

On the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz spoke with Deepdub’s cofounder and CEO, Ofir Krakowski. Deepdub uses AI-driven dubbing to help entertainment companies boost efficiency and cut costs while increasing accessibility.

WSC Sports’ Amos Bercovich on How AI Keeps the Sports Highlights Coming – Ep. 183

On this episode of the AI Podcast, host Noah Kravitz spoke with Amos Bercovich, algorithm group leader at WSC Sports, makers of an AI cloud platform that enables over 200 sports organizations worldwide to generate personalized and customized sports videos automatically and in real time.

Maya Ackerman on LyricStudio, an AI-Based Writing Songwriting Assistant – Ep. 153

Lennon and McCartney. Ashford and Simpson. Many of our all-time favorite tunes have come from songwriting duos. Now, anyone can find a snazzy compositional partner in AI. In this episode of the AI Podcast, Maya Ackerman, CEO of WaveAI, spoke with host Noah Kravtiz about WaveAI’s LyricStudio software, an AI-based lyric and poetry writing assistant.

Subscribe to the AI Podcast

Get the AI Podcast through iTunes, Google Podcasts, Google Play, Amazon Music, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn.

Make the AI Podcast better: Have a few minutes to spare? Fill out this listener survey.

Read More

Gear up, Trailblazers — Honkai: Star Rail lands on GeForce NOW this week, along with an in-game reward for members to celebrate the title’s launch in the cloud.

Stream it today, along with five new games joining the GeForce NOW library of more than 1,900 titles this week.

Five Stars

Take a galactic journey in the cloud with Honkai: Star Rail, a new Cosmic Adventure Strategy role-playing game from HoYoverse, the company behind Genshin Impact. The title seamlessly blends intricate storytelling with immersive gameplay mechanics for an epic journey through the cosmos.

Meet a cast of unique characters and explore diverse planets, each with its own mysteries to uncover. Assemble formidable teams, strategically deploying skills and resources to overcome mighty adversaries and unravel the mysteries of the Honkai phenomenon. Encounter new civilizations and face off against threats that endanger the Astral Express, overcome the struggles caused by Stellaron together, powerful artifacts that hold the keys to the universe’s fate.

Begin the trailblazing journey without needing to wait for downloads or game updates with GeForce NOW. Members who’ve opted into GeForce NOW’s Rewards program will receive an email with a code for a Honkai: Star Rail starter kit, containing 30,000 credits, three Refined Aethers and three Traveler’s Guides. All aboard the Astral Express for adventures and thrills!

A Big Cloud for New Games 

Do what cats do best in Little Kitty, Big City, the open-world adventure game from Double Dagger Studios. Explore the city as a curious little kitty with a big personality, make new friends with stray animals, and wear delightful little hats. Create a little bit of chaos finding the way back home throughout the big city.

Here’s the full list of new games this week:

• Little Kitty, Big City (New release on Steam and Xbox, available on PC Game Pass, May 9)
• Farmer’s Life (Steam)
• Honkai: Star Rail (Epic Games Store)
• Supermarket Simulator (Steam)
• Tomb Raider: Definitive Edition (Xbox, available on PC Game Pass)

What are you planning to play this weekend? Let us know on X or in the comments below.

what’s the last game you 100% completed?

— NVIDIA GeForce NOW (@NVIDIAGFN) May 8, 2024

Read More

Now’s the time to hop aboard AI, NVIDIA founder and CEO Jensen Huang declared Wednesday as ServiceNow unveiled a demo of futuristic AI avatars together with NVIDIA during a keynote at the Knowledge 24 conference in Las Vegas.

“If something is moving a million times faster every 10 years, what should you do?” Huang asked, citing rapid advancements in AI capabilities. “The first thing you should do is instead of looking at the train, from the side is … get on the train, because on the train, it’s not moving that fast.”

The demo — built on NVIDIA NIM inference microservices and NVIDIA Avatar Cloud Engine, or ACE, speech and animation generative AI technologies, all available with NVIDIA AI Enterprise software — highlighted how AI advancements support cutting-edge digital avatar communications and have the potential to revolutionize customer service interactions.

The demo showed a customer who was struggling with a slow internet connection interacting with a digital avatar. The AI customer service avatar comes to the rescue –  swiftly diagnoses the problem, offers an option for a faster internet connection, confirms the customer’s credit card number and upgrades their internet connection immediately.

The futuristic demonstration took place in front of thousands of conference attendees who were eager to learn about the latest enterprise generative AI technology advancements, which promise to empower workers across the globe.

“We’ve transitioned from instruction-driven computer coding, which very few people can do, to intention-driven computing, which is connecting with somebody through intention,” Huang said during an on-stage conversation at the conference with ServiceNow Chief Operating Officer Chirantan “CJ” Desai.

The moment is another compelling example of the ongoing collaboration between ServiceNow and NVIDIA to explore more engaging, personal service experiences across various functions, including IT services, human resources, customer support and more.

The demonstration builds upon the companies’ plan to collaborate on robust, generative AI capabilities within enterprise operations and incorporates NVIDIA ACE and NVIDIA NIM microservices.

These avatars are designed to add a human-like touch to digital interactions, improving customer experience by providing empathetic and efficient support.

These include NVIDIA Riva for automatic speech recognition and text-to-speech, NVIDIA Audio2Face for facial animation, and NVIDIA Omniverse Renderer for high-quality visual output.

ServiceNow and NVIDIA are further exploring the use of AI avatars to provide another communication option for users who prefer visual interactions.

Visit this link to watch a recording of Huang and Desai presenting the digital avatar demo at the Knowledge 24 keynote. 

###END###

Read More

AI tools accelerated by NVIDIA RTX have made it easier than ever to edit and work with video.

Case in point: Blackmagic Design’s DaVinci Resolve 19 recently added AI features that make video editing workflows more streamlined. These new features — along with all its other AI-powered effects — get a big boost from optimization for NVIDIA RTX PCs and workstations.

Editors use Blackmagic Design’s DaVinci Resolve — one of the leading nonlinear video editing platforms — to bring their creative vision to life, incorporating visual effects (VFX), color correction, motion graphics and more to their high-resolution footage and audio clips.

Resolve includes a large variety of built-in tools. Some are corrective in nature, letting editors match colors from two sets of footage, reframe footage after the fact or remove objects that weren’t meant to be in a shot. Others give editors the power to manipulate footage and audio in new ways, including smooth slow-motion effects and footage upscaling.

In the past, many of these tools required significant time and effort from users to implement. Resolve now uses AI acceleration to speed up many of these workflows, leaving more time for users to focus on creativity rather than batch processing.

Even better, the entire app is optimized for NVIDIA TensorRT deep learning inference software to get the best performance from GPU-reliant effects and other features, boosting performance by 2x.

New in Resolve 19

The newest release, DaVinci Resolve 19, adds two new AI features that make video editing more efficient: the IntelliTrack AI point tracker for object tracking, stabilization and audio panning, and UltraNR, which uses AI for spatial noise reduction.

IntelliTrack AI makes it easy to stabilize footage during the editing process. It can also be used in Resolve’s Fairlight tool to track on-screen subjects, and automatically generate audio panning within a scene by tracking people or objects as they move across 2D and 3D spaces. With AI audio panning to video, editors can quickly pan, or move audio across the stereo field, multiple actors in a scene, controlling their voice positions in the mix environment. All of this can be done by hand, but IntelliTrack’s AI acceleration speeds up the entire process.

UltraNR is an AI-accelerated denoise mode in Resolve’s spatial noise reduction palette. Editors can use it to dramatically reduce digital noise — undesired fluctuations of color or luminance that obscure detail — from a frame while maintaining image clarity. They can also combine the tool with temporal noise reduction for even more effective denoising in images with motion, where fluctuations can be more noticeable.

Both IntelliTrack and UltraNR get a big boost when running on NVIDIA RTX PCs and workstations. TensorRT lets them run up to 3x faster on a GeForce GTX 4090 laptop vs. the Macbook Pro M3 Max.

In fact, all DaVinci Resolve AI effects are accelerated on RTX GPUs by NVIDIA TensorRT. The new Resolve update also includes GPU acceleration for Beauty, Edge Detect and Watercolor effects, doubling their performance on NVIDIA GPUs.

Find out more about DaVinci Resolve 19, and try it yourself for free, at Blackmagic Design.

Learn how AI is supercharging creativity, and how to get the most from your own creative process, with NVIDIA Studio.

Generative AI is transforming gaming, videoconferencing and interactive experiences of all kinds. Make sense of what’s new and what’s next by subscribing to the AI Decoded newsletter.

Read More

Amid an AI revolution sweeping through trillion-dollar industries worldwide, NVIDIA founder and CEO Jensen Huang will deliver a keynote address ahead of COMPUTEX 2024, in Taipei, outlining what’s next for the AI ecosystem.

Slated for June 2 at the National Taiwan University Sports Center, the address kicks off before the COMPUTEX trade show scheduled to run from June 3-6 at the Taipei Nangang Exhibition Center.

The keynote will be livestreamed at 7 p.m. Taiwan time (4 a.m. PT) on Sunday, June 2, with a replay available at NVIDIA.com.

With over 1,500 exhibitors from 26 countries and an expected crowd of 50,000 attendees, COMPUTEX is one of the world’s premier technology events.

It has long showcased the vibrant technology ecosystem anchored by Taiwan and has become a launching pad for the cutting-edge systems required to scale AI globally.

As a leader in AI, NVIDIA continues to nurture and expand the AI ecosystem. Last year, Huang’s keynote and appearances in partner press conferences exemplified NVIDIA’s role in helping advance partners across the technology industry.

These partners will be out in force this year.

NVIDIA’s partners, including Acer, ASUS, Asrock Rack, Colorful, GIGABYTE, Ingrasys, Inno3D, Inventec, MSI, Palit, Pegatron, PNY, QCT, Supermicro, Wistron, Wiwynn and Zotac will spotlight new products featuring NVIDIA technology.

In addition to the exhibition and demonstrations, Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA, will take the stage at the TAITRA forum, a key segment of COMPUTEX dedicated to cutting-edge discussions in technology.

As part of the “Let’s Talk Generative AI” forum, Hamilton will present his talk, titled “Infra Build Train Go,” on June 5, from 10-10:30 a.m. at the 701 Conference Room, 7F, Taipei Nangang Exhibition Center Hall 2.

NVIDIA AI Summit

Following the keynote, the NVIDIA AI Summit on June 5 at the Grand Hilai Taipei will delve into the practical applications of AI in manufacturing, healthcare, research and more.

The summit will feature over 20 sessions from industry experts and innovators as well as training sessions for developers. Kimberly Powell, vice president of healthcare and life sciences at NVIDIA, will host a special address on how generative AI is advancing the healthcare technology industry.

Register for the AI Summit.

Read More

In support of President Biden’s executive order on AI, the U.S. government will use an NVIDIA DGX SuperPOD to produce generative AI advances in climate science, healthcare and cybersecurity.

The executive order, issued in October, is aimed at ensuring U.S. leadership in AI and managing its risks. MITRE, a nonprofit organization that operates federally funded research and development centers, is implementing a new NVIDIA DGX SuperPOD system that will provide researchers and developers access to massive computing leaps.

The DGX SuperPOD will support MITRE’s Federal AI Sandbox, a platform to improve experimentation with next-generation, AI-enabled applications across federal government agencies.

“The recent executive order on AI encourages federal agencies to reduce barriers for AI adoptions, but agencies often lack the computing environment necessary for experimentation and prototyping,” said Charles Clancy, senior vice president and chief technology officer at MITRE. “Our new Federal AI Sandbox will help level the playing field, making the high-quality compute power needed to train and test custom AI solutions available to any agency.”

The Federal AI Sandbox will deliver federal agencies the computing gains needed to train large language models and other generative AI tools to develop cutting-edge applications.

The NVIDIA DGX SuperPOD system powering the sandbox is capable of an exaflop of 8-bit AI compute, meaning it performs a quintillion math operation each second to train and deploy custom LLMs and other AI solutions at scale.

The supercomputing initiative comes as the White House recently unveiled plans, which include NVIDIA, for a $110 million partnership to help universities teach AI skills.

Read More

Cybersecurity experts at the RSA Conference this week will be on the hunt for ways to secure their operations in the era of generative AI.

They’ll find many of the latest tools use AI and accelerated computing. This intersection of security and AI is coming into focus with collaborations that companies like NVIDIA and its partners will describe at the event.

Data Science for a Data Problem

Machine learning is a great tool for cybersecurity because data is exploding.

“With more devices and users expanding the landscape to defend, cybersecurity has become a data problem; and AI is a data solution,” said David Reber, NVIDIA’s chief security officer.

Today, security analysts can be overwhelmed by a data tsunami. Generative AI can provide security copilots that act like filters, extracting from the firehose flow of information the context and anomalies that need a human expert’s attention.

Generative AI also lets security managers interrogate their data directly instead of setting rules, chasing alerts and tracking dashboards. In the age of AI, security experts will move from a command line to a conversational interface.

AI Puts Security Context on Steroids

This shift takes context-based security to a new level, according to a talk Reber delivered at GTC.

The potential is huge, but it requires work to unearth it. At GTC, Reber encouraged cybersecurity experts to begin working with AI, starting with low-risk use cases to identify and secure gaps.

He also provided suggestions for how to go about securing machine learning processes, saying security experts need to:

• secure data supply chains,
• develop tests for securing AI models and datasets across the development lifecycle,
• use model cards, data cards and software bills of materials to provide AI transparency and reliability,
• participate in community testing events such as security hackathons, and
• update policies on how to respond to AI security events.

Foundations for AI Cybersecurity

To give users a leg up, NVIDIA provides NVIDIA Morpheus, a cybersecurity AI framework that filters and classifies large volumes of real-time data. Morpheus, part of the NVIDIA AI Enterprise software suite, lets developers build applications that can detect spear phishing, insider threats and more.

Users can employ Morpheus with NVIDIA NIM and NeMo Retriever, microservices from the NVIDIA API Catalog for rapidly deploying AI. The combination can unlock new use cases, such as reducing from days to seconds the time to find and resolve common software vulnerabilities and exposures, one of many NVIDIA AI workflows.

A new release of NVIDIA DOCA — the software framework for programming NVIDIA BlueField DPUs and NVIDIA ConnectX NICs — provides another foundation for AI security. It now sports updated encryption features for network and storage data.

An Expanding AI Security Ecosystem

At RSA, many companies will show products built on NVIDIA technologies that extend security for the generative AI era, including:

• AIC will demonstrate Qrypt’s key generation for quantum secure encryption running on a BlueField DPU in an AIC-designed server.
• Anjuna will discuss how the U.S. Navy is evaluating confidential computing on the Anjuna Seaglass platform with proprietary LLMs running on NVIDIA H100 Tensor Core GPUs.
• Bloombase will show an updated version of its StoreSafe Storage Firewall powered by Morpheus and BlueField DPUs and new use cases for threat detection and fast, quantum-safe encryption of AI models and data.
• Check Point Software will show its AI Cloud Protect security solution on BlueField DPUs, Quantum Force security gateways on ConnectX NICs, and Quantum Maestro software on NVIDIA Spectrum switches.
• Cisco will feature Cisco Hypershield, an AI-native security architecture, to protect against both known and unknown attacks. It will also discuss its expanding partnership with NVIDIA to help customers harness the power of AI.
• CrowdStrike will show its CrowdStrike Falcon Foundry and CrowdStrike Falcon platform that employs NVIDIA’s GPU-optimized AI software, including NIM microservices.
• Deloitte will showcase CyberSphere, a cyber operations platform that uses Morpheus to speed detection of cyber threats.
• Palo Alto Networks will describe its collaboration with NVIDIA on two use cases, a next-generation reference architecture for securing generative AI deployments with NIM and its VM-Series Virtual Next-Generation Firewall, with expanded intelligent traffic offload (ITO), supporting BlueField-3 DPUs.
• Qrypt will demonstrate its quantum-secure key generation for securing AI workloads running on BlueField-3 DPUs using DOCA.
• Sygnia will announce the use of BlueField DPUs and Morpheus in Velocity Edge, its new hardware-accelerated MXDR service for the energy and industrial sectors.

They are part of the NVIDIA ecosystem building a new layer of security for generative AI. That community includes more than 20 companies at RSA this week from NVIDIA Inception, a program for cutting-edge startups.

At RSA, Daniel Rohrer, vice president of software product security at NVIDIA, will be part of the keynote panel on AI safety.

In addition, Kevin Deierling, senior vice president of networking at NVIDIA, will share insights on security at the Cloudflare Executive Supper Club. And NVIDIA will participate in an event about women in cybersecurity.

To get started with AI-powered cybersecurity, try a workflow in NVIDIA LaunchPad.

Read More

Intrinsic, a software and AI robotics company at Alphabet, has integrated NVIDIA AI and Isaac platform technologies to advance the complex field of autonomous robotic manipulation.

This week at the Automate trade show, in Chicago, Intrinsic is spotlighting leaps in robotic grasping and industrial scalability assisted by foundation models enabled by NVIDIA Isaac Manipulator, unlocking new value in industrial automation with AI.

NVIDIA unveiled Isaac Manipulator at GTC in March. Isaac Manipulator is a collection of foundation models and modular GPU-accelerated libraries that help industrial automation companies build scalable and repeatable workflows for dynamic manipulation tasks by accelerating AI model training and task reprogramming.

Foundation models are based on a transformer deep learning architecture that allows a neural network to learn by tracking relationships in data. They’re generally trained on huge datasets and can be used to process and understand sensor and robot information as magically as ChatGPT for text. This enables robot perception and decision-making like never before and provides zero-shot learning — the ability to perform tasks without prior examples.

NVIDIA’s collaboration with Intrinsic, a leading robotics software and AI company,  demonstrates the potential for a universally applicable robotic-grasping skill to work across grippers, environments and objects.

“For the broader industry, our work with NVIDIA shows how foundation models can have a profound impact, including making today’s processing challenges easier to manage at scale, creating previously infeasible applications, reducing development costs, and increasing flexibility for end users,” said Wendy Tan White, CEO at Intrinsic, in a blog post announcing the collaboration with NVIDIA.  (White will deliver a keynote address at Automate about what the rise of AI means for innovation and growth, on Thursday, May 9, at 7 a.m. PT.)

Developing Better Robot Grip With Isaac Manipulator

Grasping has been a long sought after robotics skill. So far it’s been time-consuming, expensive to program and difficult to scale. As a result, many repetitive pick-and-place conditions haven’t been seamlessly handled to date by robots.

Simulation is changing that. Enlisting NVIDIA Isaac Sim on the NVIDIA Omniverse platform, Intrinsic generated synthetic data for vacuum grasping using computer-aided design models of sheet metal and suction grippers. This allowed Intrinsic to create a prototype for its customer Trumpf Machine Tools, a leading maker of industrial machine tools.

The prototype uses Intrinsic Flowstate, a developer environment for AI-based robotics solutions, for visualizing processes, associated perception and motion planning. With a workflow that includes Isaac Manipulator, one can generate grasp poses and CUDA-accelerated robot motions, which can first be evaluated in simulation with Isaac Sim — a cost-saving step — before deployment in the real world with the Intrinsic platform.

Under the collaboration, NVIDIA and Intrinsic plan to bring state-of-the-art dexterity and modular AI capabilities for robotic arms, with a robust collection of foundation models and GPU-accelerated libraries to accelerate a greater number of new robotics and automation tasks.

On Tuesday, May 7, at 11 a.m. CT, NVIDIA Senior Research Scientist Adithya Murali and Intrinsic Chief Science Officer Torsten Kroeger will demonstrate the companies’ work in the session “Automating Smart Pick-and-Place With Intrinsic Flowstate and NVIDIA Isaac Manipulator ” in the Intrinsic booth 2808 at Automate. Join  our speaking sessions at Automate.

Read More