Posts in category: nVidia AI

Talk about going after low-hanging fruit. Afresh is an AI startup that helps grocery stores and retailers reduce food waste by making supply chains more efficient.

In the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz spoke with the company’s cofounder and president, Nathan Fenner, about its mission, offerings and the greater challenge of eliminating food waste.

Most supply chain and inventory management offerings targeting grocers and retailers are outdated. Fenner and his team noticed those solutions, built for the nonperishable side of the business, didn’t work as well on the fresh side — creating enormous amounts of food waste and causing billions in lost profits.

The team first sought to solve the store-replenishment challenge by developing a platform to help grocers decide how much fresh produce to order to optimize costs while meeting demand.

They created machine learning and AI models that could effectively use the data generated by fresh produce, which is messier than data generated by nonperishable goods because of factors like time to decay, greater demand fluctuation and unreliability caused by lack of barcodes, leading to incorrect scans at self-checkout registers.

The result was a fully integrated, machine learning-based platform that helps grocers make informed decisions at each node of the operations process.

The company also recently launched inventory management software that allows grocers to save time and increase data accuracy by intelligently tracking inventory. That information can be inputted back into the platform’s ordering solution, further refining the accuracy of inventory data.

It’s all part of Afresh’s greater mission to tackle climate change.

“The most impactful thing we can do is reduce food waste to mitigate climate change,” Fenner said. “It’s really one of the key things that brought me into the business: I think I’ve always had a keen eye to work in the climate space. It’s really motivating for a lot of our team, and it’s a key part of our mission.”

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Genentech, a member of the Roche Group, is pioneering the use of generative AI to discover and develop new therapeutics and deliver treatments to patients more efficiently.

A new collaboration between Genentech, the biotechnology pioneer, and NVIDIA aims to transform the discovery and development of new medicines by bringing together experts from each company to optimize and accelerate Genentech’s proprietary algorithms.

NVIDIA will work with Genentech to accelerate these models on NVIDIA DGX Cloud, which provides dedicated instances of AI supercomputing and software hosted by NVIDIA cloud service provider partners.

Genentech plans to use NVIDIA BioNeMo, which enables biotech companies to customize models at scale, and integrate BioNeMo cloud application programming interfaces directly into computational drug discovery workflows.

BioNeMo, now generally available as a training service, is a domain-specific platform that simplifies, accelerates and scales generative AI applications for computational drug discovery. It allows researchers to pretrain or fine-tune state-of-the-art models on DGX Cloud.

The collaboration will initially focus on optimizing Genentech’s drug discovery AI models in its “lab in a loop” framework. The goal: To allow its researchers to understand complex biomolecular patterns and relationships to truly disrupt drug development and improve the success rate of R&D, and to empower scientists to deliver multiplicative, rather than linear or additive, benefits for patients and the broader healthcare ecosystem.

“Our collaboration with NVIDIA builds on our long history of successfully inventing and deploying technology in ways that were not initially apparent to others,” said Aviv Regev, executive vice president and head of Genentech Research & Early Development (gRED). “We were the first biotech company to leverage molecular biology for drug discovery and development, which changed the world. We pioneered antibody therapeutics that became the paradigm of treatment. And now, we have brought AI, the lab and the clinic together to uncover otherwise inaccessible patterns in vast quantities of data, and to design experiments to test those patterns. Collaborating with NVIDIA, and introducing generative AI, has the power to turbocharge the discovery and design of therapeutics that will improve the lives of patients across the world.”

Streamlining Drug Discovery With Computation  

Drug discovery and development is currently a lengthy, complicated and costly process. Drug targets for novel medicines are difficult to predict, as is successfully developing a molecule as a potential therapeutic. AI can play a transformational role because generative and other AI models can help scientists rapidly identify potential drug molecules and interactions by training on large-scale datasets.

For Genentech, using AI helps bridge the gap between lab experiments and computational algorithms.

The company’s R&D group, gRED, has already done significant work using AI — across multiple modalities — to discover and develop novel therapeutics while learning more about the building blocks of biology and diseases.

Teams from Genentech and NVIDIA will now work together to optimize Genentech’s custom-developed models to shorten this time-consuming process of drug discovery and development and lead to greater success.

Putting AI in a Loop

Genentech’s “lab in a loop” is an iterative framework for generating and exploring molecular designs with predicted properties. It aims to use experimental data to inform generative computational models and better optimize future molecular designs. NVIDIA will help Genentech optimize its framework by accelerating training and inference of Genentech’s drug discovery models.

Through this collaboration, NVIDIA AI experts will gain insights into AI-related challenges in drug discovery and development. NVIDIA plans to use these insights to improve its BioNeMo platform and others to further accommodate the requirements of models used by the biotech industry.

“AI can play a transformational role in accelerating drug discovery and development — as it has across many parts of healthcare and life sciences,” said Kimberly Powell, vice president of healthcare at NVIDIA. “Together, NVIDIA and Genentech are unlocking scientific innovation by developing and implementing AI models and algorithms that enable us to rapidly iterate and unearth insights.”

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Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. 

It’s the season of gratitude: that time of year to give thanks for the people and small moments that make life so special.

This week’s featured In the NVIDIA Studio artist, Ravissen Carpenen, is serving up a feast of mouthwateringly photorealistic 3D food renders to the dinner table.

His delectable time-lapse videos are featured on his YouTube channel, CG Realism — presented with a side of upbeat music and a pinch of style.

Carpenen was one of several contributors to the food-themed Studio Standout video contest, alongside Roger Roque (@rogerroqueid), Nicole Morena (@nicky.blender), Heloise Cart (@isoheell) and Kris Theroin (@kristheorin).

Finally, livestreamers using OBS Studio — a free, open-source software for video recording and livestreaming — can download the latest update with HDR10 capture support, WHIP and WebRTC output and more. Learn more details.

All About That Baste

Carpenen’s wife, a pastry chef, inspired his photorealistic, food-centered works.

“My aim, one day, is to be able to create ultra-realistic renders that will be used in film and movies,” he said.

His projects begin with online research and reference gathering, mainly on Pinterest and Behance, which he then compiles into mood boards using the stand-alone image tracking program PureRef.

Before any modeling takes place, Carpenen lights the scene — but without textures.

“This is to tell the story of the artwork, as light intends to give artwork an emotional flow, alongside as well as color and materials,” he said.

Carpenen initially sculpts his models in ZBrush using customizable brushes to shape, texture and paint his virtual clay in a real-time environment with instant feedback.

He then browses the Quixel Megascans library for models that can further add realism, such as garlic cloves and rosemary garnishes for his turkey project.

Rare-in for More

Carpenen uses Marmoset Toolbag’s ambient occlusion, curvature, normal and thickness features to bake the ZBrush meshes from high-poly to low-poly models as 32-bit textures.

The process saves memory space, minimizing lag time while allowing greater flexibility in the modeling stage.

Carpenen’s GeForce RTX 3070 GPU-powered system with RTX acceleration instantly optimized his meshes. RTX-accelerated ray tracing and OptiX AI-powered denoising also enabled smoother viewport movement.

Baking a Berry Good Pie

Once the renders are ready, Carpenen imports them into Adobe Substance 3D Painter to apply custom colors and textures.

There, Carpenen uses RTX-accelerated light and ambient occlusion baking — though not in the oven — to optimize his assets, such as this berry pie, in mere seconds.

He also had the option to set up a live link connection between 3D Painter and NVIDIA Omniverse, a development platform for connecting and building Universal Scene Description (OpenUSD)-based tools and applications, via the USD Composer foundation app.

The connection would allow Carpenen’s texture work in Substance 3D Painter to directly translate to USD Composer — eliminating the need for numerous file imports, exports and reformatting.

Donut Hole in One

Carpenen uses Blender to bring his scenes together with advanced model sculpting, animations and further lighting refinement.

RTX GPUs allow smoother movement in the viewport thanks to Blender Cycles’ RTX-accelerated OptiX ray tracing.

And for exporting final files, RTX-accelerated OptiX ray tracing in Blender Cycles delivers the fastest final-frame render.

Thanks to AI, This Work Is Toast

Carpenen uses Adobe Photoshop Lightroom to put the finishing touches on his food scenes.

GPU-accelerated image processing enables dramatically more responsive adjustments on his 4K-resolution display.

Carpenen had even more RTX-accelerated AI tools at his disposal in Lightroom. The “Raw Details” feature refines the fine color details of high-resolution RAW images. And “Super Resolution” uses AI to upscale images with higher quality than traditional methods.

According to Carpenen, putting in the work is key.

“It’s equivalent to practicing football — if you don’t get enough time daily to practice, you can’t hone skills,” he said. “It’s important to know how to tackle obstacles — and that knowledge can only be gained by experience.”

Check out Carpenen’s YouTube channel, CG Realism, and ArtStation to check out more food projects.

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Generative AI is the latest turn in the fast-changing digital landscape. One of the groundbreaking innovations making it possible is a relatively new term: SuperNIC. 

What Is a SuperNIC?

SuperNIC is a new class of network accelerators designed to supercharge hyperscale AI workloads in Ethernet-based clouds. It provides lightning-fast network connectivity for GPU-to-GPU communication, achieving speeds reaching 400Gb/s using remote direct memory access (RDMA) over converged Ethernet (RoCE) technology.  

SuperNICs combine the following unique attributes: 

• High-speed packet reordering to ensure that data packets are received and processed in the same order they were originally transmitted. This maintains the sequential integrity of the data flow. 
• Advanced congestion control using real-time telemetry data and network-aware algorithms to manage and prevent congestion in AI networks. 
• Programmable compute on the input/output (I/O) path to enable customization and extensibility of network infrastructure in AI cloud data centers. 
• Power-efficient, low-profile design to efficiently accommodate AI workloads within constrained power budgets. 
• Full-stack AI optimization, including compute, networking, storage, system software, communication libraries and application frameworks. 

NVIDIA recently unveiled the world’s first SuperNIC tailored for AI computing, based on the BlueField-3 networking platform. It’s a part of the NVIDIA Spectrum-X platform, where it integrates seamlessly with the Spectrum-4 Ethernet switch system.  

Together, the NVIDIA BlueField-3 SuperNIC and Spectrum-4 switch system form the foundation of an accelerated computing fabric specifically designed to optimize AI workloads. Spectrum-X consistently delivers high network efficiency levels, outperforming traditional Ethernet environments. 

“In a world where AI is driving the next wave of technological innovation, the BlueField-3 SuperNIC is a vital cog in the machinery,” said Yael Shenhav, vice president of DPU and NIC products at NVIDIA. “SuperNICs ensure that your AI workloads are executed with efficiency and speed, making them foundational components for enabling the future of AI computing.” 

The Evolving Landscape of AI and Networking 

The AI field is undergoing a seismic shift, thanks to the advent of generative AI and large language models. These powerful technologies have unlocked new possibilities, enabling computers to handle new tasks.  

AI success relies heavily on GPU-accelerated computing to process mountains of data, train large AI models, and enable real-time inference. This new compute power has opened new possibilities, but it has also challenged Ethernet cloud networks. 

Traditional Ethernet, the technology that underpins internet infrastructure, was conceived to offer broad compatibility and connect loosely coupled applications. It wasn’t designed to handle the demanding computational needs of modern AI workloads, which involve tightly coupled parallel processing, rapid data transfers and unique communication patterns — all of which demand optimized network connectivity.  

Foundational network interface cards (NICs) were designed for general-purpose computing, universal data transmission and interoperability. They were never designed to cope with the unique challenges posed by the computational intensity of AI workloads.  

Standard NICs lack the requisite features and capabilities for efficient data transfer, low latency and the deterministic performance crucial for AI tasks. SuperNICs, on the other hand, are purpose-built for modern AI workloads. 

SuperNIC Advantages in AI Computing Environments 

Data processing units (DPUs) deliver a wealth of advanced features, offering high throughput, low-latency network connectivity and more. Since their introduction in 2020, DPUs have gained popularity in the realm of cloud computing, primarily due to their capacity to offload, accelerate and isolate data center infrastructure processing. 

Although DPUs and SuperNICs share a range of features and capabilities, SuperNICs are uniquely optimized for accelerating networks for AI. The chart below shows how they compare: 

Distributed AI training and inference communication flows depend heavily on network bandwidth availability for success. SuperNICs, distinguished by their sleek design, scale more effectively than DPUs, delivering an impressive 400Gb/s of network bandwidth per GPU.  

The 1:1 ratio between GPUs and SuperNICs within a system can significantly enhance AI workload efficiency, leading to greater productivity and superior outcomes for enterprises.  

The sole purpose of SuperNICs is to accelerate networking for AI cloud computing. Consequently, it achieves this goal using less computing power than a DPU, which requires substantial computational resources to offload applications from a host CPU.  

The reduced computing requirements also translate to lower power consumption, which is especially crucial in systems containing up to eight SuperNICs. 

Additional distinguishing features of the SuperNIC include its dedicated AI networking capabilities. When tightly integrated with an AI-optimized NVIDIA Spectrum-4 switch, it offers adaptive routing, out-of-order packet handling and optimized congestion control. These advanced features are instrumental in accelerating Ethernet AI cloud environments. 

Revolutionizing AI Cloud Computing

The NVIDIA BlueField-3 SuperNIC offers several benefits that make it key for AI-ready infrastructure: 

• Peak AI workload efficiency: The BlueField-3 SuperNIC is purpose-built for network-intensive, massively parallel computing, making it ideal for AI workloads. It ensures that AI tasks run efficiently — without bottlenecks. 
• Consistent and predictable performance: In multi-tenant data centers where numerous tasks are processed simultaneously, the BlueField-3 SuperNIC ensures that each job and tenant’s performance is isolated, predictable and unaffected by other network activities. 
• Secure multi-tenant cloud infrastructure: Security is a top priority, especially in data centers handling sensitive information. The BlueField-3 SuperNIC maintains high security levels, enabling multiple tenants to coexist while keeping data and processing isolated. 
• Extensible network infrastructure: The BlueField-3 SuperNIC isn’t limited in scope — it’s highly flexible and adaptable to a myriad of other network infrastructure needs. 
• Broad server manufacturer support: The BlueField-3 SuperNIC fits seamlessly into most enterprise-class servers without excessive power consumption in data centers.

Learn more about NVIDIA BlueField-3 SuperNICs, including how they integrate across NVIDIA’s data center platforms, in the whitepaper: Next-Generation Networking for the Next Wave of AI. 

Good news for car lovers: Two acclaimed auto shows, taking place now through next week, are delighting attendees with displays of next-generation automotive designs powered by AI.

Hundreds of thousands of auto enthusiasts worldwide are expected to visit Guangzhou, China — known as the city of flowers — to attend its auto show, running through Sunday, Nov. 26. The event will feature new developments in electric vehicles (EVs) and automated driving, with 1,100 vehicles on display.

And across the world, in the city of angels, the Los Angeles Auto Show is expected to reach its highest-ever attendee numbers. Also running through Nov. 26, the show will include classic and exotic cars from private collections, as well as a public test track where attendees can get behind the wheel of the latest EVs.

Auto Guangzhou

One of the most anticipated reveals is from Lotus, which is showcasing its new fully electric Emeya Hyper-GT that launched in September. This stunning luxury vehicle with sports-car agility achieves an impressive suite of intelligent features, powered by dual NVIDIA DRIVE Orin processors. The high-performance processing power enables drivers to enjoy the car’s safe and secure driving capabilities and supports future features through over-the-air (OTA) updates.

With an eye on safety, Emeya carries 34 state-of-the-art surround sensors for diverse and redundant sensor data processing in real time — giving drivers added confidence when behind the wheel. With DRIVE Orin embedded in the back of the vehicle, Emeya delivers advanced driver assistance system (ADAS) capabilities and also offers the built-in headroom to support an autonomous future.

Emeya Hyper-GT is built on Lotus’ innovative Electric Premium Architecture, which underpins the Eletre Hyper-SUV as well, also powered by NVIDIA DRIVE Orin.

In addition, Lotus is showcasing its full range of Lotus electric vehicles, including the Evija hypercar, Eletre Hyper-SUV and Type 136, its recently launched electric bike. Emira, Lotus’ final internal combustion engine vehicle, is also on display.

Several other NVIDIA DRIVE ecosystem members are featuring their next-gen EVs at Auto Guangzhou:

• DENZA, a joint venture between BYD and Mercedes-Benz, is highlighting the intelligent-driving features of its N7 model lineup. All N7 models can be equipped with the NVIDIA DRIVE Orin system-on-a-chip. In addition, DENZA is showcasing its next generation of car configurators built on NVIDIA Omniverse Cloud, enabling consumers to customize various aspects, such as colors, materials and more.
• Human Horizons is showcasing the HiPhi Y SUV, which includes a second-generation intelligent door system design, a range of more than 497 miles on a single charge and an autonomous-driving system powered by NVIDIA DRIVE Orin.
• JI YUE, a Geely Holding and Baidu joint venture, is displaying the ROBOCAR JiYue 01, the first model to offer the premium intelligent driving feature ROBO Drive Max, powered by NVIDIA DRIVE Orin.
• NIO is exhibiting eight models equipped with Banyan, NIO’s Smart Digital System, and Adam, an NVIDIA-powered supercomputer. Adam uses NVIDIA DRIVE Orin to enable advanced automated-driving features and allow continuous OTA upgrades.
• XPENG is unveiling and commencing pre-sales for its next-generation, super-intelligent, seven-seater MPV, the XPENG X9. The company is also showcasing the XPENG G6, the XPENG P7i and the XPENG G9, all featuring NVIDIA DRIVE Orin.
• ZEEKR introduces the ZEEKR 007. The electric sedan is ZEEKR’s first sedan and fourth model, powered by NVIDIA DRIVE Orin. The ZEEKR 007 has a sensor suite consisting of one lidar, 12 high-definition cameras and five millimeter-wave radars for advanced smart assist driving functions with multiple redundancy.

In addition, NVIDIA DRIVE ecosystem partner DeepRoute.ai, a smart driving solutions provider, is demonstrating its DeepRoute Driver 3.0, designed to offer a non-geofenced solution for automated vehicles.

Los Angeles Auto Show

At the LA Auto Show, Lucid Motors is showcasing its highly anticipated Gravity SUV, which will begin production in late 2024. Powered by NVIDIA DRIVE, the luxury SUV features supercar levels of performance and an impressive battery range to mitigate range anxiety.

In addition, Lucid is displaying its Lucid Air sedan, including the Air Pure and Air Touring models. All of these vehicles feature the future-ready DreamDrive Pro driver-assistance system, powered by the NVIDIA DRIVE platform.

Learn more about the industry-leading designs and technologies NVIDIA is developing with its automotive partners.

European startups will get a massive boost from a new generation of AI infrastructure, NVIDIA founder and CEO Jensen Huang said Friday in a fireside chat with iliad Group Deputy CEO Aude Durand — and it’s coming just in time.

“We’re now seeing a major second wave,” Huang said of the state of AI during a virtual appearance at Scaleway’s ai-PULSE conference in Paris for an audience of more than 1,000 in-person attendees.

Two elements are propelling this force, Huang explained in a conversation livestreamed from Station F, the world’s largest startup campus, which Huang joined via video conference from NVIDIA’s headquarters in Silicon Valley.

First, “a recognition that every region and every country needs to build their sovereign AI,” Huang said. Second, the “adoption of AI in different industries,” as generative AI spreads throughout the world, Huang explained.

“So the types of breakthroughs that we’re seeing in language I fully expect to see in digital biology and manufacturing and robotics,” Huang said, noting this could create big opportunities for Europe with its rich digital biology and healthcare industries. “And of course, Europe is also home of some of the largest industrial manufacturing companies.”

Praise for France’s AI Leadership

Durand kicked off the conversation by asking Huang about his views on the European AI ecosystem, especially in France, where the government has invested millions of euros in AI research and development.

“Europe has always been rich in AI expertise,” Huang said, noting that NVIDIA works with 4,000 startups in Europe, more than 400 of them in France alone, pointing to Mistral, Qubit Pharmaceuticals and Poolside AI.

“At the same time, you have to really get the computing infrastructure going,” Huang said. “And this is the reason why Scaleway is so important to the advancement of AI in France” and throughout Europe, Huang said.

Highlighting the critical role of data in AI’s regional growth, Huang noted companies’ increasing awareness of the value of training AI with region-specific data. AI systems need to reflect the unique cultural and industrial nuances of each region, an approach gaining traction across Europe and beyond.

NVIDIA and Scaleway: Powering Europe’s AI Revolution

Scaleway, a subsidiary of iliad Group, a major European telecoms player, is doing its part to kick-start that second wave in Europe, offering cloud credits for access to its AI supercomputer cluster, which packs 1,016 NVIDIA H100 Tensor Core GPUs.

As a regional cloud service provider, Scaleway also provides sovereign infrastructure that ensures access and compliance with EU data protection laws, which is critical for businesses with a European footprint.

Regional members of the NVIDIA Inception program, which provides development assistance to startups, will also be able to access NVIDIA AI Enterprise software on Scaleway Marketplace.

The software includes the NVIDIA NeMo framework and pretrained models for building LLMs, NVIDIA RAPIDS for accelerated data science and NVIDIA Triton Inference Server and NVIDIA TensorRT-LLM for boosting inference.

Revolutionizing AI With Supercomputing Prowess

Recapping a month packed with announcements, Huang explained how NVIDIA is rapidly advancing high performance computing and AI worldwide to provide the infrastructure needed to power this second wave.

These systems are, in effect,  “supercomputers,” Huang said, with AI systems now among the world’s most powerful.

They include Scaleway’s newly available Nabuchodonosor supercomputer, or “Nabu,” an NVIDIA DGX SuperPOD with 127 NVIDIA DGX H100 systems, which will help startups in France and across Europe scale up AI workloads.

“As you know the Scaleway system that we brought online, Nabu, is not your normal computer,” Huang said. “In every single way, it’s a supercomputer.”

Such systems are underpinning powerful new services.

Earlier this week, NVIDIA announced an AI Foundry service on Microsoft Azure, aimed at accelerating the development of customized generative AI applications.

Huang highlighted NVIDIA AI foundry’s appeal to a diverse user base, including established enterprises such as Amdocs, Getty Images, SAP and ServiceNow.

Huang noted that JUPITER, to be hosted at the Jülich facility, in Germany, and poised to be Europe’s premier exascale AI supercomputer, will run on 24,000 NVIDIA GH200 Grace Hopper Superchips, offering unparalleled computational capacity for diverse AI tasks and simulations.

Huang touched on NVIDIA’s just-announced HGX H200 AI computing platform, built on NVIDIA’s Hopper architecture and featuring the H200 Tensor Core GPU. Set for release in Q2 of 2024, it promises to redefine industry standards.

He also detailed NVIDIA’s strategy to develop ‘AI factories,’ advanced data centers that power diverse applications across industries, including electric vehicles, robotics, and generative AI services.

Open Source

Finally, Durand asked Huang about the role of open source and open science in AI.

Huang said he’s a “huge fan” of open source. “Let’s acknowledge that without open source, how would AI have made the tremendous progress it has over the last decade,” Huang said.

“And so the ability for open source to energize the vibrancy and pull in the research and pull in the engagement of every startup, every researcher, every industry is really quite vital,” Huang said. “And you’re seeing it play out just presently, now going forward.”

Friday’s fireside conversation was part of Scaleway’s ai-PULSE conference, showcasing the latest AI trends and innovations. To learn more, visit https://www.ai-pulse.eu/.

Europe’s startup ecosystem is getting a boost of accelerated computing for generative AI.

NVIDIA and cloud service provider (CSP) Scaleway are working together to deliver access to GPUs, NVIDIA AI Enterprise software, and services for turbocharging large language models (LLMs) and generative AI development for European startups.

Scaleway, a subsidiary of French telecommunications provider iliad Group, is offering cloud credits for access to its AI supercomputer cluster, which packs 1,016 NVIDIA H100 Tensor Core GPUs. As a regional CSP, Scaleway also provides sovereign infrastructure that ensures access and compliance with EU data protection laws — critical to businesses with a European footprint.

Sovereign Cloud, Generative AI 

Complying with regulations governing how data and metadata can be stored in cloud computing is critical. When doing business in Europe, U.S. companies, for example, need to comply with EU regulations on sovereignty to secure data against access from foreign adversaries or entities. Noncompliance risks data vulnerabilities, financial penalties and legal consequences.

Regional CSPs like Scaleway provide a strategic path forward for companies to do business in Europe with a sovereign infrastructure. iliad Group’s data centers, where Scaleway operates, are fortified by compliance certifications that ensure data security, covering key aspects like healthcare, public safety, governance and public service activities.

Delivering Sovereign Accelerated Computing 

NVIDIA is working with Scaleway to expand access to sovereign accelerated computing in the EU, enabling companies to deploy AI applications and scale up faster.   

Through the NVIDIA Inception program, startups already relying on the sovereign cloud computing capabilities of Scaleway’s NVIDIA-accelerated infrastructure include Hugging Face, with more to come. Inception is a free global program that provides technical guidance, training, discounts and networking opportunities.

Inception member Hugging Face, based in New York and with operations in France, creates tools and resources to help developers build, deploy and train AI models.

“AI is the new way of building technology, and making the fastest AI accelerators accessible within regional clouds is key to democratizing AI across the world, enabling enterprises and startups to build the experiences of tomorrow,” said Jeff Boudier, head of product at Hugging Face. “I’m really excited that selected French startups will be able to access NVIDIA H100 GPUs in Scaleway’s cluster through the new startup program Scaleway and Hugging Face just announced with Meta and Station F.”

H100 and NVIDIA AI to Scale 

Scaleway’s newly available Nabuchodonosor supercomputer, an NVIDIA DGX SuperPOD with 127 NVIDIA DGX H100 systems, will help startups in France and across Europe scale up AI workloads.

Regional Inception members will also be able to access NVIDIA AI Enterprise software on Scaleway Marketplace, including the NVIDIA NeMo framework and pretrained models for building LLMs, NVIDIA RAPIDS for accelerated data science, and NVIDIA Triton Inference Server and NVIDIA TensorRT-LLM for boosting inference.

NVIDIA Inception Services on Tap

NVIDIA Inception has more than 4,000 members across Europe. Member companies of Scaleway’s own startup program are eligible to join Inception for benefits and resources. Scaleway is earmarking companies to fast-track for Inception membership.

Inception members gain access to cloud computing credits, NVIDIA Deep Learning Institute courses, technology experts, preferred pricing on hardware and software, guidance on the latest software development kits and AI frameworks, as well as opportunities for matchmaking with investors.

How do you train an AI to understand clinical language with less clinical data? Train another AI to synthesize training data.

Artificial intelligence is changing the way medicine is done, and is increasingly being used in all sorts of clinical tasks.

This is fueled by generative AI and models like GatorTronGPT, a generative language model trained on the University of Florida’s HiPerGator AI supercomputer and detailed in a paper published in Nature Digital Medicine Thursday.

GatorTronGPT joins a growing number of large language models (LLMs) trained on clinical data. Researchers trained the model using the GPT-3 framework, also used by ChatGPT.

They used a massive corpus of 277 billion words for this purpose. The training corpora included 82 billion words from de-identified clinical notes and 195 billion words from various English texts.

But there’s a twist: The research team also used GatorTronGPT to generate a synthetic clinical text corpus with over 20 billion words of synthetic clinical text, with carefully prepared prompts. The synthetic clinical text focuses on clinical factors and reads just like real clinical notes written by doctors.

This synthetic data was then used to train a BERT-based model called GatorTron-S.

In a comparative evaluation, GatorTron-S exhibited remarkable performance on clinical natural language understanding tasks like clinical concept extraction and medical relation extraction, beating the records set by the original BERT-based model, GatorTron-OG, which was trained on the 82-billion-word clinical dataset.

More impressively, it was able to do so using less data.

Both GatorTron-OG and GatorTron-S models were trained on 560 NVIDIA A100 Tensor Core GPUs running NVIDIA’s Megatron-LM package on the University of Florida’s HiPerGator supercomputer. Technology from the Megatron LM framework used in the project has since been incorporated with the NVIDIA NeMo framework, which has been central to more recent work on GatorTronGPT.

Using synthetic data created by LLMs addresses several challenges. LLMs require vast amounts of data, and there’s a limited availability of quality medical data.

In addition, synthetic data allows for model training that complies with medical privacy regulations, such as HIPAA.

The work with GatorTronGPT is just the latest example of how LLMs — which exploded onto the scene last year with the rapid adoption of ChatGPT — can be tailored to assist in a growing number of fields.

It’s also an example of the advances made possible by new AI techniques powered by accelerated computing.

The GatorTronGPT effort is the latest result of an ambitious collaboration announced in 2020, when the University of Florida and NVIDIA unveiled plans to erect the world’s fastest AI supercomputer in academia.

This initiative was driven by a $50 million gift, a fusion of contributions from NVIDIA founder Chris Malachowsky and NVIDIA itself.

Using AI to train more AI is just one example of HiPerGator’s impact, with the supercomputer promising to power more innovations in medical sciences and across disciplines throughout the University of Florida system.

Human analysts can no longer effectively defend against the increasing speed and complexity of cybersecurity attacks. The amount of data is simply too large to screen manually.

Generative AI, the most transformative tool of our time, enables a kind of digital jiu jitsu. It lets companies shift the force of data that threatens to overwhelm them into a force that makes their defenses stronger.

Business leaders seem ready for the opportunity at hand. In a recent survey, CEOs said cybersecurity is one of their top three concerns, and they see generative AI as a lead technology that will deliver competitive advantages.

Generative AI brings both risks and benefits. An earlier blog outlined six steps to start the process of securing enterprise AI.

Here are three ways generative AI can bolster cybersecurity.

Begin With Developers

First, give developers a security copilot.

Everyone plays a role in security, but not everyone is a security expert. So, this is one of the most strategic places to begin.

The best place to start bolstering security is on the front end, where developers are writing software. An AI-powered assistant, trained as a security expert, can help them ensure their code follows best practices in security.

The AI software assistant can get smarter every day if it’s fed previously reviewed code. It can learn from prior work to help guide developers on best practices.

To give users a leg up, NVIDIA is creating a workflow for building such co-pilots or chatbots. This particular workflow uses components from NVIDIA NeMo, a framework for building and customizing large language models.

Whether users customize their own models or use a commercial service, a security assistant is just the first step in applying generative AI to cybersecurity.

An Agent to Analyze Vulnerabilities

Second, let generative AI help navigate the sea of known software vulnerabilities.

At any moment, companies must choose among thousands of patches to mitigate known exploits. That’s because every piece of code can have roots in dozens if not thousands of different software branches and open-source projects.

An LLM focused on vulnerability analysis can help prioritize which patches a company should implement first. It’s a particularly powerful security assistant because it reads all the software libraries a company uses as well as its policies on the features and APIs it supports.

To test this concept, NVIDIA built a pipeline to analyze software containers for vulnerabilities. The agent identified areas that needed patching with high accuracy, speeding the work of human analysts up to 4x.

The takeaway is clear. It’s time to enlist generative AI as a first responder in vulnerability analysis.

Fill the Data Gap

Finally, use LLMs to help fill the growing data gap in cybersecurity.

Users rarely share information about data breaches because they’re so sensitive. That makes it difficult to anticipate exploits.

Enter LLMs. Generative AI models can create synthetic data to simulate never-before-seen attack patterns. Such synthetic data can also fill gaps in training data so machine-learning systems learn how to defend against exploits before they happen.

Staging Safe Simulations

Don’t wait for attackers to demonstrate what’s possible. Create safe simulations to learn how they might try to penetrate corporate defenses.

This kind of proactive defense is the hallmark of a strong security program. Adversaries are already using generative AI in their attacks. It’s time users harness this powerful technology for cybersecurity defense.

To show what’s possible, another AI workflow uses generative AI to defend against spear phishing — the carefully targeted bogus emails that cost companies an estimated $2.4 billion in 2021 alone.

This workflow generated synthetic emails to make sure it had plenty of good examples of spear phishing messages. The AI model trained on that data learned to understand the intent of incoming emails through natural language processing capabilities in NVIDIA Morpheus, a framework for AI-powered cybersecurity.

The resulting model caught 21% more spear phishing emails than existing tools. Check out our developer blog or watch the video below to learn more.

Wherever users choose to start this work, automation is crucial, given the shortage of cybersecurity experts and the thousands upon thousands of users and use cases that companies need to protect.

These three tools — software assistants, virtual vulnerability analysts and synthetic data simulations — are great starting points for applying generative AI to a security journey that continues every day.

But this is just the beginning. Companies need to integrate generative AI into all layers of their defenses.

Attend a webinar for more details on how to get started.

Editor’s note: This post is part of Into the Omniverse, a series focused on how artists, developers and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse.

In 3D art and design, efficient workflows are essential for quickly bringing creative visions to life.

Universal Scene Description, aka OpenUSD, is a framework that enhances these workflows by providing a unified, extensible ecosystem for describing, composing, simulating and collaborating within 3D worlds. OpenUSD is a key technology in Autodesk’s suite of products and solutions, across media and entertainment; architecture, engineering and construction; and product design and manufacturing.

Unveiled at the AU 2023 conference this week, the latest OpenUSD updates to Autodesk Maya enable artists and technical professionals to create and manipulate OpenUSD assets with greater control and efficiency, while also ensuring more efficient and accurate 3D workflows.

Bridging the Digital and Real Worlds With Maya and OpenUSD

Many creators are using Maya and OpenUSD to propel their 3D workflows.

Karol Osinski is a 3D artist at S20M, an architectural and design firm that specializes in tackling unique, bold and elegant projects. When it comes to creating architectural visualizations, Osinski says the biggest challenge is matching the digital world to the real one.

Using USD and creative tools such as Maya, SideFX Houdini and Epic Games’ Unreal Engine, Osinski creates high-quality visuals for clients while accelerating his architectural workflows.

Osinski’s panoramic view from the 20th floor terrace in the Upper East Side

“OpenUSD provides the possibility of bridging different tools like never before,” said Osinski. “I love how accessible USD is for first-time users and how it opens opportunities to make designs very complex.”

“Sir Wade” Neistadt, an animator and YouTube creator, aims to make animation and 3D education more accessible through his video tutorials and industry training. The first step of his unique animation workflow is to act out his animations on camera. He then translates them in Maya to begin his animation work before using USD to export them to other 3D software, including Blender, for finishing touches.

The making of Sir Wade’s VFX robot animation

3D artists at NVIDIA are also experiencing the power of Maya and OpenUSD. Technical specialist Lee Fraser led the “Ferret-Tale Project” to showcase character creation and animation workflows enabled by OpenUSD and generative AI.

To create the demo, Fraser and his team collaborated across 3D applications like Blender, Autodesk Maya and Reallusion Character Creator through OpenUSD Connectors. This allowed them to reduce the data prep and import and export time that’s usually required when working with multiple data sources.

“My favorite thing about using OpenUSD is not having to think about where the 3D files I use originated from,” Fraser said. “It was also easy to use USD layers to experiment with applying different animation clips with different characters.”

Members of the creative community joined a recent livestream to share their workflows using Autodesk tools, OpenUSD and NVIDIA Omniverse, a development platform for connecting and building OpenUSD-based tools and applications.

Whether adjusting lighting conditions in an environment or looking at building designs from the street view, designers in architecture, engineering, construction and operations are advancing their work with AI. Learn more by watching the replay:

Shaping the Future of 3D With More Efficient Workflows

AU 2023 attendees experienced how Autodesk is enhancing Maya with its new OpenUSD plug-in to provide additional practical workflows for various production processes. The software’s latest features include:

• Simplified asset sharing: Designers can now use relative paths when creating OpenUSD stages, allowing for easy asset sharing between different systems. This includes support for sublayers, references, payloads and textures.
• Enhanced control: Plug-in developers and technical directors can overwrite the default prim writers in Maya USD to gain complete control over their OpenUSD exports.

Plus, Autodesk introduced impressive capabilities to LookdevX in Maya, a look-development tool that lets users create OpenUSD shade graphs and custom materials in Maya. These new features include:

• Streamlined shader creation: Users can employ a unified shader workflow, replacing the need for multiple shaders. They can select their desired shader type within the parameters panel, with intuitive error messages guiding them to the correct selection.
• Efficient operations: Creators can copy, paste and duplicate shaders and materials using the Outliner and LookdevX tool sets, with the option to include or exclude connections.
• Seamless color management: LookdevX in Maya integrates with color managers in other digital content creation apps to ensure accurate color representation. Color management data is precisely embedded in USD files for accurate reading.
• Advanced graphing: Users can explore advanced graphing options with the integrated component workflow, supporting multichannel Extended Dynamic Range (EXR) workflows within USD, MaterialX or Arnold shading graphs.
• Efficient troubleshooting: Solo nodes enable faster look-development workflows and efficient graph troubleshooting. Users can inspect renders of upstream nodes, supporting both Autodesk Arnold and MaterialX graphs, including materials, shaders and compounds.

Access to default prim options in Maya UI

Get Plugged Into the World of OpenUSD

Anyone can build their own Omniverse extension or Connector to enhance their 3D workflows and tools. Explore the Omniverse ecosystem’s growing catalog of connections, extensions, foundation applications and third-party tools.

Autodesk and NVIDIA are founding members of the Alliance for OpenUSD (AOUSD), together strengthening an open future with USD. To learn more, explore the AOUSD forum and check out resources on OpenUSD.

Share your Autodesk Maya and Omniverse work through November as part of the #SeasonalArtChallenge. Use the hashtag to submit an autumn harvest-themed scene for a chance to be featured on the @NVIDIAStudio and @NVIDIAOmniverse social channels.

Get started with NVIDIA Omniverse by downloading the standard license free, or learn how Omniverse Enterprise can connect your team. 

Developers can check out these Omniverse resources to begin building on the platform. 

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