Nvidia showed off its technology in Washington, D.C. today at its AI Summit to help educate the nation’s capital.

The world’s biggest maker of AI chips made seven big announcements at the summit, and we’ll summarize them here. First, it is teaming with U.S. tech leaders to help organizations create custom AI
applications and transform the world’s industries using the latest Nvidia NIM Agent Blueprints and Nvidia NeMo and Nvidia NIM microservices.

Across industries, organizations like AT&T, Lowe’s and the University of Florida are using the microservices to create their own data-driven AI flywheels to power custom generative AI applications.

U.S. technology consulting leaders Accenture, Deloitte, Quantiphi and SoftServe are adopting Nvidia NIM Agent Blueprints and Nvidia NeMo and NIM microservices to help clients in healthcare, manufacturing, telecommunications, financial services and retail create custom generative AI agents and copilots.

Data and AI platform leaders Cadence, Cloudera, DataStax, Google Cloud, NetApp, SAP, ServiceNow and Teradata are advancing their data and AI platforms with Nvidia NIM.

“AI is driving transformation and shaping the future of global industries,” said Jensen Huang, CEO of Nvidia, in a statement. “In collaboration with U.S. companies, universities and government agencies, Nvidia will help advance AI adoption to boost productivity and drive economic growth.”

New NeMo microservices — NeMo Customizer, NeMo Evaluator and NeMo Guardrails — can be paired with NIM microservices to help developers easily curate data at scale, customize and evaluate models, and manage responses to align with business objectives. Developers can then seamlessly deploy a custom NIM microservice across any GPU-accelerated cloud, data center or workstation.

Lowe’s, a home improvement company, is exploring the use of Nvidia NIM and NeMo microservices to improve experiences for associates and customers and enhance productivity of their store associates. For
example, the retailer is leveraging Nvidia NeMo Guardrails to enhance the safety and security of its generative AI solution platform.

SETI Institute researchers are also using Nvidia tech to conduct the first real-time AI search for fast radio bursts that might be a sign of life somewhere else. To better understand new and rare astronomical phenomena, radio astronomers are adopting accelerated computing and AI on Nvidia Holoscan and IGX platforms.

This summer, scientists supercharged their tools in the hunt for signs of life beyond Earth. Researchers at the SETI Institute became the first to apply AI to the real-time direct detection of faint radio signals from space. Their advances in radio astronomy are available for any field that applies accelerated computing and AI.

“We’re on the cusp of a fundamentally different way of analyzing streaming astronomical data, and the kinds of things we’ll be able to discover with it will be quite amazing,” said Andrew Siemion, Bernard M. Oliver Chair for SETI at the SETI Institute, a group formed in 1984 that now includes more than 120 scientists.

The SETI Institute operates the Allen Telescope Array (pictured above) in Northern California. It’s a cutting-edge telescope used in the search for extraterrestrial intelligence (SETI) as well as for the study of intriguing transient astronomical events such as fast radio bursts. The project started more than a decade ago, during early attempts to marry machine learning and astronomy.

Pittsburgh trades steel for AI tech

Carnegie Mellon University and the University of Pittsburgh will accelerate innovation and public-private collaboration through a pair of joint technology centers with Nvidia.

Serving as a bridge for academia, industry and public-sector groups to partner on artificial intelligence innovation, Nvidia is launching its inaugural AI Tech Community in Pittsburgh, Pennsylvania.

Collaborations with Carnegie Mellon University and the University of Pittsburgh, as well as startups, enterprises and organizations based in the “city of bridges,” are part of the new Nvidia AI Tech Community initiative, announced today during the Nvidia AI Summit in Washington, D.C.

The initiative aims to supercharge public-private partnerships across communities rich with potential for enabling technological transformation using AI. Two Nvidia joint technology centers will be established in Pittsburgh to tap into expertise in the region.

Nvidia’s Joint Center with Carnegie Mellon University (CMU) for Robotics, Autonomy and AI will equip higher-education faculty, students and researchers with the latest technologies and boost innovation in the fields of AI and robotics. And Nvidia’s Joint Center with the University of Pittsburgh for AI and Intelligent Systems will focus on computational opportunities across the health sciences, including applications of AI in clinical medicine and biomanufacturing.

CMU — the nation’s No. 1 AI university according to the U.S. News & World Report — has pioneered work in autonomous vehicles and natural language processing. CMU’s Robotics Institute, the world’s largest university-affiliated robotics research group, brings a diverse group of more than a thousand faculty, staff, students, post-doctoral fellows and visitors together to solve humanity’s toughest challenges through robotics.

The University of Pittsburgh — designated as an R1 research university at the forefront of innovation — is ranked No. 6 among U.S. universities in research funding from the National Institutes of Health, topping more than $1 billion in research expenditures in fiscal year 2022 and ranking No. 14 among U.S. universities granted utility patents. Nvidia will provide the centers with DGX for AI training, Omniverse for simulation and Jetson for robotics edge computing.

U.S. healthcare system deploys AI agents for research to rounds

Nvidia also said the U.S. healthcare system is adopting digital health agents to harness AI across the board, from research laboratories to clinical settings.

The latest AI-accelerated tools — on display at the Nvidia AI Summit taking place this week in Washington, D.C. — include Nvidia NIM, a collection of cloud-native microservices that support AI model deployment and execution, and Nvidia NIM Agent Blueprints, a catalog of pretrained, customizable workflows.

These technologies are already in use in the public sector to advance the analysis of medical images, aid the search for new therapeutics and extract information from massive PDF databases containing text, tables and graphs.

For example, researchers at the National Cancer Institute, part of the National Institutes of Health (NIH), are using several AI models built with Nvidia MonAI for medical imaging — including the Vista-3D NIM foundation model for segmenting and annotating 3D CT images. A team at NIH’s National Center for Advancing Translational Sciences (NCATS) is using the NIM Agent Blueprint for generative AI-based virtual screening to reduce the time and cost of developing novel drug molecules.

With the Nvidia tech, medical researchers across the public sector can jump-start their adoption of state-of-the-art, optimized AI models to accelerate their work. The pretrained models are customizable based on an organization’s own data and can be continually refined based on user feedback.

Massive quantities of healthcare data — including research papers, radiology reports and patient records — are unstructured and locked in PDF documents, making it difficult for researchers to quickly search for information.

The Genetic and Rare Diseases Information Center, also run by NCATS, is exploring using the PDF data extraction blueprint to develop generative AI tools that enhance the center’s ability to glean information from previously unsearchable databases. These tools will help answer questions from those affected by rare diseases.

Nvidia leaders, customers and partners are presenting over 50 sessions highlighting impactful work in the public sector.

Nvidia’s blueprint for cybersecurity

And Nvidia said Deloitte has adopted Nvidia NIM Agent Blueprint for container security to help enterprises build safe AI using open-source software.

AI is transforming cybersecurity with new generative AI tools and capabilities that were once the stuff of science fiction. And like many of the heroes in science fiction, they’re arriving just in time.

AI-enhanced cybersecurity can detect and respond to potential threats in real time — often before human analysts even become aware of them. It can analyze vast amounts of data to identify patterns and anomalies that might indicate a breach. And AI agents can automate routine security tasks, freeing up human experts to focus on more complex challenges.

All of these capabilities start with software, so Nvidia has introduced an Nvidia NIM Agent Blueprint for container security that developers can adapt to meet their own application requirements.

The blueprint uses Nvidia NIM microservices, the Nvidia Morpheus cybersecurity AI framework, Nvidia cuVS and Nvidia Rapids accelerated data analytics to help accelerate analysis of common vulnerabilities and exposures (CVEs) at enterprise scale — from days to just seconds.

All of this is included in Nvidia AI Enterprise, a cloud-native software platform for developing and deploying secure, supported production AI applications.

Deloitte is among the first to use the Nvidia NIM Agent Blueprint for container security in its cybersecurity solutions, which supports agentic analysis of open-source software to help enterprises build secure AI. It can help enterprises enhance and simplify cybersecurity by improving efficiency and reducing the time needed to identify threats and potential adversarial activity.

Software containers incorporate large numbers of packages and releases, some of which may be subject to security vulnerabilities. Traditionally, security analysts would need to review each of these packages to understand potential security exploits across any software deployment. These manual processes are tedious, time-consuming and error-prone. They’re also difficult to automate effectively because of the complexity of aligning software packages, dependencies, configurations and the operating environment.

With generative AI, cybersecurity applications can rapidly digest and decipher information across a wide range of data sources, including natural language, to better understand the context in which potential vulnerabilities could be exploited.

Enterprises can then create cybersecurity AI agents that take action on this generative AI intelligence. The NIM Agent Blueprint for container security enables quick, automatic and actionable CVE risk analysis using large language models and retrieval-augmented generation for agentic AI applications. It helps developers and security teams protect software with AI to enhance accuracy, efficiency and streamline potential issues for human agents to investigate.

CUDA-X accelerates Polars data processing library for faster AI development for data scientists

Nvidia also said Polars, one of the fastest growing data analytics tools, has just crossed 9 million monthly downloads. As a modern DataFrame library, it is designed for efficiently processing datasets that fit on a single machine, without the overhead and complexity of distributed computing systems that are required for massive-scale workloads.

As enterprises grapple with complex data problems — ranging from detecting time-boxed patterns in credit card transactions to managing quickly shifting inventory needs across a global customer base — even higher performance is essential.

Polars and Nvidia engineers just released the Polars GPU engine powered by Rapids cuDF in open beta, bringing accelerated computing to the growing Polars community with zero code change required. This brings even more acceleration to the query execution for Polars — making this speedy data processing software up to 13x faster, compared to running on CPUs. It’s like giving rocket fuel to a cheetah to help it sprint even faster.

With data science and engineering teams building more and more data processing pipelines to fuel AI applications, it’s critical to choose the right software and infrastructure for the job to keep things running smoothly. For workloads well suited to individual servers, workstations and laptops, developers frequently use libraries like Polars to accelerate iterations, reduce complexity in development environments and lower infrastructure costs.

On these single machine-sized workloads, quick iteration time is often the top priority, as data scientists often need to do exploratory analysis to guide downstream model training or decision-making. Performance bottlenecks from CPU-only computing reduce productivity and can limit the number of test/train cycles that can be completed.

For large-scale data processing workloads too large for a single machine, organizations turn to frameworks like Apache Spark to help them distribute the work across nodes in the data center. At this scale, cost- and power-efficiency are often the top priorities, but costs can quickly balloon due to the inefficiencies of using traditional CPU-based computing infrastructure.

Nvidia’s CUDA-X data processing platform is designed with these needs in mind, optimized for cost- and energy-efficiency for large-scale workloads and performance for single-machine sized workloads.

[Updated: 8:33 a.m. on 10/8/24: Nvidia noted it has not been subpoenaed in an antitrust case in D.C.]