Please tell us about your role and the team/technology at NVIDIA.
I’m on the Enterprise Computing team, which is a newer part of the business at NVIDIA. As AI continues to grow, our team is focused on helping customers accelerate data analytics, machine learning, and deep learning, and to bring this accelerated computing closer to the Edge.
In addition to accelerating computing with GPUs, NVIDIA does extensive research on optimizing software for AI.
We provide many of these tools to developers through SDKs like the NVIDIA Merlin deep recommender system and the NVIDIA Jarvis conversational AI framework.
Additionally, open source RAPIDS data science libraries, and NGC containers, models and helm charts are available to help customers leverage all of our latest software and research on accelerated AI.
We’re continuing to expand into new applications for machine learning and analytics, and working to bring our technology to an even broader audience across the enterprise.
How is NVIDIA transforming the way AI and Data Science is taught in colleges?
NVIDIA empowers and collaborates with data science professors and AI researchers at universities worldwide.
We’re focused on inspiring technological innovation, enhancing faculty research, and improving the teaching and learning experience. From partnering with leading educators on the development of AI Teaching Kits, to supporting graduate fellowships, awarding compute grants, and more, we work closely with schools worldwide.
NVIDIA and UF Infuse $70 Million (and Much More) to Build an AI Supercomputer for Higher Education
We also support learning through the NVIDIA Deep Learning Institute, which provides AI classes, workshops, and training for students and educators. We contribute broadly to open source projects, host hackathons, and participate in communities like Kaggle to support those who are learning through competitions.
We also recently announced that we’re working with the University of Florida to help UF make AI the nucleus of their curriculum. UF will be deploying an NVIDIA DGX SuperPOD of 140 DGX A100 systems to power AI research and integrate AI and data science across all fields of study.
Remote workplace and Virtualization trends are moving hand-on-hand. How is NVIDIA’s GPU technology proving to be a key component in ensuring a smooth transition to “the new normal” of remote workplace management?
All over the world, companies are supporting their remote workers through virtualization. Whether IT departments are supporting knowledge workers, graphics professionals, data scientists or AI researchers, NVIDIA virtualization software ensures that employees logging in from home have a great user experience, while also making sure that sensitive data is kept safe.
NVIDIA virtual GPU (vGPU) software is installed on a physical GPU in a server running in the enterprise data center or the cloud. It creates virtual GPUs that can be shared and allocated between multiple virtual machines. IT can then create software-defined GPU acceleration for any workflow, on any device and any location.
In addition to virtualization, some companies are providing remote access to their on-site workstations and servers, which is another great option for providing application acceleration to remote workers. And of course, some companies have chosen to provide their employees with powerful NVIDIA GPU-accelerated laptops and workstations to help them continue to stay productive.
It’s not only our accelerated computing technology that is helping IT keep workers productive. NVIDIA Mellanox networking solutions are enabling remote workplaces by providing faster access to critical data and more secure networking.
How have NVIDIA product development roadmaps evolved with the maturity of AI and Automated Machine Learning models?
AI is maturing… but, it is still in very early stages. Even with all the progress made so far, we’re just scratching the surface of what’s to come.
In the future, all businesses will be driven by data.
AI will continue to scale to enable this shift. For example, most of us are now shopping online more than ever before.
With everyone staying at home, online shopping has actually experienced about ten years of growth in mere weeks. AI-powered recommender systems help us find what we’re looking for online, whether we’re hunting for goods, news, entertainment, or even friends.
We’re investing in how to help businesses develop their pipelines for delivering AI services. From the edge, to the data center, to the cloud, we’re accelerating it all with infrastructure like our A100 GPUs, NVIDIA Mellanox networking, software development kits, libraries – it’s an extensive ecosystem, all built to accelerate AI.
As we look forward, we’re expanding our focus on enabling speedups in data preparation, which is related to our work in bringing GPU acceleration to Apache Spark 3.0, the world’s leading analytics platform. We’ll continue to optimize applications and infrastructure across the data pipeline to help our customers transform their businesses with AI.
Hear it from the pro: How do modern security and privacy measures impact Enterprise-grade App platform development?
It’s critically important to provide security from Core to Edge to Cloud.
NVIDIA Mellanox has recently introduced powerful new products that ensure security across the network and inside the data center without impacting application performance.
For example, the recently-launched NVIDIA Mellanox ConnectX-6 Lx SmartNIC is a highly secure and efficient 25/50 gigabit per second (Gb/s) Ethernet smart network interface controller (SmartNIC) built to meet surging growth in enterprise and cloud scale-out workloads. It provides accelerated security features like IPsec in-line cryptography and Hardware Root of Trust, and a 10x performance improvement for connection tracking to enable Zero Trust security throughout the data center. The NVIDIA Mellanox ConnectX-6 Dx also adds in-line Transport Layer Security (TLS) cryptography which is used extensively for securing web applications.
We also recently launched the NVIDIA Mellanox UFM Cyber-AI platform, which minimizes downtime in InfiniBand-powered data centers by harnessing AI-powered analytics to detect security threats and operational issues, as well as predict network failures.
What is the Future of Digital Transformation? Which technologies are you particularly keen to explore and adopt for your businesses?
AI is the future of digital transformation. Data centers will transform to support it. AI is now driving an architecture change from Hyper-Converged Infrastructures to Accelerated-Disaggregated Infrastructure.
This is an existential shift. Businesses will leverage data to become more automated and intelligent, or they might not exist in the future.
AI isn’t just about gaining a competitive advantage today. It’s about operating at the speed and level of efficiency that will be critical for companies to exist and thrive.
We’re conducting extensive research in AI. Across autonomous driving, conversational AI, recommender systems, smart cities, and intelligent healthcare, we’re working to help leaders all over the world create the AI breakthroughs that we’ll all take for granted tomorrow.
Tag a person whose answers you would like to read here:
I’m interested in what’s happening out in the world where people are using NVIDIA technology to change their business.
For example, Dominos has been adding AI and machine learning to optimize pizza orders and production. This year, they improved order prediction accuracy from 75% to 95%, helping hungry customers know exactly when their pizza will be ready. Zack Fragoso, a data science and AI manager at Dominos, is doing really interesting work.
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Thank you, Erik! That was fun and we hope to see you back on AiThority.com soon.
Erik Pounds is head of product marketing, Enterprise Computing, at NVIDIA, where he’s focused on data science and edge computing platforms.
Prior to NVIDIA, Erik led marketing at SwiftStack, and he has held numerous leadership roles in product management throughout his career at companies including EMC, Brocade, Drobo and BitTorrent. Erik holds a Bachelor of Science in business administration from the University of San Francisco.
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world.
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