NVIDIA GTC21 AI Conference Highlights

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NVIDIA GTC21
AI Conference Highlights
Alex G. Lee

©2021 TechIPm, LLC All Rights Reserved
Selected Topics
Modern Artificial Intelligence 1980s-2021 and Beyond
A Vision for the Next Decade of Computing
The Next Decade in AI: Four Steps Toward Robust Artificial Intelligence
Keras and TensorFlow: The Next Five Years
A Vision for the Future of ML Frameworks
AI Implementation at Scale: Lessons from the Front Lines
A Future with Self-Driving Vehicles
Advances in Renewable Energy: Enabling Our Decarbonized Energy Future with Technology Innovations and Smart Operations
Accelerating Health Care at Bayer with Science@Scale and Federated Learning
Large-Scale Deep Learning Recommendation Models at Facebook
Is AI at the Edge the Killer App for 5G?
Deep Learning for Anomaly Detection
From Storytelling to StoryLiving: A Vision for the Future of Immersive Entertainment
A New Era in Virtual Cinematography
Digital Transformation Is Here: Augmenting Human Capacity with Exponential Compute
Rethinking Drug Discovery in the Era of Digital Biology
Representation Learning for Autonomous Robots
Architecting the Secure Accelerated Data Center of the Future
Convergence of AI and HPC to Solve Grand Challenge Science Problems
Presenting US HHS Artificial Intelligence Strategy 2021: AI Mission and Ambition Commentary by the CAIO

Modern Artificial Intelligence 1980s-2021 and Beyond
Significant historic events appear to be occurring more frequently as time goes on. Interestingly, it
seems like subsequent intervals between these events are shrinking exponentially by a factor of
four. This process looks like it should converge around the year 2040. The last of these major
events can be said to have occurred around 1990 when the cold war ended, the WWW was born,
mobile phones became mainstream, the first self-driving cars appeared, and modern AI with very
deep neural networks came into being. In this talk, I'll focus on the latter, with emphasis on
Metalearning since 1987 and what I call "the miraculous year of deep learning" which saw the birth
of—among other things—(1) very deep learning through unsupervised pre-training, (2) the
vanishing gradient analysis that led to the LSTMs running on your smartphones and to the really
deep Highway Nets/ResNets, (3) neural fast weight programmers that are formally equivalent to
what’s now called linear Transformers, (4) artificial curiosity for agents that invent their own
problems (familiar to many nowadays in the form of GANs), (5) the learning of sequential neural
attention, (6) the distilling of teacher nets into student nets, and (7) reinforcement learning and
planning with recurrent world models. I’ll discuss how in the 2000s much of this has begun to
impact billions of human lives, how the timeline predicts the next big event to be around 2030, what
the final decade until convergence might hold, and what will happen in the subsequent 40 billion
years. Take all of this with a grain of salt though.

A Vision for the Next Decade of Computing
AI, 5G, and the internet of things are sparking the world’s potential. And for many hardware
engineers and software developers, these technologies will also become the challenge of their
careers. The question is how to invisibly integrate the new intelligence everywhere by creating more
responsive infrastructure that links people, processes, devices, and data seamlessly. Getting there
will require architectural leaps, new partnerships, and plenty of creativity. Arm President Rene Haas
will discuss the forces pushing these advances and how Arm’s global developer ecosystem will
react to drive the next wave of compute.

The Next Decade in AI: Four Steps Toward Robust Artificial Intelligence
Recent research in artificial intelligence and machine learning has largely emphasized general-
purpose learning and ever-larger training sets and more and more compute. In contrast, I propose
a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that
could provide the substrate for a richer, more robust AI than is currently possible.

Keras and TensorFlow: The Next Five Years
The field of deep learning is still evolving rapidly, and increasingly large numbers of developers are
starting to leverage deep learning in their applications across an ever-expanding set of use cases.
Learn about the latest developments in the Keras and TensorFlow ecosystem, and you'll find out
how Google is preparing for the next generation of deep learning research and applications.

A Vision for the Future of ML Frameworks
Machine learning frameworks have gone through various transitions over the years. The
"mainstream" ML framework has changed every five years. We'll recap ML frameworks through the
lens of a few variables and factors, and then project the future for ML frameworks as a distribution
over these variables. Finally, we'll float a few ideas of how the future might look like, based on
some general bets.

AI Implementation at Scale: Lessons from the Front Lines
AI can transform products, customer experiences, and entire business models. That’s why the
trend toward AI adoption is on an accelerated path, according to Gartner. By the end of 2024, 75
percent of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in
streaming data and analytics infrastructures. But, getting AI off your roadmap and into your
products with speed, scale and rich customer value is no small feat. Even the best AI strategy can
be derailed by complexity, weak business cases, missing or disparate data, competition for talent,
cost, and ROI considerations. Entrusted with the financial data of millions of customers around the
world, Intuit has been in a unique position to take advantage of AI to help solve some of the biggest
financial pain points for small businesses, consumers and self-employed individuals – especially
throughout the global pandemic and economic crisis.

A Future with Self-Driving Vehicles
We're on the verge of a new era, in which robotics and artificial intelligence will play an important
role in our daily lives. Self-driving vehicles have the potential to redefine transportation as we
understand it today. Our roads will become safer and less congested, while parking spots will be
repurposed as leisure zones and parks. However, many technological challenges remain as we
pursue this future. We'll showcase our latest advances in the quest toward self-driving vehicles at
scale.

Advances in Renewable Energy: Enabling Our Decarbonized Energy Future with Technology
Innovations and Smart Operations
The cost of renewable energy has fallen sharply in the last decade, reaching all-time lows. Key
drivers of this trend have included increased manufacturing scale and improved system efficiency.
As the industry continues to advance, technologies that enable higher penetration on the grid will
become even more critical to progress. We'll share a vision for the future of energy and explore the
key digital and physical technology advances needed to drive continued growth. We'll cover
examples of how using abundance of data, HPC, and AI algorithms married with physics are
transforming the way we design, manufacture, and operate mission-critical systems that lead to
better reliability, predictability, automation, and efficiency for our operations and help us achieve our
carbon-neutrality goals.

Accelerating Health Care at Bayer with Science@Scale and Federated Learning
Bayer is a large pharmaceutical company with 150+ years of history. We'll present the effort to
transform a traditional pharmaceutical business to a digital player, looking at a cloud strategy and
federated learning. Learn how Bayer is using GPUs both in cloud and on-premises to maximize the
performance of scientific discovery.

Large-Scale Deep Learning Recommendation Models at Facebook
Learn about the efforts and challenges of using GPUs for training large-scale recommendation
models at Facebook. The use of GPUs has proliferated for machine learning workflows and is now
considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art
personal recommendation models, which consume the highest number of compute cycles at our
large-scale datacenters, the use of GPUs came with various challenges due to having both
compute-intensive and memory-intensive components. GPU performance and efficiency of these
recommendation models are largely affected by model architecture configurations such as dense
and sparse features, MLP dimensions. Furthermore, these models often contain large embedding
tables that do not fit into limited GPU memory. The goal of this talk is to explain the intricacies of
using GPUs for training recommendation models, factors affecting hardware efficiency at scale.

Is AI at the Edge the Killer App for 5G?
There's massive investment going into the "edge" in the 5G era. IDC reports that the share of new
enterprise IT infrastructure at the edge will reach 50% in 2023, up from a paltry 10% in 2020. For
the telecoms industry, this development illustrates the growing demand for computational resources
at the edge to power two AI use cases: first, for physical layer L1 processing, channel estimation for
Cloud RAN and to boost overall network capacity utilization; second, to support analytics for smart
cities, security systems, retail intelligence, and industrial efficiency applications. Across industries,
AI is enabling automation, data analysis, and efficiency gains at a precision, speed, and scale not
previously possible. Hear our panel of senior industry leaders explore how AI and edge will
interplay with 5G networks.

Deep Learning for Anomaly Detection
Anomaly detection methods are broadly used to identify opportunities and risks across many
industries. Timely, accurate methods are required in order to produce actionable predictions that
don't simply add to an already noisy data environment. We'll survey current anomaly detection
methods applied in time-sensitive, data-intensive, financial services use cases, and briefly
extrapolate to use cases in other industries before showing an example of a deep neural network
implemented with PyTorch and CUDA. Anomaly detection comprises a number of mathematical
techniques for distinguishing significant events that stand out from a statistically mundane
background of peer events. These significant events can represent opportunities to pursue or risks
to mitigate. Used effectively, anomaly detection supports more effective customer engagement, risk
management, fraud avoidance, cybersecure networks, operational reliability, and a host of other
applications. However, there are significant challenges to that effective use. You should be
generally familiar with the technical challenges and benefits of anomaly detection. You don't need
familiarity with DL, PyTorch, or DNN.

From Storytelling to StoryLiving: A Vision for the Future of Immersive Entertainment
Spark your curiosity and imagination in this visionary talk by Vicki Dobbs Beck, executive in charge
of Lucasfilm’s ILMxLAB. Learn why many believe XR to be the greatest disruptor of entertainment
in our generation. The transition from storytelling to "storyliving" will be supported by examples from
Star Wars — one of the richest and deepest fictional universes ever brought to life. Vicki will share
insights from ILMxLAB’s internal experiments as well as polished experiences like the award-
winning "Vader Immortal — A Star Wars VR Series" and the recently launched "Star Wars: Tales
from The Galaxy’s Edge." You’ll also get a glimpse into how ILMxLAB sees the future and their
approach to bringing that vision to life.
A New Era in Virtual Cinematography
Three-time Academy Award-winning VFX Supervisor Rob Legato has continuously reinvented
cinematography — from blurring the lines between live action and photorealistic CG worlds to
groundbreaking new techniques in virtual production. Learn from one of the masters of movie-
making as he shares insights on technical innovation and the rapidly evolving process of
filmmaking. Rob will also discuss some of the technological and creative breakthroughs that are
enabling all-new film production pipelines, and he’ll provide a view into the future of entertainment.

Digital Transformation Is Here: Augmenting Human Capacity with Exponential Compute
Join Amy Bunszel, Autodesk executive vice president of Architecture, Engineering, and
Construction Design Solutions, to learn how computing power is enhancing the way we work and
preparing us for a more resilient future. Technology companies like Autodesk and NVIDIA
understand the importance of digital transformation and have been equipping customers and
industries for this evolution. With COVID-19 rapidly accelerating the move to remote work, data-
driven platforms went from a “nice-to-have” to a “must-have,” and many businesses have rapidly
adjusted to new working models that take advantage of this digital transformation.
Rethinking Drug Discovery in the Era of Digital Biology
Featuring Daphne Koller of Insitro, Coursera and Stanford. Modern medicine can now treat some of
the most burdensome diseases. At the same time, developing new therapeutics is becoming
increasingly harder, because the drug development process involves multiple long and complex
steps that often ultimately fail. At insitro, we aim to combine high-throughput data production with
machine learning models to help predict the outcome of these experiments. We're bringing together
high-quality data from human cohorts, while also developing cutting-edge methods in high-
throughput biology and chemistry that can produce massive amounts of in vitro data relevant to
human disease and therapeutic interventions. Those are then used to train machine learning
models that make predictions about novel targets, coherent patient segments, and the clinical effect
of molecules. Our goal is to develop a new approach to drug development that uses high-quality
data and ML models to design transformative therapies that help more people, more quickly, and at
a lower cost.

Representation Learning for Autonomous Robots
Autonomous robots need to be efficient and agile, and be able to handle a wide range of tasks and
environmental conditions. This requires the ability to learn good representations of domains and
tasks using a variety of sources, such as demonstrations and simulations. Representation learning
for robotic tasks needs to be generalizable and robust. I will describe some key ingredients to
enable this: (1) robust self-supervised learning; (2) uncertainty awareness; and (3) compositionality.
We utilize NVIDIA Isaac for GPU-accelerated robot learning at scale on a variety of tasks and
domains.
Architecting the Secure Accelerated Data Center of the Future
AI is transforming businesses and organizations of every kind. Accelerated computing is critical to
harnessing the power of AI, and requires new thinking about server and data center architecture.
Join NVIDIA CTO Michael Kagan for a featured talk that will cover design considerations to achieve
optimal scale and performance from your data centers. The combination of CPU, GPU, and DPUs
is critical to realizing optimal accelerated data centers. This talk will cover design trends including
networking, security, and balancing compute resources within servers and across data centers of
the future.

Convergence of AI and HPC to Solve Grand Challenge Science Problems
The response to the COVID-19 pandemic poses a Grand Challenge Science problem with
immediate impact for global health and well-being. Multiple teams used the fastest supercomputers
in the world and applied the latest methods of computational simulation and artificial intelligence to
build a foundation that can help us respond to this pandemic, as well as prepare for mutations and
future viral pathogens. We'll discuss the award-winning work that was recognized at the recent
SC20 event with the Gordon Bell for COVID competition, and provide insight into how the methods
were applied, and their future plans.

Presenting US HHS Artificial Intelligence Strategy 2021: AI Mission and Ambition Commentary by
the CAIO
The U.S. Department of Health and Human Services' chief AI officer presents his recently-
published AI strategy for HHS, including the mission and overall planning for AI goals.

Thank you!

NVIDIA GTC21 AI Conference Highlights

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