The AI Podcast

Jeff Herbst is a fixture of the AI startup ecosystem. Which makes sense since he’s the VP of business development at NVIDIA and head of NVIDIA Inception, a virtual accelerator that currently has over 6,000 members in a wide range of industries. Ahead of the GPU Technology Conference, taking place Oct. 5-9, Herbst joined AI Podcast host Noah Kravitz to talk about what opportunities are available to startups at the conference, and how NVIDIA Inception is accelerating startups in every industry. Learn more about NVIDIA Inception at https://www.nvidia.com/en-us/deep-learning-ai/startups/ Follow Jeff Herbst at @jeffatNvdia

Today we're talking graphics, the intersection of AI and graphics specifically. There may be no better guest to talk AI and graphics than our guest today, David Luebke. David is vice president of graphics research at NVIDIA. He co-founded NVIDIA Research in 2006, after eight years on the faculty of the University of Virginia.

It’s a modern television trope – detectives trying to solve a case “enhance” a blurry image, digitally, giving them a crystal clear image of their suspect. Until recently, this was little more than science fiction. Now, however, it’s a key tool for photographers around the world. Topaz Labs pioneered the intersection of deep learning and photo noise reduction. Their sprawling suite of image editing plugins are relied by pro and amateur photographers alike. We spoke with Topaz founder and CTO, Feng “Albert” Yang, to learn where AI and photography are going next.

It's all come full circle. Real-time graphics technology, namely, GPUs, sparked the modern AI boom. Now modern AI, driven by GPUs, is remaking graphics. This episodes guest is Aaron Lefohn, senior director of realtime rendering research at NVIDIA. Aaron's international team of scientists played a key role in founding the field of AI computer graphics. They were the first to bring AI to real-time computer graphics. They invented key technologies that brought ray tracing to real time computer graphics. Now they're at the forefront of combining AI and ray tracing to rapidly increase the realism of real-time graphics.

Hugging Face is more than just an adorable emoji — it’s a company that’s demystifying AI by transforming the latest developments in deep learning into usable code for businesses and researchers. Research engineer Sam Shleifer spoke with AI Podcast host Noah Kravitz about Hugging Face NLP technology, which is in use at over 1,000 companies, including Apple, Bing and Grammarly, across fields ranging from finance to medical technology. Hugging Face’s models serve a variety of purposes for their customers, including autocompletion, customer service automation and translation. Their popular web application, Write with Transformer, can even take half-formed thoughts and suggest options for completion. Shleifer is currently at work developing models that are accessible to everyone, whether they are proficient coders or not. In the next few years, Shleifer envisions the continued growth of smaller NLP models that power a wave of chat apps with state-of-the-art translation capabilities.

Michael Kirk and Raphael Attie, scientists at NASA’s Goddard Space Flight Center, regularly face terabytes of data in their quest to analyze images of the sun. This computational challenge, which could take a year or more on a CPU, has been reduced to less than a week on Quadro RTX data science workstations. Kirk and Attie spoke to AI Podcast host Noah Kravitz about the workflow they follow to study these images, and what they hope to find. The lessons they’ve learned are useful for those in both science and industry grappling with how to best put torrents of data to work.

Jared Dame, Z by HP's director of business development and strategy for AI, data science and edge technologies, spoke to AI Podcast host Noah Kravitz about the role HP’s workstations play in cutting-edge AI and data science.

Understanding the U.S. tax code can take years of study — it’s 80,000 pages long. Software company Intuit has decided that it’s a job for AI. Ashok Srivastava, its senior vice president and chief data officer, spoke to AI Podcast host Noah Kravitz about how the company is utilizing machine learning to help customers with taxes and aid small businesses through the financial effects of COVID-19.

Imagine building an engine with 54 billion parts. Now imagine each piece is the size of a gnat’s eyelash. That gives you some idea of the scale Jonah Alben works at. Jonah is the co-leader of GPU engineering at Nvidia. The engines he builds are GPUs. Without these chips your favorite computer games and special-effects movies would look pretty lame. GPUs also power scientific simulations of everything from a Mars lander to a protein spike on the coronavirus… and, oh yes, these days they do much of the heavy lifting for the latest and greatest form of computing: AI.

As NVIDIA’s vice president of worldwide AI initiatives, Keith Strier is thinking on a global scale. He leads an initiative called AI Nations, a worldwide program that helps government leaders and stakeholders develop plans to implement AI to advance national priorities and drive economic growth. Strier spoke to AI Podcast host Noah Kravitz about AI Nations, and how NVIDIA helps countries harness all the capabilities of AI — from enhancing their local startup ecosystems to developing autonomous public transportation systems.

Whether you're using the latest generation of AI enabled mobile apps or robust business systems powered on banks of powerful servers, chances are your technology was built, first, on a workstation. We spoke with Lenovo’s Mike Leach about how these workhorses are adapting to support a plethora of new kinds of AI applications.

Autonomous vehicles require a massive amount of data and computing power. Teaching a vehicle to see what’s on the road in front of it is a big part of the puzzle. Our guests today, Ford’s Nikita Jaipuria and Rohan Bhasin, are using Generative Adversarial Networks to help autonomous vehicle systems see as well in rain and snowy conditions as they do when it’s clear out.

Cleaning up our oceans, rivers, and waterways have become a major environmental issue. Leaders in the field are now looking to begin harnessing modern AI and machine learning techniques to help tackle this immense challenge. We spoke with the San Francisco Estuary Institute’s Lorenzo Flores on using machine learning to find trash in environmentally sensitive waterways.

We spoke with Jonathan Frankle, a PhD student at MIT and coauthor of a seminal paper outlining a technique, known as the “The Lottery Ticket,” hypothesis that promises to help advance our understanding of why neural networks, and deep learning, works so well.

We spoke with Capital One Senior Software Engineer Kyle Nicholson on how modern machine learning techniques have become a key tool for financial and credit analysis.