The University of Florida and NVIDIA Tuesday unveiled a plan to build the world's fastest AI supercomputer in academia, delivering 700 petaflops of AI performance. The effort is anchored by a $50 million gift: $25 million from alumnus and NVIDIA co-founder Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA. "We've created a replicable, powerful model of public-private cooperation for everyone's benefit," said Malachowsky, who serves as an NVIDIA Fellow, in an online event featuring leaders from both the UF and NVIDIA. UF will invest an additional $20 million to create an AI-centric supercomputing and data center. The $70 million public-private partnership promises to make UF one of the leading AI universities in the country, advance academic research and help address some of the state's most complex challenges.

At Oracle, customer service chatbots use conversational AI to respond to consumers with more speed and complexity. Suhas Uliyar, vice president of bots, AI and mobile product management at Oracle, stopped by to talk to AI Podcast host Noah Kravitz about how the newest wave of conversational AI can keep up with the nuances of human conversation. Many chatbots frustrate consumers because of their static nature. Asking a question or using the wrong keyword confuses the bot and prompts it to start over or make the wrong selection. Uliyar says that Oracle's digital assistant uses a sequence-to-sequence algorithm to understand the intricacies of human speech, and react to unexpected responses.

Kicking off the Washington edition of our GPU Technology Conference, Buck, NVIDIA's VP for accelerated computing, detailed a new generation of technologies that will help companies put modern AI to work. Buck also announced that the United States Postal Service -- the world's largest delivery service, with 146 billion pieces of mail processed and delivered annually -- is adopting end-to-end AI technology from NVIDIA. "The challenge is how do we take AI from innovation to actually applying AI," Buck told an audience of more than 3,500 developers, CIOs and federal employees at the three-day GTC DC. "Our challenge, NVIDIA's challenge, and my challenge is'How can I bring AI to industries and activate it.'" Over the course of his hour-long talk, Buck explained how modern AI is trained and deployed, and described how NVIDIA is adapting AI for the automotive, healthcare, robotics, and 5G industries, among others. The U.S. Postal Service offers a glimpse at what's possible. Buck said the U.S. Postal Service will roll out a deep learning solution based on NVIDIA EGX to 200 processing facilities that should be operational in 2020.

Think of it as a chance to rest your eyes up ahead of the New Year's Day college football frenzy in front of that new big screen TV. Simple story: our podcast does best when, and where, other media don't -- while listeners are trapped in a commute and catching up on the latest episode of Jack Ryan isn't just dangerous, it's illegal. Still, if you're tooling around town picking up supplies for New Year's festivities, or just looking to stick in your earbuds and chill out amidst the bustle of the season, we've had more than a few episodes over the past year that entertain -- as well as enlighten. But sarcasm is no joke. Long before today's sentiment analysis systems struggled to accurately understand human communication, people struggled to understand one another's sarcasm.

When you hear of AI and machine learning, it's easy to think of technology companies leading the charge. Capital One is determined to change that. "My first thought when Capital One approached me was'well it makes sense they use machine learning to predict lending decisions and other financial forecasting and that'll be about it.' So I wasn't terribly enthusiastic," said Nitzan Mekel, managing vice president of machine learning at Capital One. "But what really turned me on to the opportunity, and what gets me excited everyday now, is the fact that we use AI and machine learning pretty much in every facet of our business."

I am fortunate today to be in Washington with leaders from three dozen companies, including many of our close partners, to discuss with administration officials how the U.S. can continue to lead the world in research, development and the adoption of artificial intelligence. I'd like to thank the Office of Science and Technology Policy for convening this important meeting. AI is becoming the world's most important computational tool -- applicable to a wide variety of industries including transportation, energy and healthcare. But AI is enormously demanding in terms of computation -- it requires processing hundreds of millions of data points to extract insight. Therefore, it's important for us to discuss how to improve our nation's computing infrastructure to support AI and maintain leadership in this space.

I am fortunate today to be in Washington with leaders from three dozen companies, including many of our close partners, to discuss with administration officials how the U.S. can continue to lead the world in research, development, and the adoption of artificial intelligence. I'd like to thank the Office of Science and Technology Policy for convening this important meeting. AI is becoming the world's most important computational tool -- applicable to a wide variety of industries including transportation, energy and healthcare. But AI is enormously demanding in terms of computation -- it requires processing hundreds of millions of data points to extract insight. Therefore it's important for us to discuss how to improve our nation's computing infrastructure to support AI and maintain leadership in this space.

That was the message one young officer gave Lt. General John "Jack" Shanahan -- the Pentagon's director for defense for warfighter support -- who is hustling to put artificial intelligence and machine learning to work for the U.S. Defense Department. Highlighting the growing role AI is playing in security, intelligence and defense, Shanahan spoke Wednesday during a keynote address about his team's use of GPU-driven deep learning at our GPU Technology Conference in Washington. Shanahan leads Project Maven, an effort launched in April to put machine learning and AI to work, starting with efforts to turn the countless hours of aerial video surveillance collected by the U.S. military into actionable intelligence. "We have analysts looking at full-motion video, staring at screens 6, 7, 8, 9, 10, 11 hours at a time. They're doing the same thing photographic interpreters were doing in World War II," Shanahan said.

The rise of modern business intelligence (BI) has seen the emergence of a number of component parts designed to support the different analytical functions necessary to deliver what enterprises require. Perhaps the most fundamental component of the BI movement is the traditional frontend or visualization application. Companies like Tableau, Qlik, Birst, Domo and Periscope provide these. There are dozens more -- all with essentially equivalent capabilities: the ability to make spreadsheets look beautiful. Some of these companies have been tremendously successful, primarily differentiating themselves on the axis of usability.

Take smart people, add the most disruptive new technology of our time, and inject money. Welcome to NVIDIA's AI startup challenge. Of the 12 semi-finalists who gave their pitches -- live before an audience of investors and press at NVIDIA's Silicon Valley campus -- six were selected to go on to the finals, to be held on March 27 at GTC, our annual GPU developers conference. There, the two finalists in three categories -- autonomous systems, enterprise and healthcare -- will get winnowed down to a single winner, who will get $330,000. Kicking off the event NVIDIA founder and CEO Jensen Huang said: ""AI is enjoying a revolution: software that writes software, machines that learn by themselves, solving problems that human software engineers had no possibility of addressing until now have finally hit the scene.