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Take a look at all the pictures from last years’ 3D Digital Showcase, hosted by the National Stereoscopic Association and NVIDIA. Congratulations to showcase winners Tim Farrell and Lee Pratt, and to all the photographers that submitted pics!

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How do you instruct your robot to learn? This is the question that Sergey Levine, an assistant professor in the department of electrical engineering and computer science at UC Berkeley is attempting to response.

“One of the most significant things is that you have to somehow communicate to the robot what it means to succeed,” Levine said in a conversation with AI Podcast host Michael Copeland. “That’s one of the most basic things …You need to tell it what it should be doing.”

During Levine’s research, he explored reinforcement learning, in which robots learn what functions are desired to fulfill a particular task. He’s also quick to point out that it’s significant that the robots don’t just repeat what they learn in training, but understand why a task requires certain deeds.

“So you observe people doing a task, attempt to infer what are the goals these people are optimizing, and then the robot can attempt to optimize the same aim itself,” said Levine.

It’s not effortless, however. Training a robot to learn, instead of to just recognize pictures, is more difficult than building a deep learning system that can recognize photos. In reality, the learning process is a lot of “trial and error.”

“If you want to get a robot to do interesting things, you kind of need it to learn on its own,” Levine said. “This is not something where people can just give you these ideal labels like they do for picture recognition.”

Levine’s fascination with robots and his current area of research stemmed from two things: his work in computer graphics, and a Disney Animation film titled Big Hero 6.

One of the film’s characters is Baymax, a robot built by a graduate student to assist with people’s medical needs.

“The thing that I truly liked about that movie…[is] this idea that scientists can build a robot as an experiment but for the purpose of actually helping people,” said Levine. “The fact that something like that makes it in popular culture and a lot of people see it, that to me is actually fairly inspiring. I think we should be thinking more about robots in this way.”

AI Podcast: Budge Over Sherlock, AI Is Here

And if you are a frequent PayPal user, give last week’s gig a listen: Vadim Kutsyy, a data scientist at PayPal, discusses how the online payments company uses AI to crack down on suspicious transactions.

How to Tune in to the AI Podcast

The AI Podcast is available through iTunes, DoggCatcher, Google Play Music, Overcast, PlayerFM, Podbay, Pocket Casts, PodCruncher, PodKicker, Stitcher and Soundcloud. If your beloved isn’t listed here, email us at [email protected].

If you’re a university student with a passion for AI, we want to talk to you.

Universities are only recently reopening for learning, but our recruiters have already launched their fall search for interns and fresh hires. We’re seeking the best and brightest students at twenty top schools in the U.S. and Canada to help us build the future of AI, and NVIDIA.

“Students are about as close as you can get to the latest AI technologies coming out of universities,” said Jennifer Griffin, our director of university recruiting. “We want people to take what they’ve been doing in school, come here and make it real.”

Where We’ll Be on Campus

Half of the universities we’ll visit this fall are renowned for their pioneering work in AI, including Stanford University, the University of Washington, the University of Toronto, Fresh York University, and the University of California, Berkeley.

As football season rages, we’ll journey from the home of the Gators at the University of Florida to the Aggies’ base at Texas A&M to the Trojans’ home at University of Southern California. Other stops include MIT, Georgia Tech, Harvard, Purdue, the University of Montreal and the University of Illinois at Urbana-Champaign. And that’s just a partial list.

We’re especially focused on boosting recruiting at universities that tend to serve groups underrepresented in the technology industry. In our search for African-American and Hispanic engineers, we’re visiting top schools such as Howard University, the University of California, Riverside, and the University of Puerto Rico. We also work with the Society for Women Engineers to reach technical women at our target campuses.

You’ll find us at career fairs, of course, but we’re also bringing NVIDIA events like GPUniversity and our Deep Learning Institute to campuses. GPUniversity is a day of technical talks, workshops and demos of the latest technologies. Our Deep Learning Institute classes train people how to incorporate deep learning into their research.

Depending on the school, students will have a chance to build robots at workshops led by our Jetson embedded systems team. And we’ll be showcasing BB8, our self-driving car, and explaining how AI (and specifically deep learning) will revolutionize driving. If you’re fortunate, you might even get the scoop on NVIDIA and AI from our CEO, Jensen Huang.

Besides the free t-shirts, we bring technical talks, demos and classes to campus. How to Get on Our Radar

We’re especially interested in students with abilities in deep learning or computer vision, but we hire from all technical disciplines. Of the four hundred interns who joined us over the summer — our largest group yet — eighty focused on deep learning projects such as robotics and self-driving cars. But interns work all over the company, contributing their energy and intellect to everything from GPU architecture to system software to VR.

Among their assignments this year: working on simulation models to test GPU spectacle; tackling tests to run AI applications better; building navigation systems for autonomous driving using recurrent neural networks; and creating VR demos showcased at international conferences.

No matter what your specialty, we look for students with the right technical background. But it’s about more than that.

“We want to see how you solve problems,” said recruiter Linh Nguyen. “How you figure out the response is more significant than getting the right response.”

How to Get a Job at NVIDIA

The best way to graduate with a job at NVIDIA is to begin here as an intern. When we hire fresh college graduates, we look very first to our interns. (To learn more about interning at NVIDIA our internship program, read the latest blog by one of our interns or see our University Recruiting website.)

“We need indeed brainy, sultry people who thrive on challenges and want to put down deep roots at NVIDIA so they do their life’s work here,” said Lindsey Duran, our senior director of staffing.

NVIDIA’s Shashank Prasanna demonstrates our Deep Learning GPU Training System (DIGITS) at a university recruiting event. From Intern to Employee

Sarah Adams was an electrical electrical engineering student at the University of San Diego when she joined a team dedicated to our SHIELD streaming-media device. In July, she continued her work as a full-time employee.

“I loved every single minute of my internship,” she said. “My team had a breadth of skill you don’t indeed get in college.”

Richard Wan interned here three times before joining the company in April to work on GPU architecture. As a master’s student at Stanford University, he had slew of opportunities. But after interviewing with a few companies, he determined to come back to NVIDIA.

“NVIDIA was familiar. I knew I’d love it,” he said. “And in the end, I was more interested in the work at NVIDIA than other places.”

NVIDIA University Recruiting

Last year, our recruiters met with more than Five,000 students, and we plan to talk with even more this year when we bring our people and technologies to campus.

“We want to hear about the work you’re doing, and we’ll tell you about what we’re doing,” Griffin said. “If it fits with where you see yourself in the future, we’ll see if we can find a place in NVIDIA where you can pursue your passion.”

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