AI Podcast: Margot Gerritsen’s Got Binders Full of Women in Data Science — and She’s Serious

This week’s AI Podcast guest is a renaissance woman with a special passion for data science.

Margot Gerritsen is senior associate dean for educational affairs and professor of energy resources engineering at Stanford University. She’s the co-founder and co-director of the organization Women in Data Science (WiDS). And she’s the host of the WiDS podcast.

Gerritsen spoke to AI Podcast host Noah Kravitz about WiDS, the projects she’s overseeing at Stanford, and what she’s excited about in the current era of data science: the democratization of data.

Gerritsen sees today’s vast quantities of data, open source code and computational power as a “perfect storm” for groundbreaking analytical work.

Key Points From This Episode:

  • The idea for WiDS was born during a conversation at Stanford’s Coupa Cafe, in which Gerritsen lamented the lack of female speakers at technology conferences and was inspired to take action.
  • WiDS hosted its major technical conference at Stanford earlier this month. Conference sessions are available to watch for free. This event is traditionally followed by a series of over 150 regional events across the world through the month of March.

Tweetables:

“We wanted to create binders of women in data science so that we could help promote them, and that’s a very serious thing because we want to make sure that these women who are making outstanding contributions are being seen, and listened to.” — Margot Gerritsen [3:23]

“You know, when you can use your data skills and your modeling and simulation skills to come up with better policies — that’s the golden spot. That’s the best place to be.” — Margot Gerritsen [30:50]

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Entrepreneur Brings GPUs to Fashion

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Pod Squad: Descript Uses AI to Make Managing Podcasts Quicker, Easier

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The post AI Podcast: Margot Gerritsen’s Got Binders Full of Women in Data Science — and She’s Serious appeared first on The Official NVIDIA Blog.

Keeping an Eye on AI: Building Ethical Technology at Salesforce

Kathy Baxter, the architect of the ethical AI practice at Salesforce, is helping her team and clients create more responsible technology. To do so, she supports employee education, the inclusion of safeguards in Salesforce technology, and collaboration with other companies to improve ethical AI across industries. Baxter spoke with AI Podcast host Noah Kravitz about Read article >

The post Keeping an Eye on AI: Building Ethical Technology at Salesforce appeared first on The Official NVIDIA Blog.

Keeping an Eye on AI: Building Ethical Technology at Salesforce

Kathy Baxter, the architect of the ethical AI practice at Salesforce, is helping her team and clients create more responsible technology. To do so, she supports employee education, the inclusion of safeguards in Salesforce technology, and collaboration with other companies to improve ethical AI across industries.

Baxter spoke with AI Podcast host Noah Kravitz about her role at the company, a position she helped create as the need for AI ethicists became apparent.

She’s helped construct practices such as release readiness planning, in which teams brainstorm any potential unintended negative consequences, along with ways to mitigate them.

In the future, Baxter predicts more global policies that will help companies define ethical AI and guide them in creating responsible technology.

Kathy Baxter, architect of ethical AI at Salesforce.

Key Points From This Episode:

  • There are several ways to correct bias in AI. This includes making edits to the training data or editing the model itself (for example, not using race or gender as a factor).
  • Einstein is Salesforce’s AI platform. The company implements in-app guidance through a feature called Einstein Discovery. One of its functions is to alert users when they might be using sensitive variables such as age, race or gender. Administrators can also select the variables they don’t want to include in their model, to avoid accidental bias.

Tweetables:

“We have to understand that everything that we build and bring into society has an impact,” — Kathy Baxter [2:29]

“One of the magical things about AI is that we can become aware of biases that we might not have known even existed in our business processes in the first place.” — Kathy Baxter [10:18]

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How Federated Learning Can Help Keep Data Private

Walter De Brouwer, CEO of Doc.ai — a company building a medical research platform that addresses the issue of data privacy with federated learning — talks about the complications of putting data to work in industries such as healthcare.

Good News About Fake News: AI Can Now Help Detect False Information

If only there was a way to filter the fake news from the real. Thanks to Vagelis Papalexakis, a professor of computer science at the University of California, Riverside, there is. He discusses his algorithm that can detect fake news with 75 percent accuracy.

Teaching Families to Embrace AI

Tara Chklovski is CEO and founder of Iridescent, a nonprofit that provides access to hands-on learning opportunities to prepare underrepresented children and adults for the future of work. She talks about Iridescent, the UN’s AI for Good Global Summit and the AI World Championship — part of the AI Family Challenge.

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The post Keeping an Eye on AI: Building Ethical Technology at Salesforce appeared first on The Official NVIDIA Blog.

An AI for Detail: Nanotronics Brings Deep Learning to Precision Manufacturing

Matthew Putman, this week’s guest on the AI Podcast, knows that the devil is in the details. That’s why he’s the co-founder and CEO of Nanotronics, a Brooklyn-based company providing precision manufacturing enhanced by AI, automation and 3D imaging.

He sat down with AI Podcast host Noah Kravitz to discuss how running deep learning networks in real-time on factory floors produces the best possible products, and how Nanotronics models and equipment are finding success in fields ranging from the semiconductor industry to genome sequencing.

SUBHEAD: Key Points From This Episode:

Nanotronics develops universal AI models that can be customized depending on individual customers’ processes and deployments.

The AI models that Nanotronics deploys at a customer site can be communicated directly from the GPU to the machine, without the cloud, to ensure security and speed.

When the new Nanotronics factory is finished (pictured, above), they’ll use their own deep learning models to ensure precision manufacturing as they construct their equipment.

Tweetables:

  • “It’s a great advantage to our customers to actually have a smaller footprint because we have a computationally driven system, rather than a system that requires a lot of very expensive large hardware” — Matthew Putman [7:14]
  • “We can adjust actual controls in real time to make corrective actions for any type of anomalies that occur. It’s not so important to us what the absolute value is on each of the stations, it’s that by the end, the product has the most reproducibility and highest quality possible” Matthew Putman [8:47]

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No More Trying Taxes: Intuit Uses AI for Smarter Finances

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UC Berkeley’s Pieter Abbeel on How Deep Learning Will Help Robots Learn

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Astronomers Turn to AI as New Telescopes Come Online

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The post An AI for Detail: Nanotronics Brings Deep Learning to Precision Manufacturing appeared first on The Official NVIDIA Blog.

An AI for Detail: Nanotronics Brings Deep Learning to Precision Manufacturing

Matthew Putman, this week’s guest on the AI Podcast, knows that the devil is in the details. That’s why he’s the co-founder and CEO of Nanotronics, a Brooklyn-based company providing precision manufacturing enhanced by AI, automation and 3D imaging. He sat down with AI Podcast host Noah Kravitz to discuss how running deep learning networks Read article >

The post An AI for Detail: Nanotronics Brings Deep Learning to Precision Manufacturing appeared first on The Official NVIDIA Blog.

NVIDIA’s Neda Cvijetic Explains the Science Behind Self-Driving Cars

What John Madden was to pro football, Neda Cvijetic is to autonomous vehicles. No one’s better at explaining the action, in real time, than Cvijetic.

Cvijetic, senior manager of autonomous vehicles at NVIDIA, drives our NVIDIA DRIVE Labs series of videos and blogs breaking down the science behind autonomous vehicles.

A Serbian-American electrical engineer, Cvijetic seems destined for this role. She literally grew up in the shadow of Nikola Tesla. His statue in Belgrade stood across the street from her childhood home.

On this week’s AI Podcast, Cvijetic spoke to host Rick Merritt about what’s driving autonomous vehicles. She also shared her perspective on how both broad initiatives and day-to-day actions can promote diversity in AI.

 Key Points From This Episode:

  • Autonomous vehicles use three key techniques: perception, localization, and control and planning.
  • Each self-driving car runs on dozens of deep neural networks, which are each trained on thousands of hours of real-world driving data and on NVIDIA DRIVE Constellation, which provides extensive testing in virtual reality before the car even hits the road.
  • Autonomous vehicles drive safely because diversity and redundancy are designed into their systems. Multiple cameras with overlapping fields of view, radar, and more provide a wealth of perception data for the highest level of accuracy.

Tweetables:

“I want every driver out there to feel that they understand AI, to understand how AI works in self-driving cars, and feel empowered by that understanding” — Neda Cvijetic [1:56]

“The NVIDIA DRIVE simulator seeks to create some of these corner cases that might take years to actually observe” — Neda Cvijetic [10:36]

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Deep Learning 101: Will Ramey, NVIDIA Senior Manager for GPU Computing

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How AI Turns Kiddie Cars Into Fast and Frugal Autonomous Racers

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Get the AI Podcast through iTunesGoogle PodcastsGoogle PlayCastbox, DoggCatcher, OvercastPlayerFM, Pocket Casts, PodbayPodBean, PodCruncher, PodKicker, SoundcloudSpotifyStitcher and TuneIn.

  

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The post NVIDIA’s Neda Cvijetic Explains the Science Behind Self-Driving Cars appeared first on The Official NVIDIA Blog.

NVIDIA’s Neda Cvijetic Explains the Science Behind Self-Driving Cars

What John Madden was to pro football, Neda Cvijetic is to autonomous vehicles. No one’s better at explaining the action, in real time, than Cvijetic. Cvijetic, senior manager of autonomous vehicles at NVIDIA, drives our NVIDIA DRIVE Labs series of videos and blogs breaking down the science behind autonomous vehicles. A Serbian-American electrical engineer, Cvijetic Read article >

The post NVIDIA’s Neda Cvijetic Explains the Science Behind Self-Driving Cars appeared first on The Official NVIDIA Blog.

NVIDIA to Host 10,000 AI Developers at GTC 2020 in Silicon Valley

NVIDIA’s GPU Technology Conference — the world’s premier AI event — returns to San Jose in March, bringing together leading developers, researchers and executives to share the latest advancements in AI, high performance computing and computer graphics across a broad range of industries.

More than 10,000 attendees from 70 countries are expected to attend the 11th annual GTC, taking place from March 22-26 at the San Jose McEnery Convention Center. Over 250 companies — including AWS, Google Cloud, and Microsoft — will exhibit the latest technology for cloud and enterprise computing, as well as autonomous cars, robotics, professional graphics and gaming, embedded and edge AI applications and more.

NVIDIA founder and CEO Jensen Huang will deliver a keynote address on Monday, March 23.

In addition to 600 sessions and full-day NVIDIA Deep Learning Institute workshops, GTC 2020 includes a range of new initiatives. Among them: an AI art gallery displaying the work of renowned artists; an HPC Summit giving attendees a chance to network and learn from industry luminaries; and new events for underrepresented communities in AI, such as NVIDIA DRIVE training for women in AI, sponsored by Ford.

A variety of industries will be represented in the talks, networking events, and experts attending GTC. This year’s topics include everything from data science to gaming to visualization, and industries ranging from entertainment to healthcare to retail.

Early Bird Discount

Registration is now open, with a 15 percent discount available until Feb. 13 and priority keynote access for the first 1,000 registrants.

Named one of Forbes’ top conferences for women in tech, GTC will continue its commitment to under-represented communities in AI. Various activities — including Deep Learning Institute training and unique networking events — will provide opportunities for female, Black, and Latinx AI developers, with more to come. Experts from many of the world’s leading organizations — including BMW, Epic Games, NASA, Stanford and Wells Fargo — will present technical talks on topics ranging from autonomous vehicles and robotics to CUDA Python programming and natural language processing.

Summit for Science and Technology

This year, GTC will feature the HPC Summit, a full-day event on Thursday, March 26, bringing together hundreds of industry leaders, developers and researchers to discuss innovations in high performance computing.

HPC experts from around the world will lead sessions and a forum, including:

  • Thomas Schulthess, Director of ETH Zurich, CSCS
  • Ian Buck, NVIDIA’s VP of Accelerated Computing and the inventor of CUDA
  • Michael Kagen, CTO, Mellanox Technologies
  • Nidhi Chappell, Head of Product, Specialized Azure Compute, Microsoft

AI for Artists

Refik Anadol will be presenting a display titled “Machine Hallucination.”

GTC 2020 will also feature its first AI art gallery showcasing a variety of artists, including Refik Anadol, Memo Atken, Mario Klingemann and Helena Sarin.

Anadol’s immersive display, called “Machine Hallucination,” is powered by NVIDIA Quadro GPUs and reimagines cities of past, present and future.

Hands-On Training

GTC 2020 offers developers and data scientists the chance to get hands on with deep learning, accelerated computing and accelerated data science. Attendees can register for 70+ instructor-led trainings and 30+ self-paced courses offered by the Deep Learning Institute.

There are also six full-day DLI workshops taking place Sunday, March 22, where attendees can earn certificates in topics ranging from anomaly detection to CUDA C/C++.

Beyond workshops, GTC will offer a variety of networking opportunities. These include evening receptions, birds of a feather dinners and “Connect with the Experts” — hour-long Q&A sessions with NVIDIA engineers and researchers on topics like autonomous vehicles, computer vision and robotics.

To learn more about the conference and reserve your spot, visit the GTC 2020 website.

The post NVIDIA to Host 10,000 AI Developers at GTC 2020 in Silicon Valley appeared first on The Official NVIDIA Blog.

NVIDIA to Host 10,000 AI Developers at GTC 2020 in Silicon Valley

NVIDIA’s GPU Technology Conference — the world’s premier AI event — returns to San Jose in March, bringing together leading developers, researchers and executives to share the latest advancements in AI, high performance computing and computer graphics across a broad range of industries. More than 10,000 attendees from 70 countries are expected to attend the Read article >

The post NVIDIA to Host 10,000 AI Developers at GTC 2020 in Silicon Valley appeared first on The Official NVIDIA Blog.

AI’s Mild Ride: RoadBotics Puts AI on Pothole Patrol

National Pothole Day is Jan. 15. Its timing is no accident.

All over the Northern hemisphere, potholes are at their suspension-wrecking, spine-shaking worst this month.

Thanks to AI, one startup is working all year long to alleviate this menace. Benjamin Schmidt, president and co-founder of RoadBotics, is using the tech to pave the way to better roads.

His startup is identifying areas at risk of potholes, so city governments can improve roads before damage worsens.

Schmidt spoke with AI Podcast host Noah Kravitz about how RoadBotics is working with over 160 governments across the world to collect and analyze video data to improve preventative maintenance.

 Key Points From This Episode:

  • Using smartphones placed against car windshields, RoadBotics collects and analyzes video data to assign each road a score, which local governments can use to inform infrastructure decisions.
  • RoadBotics protects privacy by blurring people, cars and other sensitive data so only roads are analyzed.
  • Early this year, RoadBotics will be release an app so anyone can use smartphones to collect data and submit to their neural network to help improve analysis.

Tweetables:

“The sooner you can detect [surface distresses], the sooner you can put a cheaper intervention in now that really just saves the life of the road.” — Benjamin Schmidt [5:00]

“RoadBotics was founded at exactly the right moment with the right tech, the right hardware. So we’re now in this sweet spot where we can actually deploy a solution” — Benjamin Schmidt [6:46]

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How AI Will Revolutionize Driving

Danny Shapiro, senior director of automotive at NVIDIA, explains the capabilities necessary for autonomous driving, from object detection to AI to high performance computing.

Where Is Deep Learning Going Next?

Bryan Catanzaro, head of applied deep learning research at NVIDIA, explains his journey in AI from UC Berkeley, to Baidu, to NVIDIA. He’s striving for AI that works so seamlessly that users don’t even notice it, and he explains how GPUs are helping to make that happen.

Featured image credit: Santeri Viinamäki, some rights reserved.

The post AI’s Mild Ride: RoadBotics Puts AI on Pothole Patrol appeared first on The Official NVIDIA Blog.