The center of the AI ecosystem shifts to D.C. this fall, when the GPU Technology Conference arrives at the Reagan Center in Washington, from Nov. 4-6.

GTC DC will bring together some 3,000 attendees from government and industry for three days of networking and more than 100 sessions, including presentations, panel talks and workshops focused on implementing AI in government and business.

Here are five of the top reasons to attend:

Keynote

Ian Buck, vice president of accelerated computing at NVIDIA, will be giving this year’s keynote at GTC DC.

This is a rare chance to receive concrete advice on how organizations can use AI to boost competitiveness and improve services from the man who invented CUDA, the world’s leading platform for accelerated parallel computing.

Buck has devoted his career to helping many of the world’s leading organizations accelerate critical compute workloads. He’s testified before Congress on AI and advised the White House.

100+ Sessions

Leading thinkers from the White House Office of Science and Technology, National Institute of Standards and Technology, NASA Langley Research Center, the Pacific Northwest National Laboratory and more will be discussing their technology and the future of AI in a series of over 100 sessions.

With a heavy focus on autonomous machines, cybersecurity and disaster relief, there will be panels on “The National AI Strategy: What’s Happening, What to Expect and How to Engage” and “AI and Cybersecurity: Opportunities and Threats to Businesses, Government and Individuals,” among others.

A few of the confirmed speakers include:

• Suzette Kent, U.S. chief information officer — U.S. Office of Management and Budget
• Lynne E. Parker, assistant director for AI — White House Office of Science and Technology Policy
• Gregg Cohen, CTO and staff scientist — National Institutes of Health
• Elham Tabassi, chief of staff in the Information Technology Laboratory — National Institute of Standards and Technology
• Kimberly Powell, vice president of healthcare — NVIDIA
• Sertac Karaman, associate professor of aeronautics and astronautics — MIT
• John Ferguson, CEO — Deepwave Digital
• Joshua Patterson, director of AI infrastructure — NVIDIA
• Moira Bergin, subcommittee director — U.S. House of Representatives Committee on Homeland Security

Exhibits

GTC DC won’t be all talk and no action, though — attendees will have access to demos of the latest innovations in AI. Over 50 companies will be exhibiting their technology in AI, robotics and high performance computing, including Booz Allen Hamilton, Lockheed Martin and Dell.

NVIDIA will demonstrate its RTX-powered lunar landing demo, which stole the show at SIGGRAPH earlier this year.

A celebration of the Apollo 11 moon landing, the demo uses a single camera to capture a participant’s movement and match it using AI pose estimation technology to a 3D-rendered astronaut in real time.

Also in the spotlight will be NVIDIA Clara AI, in a demo called “Enhancing Radiology with Cinematic Rendering and AI.” Clara uses NVIDIA GPUs and AI to enable views of the body that traditional medical imaging techniques cannot produce. These cinematic 3D renderings of medical images can transform the way we diagnose and recommend treatment.

Training

GTC DC is offering both AI beginners and experts the chance to work on their skills with seven day-long NVIDIA Deep Learning Institute workshops that will take place on Nov. 4. Led by certified DLI instructors, participants can earn a certificate of competency by completing the built-in assessment at the end of each session. Workshops include: “Getting Started with AI on Jetson Nano,” “Deep Learning for Intelligent Video Analytics” and “Deep Learning for Healthcare Image Analysis.”

There will also be dozens of two-hour hands-on training sessions throughout GTC DC. Instructors will train participants in the application of data science and accelerated computing to address the most difficult governmental and industrial challenges. Popular sessions available for registration are “Accelerating Data Science Workflows with RAPIDS” and “Introduction to CUDA Python with Numba.”

Networking

For many, the biggest benefit of GTC DC is being able to talk with a unique cross-section of technical experts, elected representatives, agency and department heads, staffers, corporate executives and academic leaders.

Attendees can engage with representatives from the White House, Department of Energy, Oak Ridge National Laboratory, Microsoft, Carnegie Mellon University, Amazon Web Services and many others in government, research and business.

The conference also hosts an annual Women in AI breakfast, bringing together women speakers from a variety of industries and research fields.

After hours, evening receptions offer attendees the chance to continue networking.

To see all of this and more, come join us at GTC DC from Nov. 4 to Nov. 6.

The post AI Goes to Washington: Top 5 Things to See at GTC DC appeared first on The Official NVIDIA Blog.

Great processors — and great hardware — won’t be enough to propel the AI revolution forward, Ian Buck, vice president and general manager of NVIDIA’s accelerated computing business, said Wednesday at the AI Hardware Summit.

“We’re bringing AI computing way down in cost, way up in capability and I fully expect this trend to continue not just as we advance hardware, but as we advance AI algorithms, AI software and AI applications to help drive the innovation in the industry,” Buck told an audience of hundreds of press, analysts, investors and entrepreneurs in Mountain View, Calif.

Buck — known for creating the CUDA computing platform that puts GPUs to work powering everything from supercomputing to next-generation AI — spoke at a showcase for some of the most iconic computers of Silicon Valley’s past at the Computer History Museum.

AI Training Is a Supercomputing Challenge

The industry now has to think bigger — much bigger — than the boxes that defined the industry’s past, Buck explained, weaving together software, hardware,and infrastructure designed to create supercomputer-class systems with the muscle to harness huge amounts of data.

Training, or creating new AIs able to tackle new tasks, is the ultimate HPC challenge – exposing every bottleneck in compute, networking, and storage, Buck said.

“Scaling AI training poses some hard challenges, not only do you have to build the fast GPU, but optimize for the full data center as the computer,” Buck said. “You have to build system interconnections, memory optimizations, network topology, numerics.”

That’s why NVIDIA is investing in a growing portfolio of data center software and infrastructure, from interconnect technologies such as NVLink and NVSwitch to NVIDIA Collective Communications Library, or NCCL, which optimizes the way data moves across vast systems.

From ResNet-50 to BERT

Kicking off his brisk, half-hour talk, Buck explained that GPU computing has long served the most demanding users — scientists, designers, artists, gamers. More recently that’s included AI. Initial AI applications focused on understanding images, a capability measured by benchmarks such as ResNet-50.

“Fast forward to today, with models like BERT and Megatron that understand human language – this goes way beyond computer vision but actually intelligence,” Buck said. “When I said something, what did I mean? This is a much more challenging problem, it’s really true intelligence that we’re trying to capture in the neural network.”

To help tackle such problems, NVIDIA yesterday announced the latest version of NVIDIA’s inference platform, TensorRT 6. On the T4 GPU, it runs BERT-Large, a model with super-human accuracy for language understanding tasks, in only 5.8 milliseconds, nearly half the 10 ms threshold for smooth interaction with humans. It’s just one part of our ongoing effort to accelerate the end-to-end pipeline.

Accelerating the Full Workflow

Inference tasks, or putting trained AI models to work, are diverse, and usually part of larger applications application that obeys Amdhahl’s Law — if you accelerate only one piece of the pipeline, for example matrix multipliers, you’ll still be limited by the rest of the processing steps.

Making an AI that’s truly conversational will require a fully accelerated speech pipeline able to bounce from one crushingly compute-intensive task to another, Buck said.

Such a system could require 20 to 30 containers end to end, harnessing assorted convolutional neural networks and recurrent neural networks made up of multilayer perceptrons working at a mix of precisions, including INT8, FP16 and FP32. All at a latency of less than 300 milliseconds, leaving only 10 ms for a single model.

Data Center TCO Is Driven by Its Utilization

Such performance is vital as investments in data centers will be judged by the amount of utility that can be wrung from their infrastructures, Buck explained. “Total cost of ownership for the hyperscalers is all about utilization,” Buck said. ”NVIDIA having one architecture for all the AI powered use cases drives down the TCO.”

Performance — and flexibility — is why GPUs are already widely deployed in data centers today, Buck said. Consumer internet companies use GPUs to deliver voice search, image search, recommendations, home assistants, news feeds, translations and ecommerce.

Hyperscalers are adopting NVIDIA’s fast, power-efficient T4 GPUs — available on major cloud service providers such as Alibaba, Amazon, Baidu, Google Cloud and Tencent Cloud. And inference is now a double-digit percentage contributor to NVIDIA’s data center revenue.

Vertical Industries Require Vertical Platforms

In addition to delivering supercomputing-class computing power for training, and scalable systems for data centers serving hundreds of millions, AI platforms will need to grow increasingly specialized, Buck said.

Today AI research is concentrated in a handful of companies, but broader industry adoption needs verticalized platform, he continued.

“Who is going to do the work,” of building out all those applications? Buck asked. “We need to build domain-specific, verticalized AI platforms, giving them an SDK that gives them a platform that is already tuned for their use cases,” Buck said.

Buck highlighted how NVIDIA is  building verticalized platforms for industries such as automotive, healthcare, robotics, smart cities, and 3D rendering, among others.

Zooming in on the auto industry as an example, Buck touched on a half dozen of the major technologies NVIDIA is developing. They include the NVIDIA Xavier system on a chip, NVIDIA Constellation automotive simulation software, NVIDIA DRIVE IX software for in-cockpit AI and NVIDIA DRIVE AV software to help vehicles safely navigate streets and highways.

Wrapping up, Buck offered a simple takeaway: the combination of AI hardware, AI software, and AI infrastructure promise to make more powerful AI available to more industries and, ultimately, more people

“We’re driving down the cost of computing AI, making it more accessible, allowing people to build powerful AI systems and I predict that cost reduction and improved capability will continue far into the future.”

The post Now for the Soft Part: For AI, Hardware’s Just the Start, NVIDIA’s Ian Buck Says appeared first on The Official NVIDIA Blog.

We’ve all chosen the self-checkout stand over the human cashier, thinking it’ll take less time.

But somehow, things take a terrible turn. The barcodes aren’t scanning, there’s a pop-up scolding you for not placing the product in the bagging area (though you did, of course), and an employee is coming over to fix the chaos.

It would’ve taken less time to go to the cashier.

Focal Systems is applying deep learning and computer vision to automate portions of retail stores to streamline operations and get customers in and out more efficiently, without the pitfalls of the traditional self-checkout.

CEO Francois Chaubard sat down with AI Podcast host Noah Kravitz to talk about how the company is changing retailers.

As labor costs increase, the traditional solution is twofold: automation and human staff reduction. But Chaubard explains that self-checkout systems don’t actually compensate for fewer employees. Instead, “you get more out-of-stocks, because you’ve got less people,” he says.

Focal Systems started by applying AI to a different area of the store: shelves. Chaubard notes that, for store employees, one of the first tasks every day is checking what items are out of stock, and “knowing that answer takes about four hours a day.”

To prevent this, Focal Systems installs small, inexpensive cameras throughout the store, with a focus on high-moving areas like the soda aisle. The cameras “take an image once every half hour” and produce a chart that notes either “in” or “out.”

“Every single hour that you don’t have a product on the shelf is lost sales,” Chaubard emphasizes. This aspect of Focal Systems alerts employees that they need to restock, and helps identify common “out-of-stock hours” so that stores can recognize the pattern and avoid it altogether.

This shelf camera system is already in 11 major retailers across the world.

The other component to Focal Systems is the Focal Scan. While barcode scanning takes three seconds an item, on average, Focal Systems installs a camera on top of the conveyor belt. “You’re just using deep learning and computer vision to detect amongst a hundred thousand different SKUs in 0.1 seconds with 99.9 percent precision recall,” Chaubard explains.

The cashier can just focus on bagging, reducing the total time of the transaction by 60 percent.

Chaubard thinks that the future holds even more automation, but only where it would be cheaper than human labor. “People are hard to beat in certain tasks,” he laughs.

Visit Focal Systems’ website for more information and to find videos of their technology in action.

Help Make the AI Podcast Better

Have a few minutes to spare? Fill out this short listener survey. Your answers will help us make a better podcast.

How to Tune in to the AI Podcast

Get the AI Podcast through iTunes, Castbox, DoggCatcher, Overcast, PlayerFM, Podbay, PodBean, Pocket Casts, PodCruncher, PodKicker, Stitcher, Soundcloud and TuneIn. Your favorite not listed here? Email us at aipodcast [at] nvidia [dot] com.

The post Food’s Flying Off the Shelves: Focal Systems Brings AI to Grocery Stores appeared first on The Official NVIDIA Blog.

NVIDIA-developed AI — and NVIDIA GPUs — plays a starring role in the opening this month in New York City of a permanent venue for new media art.

“Machine Hallucination” made its debut this month in the 6,000-square-foot Chelsea Market boiler room — an expansive space beneath the Manhattan landmark’s main concourse.

Created by Turkish media artist and director Refik Anadol and his studio, the installation uses six custom media servers powered by NVIDIA Quadro GPUs.

“People have never seen something like this before. It’s not  hyperbole to say it’s the future of cinema,” said Anadol.

These servers — paired with 18 4K projectors — splash stunning digital images based on some of New York’s most iconic architecture across the cavernous space’s brick walls and terracotta ceiling.  

The story behind the immersive spectacle involves even more NVIDIA technology.

Starting with more than 110 million images of New York — collected, prepared and sorted by an NVIDIA DGX Station — Anadol modified an NVIDIA-developed deep learning algorithm known as a StyleGAN.

“We wrote a latent space browser, a custom program to work with StyleGAN that has the capacity to animate every layer of the neural network and be able to choose latent coordinates to narrate our AI. Basically, we put a camera inside StyleGAN and allowed us to navigate latent space purposefully,” he said.

The exhibit — the inaugural exhibition for ARCTECHOUSE in New York — runs through Nov. 17. Admission is $24..

The post Artist’s NVIDIA-Powered AI Images of New York City Blanket Manhattan Landmark appeared first on The Official NVIDIA Blog.

At Pure Accelerate 2019, IT organizations learned how they can help their businesses bring AI development out of the shadows and into an “AI-first” mindset.

Most organizations and their IT leaders want to lean into embracing AI, positioning IT as an enabler rather than an inhibitor. This week, we announced important capabilities that will make it simpler for every enterprise to develop their best AI-powered applications faster — and deploy them in production at-scale sooner.

To get there, we’re making it easier for data scientists to develop models with greater iterative speed and, ultimately, maximum business impact. At the same time, we’re continuing to make it easier for organizations to access world-class AI-ready infrastructure facilities that ease and accelerate deployments. It’s a win-win for everyone involved in turning data into business insights at enterprise scale.

AI Data Hub

AI Data Hub is an end-to-end AI data pipeline — spanning initial exploration and prototyping to model training and inference — from Pure Storage that’s powered by NVIDIA GPUs, systems and software. By enabling accelerated movement of massive amounts of data through every phase of the development workflow, AI Data Hub can help organizations break down data storage silos associated with legacy architectures.

NVIDIA supercharges the AI Data Hub architecture, beginning with our RAPIDS suite of data science libraries built on CUDA-X AI, to deliver GPU-accelerated data ingest, manipulation and model training. AI Data Hub uses Pure Storage AIRI, built on NVIDIA DGX systems, to offer the fastest performance for training with multi-system scale. And it deploys effortlessly on NVIDIA T4 servers running inference.

AIRI-as-a-Service

In addition to streamlining AI development, Pure and NVIDIA are removing a fundamental implementation roadblock faced by many customers whose data centers aren’t AI-ready.

Extending the successful model introduced by the DGX-Ready Data Center Program, we’re partnering with Pure on the new AIRI-as-a-Service offering. This taps into a network of proven DGX colocation providers to offer a spectrum of services ranging from hosting customer-owned AIRI infrastructure to delivering AIRI-as-a-Service in a utility consumption model.

The offering will help customers of any size deploy AI infrastructure sooner by eliminating the burden of transforming their data centers to support the unique facilities demands of AI compute and affordably offering the capacity they need.

Learn more at the links below:

• DGX Systems
• DGX POD
• DGX-Ready Data Center program
• NVIDIA Data Science

The post Simplifying the ‘AI-First’ World for Every Enterprise appeared first on The Official NVIDIA Blog.

Three decades and hundreds of millions of lines of computer code after graduating from the Milwaukee School of Engineering, NVIDIA’s Dwight Diercks returned today to celebrate a donation that will put his alma mater at the forefront of AI undergraduate education.

Diercks, who grew up the son of a mailman, working on his family’s pig farm in Red Wing, Minnesota, came to NVIDIA as its 22nd employee. Today, he oversees a team of some 5,000 software engineers around the world who ship tens of millions of lines of code each month that help accelerate the world’s computing.

Diercks’ $34 million gift, the largest from an alum in MSOE’s 116-year history, is the keystone in the school’s efforts to infuse its engineering program with artificial intelligence. Two years ago, MSOE became one of the very few programs, together with Carnegie Mellon, to offer a computer science degree focused on AI.

As a result, at a time when many smaller schools wrestle with getting students in the door and financial pressures, MSOE is on a roll. Enrollment in computer science-related programs at the 2,800-student school — based in the heart of downtown Milwaukee, just a few blocks from the green parkland alongside Lake Michigan — is up 67 percent since the program was introduced. Other key admissions indicators are also up by strong double digits.

Speaking ahead of a ceremony to mark the donation, MSOE President John Walz said, “AI has very quickly become huge for us.” He noted that the new computer science program is already on pace to be the school’s second largest program and that the number of companies now recruiting there is approaching the number in its graduating class.

Central to MSOE’s focus on AI is the spanking new NVIDIA-powered AI supercomputer housed in a glass-walled area within the newly constructed four-story Diercks Hall. The system includes three NVIDIA DGX-1 pods, each with eight NVIDIA V100 Tensor Core GPUs, and 20 servers each with four NVIDIA T4 GPUs. The nodes are joined together by Mellanox networking fabric and share 200TB of network-attached storage.

Rare among supercomputers in higher education, the system —which provides 8.2 petaflops of deep learning performance — will be used for teaching undergrad classes.

Diercks, who made the donation with his wife, Dian, initiated the AI initiative because of the school’s highly practical, hands-on approach to teaching future engineers, leading them to spend more time in labs than classrooms. His own immersion in NVIDIA’s evolution in recent years into an AI powerhouse from its roots in computer gaming helped him encourage MSOE to reshape its approach around preparing students for the brave new age of artificial intelligence.

“We knew MSOE needed a supercomputer and one that can expand to scale out for students and scale up for local industries and professors,” Diercks said. In an emotional speech, he thanked a high school teacher, MSOE professor and NVIDIA founder and CEO Jensen Huang for reinforcing what his parents taught him about the importance of hard work and continuous learning.

“You don’t ever take a day off learning,” he quoted his former math teacher, Ron Gray, as telling him when he tried to skip out on a test. The long-retired teacher shyly stood up in the back of the hall.

While MSOE students come to the school from across the Midwest, with a smattering from California and Texas, many choose to stay in the Milwaukee area. The largely deindustrialized city of German church spires — which a century ago represented American innovation, giving birth to the typewriter, steam shovel and motorcycle — is home to thriving companies like Northwestern Mutual, Harley-Davidson and Rockwell Automation that hire many grads.

While not widely recognized as tech companies, these regional giants collect oceans of data that need to be crunched using the latest tools of deep learning and data science.

Huang, who delivered a keynote after the ceremony, called AI the fourth industrial revolution that will sweep across the work of virtually every industry. MSOE’s new AI push and supercomputer will help it enable generations of computer scientists trained for tomorrow’s challenges.

“MSOE now has the single most important instrument of knowledge today,” Huang said, delivering the first address in the NVIDIA auditorium. “Without access to the correct instrument, you can’t access knowledge.”

Outside the auditorium, Kyle Rodrigues, a sophomore from suburban Chicago enrolled in the new computer science program, said it was AI that drew him to MSOE. He exclaimed how thrilled he was to get his hands on the supercomputer, which MSOE is christening “Rosie,” the term used for a half dozen pioneering women who worked in the 1940s programming the early ENIAC computer — and which was also the name of Dierck’s mother.

The post NVIDIA Software Head Helps Transform Alma Mater into Leading AI Center with $34M Gift  appeared first on The Official NVIDIA Blog.

Doctors’ handwriting is notoriously difficult to read. Even more cryptic is medical coding — the process of turning a clinician’s notes into a set of alphanumeric codes representing every diagnosis and procedure.

Although this system is used in over 100 countries worldwide, accurate coding is of particular significance in the U.S., where medical codes form the basis for the bills doctors, clinics and hospitals issue to insurance providers and patients.

More than 150,000 codes are used in the U.S.’s adaptation of the International Classification of Diseases, a cataloging standard developed by the World Health Organization.

The diagnostic code for a pedestrian hit by a pickup truck? V03.10XA. Type 2 diabetes diagnosis? E11.9. There are also a set of procedural codes for everything a doctor might do, like put a cast on a patient’s broken right forearm (2W3CX2Z) or insert a pacemaker into a coronary vein (02H40NZ).

After every doctor’s appointment or procedure, a clinician’s summary of the interaction is converted into these codes. When done by humans, the turnaround time for medical chart coding — within a healthcare organization or at a private firm — is often two days or more. Natural language processing AI, accelerated by GPUs, can shrink that time to minutes or seconds.

San Francisco-based Fathom is developing deep learning tools to automate the painstaking medical coding process while increasing accuracy. The startup’s tools can help address the shortage of trained clinical coders, improve the speed and precision of billing, and allow human coders to focus on complex cases and follow-up queries.

“Sometimes you have to go back to the doctor to ask for clarification,” said Christopher Bockman, co-founder and chief technology officer of Fathom, a member of the NVIDIA Inception virtual accelerator program. “The longer that process takes, the harder it is for the doctor to remember what happened.”

Fathom uses NVIDIA P100 and V100 Tensor Core GPUs in Google Cloud for both training and inference of its deep learning algorithms. Founded in 2016, the company now works with several of the largest medical coding operations in the U.S., representing more than 200 million annual patient encounters. Its tools can reduce human time spent on medical coding by as much as 90 percent.

Deciphering the Doctor

At any doctor’s appointment, emergency room visit or surgical procedure, healthcare providers type up notes describing the interaction. While there are some standardized formats, these medical records differ by hospital, by type of appointment or procedure, and by whether the note is written during the patient interaction or after.

Medical coders make sense of this unstructured text, categorizing every test, treatment and procedure into a list of codes. Once coded, a healthcare provider’s billing department turns the reports into an invoice to collect payments from insurance providers and patients.

It’s a messy process — for a human or an AI. Human coders agree with each other less than two-thirds of the time in key scenarios, studies show. And research has found that half or more medical charts have coding errors.

“The challenge for us is these notes can vary quite a bit,” Bockman said. “There’s a push to standardize, but that tends to make the doctor’s job a lot harder. Human health is complex, so it’s hard to come up with a format that works for every case.”

Coding an AI that Codes

As a machine learning problem, medical coding shares elements of two kinds of tasks: multilabel classification and sequence-to-sequence NLP. An effective AI must understand the text in a doctor’s note and accurately tag it with a list of diagnoses and procedures organized in the right order for billing.

Fathom is tackling this challenge, aided by tools such as NVIDIA’s GPU-optimized version of BERT, a leading natural language understanding model. The team uses the TensorFlow deep learning framework and relies on the mixed-precision training provided by Tensor Cores to accelerate the large-scale processing of medical documents that vary widely in size.

Using NVIDIA GPUs for inference allows Fathom to easily scale up to process upwards of millions of healthcare encounters per hour.

“While lowering costs matter, the ability to instantly add the capacity of thousands of medical coders to their operations has been the game-changer for our clients,” said Andrew Lockhart, Fathom’s co-founder and CEO.

Relying on NVIDIA GPUs on Google Cloud helps the team ramp its usage up and down based on demand.

“We have very bursty needs,” Bockman said, referring to the team’s fluctuating computational workload. “Sometimes we might be trying to retrain different variants of the same large model, while other times we’re doing a lot of experimentation or just doing inference. We might need a single GPU or many dozens of them.”

The startup chose Google Cloud, Bockman said, in part because the data is encrypted by default — one of the requirements for compliance with HIPAA and SOC 2 privacy requirements.

While medical coding is the main activity done today with doctor’s notes, unlocking the information contained in these health records could enable a wide range of use cases beyond billing and reimbursement, Bockman says.

AI that quickly and accurately analyzes medical charts and appointment records at scale can help doctors spot patient illnesses that may otherwise have been missed, predict likely patient outcomes, suggest treatment options — and even identify promising patient candidates for clinical trials.

The post Cure for the Common Code: San Francisco Startup Uses AI to Automate Medical Coding appeared first on The Official NVIDIA Blog.

College students are many things — sleepy, overly caffeinated, stressed — but above all, they are hungry. Kiwi Campus is here to help.

Co-founder and CEO of Kiwi Campus, Felipe Chávez, joined AI Podcast host Noah Kravitz to talk about Kiwi and its delivery service.

Based in Berkeley, Calif., the company specializes in creating a robotic ecosystem for last-mile delivery. Its solution is the Kiwibot. The small autonomous robot delivers orders seven days a week from 10 a.m. to 8 p.m. Its coverage area includes UC Berkeley and the surrounding streets.

Chávez, originally from Colombia, noticed how expensive it was to have food delivered in the US. He says online food ordering ranks at about 20 percent in Latin America’s largest cities. By contrast, when he moved to America, “it was 6 percent two years ago, and now it’s 9 percent.”

Given the American economy and level of productivity, Chávez says, “It’s insane that we’re not ordering several times per day.”

Kiwi Campus has a unique delivery system. It starts with Kiwi Trike, an autonomous tricycle, that brings Kiwibots to restaurants. Kitchen staff load the order into the Kiwibots, which then complete the final legs of the journey.

The Kiwibot runs on a blend of AI and human input. The bots themselves use a Jetson TX2, six ultra-HD cameras, and radar to navigate the streets of Berkeley. Chávez realized that the best way to avoid high-risk situations would be to incorporate human input.

Kiwi’s human workers are based in Colombia. Each person is assigned to three robots and provides observations, with a latency of just five seconds. Their role is to ensure that the robots “are in the correct direction. Also, sometimes we have a behavioral neural network that keeps the robot centered in the sidewalk but sometimes it’s not, so they keep it centered, and also giving extra input about position.”

Human observations also are key in crossing the street. Kiwibots are “crossing 2,000 streets per day,” says Chávez. Before each crossing, humans confirm the input each Kiwibot receives from traffic lights. They then cross the street safely.

This approach seems to be working — Kiwi Campus has had more than 30,000 orders in the last 10 months.

Chávez promises that Kiwi Campus will soon be in more than 10 campuses. In the meantime, you can visit their website to learn more, or connect with Chávez on twitter at @felipekiwi90.

The post Let Them Eat Take-Out: Kiwibots Bring Sustenance to Students appeared first on The Official NVIDIA Blog.

Choosing between an on-premises GPU system and the cloud is a bit like deciding between buying or renting a home.

Renting takes less capital up front. It’s pay as you go, and features like the washer-dryer unit or leaky roof repair might be handled by the property owner. If their millennial children finally move out and it’s time to move to a different-sized home, a renter is only obligated to stick around for as long as contract terms dictate.

Those are the key benefits of renting GPUs in the cloud: a low financial barrier to entry, support from cloud service providers and the ability to quickly scale up or down to a different-sized computing cluster.

Buying, on the other hand, is a one-time, fixed cost — once you purchase a property, stay there as long as you’d like. Unless they’re living with teenagers, the owner has full sovereignty over what goes on inside. There’s no lease agreement, so as long as everyone fits in the house, it’s okay to invite over a few friends and relatives for an extended stay.

And that’s the same reasoning for investing in GPUs on premises. An on-prem system can be used for as much time and as many projects as the hardware can handle, making it easier to iterate and try different methods without considering cost. For sensitive data like financial information or healthcare records, it might be essential to keep everything behind an organization’s firewall.

Depending on the use case at hand and the kind of data involved, developers may choose to build their AI tools on a deskside system, on-prem data center or in the cloud. More likely than not, they’ll move from one environment to another at different points in the journey from initial experimentation to large-scale deployment.

Using GPUs in the Cloud

Cloud-based GPUs can be used for tasks as diverse as training multilingual AI speech engines, detecting early signs of diabetes-induced blindness and developing media-compression technology. Startups, academics and creators can quickly get started, explore new ideas and experiment without a long-term commitment to a specific size or configuration of GPUs.

NVIDIA data center GPUs can be accessed through all major cloud platforms, including Alibaba Cloud, Amazon Web Services, Google Cloud, IBM Cloud, Microsoft Azure and Oracle Cloud Infrastructure.

Cloud service providers aid users with setup and troubleshooting by offering helpful resources such as development tools, pre-trained neural networks and technical support for developers. When a flood of training data comes in, a pilot program launches or a ton of new users arrive, the cloud lets companies easily scale their infrastructure to cope with fluctuating demand for computing resources.

Adding to cost-effectiveness, developers using the cloud for research, containerized applications, experiments or other projects that aren’t time-sensitive can get discounts of up to 90 percent by using excess capacity. This usage, known as “spot instances,” effectively subleases space on cloud GPUs not in use by other customers.

Users working on the cloud long term can also upgrade to the latest, most powerful data center GPUs as cloud providers update their offerings — and can often take advantage of discounts for their continued use of the platform.

Using GPUs On Prem 

When building complex AI models with huge datasets, operating costs for a long-term project can sometimes escalate. That might cause developers to be mindful of each iteration or training run they undertake, leaving less freedom to experiment. An on-prem GPU system gives developers unlimited iteration and testing time for a one-time, fixed cost.

Data scientists, students and enterprises using on-prem GPUs don’t have to count how many hours of system use they’re racking up or budget how many runs they can afford over a particular timespan.

If a new methodology fails at first, there’s no added investment required to try a different variation of code, encouraging developer creativity. The more an on-prem system is used, the greater the developer’s return on investment.

From powerful desktop GPUs to workstations and enterprise systems, on-prem AI machines come in a broad spectrum of choices. Depending on the price and performance needs, developers might start off with a single NVIDIA GPU or workstation and eventually ramp up to a cluster of AI supercomputers.

NVIDIA and VMware support modern, virtualized data centers with vComputeServer software and the NVIDIA NGC container registry. These help organizations streamline the deployment and management of AI workloads on virtual environments using GPU servers.

Healthcare companies, human rights organizations and the financial services industry all have strict standards for data sovereignty and privacy. On-prem deep learning systems can make it easier to adopt AI while following regulations and minimizing cybersecurity risks.

Using a Hybrid Cloud Architecture

For many enterprises, it’s not enough to pick just one method. Hybrid cloud computing combines both, taking advantage of the security and manageability of on-prem systems while also leveraging public cloud resources from a service provider.

The hybrid cloud can be used when demand is high and on-prem resources are maxed out, a tactic known as cloud bursting. Or a business could rely on its on-prem data center for processing its most sensitive data, while running dynamic, computationally intensive tasks in the hybrid cloud.

Many enterprise data centers are already virtualized and looking to deploy a hybrid cloud that’s consistent with the business’ existing computing resources. NVIDIA partners with VMware Cloud on AWS to deliver accelerated GPU services for modern enterprise applications, including AI, machine learning and data analytics workflows.

The service will allow hybrid cloud users to seamlessly orchestrate and live-migrate AI workloads between GPU-accelerated virtual servers in data centers and the VMware Cloud.

Get the Best of Both Worlds: A Developer’s AI Roadmap

Making a choice between cloud and on-prem GPUs isn’t a one-time decision taken by a company or research team before starting an AI project. It’s a question developers can ask themselves at multiple stages during the lifespan of their projects.

A startup might do some early prototyping in the cloud, then switch to a desktop system or GPU workstation to develop and train its deep learning models. It could move back to the cloud when scaling up for production, fluctuating the number of clusters used based on customer demand. As the company builds up its global infrastructure, it may invest in a GPU-powered data center on premises.

Some organizations, such as ones building AI models to handle highly classified information, may stick to on-prem machines from start to finish. Others may build a cloud-first company that never builds out an on-prem data center.

One key tenet for organizations is to train where their data lands. If a business’s data lives in a cloud server, it may be most cost-effective to develop AI models in the cloud to avoid shuttling the data to an on-prem system for training. If training datasets are in a server onsite, investing in a cluster of on-prem GPUs might be the way to go.

Whichever route a team takes to accelerate their AI development with GPUs, NVIDIA developer resources are available to support engineers with SDKs, containers and open-source projects. Additionally, the NVIDIA Deep Learning Institute offers hands-on training for developers, data scientists, researchers and students learning how to use accelerated computing tools.

Visit the NVIDIA Deep Learning and AI page for more.

Main image by MyGuysMoving.com, licensed from Flickr under CC BY-SA 2.0.

The post What’s the Difference Between Developing AI on Premises and in the Cloud? appeared first on The Official NVIDIA Blog.

Charter Communications is working to make customer service smarter even before an operator picks up the phone.

Senior Director of Wireless Engineering Jared Ritter took a break from his presentations at GTC in Santa Clara to talk to AI Podcast host Noah Kravitz about Charter’s perspective on customer relations.

Multi-service operators — operators that run multiple cable television systems — revolve around client relations. And when it comes to customer service, “cable companies don’t have the best reputations,” Ritter admits.

Charter Communications, also known as Spectrum, is using AI to improve their customer service and process data more intelligently.

The most common basis for customer service at a standard telecommunications company is called interactive voice response. This automated voice lists a menu to route customers to the correct line.

But this often takes too long or routes customers incorrectly. Kravitz admits that when he hears the automated voice, “I just start yelling ‘representative’ at it until someone answers or my wife takes the phone away.”

Charter wants to make it easier for clients to call and get help. “You want your customers to talk to you,” Ritter says. “And no matter how good your network is, you’re never gonna have a day where you don’t receive calls or questions from customers.”

The other aspect of customer service is called agency, which is what the company’s AI can do. Charter wants to move past the traditional use of AI to route customers to yet another menu.

To do so, Charter is challenging the data lake model. Ritter explains that, in this traditional setup, networks generate a large amount of data that pours into a lake and stays in its native format until it’s needed. It’s then more challenging to recognize and access important data.

“We’ve flipped the script on that, and we’ve got the antithesis of a data lake, where we’ve got the AI looking through all that data before we ever store it,” Ritter explains. Their AI is trained to look for key issues or trends, allowing customer service representatives to be better informed to help clients.

Their reps can then preemptively predict customer problems, rather than learning about network outages or other issues after the fact.

When asked what else AI can make possible for Charter’s customer service, Ritter reflects, “I can’t even think about what it’ll look like in five years, because every week something new happens.”

To find out more about what Charter is making possible, visit their newsroom.

Help Make the AI Podcast Better

Have a few minutes to spare? Fill out this short listener survey. Your answers will help us make a better podcast.

How to Tune in to the AI Podcast

Get the AI Podcast through iTunes, Castbox, DoggCatcher, Overcast, PlayerFM, Podbay, PodBean, Pocket Casts, PodCruncher, PodKicker, Stitcher, Soundcloud and TuneIn. Your favorite not listed here? Email us at aipodcast [at] nvidia [dot] com.

The post Dial A for AI: Charter Boosts Customer Service with AI appeared first on The Official NVIDIA Blog.