Intel Helps IntellectEU Fight Insurance Fraud with ClaimShare

security cyberweek 2x1
What’s New: IntellectEU, a technology company focused on emerging technologies, digital finance and insurtech, has implemented Intel® Software Guard Extensions (Intel® SGX) to secure ClaimShare, its new insurance fraud detection platform. ClaimShare uses R3’s Conclave confidential computing platform powered by Intel SGX and enabled on Microsoft Azure. Additionally, ClaimShare utilizes Corda blockchain and artificial intelligence to help solve the insurance industry’s growing problem with fraudulent duplicate claims.

“The application of Intel SGX technology and confidential computing to help combat this prominent form of insurance fraud will be a game changer for insurance companies. GDPR (General Data Protection Regulation) and strict data privacy compliance is critical in the insurance industry, and innovative solutions like ClaimShare will support collaboration, communication and further privacy.”
–Michael Reed, Intel director of Confidential Computing

Why It Matters: According to the FBI, the total cost of non-health insurance fraud is estimated to be more than $40 billion a year, meaning insurance fraud costs the average U.S. family between $400 and $700 a year in increased premiums. And while insurance companies invest in fraud-prevention technologies to identify patterns of fraudulent behavior, they are often limited to internal data. Coupled with a lack of collaboration, this proves problematic when bad actors create multiple claims for the same loss event at multiple insurers – a duplicate claims fraud also called “double dipping.”

IntellectEU launched its innovative solution ClaimShare to solve this problem of “double-dipping.” ClaimShare’s industrywide platform facilitates secure data sharing between insurers, powered by confidential computing and Intel SGX. Confidentiality is crucial given regulatory and privacy constraints when sharing sensitive, personal insurance information.

“ClaimShare is the first industrywide platform that addresses these fraudulent challenges in the insurance industry while respecting business and client privacy. Until recently, there was no technology that supported this way of data exchange. With the recent advancements and adoption of enterprise blockchain and confidential computing, insurers can now securely and privately share and match data. We are fighting insurance fraud head-on,” said Chaim Finizola, director of ClaimShare.

How It Works: Once the insurer validates the claims, ClaimShare separates claims data into personally identifiable information (PII) and non-personally identifiable information (non-PII). Using the Corda distributed ledger, the non-PII is shared between the insurers and matched using fuzzy matching algorithms to identify suspicious claims. Once claims are suspected of being fraudulent, confidential computing is used to match the PII, confirming the fraud attempt before the second payout happens for the same claim.

ClaimShare offers a duplicate fraud claim verification solution across insurers, significantly decreasing the number of fraudulent claim payouts by enabling industry collaboration. This allows insurers to put public claims data on the ClaimShare ledger after verification so other insurers can check if the claim has already been paid.

Intel SGX uses a hardware-based trusted execution environment or enclave – an area of memory with a higher level of security protection – to help isolate and protect specific application code and data in memory. By creating a confidential computing environment with Intel SGX, ClaimShare can improve the security of encrypted data sharing and collaboration between insurers and help ensure privacy so that no competitive or sensitive information is leaked. The pilot detection program focused on auto insurance but can be replicated for other insurance products.

More Context: Security News at Intel

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The post Intel Helps IntellectEU Fight Insurance Fraud with ClaimShare appeared first on Intel Newsroom.

Meet the Maker: DIY Builder Takes AI to Bat for Calling Balls and Strikes

Baseball players have to think fast when batting against blurry-fast pitches. Now, AI might be able to assist.

Nick Bild, a Florida-based software engineer, has created an application that can signal to batters whether pitches are going to be balls or strikes. Dubbed Tipper, it can be fitted on the outer edge of glasses to show a green light for a strike or a red light for a ball.

Tipper uses image classification to alert the batter before the ball has traveled halfway to home plate. It relies on the NVIDIA Jetson edge AI platform for split-second inference, which triggers the lights.

He figures his application could be used to help as a training aid for batters to help recognize good pitches from bad. Pitchers also could use it to analyze whether any body language tips off batters on their delivery.

“Who knows, maybe umpires could rely on it. For those close calls, it might help to reduce arguments with coaches as well as the ire of fans,” said Bild.

About the Maker

Bild works in the telecom industry by day. By night, he turns his living room into a laboratory for Jetson experiments.

And Bild certainly knows how to have fun. And we’re not just talking about his living room-turned-batting cage. Self-taught on machine learning, Bild has applied his ML and Python chops to Jetson AGX Xavier for projects like ShAIdes, enabling gestures to turn on home lights.

Bild says machine learning is particularly useful to solve problems that are otherwise unapproachable. And for a hobbyist, he says, the cost of entry can also be prohibitively high.

His Inspiration

When Bild first heard about Jetson Nano, he saw it as a tool to bring his ideas to life on a small budget. He bought one the day it was first released and has been building devices with it ever since.

The first Jetson project he created was called DOOM Air. He learned image classification basics and put that to work to operate a computer that was projecting the blockbuster video game DOOM onto the wall, controlling the game with his body movements.

Jetson’s ease of use enabled early successes for Bild, encouraging him to take on more difficult projects, he says.

“The knowledge I picked up from building these projects gave me the basic skills I needed for a more elaborate build like Tipper,” he said.

His Favorite Jetson Projects

Bild likes many of his Jetson projects. His Deep Clean project is one favorite. It uses AI to track the places in a room touched by a person so that it can be sanitized.

But Tipper is Bild’s favorite Jetson project of all. Its pitch predictions are aided by a camera that can capture 100 frames per second. Facing the camera at the ball launcher — a Nerf gun —  it can capture two successive images of the ball early in flight.

Tipper was trained on “hundreds of images” of balls and strikes, he said. The result is that Jetson AGX Xavier classifies balls in the air to guide batters better than a first base coach.

As far as fun DIY AI, this one is a home run.

The post Meet the Maker: DIY Builder Takes AI to Bat for Calling Balls and Strikes appeared first on The Official NVIDIA Blog.

In the Drink of an AI: Startup Opseyes Instantly Analyzes Wastewater

Let’s be blunt. Potentially toxic waste is just about the last thing you want to get in the mail. And that’s just one of the opportunities for AI to make the business of analyzing wastewater better.

It’s an industry that goes far beyond just making sure water coming from traditional sewage plants is clean.

Just about every industry on earth — from computer chips to potato chips — relies on putting water to work, which means we’re all, literally, swimming in the stuff.

Just What the Doctor Ordered

That started to change, however, thanks to a conversation Opseyes founder Bryan Arndt, then a managing consultant with Denmark-based architecture and engineering firm Ramboll, had with his brother, a radiologist.

Arndt was intrigued when his brother described how deep learning was being set loose on medical images.

Arndt quickly realized that the same technology — deep learning — that helps radiologists analyze images of the human body faster and more accurately could almost instantly analyze images, taken through microscopes, of wastewater samples.

Faster Flow

The result, developed by Arndt and his colleagues at Ramboll, a wastewater industry leader for more than 50 years, dramatically speeds up an industry that’s long relied on sending tightly sealed samples of some of the stinkiest stuff on earth through the mail.

That’s critical when cities and towns and industries of all kinds are constantly taking water from lakes and rivers, like the Mississippi, treating it, and returning it to nature.

“We had one client find out their discharge was a quarter-mile, at best, from the intake for the next city’s water supply,” Arndt says. “Someone is always drinking what your tube is putting out.”

That makes wastewater enormously important.

Water, Water, Everywhere

It’s an industry that was kicked off by the 1972 U.S. Clean Water Act, a landmark not just in the United States, but globally.

Thanks to growing awareness of the importance of clean water, analysts estimate the global wastewater treatment market will be worth more than $210 billion by 2025.

The challenge: while almost every industry creates wastewater, wastewater expertise isn’t exactly ubiquitous.

Experts who can peer through a microscope and identify, say, the six most common bacterial “filaments” as they’re known in the industry, or critters such as tardigrades, are scarce.

You’ve Got … Ugh

That means samples of wastewater, or soil containing that water, have to be sent through the mail to get to these experts, who often have a backlog of samples to go through.

While Ardnt says people in his industry take precautions to seal potentially toxic waste and track it to ensure it gets to the right place, it’s still time-consuming.

The solution, Arndt realized, was to use deep learning to train an AI that could yield instantaneous results. To do this, last year Arndt reached out on social media to colleagues throughout the wastewater industry to send him samples.

Least Sexy Photoshoot Ever

He and his small team then spent months creating more than 6,000 images of these samples in Ramboll’s U.S. labs, where they build elaborate models of wastewater systems before deploying full-scale systems for clients. Think of it as the least sexy photoshoot, ever.

These images were then labeled and used by a data science  team lead by Robin Schlenga to train a convolutional neural network accelerated by NVIDIA GPUs. Launched last September after a year-and-a-half of development, Opseyes allows customers to use their smartphone to take a picture of a sample through a microscope and get answers within minutes.

It’s just another example of how expertise in companies seemingly far outside of tech can be transformed into an AI. After all, “no one wants to have to wait a week to know if it’s safe to take a sip of water,” Arndt says.

Bottoms up.

Featured image credit: Opseyes

The post In the Drink of an AI: Startup Opseyes Instantly Analyzes Wastewater appeared first on The Official NVIDIA Blog.

NVIDIA Deep Learning Institute Releases New Accelerated Data Science Teaching Kit for Educators

As data grows in volume, velocity and complexity, the field of data science is booming.

There’s an ever-increasing demand for talent and skillsets to help design the best data science solutions. However, expertise that can help drive these breakthroughs requires students to have a foundation in various tools, programming languages, computing frameworks and libraries.

That’s why the NVIDIA Deep Learning Institute has released the first version of its Accelerated Data Science Teaching Kit for qualified educators. The kit has been co-developed with Polo Chau, from the Georgia Institute of Technology, and Xishuang Dong, from Prairie View A&M University, two highly regarded researchers and educators in the fields of data science and accelerating data analytics with GPUs.

“Data science unlocks the immense potential of data in solving societal challenges and large-scale complex problems across virtually every domain, from business, technology, science and engineering to healthcare, government and many more,” Chau said.

The free teaching materials cover fundamental and advanced topics in data collection and preprocessing, accelerated data science with RAPIDS, GPU-accelerated machine learning, data visualization and graph analytics.

Content also covers culturally responsive topics such as fairness and data bias, as well as challenges and important individuals from underrepresented groups.

This first release of the Accelerated Data Science Teaching Kit includes focused modules covering:

  • Introduction to Data Science and RAPIDS
  • Data Collection and Pre-processing (ETL)
  • Data Ethics and Bias in Data Sets
  • Data Integration and Analytics
  • Data Visualization
  • Distributed Computing with Hadoop, Hive, Spark and RAPIDS

More modules are planned for future releases.

All modules include lecture slides, lecture notes and quiz/exam problem sets, and most modules include hands-on labs with included datasets and sample solutions in Python and interactive Jupyter notebook formats. Lecture videos will be included for all modules in later releases.

DLI Teaching Kits also come bundled with free GPU resources in the form of Amazon Web Services credits for educators and their students, as well as free DLI online, self-paced courses and certificate opportunities.

“Data science is such an important field of study, not just because it touches every domain and vertical, but also because data science addresses important societal issues relating to gender, race, age and other ethical elements of humanity,“ said Dong, whose school is a Historically Black College/University.

This is the fourth teaching kit released by the DLI, as part of its program that has reached 7,000 qualified educators so far. Learn more about NVIDIA Teaching Kits.

The post NVIDIA Deep Learning Institute Releases New Accelerated Data Science Teaching Kit for Educators appeared first on The Official NVIDIA Blog.

New Training Opportunities Now Available Worldwide from NVIDIA Deep Learning Institute Certified Instructors

For the first time ever, the NVIDIA Deep Learning Institute is making its popular instructor-led workshops available to the general public.

With the launch of public workshops this week, enrollment will be open to individual developers, data scientists, researchers and students. NVIDIA is increasing accessibility and the number of courses available to participants around the world. Anyone can learn from expert NVIDIA instructors in courses on AI, accelerated computing and data science.

Previously, DLI workshops were only available to large organizations that wanted dedicated and specialized training for their in-house developers, or to individuals attending GPU Technology Conferences.

But demand for in-depth training has increased dramatically in the last few years. Individuals are looking to acquire new skills and organizations are seeking to provide their workforces with advanced software development techniques.

“Our public workshops provide a great opportunity for individual developers and smaller organizations to get industry-leading training in deep learning, accelerated computing and data science,” said Will Ramey, global head of Developer Programs at NVIDIA. “Now the same expert instructors and world-class learning materials that help accelerate innovation at leading companies are available to everyone.”

The current lineup of DLI workshops for individuals includes:

March 2021

  • Fundamentals of Accelerated Computing with CUDA Python
  • Applications of AI for Predictive Maintenance

April 2021

  • Fundamentals of Deep Learning
  • Applications of AI for Anomaly Detection
  • Fundamentals of Accelerated Computing with CUDA C/C++
  • Building Transformer-Based Natural Language Processing Applications
  • Deep Learning for Autonomous Vehicles – Perception
  • Fundamentals of Accelerated Data Science with RAPIDS
  • Accelerating CUDA C++ Applications with Multiple GPUs
  • Fundamentals of Deep Learning for Multi-GPUs

May 2021

  • Building Intelligent Recommender Systems
  • Fundamentals of Accelerated Data Science with RAPIDS
  • Deep Learning for Industrial Inspection
  • Building Transformer-Based Natural Language Processing Applications
  • Applications of AI for Anomaly Detection

Visit the DLI website for details on each course and the full schedule of upcoming workshops, which is regularly updated with new training opportunities.

Jump-Start Your Software Development

As organizations invest in transforming their workforce to benefit from modern technologies, it’s critical that their software and solutions development teams are equipped with the right skills and tools. In a market where developers with the latest skills in deep learning, accelerated computing and data science are scarce, DLI strengthens their employees’ skillsets through a wide array of course offerings.

The full-day workshops offer a comprehensive learning experience that includes hands-on exercises and guidance from expert instructors certified by DLI. Courses are delivered virtually and in many time zones to reach developers worldwide. Courses are offered in English, Chinese, Japanese and other languages.

Registration fees cover learning materials, instructors and access to fully configured GPU-accelerated development servers for hands-on exercises.

A complete list of DLI courses are available in the DLI course catalog.

Register today for a DLI instructor-led workshop for individuals. Space is limited so sign up early.

For more information, visit the DLI website or email nvdli@nvidia.com.

The post New Training Opportunities Now Available Worldwide from NVIDIA Deep Learning Institute Certified Instructors appeared first on The Official NVIDIA Blog.

Miracle Qure: Founder Pooja Rao Talks Medical Technology at Qure.ai

Pooja Rao, a doctor, data scientist and entrepreneur, wants to make cutting-edge medical care available to communities around the world, regardless of their resources. Her startup, Qure.ai, is doing exactly that, with technology that’s used in 150+ healthcare facilities in 27 countries.

Rao is the cofounder and head of research and development at the Mumbai-based company, which started in 2016. Qure.ai is also a member of the NVIDIA Inception startup accelerator program. The company develops AI technology that interprets medical images, with a focus on pulmonary and neurological scans.

Qure.ai technology has proven extremely useful in rapidly diagnosing tuberculosis, a disease that infects millions each year and can cause death if not treated early. By providing fast diagnoses and compensating in areas with fewer trained healthcare professionals, Qure.ai is saving lives.

Their AI is also helping to prioritize critical cases in teleradiology. Teleradiologists remotely analyze large volumes of medical images, with no way of knowing which scans might portray a time-sensitive issue, such as a brain hemorrhage. Qure.ai technology analyzes and prioritizes the scans for teleradiologists, reducing the time it takes them to read critical cases by 97 percent, according to Rao.

Right now, a major focus is helping fight COVID-19 — Qure.ai’s AI tool qXR is helping monitor disease progression and provide a risk score, aiding triage decisions.

In the future, Rao anticipates eventually building Qure.ai technology into medical imaging machinery to identify areas that need to be photographed more closely.

Key Points From This Episode:

  • Qure.ai has just received its first U.S. FDA approval. Its technology has also been acknowledged by the World Health Organization, which recently officially endorsed AI as a means to diagnose tuberculosis, especially in areas with fewer healthcare professionals.
  • Because Qure.ai’s mission is to create AI technology that can function in areas with limited resources, it has built systems that have learned to work with patchy internet and images that aren’t of the highest quality.
  • In order to be a global tool, Qure.ai partnered with universities and hospitals to train on data from patients of different genders and ethnicities from around the world.

Tweetables:

“You can have the fanciest architectures, but at some point it really becomes about the quality, the quantity and the diversity of the training data.” — Pooja Rao [7:46]

“I’ve always thought that the point of studying medicine was to be able to improve it — to develop new therapies and technology.” — Pooja Rao [18:57]

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The post Miracle Qure: Founder Pooja Rao Talks Medical Technology at Qure.ai appeared first on The Official NVIDIA Blog.

Demetics Protects AI-Based Medical Innovation with Intel SGX

What’s New: Demetics Medical Technology Co. Ltd. is using Intel® Software Guard Extension (Intel® SGX) and Intel® oneAPI Math Kernel Library (oneMKL) to protect its medical artificial intelligence (AI) algorithms and intellectual property (IP) in medical devices at the edge. A pioneer in China of AI-based ultrasonography, Demetics accelerated adoption of DE-Light, its independently developed deep-learning framework that has shown outstanding performance and improved the accuracy of thyroid nodule detection under an open source framework by 30% to 40%.1

“Due to the nature of the medical industry, some artificial intelligence vendors have to deploy their solutions on the client side. Therefore, protecting the core intellectual property of manufacturers’ AI models has become an urgent need. The trusted execution environment technology based on Intel SGX technology has become the most cost-effective and ultimate solution for runtime protection. With the support of Intel, they [Demetics] strategically apply SGX technology to a series of AI products, including its ultrasonic AI products, greatly enhancing its market competitiveness and innovation capabilities.”
–Yali Liang, Intel vice president in the Sales, Marketing and Communications Group and general manager of PRC Business Consumption

Why It Matters: Ultrasonic imaging diagnosis and analysis are widely used and an important part of current healthcare diagnosis and treatment; however, imaging examination depends heavily on manual analysis and can be inefficient.

One of AI’s greatest values in medicine is its quick analysis of a large amount of data and its ability to obtain accurate results. By using intelligent imaging diagnostics, AI technology in diagnosis and analysis can help medical institutions accelerate detection and treatment. Doctors can improve the efficiency of film reading, reduce the probability of misdiagnosis and obtain diagnostic assistance, while patients can receive more accurate diagnoses and personalized treatment recommendations.

Demetics has successfully deployed AI medical application products in more than 400 hospitals across China, and it plans to deploy in more than 1,000 hospitals in the coming year.

How It Works: Demetics launched the AI-SONIC™ computer-aided diagnosis system for ultrasonic imaging and also developed several other AI products that use Intel SGX to protect algorithms. In the ultrasonic AI model solution, to ensure that the algorithm in Intel SGX can make full use of the computing power of Intel® processors, the project team, with the help of the Intel® C++ Compiler, manually optimized the core calculation of the algorithm floating-point matrix multiplication modeled on oneMKL.

demetics ultrasonic model protection
AI-powered ultrasonic model protection solution based on Intel architecture.
» Click for full image

oneMKL is a highly optimized and parallel library of mathematical functions for Intel and its compatible processors. In different operating environments, oneMKL can automatically run runtime processor detection and specifically optimized programs on different processors, thus enabling better performance across all its processors. Intel® oneAPI Math Kernel Library (oneMKL) and Intel® oneAPI Deep Neural Network Library (oneDNN) provide underlying support for commonly used deep learning frameworks and numerous self-developed AI applications.

Intel SGX is a set of instructions that increase the security of application code and data, offering them more protection from disclosure or modification. Developers can partition sensitive information into a hardware-based trusted execution environment (TEE) or enclave — an area of memory with a higher level of security protection. The technology helps ensure the root of trust is limited to a small portion of the central processing unit’s hardware, better protecting the confidentiality and integrity of code and data.

More Context: Security News

Intel Partner Stories: Intel Customer Spotlight on Intel.com | Partner Stories on Intel Newsroom

The Small Print:

1 The test results are quoted from internal evaluation of Demetics. For more details, please contact Demetics.

The post Demetics Protects AI-Based Medical Innovation with Intel SGX appeared first on Intel Newsroom.

A Capital Calculator: Upstart Credits AI with Advancing Loans

With two early hits and the promise of more to come, it feels like a whole new ballgame in lending for Grant Schneider.

The AI models he helped create as vice president of machine learning for Upstart are approving more personal loans at lower interest rates than the rules traditional banks use to gauge credit worthiness.

What’s more, he’s helping the Silicon Valley startup, now one of the newest public companies in the U.S., pioneer a successful new hub of AI development in Columbus, Ohio.

A Mentor in the Midwest

Schneider’s career has ridden an AI rocket courtesy of two simple twists of fate.

“In the 2009 downturn, I was about to graduate from Ohio State in finance and there were no finance jobs, but a mentor convinced me to take some classes in statistics,” he said.

He wound up getting a minor, a master’s and then a Ph.D. in the field in 2014, just as machine learning was emerging as the hottest thing in computing.

“Then I read about Upstart in a random news article, sent them a cold email and got a response — I was blown away by the team,” he said.

A Breakthrough with Big Data

Schneider signed on as a data scientist, experimenting with ways to process online loan requests from the company’s website. He trained AI models on publicly available datasets while the startup slowly curated its own private trove of data.

The breakthrough came with the first experiment training a model on Upstart’s own data. “Overnight our approval rates nearly doubled … and over time it became clear we were actually moving the needle in improving access to credit,” he said.

As the business grew, Upstart gathered more data. That data helped make models more accurate so it could extend credit to more borrowers at lower rates. And that attracted more business.

Riding the Virtuous Cycle of AI

The startup found itself on a flywheel it calls the virtuous cycle of AI.

“One of the coolest parts of working on AI models is they directly drive the interest rates we can offer, so as we get better at modeling we extend access to credit — that’s a powerful motivator for the team,” he said.

Borrowers like it, too. More than 620,000 of them were approved by Upstart’s models to get a total $7.8 billion in personal loans so far, about 27 percent more than would’ve been approved by traditional credit models, at interest rates 16 percent below average, according to a study from the U.S. Consumer Financial Protection Bureau.

The figures span all demographic groups, regardless of age, race or ethnicity. “Our AI models are getting closer to the truth of credit worthiness than traditional methods, and that means there should be less bias,” Schneider said.

Betting on the Buckeyes

As it grew, the Silicon Valley company sought a second location where it could expand its R&D team. A study showed the home of Schneider’s alma mater could be a good source of tech talent, so the Ohio State grad boomeranged back to the Midwest.

Columbus exceeded expectations even for a bullish Schneider. What was going to be a 140-person office in a few years has already hit nearly 250 people primarily in AI, software engineering and operations with plans to double to 500 soon.

“Having seen the company when it was 20 people in a room below a dentist’s office, that’s quite a change,” Schneider said.

GPUs Slash Test Time

Upstart has experience with nearly a dozen AI modeling techniques and nearly as many use cases. These days neural networks and gradient-boosted trees are driving most of the gains.

The models track as many as 1,600 variables across data from millions of transactions. So Upstart can use billions of data points to test competing models.

“At one point, these comparisons took more than a day to run on a CPU, but our research found we could cut that down by a factor of five by porting the work to GPUs,” Schneider said.

These days, Upstart trains and evaluates new machine-learning models in a few hours instead of days.

The Power of Two

Looking ahead, the company’s researchers are experimenting with NVIDIA RAPIDS, libraries that quickly move data science jobs to GPUs.

Schneider gives a glowing report of the “customer support on steroids” his team gets from solution architects at NVIDIA.

“It’s so nice for our research team to have experts helping us solve our problems. Having a proactive partner who understands the technology’s inner workings frees us up to focus on interesting business problems and turn around model improvements that affect our end users,” he said.

Early Innings for AI Banking

As a startup, the company built and tested models on GPU-powered laptops. These days it uses the cloud to handle its scaled up AI work, but Schneider sees the potential for another boomerang in the future with some work hosted on the company’s own systems.

Despite its successful IPO in December, it’s still early innings for Upstart. For example, the company started offering auto loans in September.

Going public amid a global pandemic “was a very surreal and exciting experience and a nice milestone validating years of work we’ve put in, but were still early in this company’s lifecycle and the most exciting things are still ahead of us,” he said. “We’re still far from perfectly predicting the future but that’s what we’re aiming at,” he added.

Visit NVIDIA’s financial services industry page to learn more.

The post A Capital Calculator: Upstart Credits AI with Advancing Loans appeared first on The Official NVIDIA Blog.

Fetching AI Data: Researchers Get Leg Up on Teaching Dogs New Tricks with NVIDIA Jetson

AI is going to the dogs. Literally.

Colorado State University researchers Jason Stock and Tom Cavey have published a paper on an AI system to recognize and reward dogs for responding to commands.

The graduate students in computer science trained image classification networks to determine whether a dog is sitting, standing or lying. If a dog responds to a command by adopting the correct posture, the machine dispenses it a treat.

The duo relied on the NVIDIA Jetson edge AI platform for real-time trick recognition and treats.

Stock and Cavey see their prototype system as a dog trainer’s aid — it handles the treats — or a way to school dogs on better behavior at home.

“We’ve demonstrated the potential for a future product to come out of this,” Stock said

Fetching Dog Training Data

The researchers needed to fetch dog images that exhibited the three postures. They found the Stanford Dogs datasets had more than 20,000 at many positions and image sizes, requiring preprocessing. They wrote a program to help quickly label them.

To refine the model, they applied features of dogs from ImageNet to enable transfer learning. Next, they applied post-training and optimization techniques to boost the speed and reduce model size.

For optimizations, they tapped into NVIDIA’s Jetpack SDK on Jetson, offering an easy way to get it up and running quickly and to access the TensorRT and cuDNN libraries, Stock said. NVIDIA TensorRT optimization libraries offered “significant improvements in speed,” he added.

Tapping into the university’s computing system, Stock trained the model overnight on two 24GB NVIDIA RTX 6000 GPUs.

“The RTX GPU is a beast — with 24GB of VRAM, the entire dataset can be loaded into memory,” he said. “That makes the entire process way faster.”

Deployed Models on Henry

The researchers tested their models on Henry, Cavey’s Australian Shepherd.

They achieved model accuracy in tests of up to 92 percent and an ability to make split-second inference at nearly 40 frames per second.

Powered by the NVIDIA Jetson Nano, the system makes real-time decisions on dog behaviors and reinforces positive actions with a treat, transmitting a signal to a servo motor to release a reward.

“We looked at Raspberry Pi and Coral but neither was adequate, and the choice was obvious for us to use Jetson Nano,” said Cavey.

Biting into Explainable AI 

Explainable AI helps provide transparency about the makeup of neural networks. It’s becoming more common in the financial services industry to understand fintech models. Stock and Cavey included model interpretation in their paper to provide explainable AI for the pet industry.

They do this with images of the videos that show the posture analysis. One set of images relies on GradCAM — a common technique to display where a convolutional neural network model is focused. Another set of images explains the model by tapping into Integrated Gradients, which helps analyze pixels.

The researchers said it was important to create a trustworthy and ethical component of the AI system for trainers and general users. Otherwise, there’s no way to explain your methodology should it come into question.

“We can explain what our model is doing, and that might be helpful to certain stakeholders — otherwise how can you back up what your model is really learning?” said Cavey.

The NVIDIA Deep Learning Institute offers courses in computer vision and the Jetson Nano.

The post Fetching AI Data: Researchers Get Leg Up on Teaching Dogs New Tricks with NVIDIA Jetson appeared first on The Official NVIDIA Blog.

Making Machines More Human: Author Brian Christian Talks the Alignment Problem

Not many can claim to be a computer programmer, nonfiction author and poet, but Brian Christian has established himself as all three.

Christian has just released his newest book, The Alignment Problem, which delves into the disparity that occurs when AI models don’t do exactly what they’re intended to do.

The book follows on the success of Christian’s previous work, The Most Human Human and Algorithms to Live By. Now a visiting scholar at UC Berkeley, Christian joined AI Podcast host Noah Kravitz to talk about the alignment problem and some new techniques being used to address the issue.

The alignment problem can be caused by a range of reasons — such as data bias, or datasets used incorrectly and out of context. As AI takes on a variety of tasks, from medical diagnostics to parole sentencing decisions, machine learning researchers are expressing concern over the problem.

Listen to the full podcast to hear about this and more — including Christian’s book club experience with Elon Musk and why he chose to double major in philosophy and computer science.

Key Points From This Episode:

  • The Alignment Problem features insights from hundreds of interviews Christian did with those he calls “first responders” to the ethical and scientific concerns surrounding the issue. He believes this group is evolving into a new interdisciplinary field.
  • Christian is also director of technology at McSweeney’s Publishing and scientific communicator in residence at Simon’s Institute for the Theory of Computing. He talks to Kravitz about how he managed to combine his love for both computer science and creative writing in his current career.

Tweetables:

“Philosophy and computer science are really on a collision course.” — Brian Christian [20:23]

“This new interdisciplinary field, thinking about … how exactly are we going to get human norms into these ML systems.” — Brian Christian [26:25]

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The post Making Machines More Human: Author Brian Christian Talks the Alignment Problem appeared first on The Official NVIDIA Blog.