University of Waterloo researcher Alexander Wong didn’t have enough processing power for his computer vision startup, so he developed a workaround. That workaround is now the company’s product.

Ontario-based DarwinAI, founded by a team from the Ontario-based university, provides a platform for developers to generate slimmed-down models from neural networks. This offers a quicker way for developers to spin out multiple networks with smaller data footprints.

The company’s lean models are aimed at businesses developing AI-based edge computing networks to process mountains of sensor data from embedded systems and mobile devices.

Industries of all stripes — autonomous vehicles, manufacturing, aerospace, retail, healthcare and consumer electronics — are developing next-generation businesses with AI computing at the edge of their GPU-powered networks.

It’s estimated that by 2025 some 150 billion machine sensors and IoT devices will stream continuous data for processing.

Yet many find that talent and computing resources run high to build these various models.

DarwinAI’s position is that companies can reduce development time and costs — like DarwinAI did for themselves — by using its platform to spin out compact models from full-sized ones.

“We can enable AI at the edge for mobile devices and clients who need to put powerful neural networks into cars, watches, airplanes and other areas,” said Sheldon Fernandez, CEO and co-founder at DarwinAI.

Generative Synthesis: Hello, World 

DarwinAI’s platform, dubbed GenSynth, is the result of pioneering research on what’s called generative synthesis. There’s an easy way to think of generative synthesis: It’s AI to create AI.

The startup’s founders late last year released a research paper on generative synthesis and then fused that with its proprietary research to launch the company’s offering.

DarwinAI’s platform relies on machine learning to probe and understand the architecture of neural networks for customers. Then its AI generates a new family of neural networks that are functionally equivalent to the original but smaller and faster, according to the company.

The company is a member of the NVIDIA Inception program that helps startups move to market faster.

Audi Rides DarwinAI Networks 

The startup’s research has attracted interest from consumer electronics companies, aerospace and automakers, including Audi.

Audi’s case study with DarwinAI used the GenSynth platform to accelerate design of custom, optimized deep neural networks for object detection in autonomous driving.

The GenSynth platform helped Audi developers train models 4x faster and slash GPU processing time by three-fourths.

“They worked with two terrabytes of data, and we really reduced the testing time,” said Fernandez. “There’s real savings for their GPU training time and real benefits for the developers.”

GPUs for GenSynth

DarwinAI developed GenSynth to reduce its own development time, tapping into NVIDIA GPUs on AWS and Microsoft Azure and local instances on premises to boost its coding cycles.

Many of DarwinAI’s early customers are now using the platform to speed their development. It also helps reduce the data processed on customers’ systems running NVIDIA Jetson modules on site and NVIDIA V100 Tensor Core GPUs in the cloud for training and inference.

“Deep learning is so complex that you need to collaborate with AI enabled by GPUs to do it properly — it will free up your time to do the creative work,” said Fernandez.

Image credit: Taken at the University of Waterloo by Victor Vucicevich; licensed under Creative Commons Attribution-Share Alike 3.0 Unported.

The post AI Builds AI: Startup’s AI Generates Compact Neural Networks appeared first on The Official NVIDIA Blog.

Security guards. Closed circuit TV. Anti-theft tags and alarms.

Retailers are constantly battling shoplifters to protect store profits — up to 50 percent of which are lost to theft.

Now, they have a new weapon in their armory.

ThirdEye Labs, a London-based company and member of Inception, NVIDIA’s startup incubator, is combining off-the-shelf CCTV cameras with state-of-the-art AI algorithms to detect fraudulent activities in stores.

Caught AI Handed

Every year, U.S. retailers lose up to $32.25 billion due to theft.

In addition to those pocketing items straight from the shelves, it’s estimated that one percent of all customers who visit self-service checkouts steal. Sometimes it’s accidental — an item doesn’t scan through properly or the wrong type of pastry is selected from the bakery menu.

But some supermarket stealers are more slick — following schemes such as “the banana trick” (steaks scanned as potatoes) or “the switcheroo” (scanning the barcodes of cheaper items, instead of a pricier purchase).

To date, retailers’ attempts to deter thieves have had little effect. Hiring more security personnel is expensive and creates unpleasant shopping experiences. While security alarms are evaded and self-service counters continue to be deceived.

ThirdEye Labs’ AI algorithms help security staff work more effectively and efficiently. Trained on NVIDIA GPUs, the company’s deep learning networks can detect specific indicators of fraudulent behavior from CCTV footage and then alert staff, who can take appropriate action on the spot.

“We chose to train our algorithms on NVIDIA GPUs as they are fast, reliable and effective,” said Raz Ghafoor, CEO and co-founder at ThirdEye Labs. “Without the power of these GPUs, our development time would have doubled.”

ThirdEye Labs’ AI software can be used with existing security infrastructure — no additional hardware or software is needed. None of the video footage used is recorded or stored anywhere and the system doesn’t perform any facial recognition, meaning the system is GDPR compliant.

At stores where ThirdEye Labs’ system has been introduced at self-service checkouts, the AI technology analyzes every scan to detect non-scans, non-payments, substitute scanning and fraudulent refunds. Over the course of a month, two stores caught 27 thieves in action, up from basically zero, by implementing ThirdEye Labs’ point-of-sale system.

In the aisles, too, fraudulent behavior hasn’t gone unnoticed. ThirdEye Labs’ “In-Aisle Theft Detector” sends security guards push notifications every time someone picks up high-risk items, like champagne bottles or fresh meat. They can then decide whether or not to take action, helping them work more efficiently and effectively.

The service has saved stores tens of thousands of dollars in losses by helping security guards have their eyes on the right person, at the right time.

The Future of Convenient Shopping

ThirdEye Labs plans to expand its technology further to improve customer shopping experiences.

Its “Queue Detector” will predict when lots of customers are about to get in checkout lines. By alerting staff, tills can be manned before the rush.

Its “Stock-out Detector” will help stores monitor their shelves and identify when stock is low. Empty shelves cost retailers an estimated three percent of their total revenue each year, so optimizing stock replenishment has big benefits for sellers as well as those looking to purchase.

Image credit: kc0uvb

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