Entries by Xnor.ai

Auto-evolution: the surprising things cars can do with AI at the Edge

I’ve loved cars since I was a little boy. From classic cars to custom hot rods, I loved them all, but I was especially fascinated by the futuristic vehicles featured on TV. Depending on which generation you identify with, you might remember Kitt from Knight Rider, the Batmobile, or the nameless Delorean from Back to […]

,

How Retailers Can Get a Competitive edge with the Next Generation in AI

Search for the term “the future of retailing” and you’ll see plenty of stories about physical retailers being marginalized by their dot-com counterparts. Some would say that physical stores are fading from the retail landscape. Quaint, but doomed. To understand why consider the shopping experiences offered by each channel. Online vs. Offline For example, while checking […]

,

AI at the Edge: Accurate Face Recognition on Resource-Constrained Devices with Xnor.ai

  2010 was a milestone year for face recognition. That’s when Facebook introduced a photo tagging feature with the ability to identify individuals in a photograph by matching faces to the pictures stored in a user’s profile. The feature was popular but frequently inaccurate. Getting the best results required the people in the photograph to […]

,

How Xnor.ai Enables Object Detection at the Edge

Machine vision has long been the holy grail to unlocking a number of real-world use cases for AI – think of home automation and security, autonomous vehicles, crop picking robots, retail analytics, delivery drones or real time threat detection. However, until recently, AI models for computer vision have been constrained to expensive hardware with sophisticated […]

,

The Intelligence that Makes Smart Homes Smart

Much of the convenience and security that Smart Homes have claimed to promise has yet to become a reality. To understand why, consider that the technology behind a Smart Home historically required significant CPU power combined with a GPU or an accelerator chip to provide capabilities like object detection and face identification. To keep solutions […]

,

Take Video Conferencing to the Next Level with AI Image Segmentation

Andrew, one of Xnor.ai’s engineers, showing a demo of Image Segmentation running off a webcam video feed, using 60 MB of memory and just the CPU – no GPU necessary. With so many meetings involving participants from multiple locations, it’s no surprise that video conferencing has quickly become an essential collaboration tool. Best-in-class solutions allow […]

, ,

Real-Time Image Segmentation for Mobile, Retail and Videoconferencing

Imagine being able to create more focus in your video-conference, or transport users to a different world in a mobile app experience. Image segmentation, a computer vision machine learning task, makes this a reality by creating pixel-accurate image masks of detected objects. Computer vision is progressing at such a rapid rate that these tasks can […]

,

Xnor.ai and Toradex at Arm TechCon 2018

Today we’re featuring Toradex, one of our hardware partners who will be exhibiting at Arm TechCon this week in San Jose, California. If you’re at the conference, come visit booth #1134 to see a joint demonstration of Xnor running on Toradex’s efficient Arm system-on-modules. On a single board we have been able to get Xnor object-detection models running real-time on […]

,

AI At the Edge On a Raspberry Pi Zero

At Xnor.ai we work on every aspect of computing platforms to optimize artificial intelligence and machine learning, from the software down to the hardware. We have a diverse set of skills, so it is easy to quickly build a prototype for an end-to-end project. Saman Naderiparizi, PhD, is an Xnor.ai hardware engineer and is here […]