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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 the Future. Not only were these cars fast, they could think, talk and sometimes even see.

AI has given us the first generation of autonomous cars — and it’s pretty impressive. But there is a host of next generation of AI-enhanced features that go even further in providing convenience and ensuring passenger safety.

Auto-evolution: AI at the edge for cars

Xnor is focused on bringing computer vision to edge devices, so our technology is particularly valuable for automobiles and commercial vehicles. Every AI capability we offer – whether it involves people, object or face recognition – delivers a degree of speed and accuracy that until recently, was only possible using a high-end processor augmented by a neural accelerator. We take that same level of performance, improve upon it, and make it available on an edge device, such as a 1 GHz ARM processor or a simple onboard computer.

Check out this demo of our computer vision technology:

Object detection capabilities

Crime prevention

For car sharing companies or taxis, the system can enforce security regulations by recognizing when passengers hold weapons or other objects that present a safety hazard.

Loss prevention

Using object detection, the system can remind a passenger to retrieve the phone or purse they left on the seat. Transportation and logistics companies could receive an alert if a package was not delivered at the end of a route.

Face recognition capabilities

Here are a few of the capabilities that can be incorporated into a line of vehicles using Xnor’s face recognition or action detection models.

Secure access

Using face recognition, a driver can be authenticated even before they enter a vehicle. The door could automatically open for people recognized by the car, making hands-free entry possible. Our technology would even allow the car to differentiate between children and adults. Commercial vehicles could use that information to control access to certain areas by authorizing drivers.

Because all of this is done on-device, the data doesn’t need to be transmitted to the cloud, making it significantly more secure and practical of a feature.

Personalization

Once a driver or passenger is authenticated, the car could adjust settings to align with personal preferences, such as the position of the seat and steering column, interior temperature and infotainment system settings.

Driver awareness

ML-powered driver monitoring can tell when a driver is looking at a phone, instead of the road ahead. And if the driver becomes drowsy and their eyelids start to close, the system will know that too.

Emergency response

In the event of a crash or another emergency, the system can generate a passenger list, and notify someone if the driver does not respond to an audible alarm.

Passenger safety

Action detection models can be trained to detect specific gestures like fastening a seatbelt to ensure that everyone is buckled in.

Person and pet detection models can identify if a pet is left inside a car (a potentially dangerous situation on a hot day) or if an infant or small child is left behind, and then sound an alarm to notify the driver.

AI at the edge drives automotive innovation

Without recent advances in deep learning for computer vision, many of these features would be too difficult or expensive to implement.

Xnor’s AI technology is unique in that it delivers state-of-the-art performance on a commodity processor, using only the bare minimum for energy and memory.

Even with a simple onboard computer, Xnor models execute at up to 10x faster than conventional solutions – while using up to 15x less memory and 30x less energy.

Taken together, all these capabilities make it both practical and profitable for automobile manufacturers to incorporate high-performance computer vision into a variety of applications for the commercial and consumer vehicle markets.

At Xnor, we’re fascinated by the creative and powerful ways our customers are working to incorporate machine learning into their line of cars and commercial vehicles. It’s not as cool as owning one of the super-smart, fast-talking exotic cars that my TV heroes used to drive, but it comes pretty close.

Read more about how you can incorporate the latest in computer vision into your line of vehicles.

Mention Smart Appliance, and most people think of using a smartphone to turn on house lights as they pull in the driveway, arm security systems, control thermostats, or check if Amazon left a package on the front porch. Initially, that level of functionality was impressive. But so far, the value associated with Smart Appliances has been centered around heightened security and managing your home from a remote location.

It’s time Smart Appliances got an upgrade.

Smart Appliances V1

The first iterations of Smart Appliances were hampered by technical limitations. In some cases, the only smart thing about the earliest versions was touch screen interfaces, Bluetooth connectivity and the option to use a mobile device to control the appliance. Advanced features like food detection, if it was used at all, was constrained by the limitations inherent in AI technology at that time. One of those factors was the processing power needed to run an AI application. AI apps that could recognize and identify specific varieties of food required a robust processor with a neural or GPU accelerator, as well as an ample power source. Incorporating a power-hungry processor into the design of an energy-efficient appliance wasn’t practical. It also required a persistent, high bandwidth connection to the cloud. The resulting latency could delay system response to user input and create a poor customer experience. At any rate, aside from the onerous compute requirement, food detection models were still in their infancy. They were often inconsistent, and it was difficult to train them to identify new items.

The new generation of food identification technology promises to break through those barriers. With highly efficient algorithms, AI apps can be run on a small embedded device inside the appliance, without a persistent, high-bandwidth, internet connection.

Here are a few ways AI on the Edge can make a Smart refrigerator a little smarter:

  • Add items to a shopping list when they need to be replenished
  • Suggest a recipe based on the items you already have in your refrigerator
  • Make grocery shoppers faster and more informed
  • Make recommendations for how best to store certain produce
  • Provide cooking tips for certain foods
  • Detect when there’s a spill inside

With this kind of upgrade, homeowners can use the new generation of Smart Appliances to reduce their monthly grocery bill, reduce waste, and save time at the grocery store.

Compact, efficient algorithms are the brains behind smart appliances

With Xnor’s efficient, on-device computer vision models, smart appliances are now becoming a reality. Xnor’s food identification models offer appliance manufacturers some specific advantages over conventional AI solutions:

Improved performance

The new generation of food identification technology brings AI to edge devices, so there’s no need for internet connectivity. When Smart Appliances aren’t tethered to an internet connection, they are more responsive. Plus, there’s no risk of downtime due to a network or service outage. That translates into a better experience for consumers.

Improved accuracy

Even an item as ubiquitous as a Granny Smith apple comes in a variety of shades, sizes, and shapes. Our highly efficient training models deliver substantially higher accuracy, making it possible to visually identify food items in less than ideal lighting conditions, even if they are partially obscured.

Reduced energy use

Keeping energy consumption to a minimum is a top priority for appliance manufacturers. Xnor’s food detection models have been shown to be up to 30x more energy efficient than conventional AI technology.

Lower costs

Without the need for fast, power-hungry processors, the cost of introducing these features comes way down. Combined with low energy use and internet-free, on-device computing, its now possible to incorporate advanced food detection capabilities into a range of products at multiple price points.

Bon-Appetit

There’s a multitude of tasks involved in preparing a meal. By going beyond preserving and cooking food, refrigerators will begin to behave less like an appliance, and more like a virtual sous-chef. As a company that’s invested a significant amount of research in this area, we’d like to say, “Bon-Appetit!”

Visit us to learn how the next generation in food detection technology can boost the performance of your Smart Appliance.

Nearly forty years ago Paul Allen and Bill Gates set an audacious goal to put a computer on every desk and in every home. Since then we’ve seen our lives change as computers became increasingly available, miniaturizing from expensive mainframes to tremendously powerful handheld smartphones that nearly anyone can access.

I believe we’re on the brink of a similar breakthrough with artificial intelligence, and we are about to witness the next computer revolution. Until now, AI has required vast amounts of computing power to create and run deep learning models, relegating it to research, running in expensive data centers, or controlled by an elite group of cloud computing vendors. Where AI is truly needed is at the edge — cameras, sensors, mobile devices and IoT — where AI can interact with the real world in real-time

Jon Gelsey, Carlo C del Mundo, and Stephanie Wang in Xnor’s office

I’m excited and honored to be joining Xnor as CEO, joining Xnor’s founders — Professor Ali Farhadi and Dr. Mohammad Rastegari — to enable AI on billions of devices such as cameras, phones, wearables, autonomous vehicles and IoT devices that previously wasn’t feasible. Ali and Mohammad’s breakthrough discoveries have dramatically shrunk the compute requirements for advanced AI functions such as computer vision and speech recognition. Xnor is revolutionizing what’s possible on edge devices, delivering sophisticated AI on small and inexpensive devices, e.g. powerful computer vision even on something like a $5 Raspberry Pi Zero. We are already working with companies accomplishing amazing things on autonomous vehicles, home security, and on mobile devices.

Can AI Save Lives?

I’m also incredibly optimistic about the good that AI can bring to the world. Movies and science fiction often paint a dystopian future of how AI can be misused. Instead, I see many possibilities to improve lives — perhaps even save them. One of my friends is an avid sailor and I sometimes worry about what would happen if his boat capsized in a storm. Similar incidents in the recent past innovated by organizing crowdsourcing efforts enlisting people to scour satellite images of oceans spanning thousands of square miles to search for signs of survivors. As noble as these efforts were it was still looking for a needle in a haystack, with human eyes susceptible to fatigue reviewing imagery that quickly became out of date. I envision a future, already possible today, where autonomous search and rescue drones tirelessly traverse large expanses of ocean, equipped with cameras and utilizing deep machine learning to detect human life, boat wreckage, and survival gear in real-time to expedite a rescue.

Imagine drones using ai for search and rescue missions

What else is in the realm of possibility to improve our existence? One of the emerging areas of AI is human emotion detection and behavioral intent to improve retail experiences, utilizing deep learning models that measure consumer intent and engagement through movement and behavior. Those same concepts could be used to alert us to potential terrorist activity, human trafficking, and identify people in distress.

As with most exciting journeys, they’re rarely straight and can take a few surprise turns — but they are always memorable and worth venturing on. I’m looking forward to starting this one.

Learn more in our press release.

About Xnor.ai

Xnor.ai brings highly efficient AI to edge devices such as cameras, cars, drones, wearables and IoT devices. The Xnor platform allows product developers to run complex deep learning algorithms — previously restricted to the cloud — locally, on a wide range of mobile and low-energy devices. Xnor is a venture funded startup, founded on award winning research conducted at the University of Washington and the Allen Institute for Artificial Intelligence. Xnor’s industry-leading technology is used by global corporations in aerospace, automotive, retail, photography, and consumer electronics.