The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds — and looks — surprisingly low-tech.
This window to the future is none other than a piece of glass. University of Wisconsin–Madison engineers have devised a method to create pieces of “smart” glass that can recognize images without requiring any sensors or circuits or power sources.
“We’re using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass,” says UW-Madison electrical and computer engineering professor Zongfu Yu.
Yu and colleagues published details of their proof-of-concept research today in the journal Photonics Research.
Embedding artificial intelligence inside inert objects is a concept that, at first glance, seems like something out of science fiction. However, it’s an advance that could open new frontiers for low-power electronics.
Now, artificial intelligence gobbles up substantial computational resources (and battery life) every time you glance at your phone to unlock it with face ID. In the future, one piece of glass could recognize your face without using any power at all.
“This is completely different from the typical route to machine vision,” says Yu.
He envisions pieces of glass that look like translucent squares. Tiny strategically placed bubbles and impurities embedded within the glass would bend light in specific ways to differentiate among different images. That’s the artificial intelligence in action.
For their proof of concept, the engineers devised a method to make glass pieces that identified handwritten numbers. Light emanating from an image of a number enters at one end of the glass, and then focuses to one of nine specific spots on the other side, each corresponding to individual digits.
The glass was dynamic enough to detect, in real-time, when a handwritten 3 was altered to become an 8.
“The fact that we were able to get this complex behavior with such a simple structure was really something,” says Erfan Khoram, a graduate student in Yu’s lab.
Designing the glass to recognize numbers was similar to a machine-learning training process, except that the engineers “trained” an analog material instead of digital codes. Specifically, the engineers placed air bubbles of different sizes and shapes as well as small pieces of light-absorbing materials like graphene at specific locations inside the glass.
“We’re accustomed to digital computing, but this has broadened our view,” says Yu. “The wave dynamics of light propagation provide a new way to perform analog artificial neural computing”
One such advantage is that the computation is completely passive and intrinsic to the material, meaning one piece of image-recognition glass could be used hundreds of thousands of times.
“We could potentially use the glass as a biometric lock, tuned to recognize only one person’s face” says Yu. “Once built, it would last forever without needing power or internet, meaning it could keep something safe for you even after thousands of years.”
Additionally, it works at literally the speed of light, because the glass distinguishes among different images by distorting light waves.
“We could potentially use the glass as a biometric lock, tuned to recognize only one person’s face”
Although the up-front training process could be time consuming and computationally demanding, the glass itself is easy and inexpensive to fabricate.
In the future, the researchers plan to determine if their approach works for more complex tasks, such as facial recognition.
“The true power of this technology lies in its ability to handle much more complex classification tasks instantly without any energy consumption,” says Ming Yuan, a collaborator on the research and professor of statistics at Columbia University. “These tasks are the key to create artificial intelligence: to teach driverless cars to recognize a traffic signal, to enable voice control in consumer devices, among numerous other examples.”
Unlike human vision, which is mind-bogglingly general in its capabilities to discern an untold number of different objects, the smart glass could excel in specific applications — for example, one piece for number recognition, a different piece for identifying letters, another for faces, and so on.
“We’re always thinking about how we provide vision for machines in the future, and imagining application specific, mission-driven technologies.” says Yu. “This changes almost everything about how we design machine vision.”
The Latest on: Embedded artificial intelligence
via Google News
The Latest on: Embedded artificial intelligence
- AI enables banks to spot bias claims in customers' complaintson April 19, 2021 at 6:00 pm
You all will not let me breathe” is just one example in the CFPB’s complaint database where a consumer likened alleged mistreatment by a financial institution to social injustice. An artificial ...
- Healthcare Virtual Assistant Industry Size Worth USD 2830.1 Million by 2027 : Amazon, eGain Corporation, Nuance Communications, Etcon April 19, 2021 at 12:39 pm
Healthcare Virtual Assistant market was valued at USD 397.2 million in 2019 and is expected to reach USD 2830.1 million by the year 2027, at a CAGR of 27.2%.
- Crystallography companion agent for high-throughput materials discoveryon April 19, 2021 at 11:52 am
Crystallography companion agent enables autonomous characterization of powder X-ray diffraction data for organic and inorganic materials.
- Connectors of smart design and smart systemson April 19, 2021 at 10:20 am
Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them ...
- Tevatron Embedded & Product Design Serviceson April 18, 2021 at 9:44 pm
Tevatron Embedded & Product Design Services - Expertise in Engineering Development across ST MCU and MEMS portfolio, PP-TEVA-ENGSERV, STMicroelectronics ...
- Arduino customisable artificial intelligence and gesture recognitionon April 16, 2021 at 2:02 am
If you are interested in artificial intelligence and gesture recognition you may be interested in a new article published to the official Arduino blog ...
- Designing Embedded AI PCBs Made Easy with Coral from Google and Upverteron April 14, 2021 at 7:30 am
Upverter, now part of the Coral Partnership Program, is launching a new series of embedded AI-enabled board designs that work with Coral Intelligence.
- The future of artificial intelligence requires the guidance of sociologyon April 14, 2021 at 6:30 am
In the race to out-compete other companies– artificial intelligence (AI) design is lacking a deep understanding of what data about humans mean and its relation to equity. Two Drexel University ...
- How IoT And Artificial Intelligence Are the Perfect Partners To Boost Business Productivityon April 12, 2021 at 10:23 pm
Both the IoT and AI are playing a crucial role in letting businesses run their function more efficiently and drive success ...
- NVIDIA Announces Technology For Training Giant Artificial Intelligence Modelson April 12, 2021 at 4:21 pm
Goodson discusses NVIDIA GTC, the premier annual conference for developers, scientists, and businesses interested in machine learning or AI. He dives into one of Huang’s most exciting announcements of ...
via Bing News