December 14, 2017 -
This is a guest post by George Brostoff, CEO of SensibleVision.
It is safe to say that AI has experienced a tremendous growth spurt in 2017. While some of the talk seems to border on marketing hype that has gotten way ahead of the actual functionality being delivered, there is no doubt that AI and machine learning capabilities are expanding across a wide range of use cases. From Google Home all the way up to that gaggle of Power 7 servers sitting in a refrigerated room called IBM Watson – AI is here.
As an entrepreneur and security geek with over 20 years’ experience working in the biometric-driven authentication space, I am very excited about AI’s potential to dramatically transform and improve secure authentication on mobile devices. Increasingly powerful AI and machine learning solutions have been making their way into smaller and smaller form factors and are finally being deployed in smartphones and tablets.
There are certainly many different players exploring this space. Apple’s new iPhone X has a Neural Engine as part of its A11 Bionic chip. Chinese handset maker Huawei has a Neural Processing Unit (NPU) on its Kiri 970 chip and describes its upcoming Mate 10 as a “real AI phone.” Google’s Pixel 2 handset boasts an AI-powered imaging chip. Samsung’s next Exynos SoC is rumored to feature a dedicated AI chip, too. Intel, Nvidia, and others are all working on their own versions of artificial intelligence processing products. The race is definitely underway and speeding up every day.
While these new AI chips have initially gained traction delivering improved voice and image recognition as well as facilitating language translation, their real transformational value, in my opinion, is their ability to enable fast, secure, user-friendly and virtually spoof proof security. Connecting the current generation of 3D cameras to AI chips will deliver the next generation of secure authentication on mobile devices.
All this aside, we do need to take a step back and look at the AI transformation in a larger context. While today’s developments are remarkable, they are based on the simple fact that it makes more sense to create a custom processor focused on a specific type of task. It is a much smarter approach to design a single dedicated component that can handle a specific set of tasks very efficiently and that is what we are seeing in the case of chips with AI and machine learning capabilities.
Mobile AI allows smartphones to compare and map distances in order to detect masks or printouts that are not the exact size of the authorized user. These chips allow a device to identify differences in IR reflectance to detect live skin versus latex, plastic or other materials, as well as capture and compare unique 3D facial contours. In addition, by tracking the dimensions of and differences between a person’s various facial features, a confirmed template of an authorized user can be created, thus reducing the risk of false positives or false rejections when a user tries to access their device.
The broader business implications are tremendous. Imagine the impact to mobile banking and other financial transactions as well as retail and healthcare. If consumers felt that AI was working alongside them to deliver virtually spoof proof access to applications and data, it would drive increased user confidence which translates to more meaningful interaction which in turn results in attributable revenue.
While we are still in the early stages, I am excited about the potential of mobile AI to usher in a new era of secure and user-friendly mobile authentication, and the implications for the broader business benefits that will result.
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