CES 2022: AI is driving innovation in smart tech – VentureBeat

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Despite all the stories about big companies bailing out of CES 2022 amidst the latest surge in COVID-19 cases, the consumer electronics show in Las Vegas is still the place to be for robots, autonomous vehicles, smart gadgets, and their inventors an opportunity to take stock of whats required to build practical machine intelligence into a consumer product.

A sampling of the innovations VentureBeat heard about in advance briefings:

OrCam and Sonatus are among the companies no longer planning to travel to Las Vegas or announce products at CES, and its possible some of the other vendors VentureBeat interviewed in advance of the event will also be no-shows. Big names like Microsoft, Google, Intel, Amazon, and T-Mobile backed out in recent weeks. Augmented reality, virtual reality, and the metaverse will be topics of discussion that will have to proceed without Meta (the company formerly known as Facebook). Automotive tech will be a big theme of the event, but General Motors, BMW, and Mercedes-Benz decided not to make the drive (GMs all-digital presence is still supposed to include a video keynote from CEO Mary Barra on Wednesday). On the other hand, some like Perrone Robotics had already shipped vehicles and a test track set up, indicating their commitment

Still, the Consumer Technology Association, which sponsors the event, determined that the show must go on. Despite the big company drama, exhibiting and networking at CES remains an opportunity for thousands of smaller companies, entrepreneurs, and innovators who have made investments in building their exhibits and are counting on CES for their business, inspiration, and future, CTA CEO Gary Shapiro wrote in an op-ed for the Las Vegas Review-Journal.

Although CES exhibitors pitched VentureBeat on everything, including sexual wellness products, VB sought out briefings related to the uses of data and AI that readers can learn from. In particular, consumer device makers tend to want to take advantage of the cloud for software and data updates without being dependent on the cloud with the smarts of the smart device resident on the device itself. So theyre worth studying as pioneers in edge computing.

Much as enterprise tech may have to learn from the consumer technology world, the opposite is also true. For example, Jeffrey Chou, CEO, and founder at Sonatus, said that one way for computerized systems in automobiles to improve is by learning from the model of the enterprise datacenter. In other words, siloed software running on lots of little computers (electronics control units or ECUs in automotive jargon) needs to be simplified and tied together with middleware, which Sonatus provides. That unification has to happen while preserving real-time performance for vehicle safety systems and addressing new concerns like vehicle cybersecurity.

Theres no short-term fix. The long-term fix is to do software right, Chou said.

Apex.ai has a somewhat similar story about improving the software foundations for autonomous driving and other smart vehicle tech, which, in its case, involved a series of enhancements and optimizations for ROS, the open source Robotics Operating System (considered as more of a programming framework). There isnt a company in automotive that doesnt use ROS for prototyping, CEO Jan Becker said, adding that his companys products help with turning successful prototypes into production products.

Auto processor consolidation is paving the way for more sophisticated software, according to Becker. The trend that we see now that Tesla introduced that a couple of years ago and everybody else is introducing that in the next three years is having those more powerful central more central computers, for infotainment for driver systems, potentially for a gateway potentially then also for vehicle safety functions like ESP and ABS anti-lock braking, he said.

At the same time, Becker noted its been years since self-driving car enthusiasts began predicting that robot taxis would be roaming the streets any day now. The truth is, the problem is really, really hard. What our industry has begun to understand better in the last couple of years is which applications are commercially reasonable, he said. For example, long before fully autonomous driving becomes available and affordable in passenger cars that need to be able to go anywhere, it can be practical for commercial vehicles navigating well-known and profitable routes.

Perrone Robotics is applying that approach to autonomous commercial vehicles that can navigate freight yards or circulate through urban or campus bus routes. Although it has partnerships with electric vehicle manufacturers like GreenPower Motor Company, Perrone also sells a retrofit kit that works with the pedals, transmission, and steering wheel of conventional vehicles to render them autonomous for low-speed operation over a known route. Theres going to be a very long path to autonomy, CEO Paul Perrone said. My focus is on, heres what you can do now.

In fact, hes a bit of a contrarian: rather than chasing bleeding edge AI applications, he leans toward deterministic software, the logic of which is easier to certify as safe for the operation of a vehicle. You cant just train it with some probabilistic learning system that will probably get it to its destination, he said.

Meanwhile, Ottobot is capitalizing on automotive innovations, such as lidar rangefinders, for use in its delivery robots, which began navigating the concourses at Cleveland International Airport in December 2020. Ottobot also recently announced a partnership with restaurant tech company Presto for curbside and parking lot delivery of food orders with less labor required.

While taking advantage of autonomous vehicle tech, Ottobot has innovated in other directions to allow its bots to go where many other delivery bots cant because, like cars, they rely on GPS navigation. To work within an airport, for example, Ottobot creates a software simulation of the floor plan. We create a digital twin and then navigate within that, CEO Ritukar Vijay said. The arrangement of sensors also needs to be different to navigate through crowds and see glass barriers.

While automobiles and robots are capturing increasing attention at CES, the show is best known for showcasing smaller gadgets.

When device manufacturers talk about embedding AI, typically that doesnt mean expecting big AI models to run on the device. A gadget may or not embed some modest machine learning capabilities, but typically the training of the model occurs in the cloud while what gets installed on the device is a much more compact inferencing model for interpreting and acting on sensor data. Even with that simplification, optimizing software to run within the size, power, and processing constraints of a given device can be a steep challenge.

For example, OrCams assistive technologies for the blind and visually impaired are in form factors the size of magic or a clip-on camera for a pair of glasses. So while the vice president of R&D, Oren Tadmor, respects the AI processors from companies like Nvidia, theyre not computers that we can ever dream of fitting into our devices, he said. Instead, the company winds up working with specialized chipsets for vision processing.

At the same time, Tamdor says, OrCam has been able to take advantage of big advances in the state of the art for deep learning as it applies to computer vision, which has made problems like face recognition much easier to solve. OrCam is an Israeli company whose cofounders Amnon Shashua and Ziv Aviram also founded Mobileye, a leader in computer vision for collision avoidance and self-driving car technology.

For computer vision, we can do anything, or almost anything, that a person can, Tamdor said. And its just a matter of finding, what are the features that our users can use?

Hardware-specific optimizations may sometimes be necessary, but that isnt stopping software tool makers from trying to promote a more standardized approach to device programmability. I think one of the exciting things here is the interplay between these two types of optimizations, said Davis Sawyer, cofounder, and chief product officer at Deeplite. Where the two meet up, thats where we see 400 to 500% increases over one or the other on their own.

At CES, Deeplite announced the Deeplite Runtime software development kit for creating efficient deep learning models based on Pytorch, particularly for computer vision applications. Where the companys previous Deeplite Neutrino product worked with GPUs and other types of processors, the new Deeplite Runtime is specifically for compiling applications to run on ARM processors, which are among the most popular on smart devices.

Given the prevalence of things like ARM CPUs, the familiarity with developers, and also the low power profile for battery-powered devices, thats where I think there [are] a lot of opportunit[ies] created, Sawyer said.

Fluent.ai, a device software player focused on voice command systems, aims to be as hardware agnostic as possible, CEO Probal Lala said. However, some hardware partners prove to be easier to work with than others. At CES, Fluent.ai is announcing a partnership with audio tech specialist Knowles, and theyll be jointly demoing voice-controlled earbuds.

For Knowles, the attraction is that Fluent.ais software operates efficiently, without being dependent on cloud services or the power and network capacity required to access them. They offer a large command set, the largest Ive ever seen thats completely offline, said Raj Senguttuvan, director of strategic marketing for audio and sensing solutions at Knowles. That opens up a wide range of entertainment and business application opportunities, he said.

Fluents key optimization is that it shortcuts the common voice application pattern of translating voice to text and then doing further processing on the text. Instead, the software does its pattern matching by working with the audio data directly.

The increasing variety of base technologies, including AI capabilities, ought to get you thinking about business opportunities.

Im a big believer that the technology doesnt mean anything to the end-user without a little imagination as to how it is going to improve their lives, Richard Browning, chief sales and marketing officer at NextBase, a maker of car dash cams.

For NextBase, that means re-imagining how the dashcam can move beyond being just a mobile security camera you can use to share crash footage with your insurance company. Just the challenge of producing good video under conditions that can range from glaring daylight to rainy daylight is steep enough and requires some AI image processing power, Browning says. The NextBase IQ product being announced at the show and readied to ship in September takes that capability further to also provide driver assistance (recognizing when other drivers are behaving badly) and spatial awareness (anticipating accidents so they can be recorded more completely).

The addition of an inside-facing camera allows the system to detect and warn drowsy or distracted drivers, but it also allows for capturing video evidence that wouldnt be captured by a front-facing camera such as road rage or aggressive road stop incidents. With a voice command, the device can be toggled into witness mode to record exactly how you behave when a cop walks up to the vehicle and asks for your license and insurance.

When in witness mode, whether triggered by voice command or sensors detecting that an accident has occurred, the video is transmitted to a cloud account for later review. Previous versions of NextBases products required the driver to manually download video data to their phone.

With these and other features, the NextBase IQ has almost outgrown the dashcam category as it was previously defined except the company cant figure out what else to call it, other than a smart dashcam, Browning says. People understand what smart is these days theyve got [a] smart home, smart security, smart health its a product that is connected and intelligent.

That will be a large part of what CES 2022 is about.

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CES 2022: AI is driving innovation in smart tech - VentureBeat