CES 2025 – A Few Thoughts Post Visit
CES 2025 – A Few Thoughts Post Visit
I attended CES 2025 in Las Vegas, Nevada, for my typical two-day run through the exhibits. I did all the LVCC main halls except for the South Hall, which is really two floors of accessories, reminding me of the Akihabara area in Tokyo in Japan. I also did the two floors of the Venetian where application-focused venues (top floor) and startups were located (bottom floor called Eureka Park).
My overall impression was that CES has now become an Asian World’s Fair where the world comes to see the many hardware products built by various Asian economies, with China and South Korea leading the way. Never before had I see such a dearth of American products (the Europeans did have some innovative presence, but it was secondary. For the Asians, having this event in the U.S. makes sense because the attendees need not worry about any government interference.
The U.S., however, is still dominant in the software components of many products, with AIas an adjective to every product description out there.
While there are a myriad of articles on the web that highlight their favorite products, I decided to skip that effort (You can see the CES Innovation Awards here). Rather, I chose to highlight 4 overarching concepts.
AI Analytics:
AI analytics was starting to emerge across many of the enterprise-related booths that I visited. There were many examples of using AI-trained systems to either provide real-time or deferred feedback on activities from sensor monitoring to human behavior across a range of industry needs.
In one example, a company targeted retail stores. They installed various sensors in the ceiling that could track customers who entered the store and navigated various aisles in search of products to fulfill their needs.
As the system gathered real-time information that could be fed into a trained AI system that had been previously trained on the content created by previous shoppers, combined with the resultant buying outcomes. This provided a real-time assessment of shopper behaviors contrasted with the previous means of analyzing information separate from the real-time activity using traditional data analytics tools. We did question the privacy issue, and the response was that once a customer enters the store, tracking of the customer does not have to be disclosed.
Robots:
Robots, both physical and virtual chatbots, were everywhere. But I was impressed by two displays, one physical and one virtual. The physical robot was made by Robotix and can be seen here. There were also several huge screens turned to portrait mode with life-sized virtual robots displayed. You could have a conversation with them, and they responded, but with human-like motions, and they replied. I suspect you may find one of these at the customer service desk of many retail stores soon.
Software Defined Vehicles (SDVs):
SDVs seemed to be the generic term used for all the self-driving vehicle technology in the North Hall, but also included many other features that AI-based software will bring to the consumer. On the self-driving side, I had a conversation with three vendors and asked them when I could fall asleep in the back of my car and the car would get me to my destination, basically level 5 (we are mostly at level 2 today).
One vendor said it was 5 years off, but felt as though the vision would be the only technology required to accomplish this feat (many vendors expect vision, rada, and Lidar to all be required). QNX (a division of BlackBerry) was finally displayed under its own name.
They felt the path to level 5 would occur much faster, mostly because the simulator technology to train the machine learning mechanisms had improved greatly. This means that the learning systems could be taught on rarely occurring situations more quickly rather than having to wait until a real-world encounter happens.
And a third vendor said that they thought it would be somewhere in between the two insights above, but also felt that the three encounter technologies would be required to get to level 5.
Multi-modal:
A visit with the Google folks on Android permitted me to see several demonstrations on how personal information across voice, video, and text could all be brought together to create predictive personal workflows for the user.
If your flight is late and you need to find what to do, the demonstration showed how AI could access all the information, not only on the phone but in other repositories, to come up with concrete suggestions on what to do. Google said that the circle to search was very popular on Android devices (just use your finger to circle an object on your screen) because it easily linked people to where they could buy a product that they are viewing or just get more information.
These examples were impressive because of the system’s ability to convert analog information to digital, understand the context of the information, and translate it to other formats when necessary. Google certainly has a leg up on the content needed to train its AI systems from the years of search and retrieval of information over the years.
Bottom Line:
Many AI-based technologies are maturing. Evaluate the business needs and scan some of the areas of interest on ces.tech, remembering that consumer technology often precedes innovation in the enterprise. It’s going to be a wild ride over the coming years, but a viable business cannot afford to miss the incorporation of these technologies into business, be they vendors or end users.
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