The researchers predict the investment growth will be necessary as telcos expand 5G networks globally and develop future 6G networks. The AI software will play a vital role in optimising network performance and security; the two most critical areas expected to account for over 50 percent of operator spending on AI by 2028.

As enterprises make increasing use of cellular connectivity for smart factories, self-driving vehicles, and other bandwidth-intensive applications, the report argues AI orchestration will be essential for telcos to maximise efficiency, reduce costs, and provide the best quality of service.

Automating functions like real-time network analysis and rapid adjustments to changing demands can minimise expenses tied to network operations and provisioning.

“As operators compete on the quality of their networks, AI will be essential to maximising the value of using a cellular network for connectivity,” said Frederick Savage, author of the report.

“High-spending users will gravitate to those networks that can provide the best service conditions.”

Telcos that fail to incorporate AI may ultimately struggle to keep pace with customer demands for performance and security.

A full copy of the report can be found here (paywall)

(Photo by Larisa Birta on Unsplash)

See also: The UK is outpacing the US for AI hiring

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“From cloud to edge, Paravision’s goal is to help our partners develop and deploy transformative solutions around face recognition and computer vision,” said Joey Pritikin, Chief Product Officer at Paravision.

“With these sweeping updates to our product family, and with what has become possible in terms of accuracy, speed, usability and portability, we see a remarkable opportunity to unite disparate applications with a coherent sense of identity that bridges physical spaces and cyberspace.”

A new Scaled Vector Search (SVS) capability acts as a search engine to provide accurate, rapid, and stable face matching on large databases that may contain tens of millions of identities. Paravision claims the SVS engine supports hundreds of transactions per second with extremely low latencies.

Another scaling solution called Streaming Container 5 enables the processing of video at over 250 frames per second from any number of streams. The solution features advanced face tracking to ensure that identities remain accurate even in busy environments.

With more enterprises than ever looking to the latency-busting and privacy-enhancing benefits of edge computing, Paravision has partnered with Teknique to co-create a series of hardware and software reference designs that enable the rapid development of face recognition and computer vision capabilities at the edge.

Teknique is a leader in the development of hardware based on designs from California-based fabless semiconductor company Ambarella.

Paravision’s Face SDK has been enhanced for smart cameras powered by Ambarella CVflow chipsets. The update enables facial recognition on CVflow-powered cameras to achieve up to 40 frames per second full pipeline performance.

A new Liveness and Anti-spoofing SDK also adds new safeguards for Ambarella-powered facial recognition solutions. The toolkit uses Ambarella’s visible light, near-infrared, and depth-sensing capabilities to determine whether the camera is seeing a live subject or whether it’s being tricked by recorded footage or a dummy image.

On the mobile side, Paravision has released its Face SDK for Android. The SDK includes face detection, landmarks, quality assessment, template creation, and 1-to-1 or 1-to-many matching. Reference applications are included which include UI/UX recommendations and tools.

Last but certainly not least, Paravision has announced the availability of its first person-level computer vision SDK. The new SDK is designed to go “beyond face recognition” to detect the presence and position of individuals and unlock new use cases.

Provided examples of real-world applications for the computer vision SDK include occupancy analysis, the ability to spot tailgating, as well as custom intention or subject attributes.

“With Person Detection, users could determine whether employees are allowed access to a specific area, are wearing a mask or hard hat, or appear to be in distress,” the company explains. “It can also enable useful business insights such as metrics about queue times, customer throughput or to detect traveller bottlenecks.”

With these exhaustive updates, Paravision is securing its place as one of the most exciting companies in the AI space.

Paravision is ranked the US leader across several of NIST’s Face Recognition Vendor Test evaluations including 1:1 verification, 1:N identification, performance for paperless travel, and performance with face masks.

(Photo by Daniil Kuželev on Unsplash)

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Most current AR applications are somewhat gimmicky, with barely a handful that have achieved any form of mass adoption. Apple’s decision to introduce LiDAR in its recent devices has given AR a boost but it’s clear that more needs to be done to make applications more useful.

A newly filed patent suggests that Apple is exploring how machine learning can be used to automatically (or “automagically,” the company would probably say) detect objects in AR.

The first proposed use of the technology would be for Apple’s own Measure app.

Measure’s previously dubious accuracy improved greatly after Apple introduced LiDAR but most people probably just grabbed an actual tape measure unless they were truly stuck without one available.

The patent suggests machine learning could be used for object recognition in Measure to help users simply point their devices at an object and have its measurements automatically presented in AR.

Specifically, Apple’s patent suggests displaying a “measurement of the object determined using one of a plurality of class-specific neural networks selected based on the classifying of the object.”

This simplicity benefit over a traditional tape measure would likely drive greater adoption.

Machine learning is already used for a number of object recognition and labelling tasks within Apple’s ecosystem. Image editor Pixelmator Pro, for example, uses it to automatically label layers.

Apple’s implementation suggests an object is measured “by first generating a 3D bounding box for the object based on the depth data”. This boundary box is then refined “using various neural networks and refining algorithms described herein.”

Not all objects are measured the same so Apple suggests that a neural network could also step in here to determine what could be useful for the user. For example, “a seat height for chairs, a display diameter for TVs, a table diameter for round tables, a table length for rectangular tables, and the like.”

To accomplish what Apple envisions here, a lot of models will need to be trained for all objects. However, there are many of the more everyday items that could be supported early on—with more added over time.

“One model may be trained and used to determine measurements for chair type objects (e.g., determining a seat height, arm length, etc.),” Apple wrote, “and another model may be trained and used to determine measurements for TV type objects (e.g., determining a diagonal screen size, greatest TV depth, etc.)”

Five inventors are credited with the patent: Amit Jain, Aditya Sankar; Qi Shan, Alexandre Da Veiga, and Shreyas V Joshi.

Apple’s patent is another example of how machine learning can be combined with other technologies to add real utility and ultimately improve lives. There’s no telling when, or even if, Apple will release an updated Measure app based on this patent—but it seems more plausible in the not-so-distant future than many of the company’s patents.

(Image Credit: Apple)

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More people than ever are relying on telecoms networks to work, play, and stay connected during the pandemic. Operators are doing all they can to ensure their existing networks have enough capacity to cope with demand.

Gorkem Yigit, Principal Analyst at Analysys Mason, said:

“Video streaming continues to experience high year on year growth and that has been exacerbated by the pandemic and resulting lock-downs,

Yes, 5G grabs the spotlight, but 4G is carrying the brunt of this traffic. So, while investment in 5G infrastructure continues, operators need intelligent ways to maximize and extend existing 4G network capabilities in the short to medium term – keeping their CAPEX to a minimum.”

8 out of 10 of the world’s largest operator groups have deployed traffic management technology from the Openwave subsidiary of Swedish firm Enea. Many of these have since upgraded to include machine learning capabilities.

Openwave claims that, based on its figures, some operators faced a 90 percent surge in peak throughput during lockdowns.

Machine learning is helping to predict and identify congestion in the RAN (Radio Access Network) which resides between user equipment such as wireless devices and an operator’s core network.

John Giere, President of Enea Openwave, commented:

“Conventional mobile data management requires manual configuration and network investment – it is no longer fit for purpose.

Machine Learning has given existing 4G networks the shot in the arm they needed. It can work dynamically without external probes or changes to the RAN, delivering additional capacity at a time that operators most need it.” 

The use of machine learning has increased operators’ 4G RAN capacity by 15 percent in congested locations—providing further evidence of how AI technology can be used to quickly tackle real-world problems.

(Photo by Adrian Schwarz on Unsplash)

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There are two new chips in the 7-series – the Snapdragon 730 and 730G – along with one in the 6-series, the Snapdragon 665.

Kedar Kondap, VP of Product Management at Qualcomm, says:

“With the introduction of the Snapdragon 730, 730G and 665 Mobile Platforms, we are bringing features such as sophisticated AI, exceptional gaming and advanced camera capabilities to a broad spectrum of devices at exceptional performance.

Each iteration of Snapdragon drives immense innovation that will surpass customer expectations.”

The base Snapdragon 730 boasts double the AI processing power of its predecessor using Qualcomm’s 4th-gen multi-core AI Engine. A Qualcomm Hexagon 688 processor inside the 730 supports improved base scalar and Hexagon Vector eXtensions (HVX) performance, as well as the new Hexagon Tensor Accelerator.

Qualcomm has also packed in the Spectra 350 which contains a dedicated ISP for computer vision. It claims to provide up to four times as much power savings as its predecessor.

As for the Snapdragon 730G, that ‘G’ stands for ‘gamer’ and intends to boost the gaming performance of devices packing it.

While the 730 features the Adreno 618 GPU, the first time a Qualcomm 7-series chip has supported the Vulkan 1.1 graphics API, the 730G claims to offer 15 percent faster rendering. Furthermore, Qualcomm claims it’s “collaborated with consumers’ favorite game-makers to optimize Snapdragon 730G for a selection of the world’s top-rated games.”

A dedicated ‘Jank Reducer’ feature boasts that it can reduce stutter by up to 90 percent in games running at 30FPS.

Perhaps most impressive, however, is the Snapdragon 665 which brings more capable AI processing abilities to lower-end devices.

While the new 7-series chips feature the 4th-gen, the Snapdragon 665 is packing the 3rd-gen of Qualcomm’s AI Engine with the Hexagon 686 DSP. It too claims to offer double the performance of its predecessor.

Packing the Spectra 165 ISP, the Snapdragon 665 provides AI-powered scene recognition and auto adjustments like HDR. To account for the growing number of unique camera setups on today’s smartphones, it supports up to three cameras which can include telephoto, wide-angle, and ultra-wide lenses.

Qualcomm has also partnered with Morpho to enhance the camera functionality of the Snapdragon chips.

Toshi Torihara, VP of Morpho, commented:

“Morpho is proud to collaborate with Qualcomm to integrate our image processing software on mobile. With the Snapdragon 665 mobile platform, we brought MovieSolid, our state-of-the-art image stabilisation implementation, to deliver industry-leading video experiences to the mobile camera industry.

We believe this partnership will further enhance smartphone camera capabilities, bringing mobile imaging & AI technologies to the next level.”

For gamers, the Snapdragon 665 features the Adreno 610 GPU which also supports the Vulkan 1.1 graphics API to help provide an improved gaming experience for those who on lower-end devices.

Commercial devices based on the Snapdragon 730, 730G, and 665 are expected to be available in mid-2019.

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AI-based tasks on most smartphones today are often carried out in the cloud without the average user even being aware. Google Photos, for example, uses deep learning to artistically crop and edit landscape panorama photos like a human photographer. Photos are categorised automatically, and — as of an update earlier this year — the app will even prompt who you may wish to share the pictures with.

Dedicated AI chips will allow faster processing of various tasks on the hardware itself without a need for the cloud.

Current Mobile AI Chips

While still in their infancy, we’re starting to see dedicated mobile AI in handsets before we even head into next year.

Huawei is leading the pack with its Kirin 970 chip which features a dedicated NPU (Neural Processing Unit). The company is claiming its NPU is able to perform an image recognition test on 2,000 images per second, 20 times faster than what the competition can do with a CPU alone.

It’s well known in the mobile industry that new technologies often don’t reach mainstream adoption until Apple gets on board. Fortunately, Apple isn’t far behind and will have dedicated AI cores in the iPhone X’s new A11 Bionic chip to power a range of new features including facial recognition and real-time analysis of 4K video for improving video recordings.

Huawei’s marketing team have been poking some fun at Apple’s expense. After the onstage failure of FaceID at the iPhone X’s announcement, Huawei posted an image of a clown with the caption “Let’s face it, facial recognition isn’t for everyone” alongside the hashtag #TheRealAIPhone ahead of the expected announcement of the Mate 10 Pro on October 16th.

We look forward to seeing whether Huawei can prove itself to be better at mobile AI than Apple, or whether Huawei will ultimately end up looking the clown.

Future Mobile AI Chips

Another given in the mobile industry is whatever Apple has, Samsung will either have or won’t be far behind.

Industry sources familiar with Samsung’s chip plans report the company has been carrying out R&D work into mobile AI chips.

“[Samsung] is in the middle of developing several types of chips that will be capable of processing massive data from AI applications on devices, eliminating the need to communicate with cloud servers,” a source from one of Samsung’s partners told The Korea Herald.

Samsung recently overtook Intel as the leading chip maker which makes this news particularly notable. We’re sure to see a dedicated AI chip debut in an upcoming Galaxy smartphone but we can also expect them in the company’s popular TVs and smart home technology.

“In the next three years or so, smartphones will have designated chips that will improve the processing of AI features by 50 percent from today,” the industry source explained. “A kind of an accelerator can be applied to empower AI devices.”

Investor Excitement

The potential for AI has excited investors who are putting their cash into chip manufacturers set to gain from this new venture.

Nvidia, who relies on the gaming market, is moving into AI and now provides chipsets for some of the world’s largest companies to power their efforts. The gaming market is often cyclical in nature but AI looks set to provide a healthy stream of revenue over the coming years.

The company’s main source of revenue is still the gaming market with $1.03 billion in sales in the first quarter of its fiscal year — although this was down from its average forecasts of $1.13 billion. However, Nvidia reported more than $400 million in quarterly revenue for its data center business selling chipsets to the many companies increasing their computing power for servers with deep learning technology.

These financial results arrive in tandem with a better-than-expected performance from Nvidia’s segment focusing on AI for self-driving cars and other automotive efforts. Combined, this more than made up for the shortfall in gaming revenue and showed investors how AI will provide a huge boon to chipmakers like Nvidia.

As a result, of any company in S&P 500 Index, Nvidia had the largest stock increase in 2016 and its shares have gained 227 percent over the past year.

“AI is the most important technology development of our time, with the greatest potential to help society,” says Nvidia Corp. Chief Executive Jensen Huang. “As the world’s leading cloud providers deploy the world’s best AI platform, with Volta GPUs and Nvidia software, we’ll see amazing breakthroughs in medicine, autonomous transportation, precision manufacturing and much more.”

Companies including Alibaba, Baidu, and Tencent are upgrading their data centers with Nvidia’s Volta-based platforms. It’s clear to us, the invasion of the dedicated mobile AI chips is nigh.

Are you excited about dedicated mobile AI chips? Share your thoughts in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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