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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In the name of research, here’s one I made earlier:

Doublicat uses a Generative Adversarial Network (GAN) to do its magic. The GAN is called RefaceAI and is developed by a company of the same name.

RefaceAI was previously used in a face swapping app called Reflect. Elon Musk once used Reflect to put his face on Dwayne Johnson’s body. 

The app is a lot of fun, but – after concerns about viral Russian app FaceApp – many will be wondering just how much data is being collected in return.

Doublicat’s developers are upfront with asking for consent to store your photos upon first opening the app and this is confirmed in their privacy policy: “We may collect the photos, that you take with your camera while using our application.”

However, Doublicat says that photos are only stored on their server for 24 hours before they’re deleted. “The rest of the time your photos used in Doublicat application are stored locally on your mobile device and may be removed any time by either deleting these photos from your mobile device’s file system.”

The app also collects data about facial features but only the vector representations of each person’s face is stored. Doublicat assures that the facial recognition data collected “is not biometric data” and is deleted from their servers within 30 calendar days.

“In no way will Doublicat use your uploaded content for face recognition as Doublicat does not introduce the face recognition technologies or other technical means for processing biometric data for the unique identification or authentication of a user.”

The amount of data Doublicat can collect is limited compared to some alternatives. Apps such as Zao require users to 3D model their face whereas Doublicat only takes a front-facing picture.

RefaceAI is now looking to launch an app which can make deepfake videos rather than just GIFs. The move is likely to be controversial given the concerns around deepfakes and how such videos could be used for things such as political manipulation.

A fake video of Nancy Pelosi, Speaker of the United States House of Representatives, went viral last year after purportedly showing her slurring her words as if she was intoxicated. The clip shows how even a relatively unsophisticated video (it wasn’t an actual deepfake in this case) could be used to cause reputational damage and even swing votes.

A report from the NYU Stern Center for Business and Human Rights last September, covered by our sister publication MarketingTech, highlighted the various ways disinformation could be used ahead of this year’s presidential elections. One of the eight predictions is that deepfake videos will be used “to portray candidates saying and doing things they never said or did”.

Earlier this month, Facebook announced new policies around deepfakes. Any deepfake video that is designed to be misleading will be banned. The problem with the rules is they don’t cover videos altered for parody or those edited “solely to omit or change the order of words,” which will not sound encouraging to anyone wanting a firm stance against manipulation.

Doublicat is available for Android and iOS.

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Applause has built a vast global community of testers for its app testing solution which is trusted by brands including Google, Uber, PayPal, and more. The company is leveraging this relatively unique asset to help overcome some of the biggest hurdles facing AI development.

AI News spoke with Kristin Simonini, VP of Product at Applause, about the company’s new solution and what it means for the industry ahead of her keynote at AI Expo North America later this month.

“Our customers have been needing additional support from us in the area of data collection to support their AI developments, train their system, and then test the functionality,” explains Simonini. “That latter part being more in-line with what they traditionally expect from us.”

Applause has worked predominantly with companies in the voice space but also their increasing expansion into things such as gathering and labelling images and running documents through OCR.

This existing breadth of experience in areas where AI is most commonly applied today puts the company and its testers in a good position to offer truly useful feedback on where improvements can be made.

Specifically, Applause’s new solution operates across five unique types of AI engagements:

• Voice: Source utterances to train voice-enabled devices, and test those devices to ensure they understand and respond accurately.
• OCR (Optimized Character Recognition): Provide documents and corresponding text to train algorithms to recognize text, and compare printed docs and the recognized text for accuracy.
• Image Recognition: Deliver photos taken of predefined objects and locations, and ensure objects are being recognized and identified correctly.
• Biometrics: Source biometric inputs like faces and fingerprints, and test whether those inputs result in an experience that’s easy to use and actually works
• Chatbots: Give sample questions and varying intents for chatbots to answer, and interact with chatbots to ensure they understand and respond accurately in a human-like way.

“We have this ready global community that’s in a position to pull together whatever information an organisation might be looking for, do it at scale, and do it with that breadth and depth – in terms of locations, genders, races, devices, and all types of conditions – that make it possible to pull in a very diverse set of data to train an AI system.”

Some examples Simonini provides of the types of training data which Applause’s global testers can supply includes voice utterances, specific documents, and images which meet set criteria like “street corners” or “cats”. A lack of such niche data sets with the diversity necessary is one of the biggest obstacles faced today and one which Applause hopes to help overcome.

A significant responsibility

Everyone involved in developing emerging technologies carries a significant responsibility. AI is particularly sensitive because everyone knows it will have a huge impact across most parts of societies around the world, but no-one can really predict how.

How many jobs will AI replace? Will it be used for killer robots? Will it make decisions on whether to launch a missile? To what extent will facial recognition be used across society? These are important questions that no-one can give a guaranteed answer, but it’s certainly on the minds of a public that’s grown up around things like 1984 and Terminator.

One of the main concerns about AI is bias. Fantastic work by the likes of the Algorithmic Justice League has uncovered gross disparities between the effectiveness of facial recognition algorithms dependent on the race and gender of each individual. For example, IBM’s facial recognition algorithm was 99.7 percent accurate when used on lighter-skinned males compared to just 65.3 percent on darker-skinned females.

Simonini highlights another study she read recently where voice accuracy for white males was over 90 percent. However, for African-American females, it was more like 30 percent.

Addressing such disparities is not only necessary to prevent things such as inadvertently automating racial profiling or giving some parts of society an advantage over others, but also to allow AI to reach its full potential.

While there are many concerns, AI has a huge amount of power for good as long as it’s developed responsibly. AI can drive efficiencies to reduce our environmental impact, free up more time to spend with loved ones, and radically improve the lives of people with disabilities.

A failure of companies to take responsibility for their developments will lead to overregulation, and overregulation leads to reduced innovation. We asked Simonini whether she believes robust testing will reduce the likelihood of overregulation.

“I think it’s certainly improved the situation. I think that there’s always going to probably be some situations where people attempt to regulate, but if you can really show that effort has been put forward to get to a high level of accuracy and depth then I think it would be less likely.”

Human testing remains essential

Applause is not the only company working to reduce bias in algorithms. IBM, for example, has a tool called Fairness 360 which is essentially an AI itself used to scan other algorithms for signs of bias. We asked Simonini why Applause believes human testing is still necessary.

“Humans are unpredictable in how they’re going to react to something and in what manner they’re going to do it, how they choose to engage with these devices and applications,” comments Simonini. “We haven’t yet seen an advent of being able to effectively do that without the human element.”

An often highlighted challenge with voice recognition is the wide variety of languages spoken and their regional dialects. Many American voice recognition systems even struggle with my accent from the South West of England.

Simonini adds in another consideration about slang words and the need for voice services to keep up-to-date with changing vocabularies.

“Teenagers today like to, when something is hot or cool, say it’s “fire” [“lit” I believe is another one, just to prove I’m still down with the kids],” explains Simonini. “We were able to get these devices into homes and really try to understand some of those nuances.”

Simonini then further explains the challenge of understanding the context of these nuances. In her “fire” example, there’s a very clear need to understand when there’s a literal fire and when someone is just saying that something is cool.

“How do you distinguish between this being a real emergency? My volume and my tone and everything else about how I’ve used that same voice command is going to be different.”

The growth of AI apps and services

Applause established its business in traditional app testing. Given the expected growth in AI apps and services, we asked Simonini whether Applause believes its AI testing solution will become as big – or perhaps even bigger – than its current app testing business.

“We do talk about that; you know, how fast is this going to grow?” says Simonini. “I don’t want to keep talking about voice, but if you look statistically at the growth of the voice market vis-à-vis the growth and adoption of mobile; it’s happening at a much faster pace.”

“I think that it’s going to be a growing portion of our business but I don’t think it necessarily is going to replace anything given that those channels [such as mobile and desktop apps] will still be alive and complementary to one another.”

Simonini will be speaking at AI Expo North America on November 13th in a keynote titled Why The Human Element Remains Essential In Applied AI. We asked what attendees can expect from her talk.

“The angle that we chose to sort of speak about is really this intersection of the human and the AI and why we – given that it’s the business we’re in and what we see day-in, day-out – don’t believe that it becomes the replacement of but how it can work and complement one another.”

“It’s really a bit of where we landed when we went out to figure out whether you can replace an army of people with an army of robots and get the same results. And basically that no, there are still very human-focused needs from a testing perspective.”

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Due to US trade restrictions, Huawei is losing access to Google’s services. The new Mate 30 smartphones announced in Munich today will launch with the open-source Android, but it will not feature the Play Store, Gmail, YouTube, Google Pay, or the many other services which Western consumers are used to.

Among the features that will be missing from the Mate 30 onwards is Google Assistant. Huawei is quickly working to fill the gaps left without access to Google’s services with its own and is launching the Huawei Assistant as a replacement for Mountain View’s virtual assistant.

Walter Ji, Director of Business, HUAWEI Consumer Business Group Western Europe, said:

“With our focus on user experience, we bring AI into mobile services so we can proactively identify user needs and thus improve their smartphone experience.

Huawei Assistant is a product that intelligently fulfils user needs at the same time as offering partners an opportunity to provide their services to users through a globally-available distribution platform.”

Huawei Assistant will launch with basic functionality compared to Google’s version, but the company is promising to expand it.

By swiping to the right of the homescreen, much like accessing Google Assistant today, users can begin interacting with Huawei Assistant. The service is powered by Huawei Ability Gallery, a service distribution platform.

There are four key features of the Huawei Assistant:

• Newsfeed – Today’s Google Assistant provides some personalised articles when you swipe to it on an Android device. The newsfeed feature is Huawei Assistant’s alternative but users can decide whether to receive custom recommendations or to select from news agencies to fill their feed with “up-to-the-minute” articles.
• Search – Users can search for information on their smartphone using Huawei Assistant. The assistant will surface things such as installed apps, memos, emails, and calendar entries, while also providing an online search feature using the default browser.
• Instant Access – Four shortcuts to a user’s choice of applications can be selected for quick access. In the future, Huawei says this can make use of AI so the shortcuts are intelligently-selected based on what the user may want at that moment.
• SmarterCare – Real-time information will be provided using AI. At launch, this will mean things such as the weather forecast, missed calls, and schedule reminders. Future planned functionality will enable more powerful abilities like booking restaurants, flights, taxis, and hotels.

The new assistant from Huawei will be pre-installed on Mate 30 series devices but it will also be downloadable from the company’s App Gallery.

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Loup Ventures asked each of the three main virtual assistants – Google Assistant, Alexa, and Siri – a total of 800 questions. The assistants understood almost every question, even if not all of the responses were correct/sufficient.

In terms of understanding the questions, these are the results:

• Google Assistant – 100 percent
• Alexa – 99.9 percent
• Siri – 99.8 percent

Loup Ventures’ say their question set it designed to comprehensively test a virtual assistant’s ability and utility. Questions are broken down into five categories:

1. Local – Where is the nearest coffee shop?
2. Commerce – Order me more paper towels.
3. Navigation – How do I get to Uptown on the bus?
4. Information – Who do the Twins play tonight?
5. Command – Remind me to call Jerome at 2 pm today.

This is the percentage of questions each assistant answered correctly:

• Google Assistant – 92.9 percent
• Siri – 83.1 percent
• Alexa –  79.8 percent

The results are a huge improvement over Assistant, Alexa, and Siri’s results last year.

In 2018, Loup Ventures found Google Assistant answered the most questions with an 86 percent success rate. This was followed by Siri at 79 percent, while Alexa trailed behind at just 61 percent.

Alexa’s jump in answering the question correctly from 61 percent last year to almost 80 percent this year is the most commendable performance improvement, even if Amazon’s assistant is still in last place overall.

The researchers explained that they’ve stopped including Cortana in their tests due to a strategy change from Microsoft earlier this year.

Microsoft said in January that it’s no longer attempting to compete with Alexa or Google Assistant in areas like smart speakers, but instead is repositioning Cortana more like a skill that can be embedded in services where she can be of assistance.

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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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The company’s flagship P-series of smartphones focuses on camera innovations. With this latest device, we’re seeing that trend continue. There are clear enhancements to existing features in addition to a few new things for us to play with.

In the Mate 20, Huawei’s previous flagship, the company expanded the number of scenes and objects its AI could recognise to 5,000 different objects and 1,500 different scenarios across 25 categories.

While impressive, many ended up switching the feature off and Huawei even released an update to do so by default. Objects were often misrecognised and images ended up looking poorer.

Huawei has made clear improvements with the P30 Pro. Aside from one amusing occurrence where my dog was recognised as a panda – and I’m fairly sure he’s not – the AI recognition has excelled, and even surprised (in a good way.)

The standout moment was taking a shot of a flower. From a distance, the AI identified the shot was of some ‘greenery’ and made the colours punchier. Framing more of the flower caused the AI to switch over to ‘flowers’ and made it sharper with a nice depth-of-field bringing the object into focus while blurring the backdrop.

Moving closer still, to capture small details of the flower, caused the AI to switch to ‘super-macro’. This mode uses the P30 Pro’s incredible macro capabilities of taking images just 2.5cm away.

Low-light photography is another area where Huawei is harnessing AI to elevate its performance. The AI image stabilisation allows the user to capture long-exposure photographs without the need for a tripod.

In the image below, both shots were taken from the same place in near pitch-black conditions:

One implementation of AI we’re unconvinced about is the smartphone’s use for HDR. As you can see below, the feature led to a washed-out image which you’d not expect from a HDR shot:

Huawei has issued updates in the past to improve the camera performance of its devices, so we’ll be keeping an eye out for one to address this. For now, it seems better to keep the HDR feature switched off.

Ahead of the P30 announcement, Huawei hyped the device’s zooming capabilities. Based on our tests, we can understand why:

In the first image, I’ve had to mark where the small daffodil is that we’re zooming in on. At 5x, due to being an optical zoom, we’re seeing no quality loss. At 10x, Huawei makes use of AI for a hybrid zoom that it claims is ‘lossless’ (we think there’s some loss, but it’s negligible.)

At 50x zoom, we’re seeing a noticeable loss of image quality. However, that is an incredible zoom for a smartphone camera and is only enabled through AI stabilisation. The resulting image is still recognisable even though it won’t be winning awards. Considering how difficult it is to pick out the flower by eye in the non-zoomed image, it’s a commendable performance.

Moving slightly away from photography into videography, the P30 Pro uses AI in combination with OIS (Optical Image Stabilisation) to keep things steady in rough conditions. We put it through some fairly extreme tests of waving the device while recording, and even going for a jog.

In both scenarios, the AI stabilisation performed admirably. While it was not perfectly stable, the result was viewable and it’s hard to imagine stabilisation getting much better (although, I have been surprised in the past.)

A clip of the AI video stabilisation is available in our YouTube review below:

Finally, we’ve benchmarked the Huawei P30 Pro against the company’s previous flagship – the Mate 20 Pro. Here are the results:

Your eyes aren’t deceiving you, the P30 Pro had a slightly lower overall result. It’s a surprise given both use the same NPU, CPU, and GPU. In fact, the P30 Pro has 2GB more RAM to play with over the Mate 20 Pro (8GB vs 6GB).

Benchmarks only tell a small part of the story and this one is overwhelmingly positive. Huawei has excelled itself and set the bar for AI on a smartphone with the P30 Pro.

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Laserlike is known for its AI-powered app which makes it easier for users to follow news topics. Most notably, it was founded by former Google engineers.

Few people think of Apple as an AI leader. The firm’s closest rival, Google, has invested heavily in AI for many years (as much for its cloud and search businesses as mobile).

Apple is, therefore, coming from a position behind in terms of AI development. However, it is quickly catching up through hoovering up talented AI companies and solutions. Let’s all remember, even Siri was acquired from Nuance.

Here are some of the other AI companies Apple has acquired in the past few years:

• Silk Labs (2018)
• Asaii (2018)
• Spektral (2017)
• Regains (2017)

Following the poaching of Google’s John Giannandrea, it’s been clear that Apple has been doubling-down on its AI efforts. Which makes sense, if you’re here on AI News then you know how important it is for Apple to become a leader in the space.

On its website, Laserlike pledges to keep personalisation at its core post-Apple acquisition:

“This is one of the things we want to fix on the Internet. Laserlike’s core mission is to deliver high quality information and diverse perspectives on any topic from the entire web. We are passionate about helping people follow their interests and engage with new perspectives.”

Apple is holding an event on March 25th where it’s rumoured to be launching a news subscription service. It will be interesting to see if this acquisition plays a part.

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If you’ve ever read a comprehensive smartphone review, you’ve likely heard of AnTuTu. The company’s smartphone benchmarking tool is often used for testing and comparing the CPU and 3D performance of devices.

With dedicated AI chips now appearing in devices from the mid-range to flagships, AnTuTu has decided it’s time for a benchmark to determine their performance.

In a blog post, AnTuTu says its benchmark uses two categories – ‘Image Classification’, and ‘Object Recognition’.

AI News tested AnTuTu’s benchmark on a Huawei Mate 20 Pro which currently ranks second on AnTuTu’s general performance leaderboard for Android devices. Huawei often brags about the AI performance of its flagship devices.

The first test classifies 200 images as fast as possible using the Inception v3 neural network:

In the second test, 600-frame video is reviewed using the MobileNet SSD neural network:

AnTuTu then delivers an overall benchmark score, along with the scores for each category.

Here is how our Mate 20 Pro fared:

• Overall – 65,222
• Image Classification – 41,717
• Object Detection – 23,505

Each of the categories is further broken down into scores for ‘speed’ and ‘accuracy’. If accuracy is traded for speed, then a lower score will be given.

AnTuTu says this helps to prevent cheating by devices processing the data fast but without providing the right answers. Smartphone manufacturers have been caught artificially-inflating their benchmarks in the past; so it provides added confidence in the results.

For a general look at the AI features in the Mate 20 Pro, see our video below:

You can download the AI benchmark from AnTuTu here.

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The company says its NPU (Neural Processing Unit) will deliver up to seven times faster AI performance than Samsung’s older Exynos 9810 chip in the S9.

Huawei became the first to debut a dedicated NPU in a flagship smartphone with their Kirin 970 chip in the Mate 10.

In our Mate 10 coverage, we noted Huawei claimed using an NPU could increase performance up to 25x over a CPU/GPU.

Huawei pitted the Mate 10 against the Galaxy Note 8 in an internal test recognising 100 photos. The Mate 10 reportedly completed the benchmark in five seconds while Samsung’s device trailed behind at 100 seconds.

Now that Samsung will have its own NPU chip, we look forward to comparing its performance against Huawei’s efforts.

Qualcomm chips are found in most other handsets – in fact, the Snapdragon 845 is even used in the US version of the Galaxy S9.

While Qualcomm is yet to launch a dedicated smartphone NPU, the company says the 845’s combination of DSP, CPU, and GPU delivers three times faster AI performance than its predecessor.

Samsung boasts the Exynos 9820 will deliver a 20 percent boost to single-core CPU performance and 15 percent improvement to multi-core. The GPU benefits from ARM’s new Mali G76 cores, which first debuted in Huawei’s Kirin 980 chip, to deliver 40 percent more performance.

The 9820 is said to be ready for production by the end of the year so it should be on track for the S10’s expected launch in late February.

It will be interesting to see how Samsung makes use of its NPU. More competition is always good, especially from a tech behemoth like Samsung, and will help drive innovations from the likes of Huawei and Apple further.

Here’s our video of Huawei’s latest AI features in the Mate 20 Pro:

 AI & >.

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