Despite incidents of reckless flying, drones unlock huge opportunities. We regularly see beautiful photography and videography shot using drones—but, of course, they can do so much more.

Sony has built a stellar reputation in media capture. The company builds great cameras – both for itself and sensors it supplies to other manufacturers (like its new IMX686) – and its software like Vegas Pro is the defacto choice for many creative professionals.

In a press release, Sony wrote:

“Airpeak will support the creativity of video creators to the fullest extent possible, aiming to contribute to the further development of the entertainment industry as well as to improve efficiency and savings in various industries.

Airpeak will also promote this project to enable drone-use with the highest level of safety and reliability in the environments where this has been difficult in the past.”

The focus on supporting video creators is to be expected from Sony, but the mention of various industries suggests the company has bigger plans.

In the photography/videography space alone, Sony will face stiff competition from established players like DJI.

Despite being the current industry leader, DJI has begun diversifying its products in recent years due to a decline in drone popularity for consumer purposes. This is mostly due to increasing restrictions in many countries around where drones can fly and even requiring permits (the FAA, for example, requires users to register all drones over a certain size.)

A patent granted to Sony back in January suggests the company may start relatively simple:

However, Sony could use its AI and robotics expertise to stand out in other exciting areas where drones have a lot of potential such as emergency response, delivering supplies, assisting in warehouses/factories, and even tackling small fires before they spread.

The language Sony uses suggests the company will target a wide range of customers from everyday consumers to large enterprise deployments.

Sony plans to reveal further details about Airpeak in the Spring of 2021.

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AI is proving itself to be an important tool in tackling some of the biggest issues the world faces today; including coronavirus and climate change. However, some public distrust remains.

“The current pandemic has shown us more needs to be done to speed up the adoption of trusted AI around the world,” said Kay Firth-Butterfield, Head of Artificial Intelligence and Machine Learning at the World Economic Forum.

“We moved from guidelines to practical tools, tested and iterated them – but this is still just a start. Now we will be working to scale them to countries around the world.”

The guidelines released today aim to “help society tackle big data problems faster” while also preparing them for future risks. The UK government adopted the guidelines across its various departments.

“The UK is a global leader in AI and I am pleased we are working with the World Economic Forum and international partners to develop guidelines to ensure its safe and ethical deployment,” said Caroline Dinenage, Digital Minister of the United Kingdom.

“By taking a dynamic approach we can boost innovation, create competitive markets and support public trust in artificial intelligence. I urge public sector organisations around the world to adopt these guidelines and consider carefully how they procure and deploy these technologies.”

For the past year, the WEF has worked alongside the UK’s Office for AI; companies such as Deloitte, Salesforce, and Splunk; 15 other countries; and more than 150 members of government, academia, civil society, and the private sector.

“As a trusted AI advisor to governments around the world, we were thrilled to collaborate with the World Economic Forum and the government of the UK in the development of procurement guidelines that help the public sector put AI at the service of its constituents in a manner that is both efficient and ethical,” said Shelby Austin, Managing Partner, Growth & Investments and Omnia AI, Deloitte, Canada.

“As our societies reorganize and make progress in our fight against COVID-19, the need for multi-stakeholder cooperation has never been more apparent. We believe in these joint efforts, and we believe in the power of data-driven decision-making to help our countries recover and thrive.”

The result of the joint effort was the “Procurement in a Box” toolkit which provides guidance from conducting drafting proposals and conducting risk assessments, all the way to purchasing AI solutions and deploying them in a trusted manner.

A proposal for a chatbot allowing executives for the Dubai Electricity and Water Authority (DEWA) to obtain answers to data-related questions was used to test the guidelines. DEWA’s chatbot was successful and serves as an early example of how rapid but safe AI deployments can be achieved using the guidelines.

“In an era that will continue to be dominated by the transformative technologies emerging from the Fourth Industrial Revolution, integrating AI into the public sector for everyday use will significantly elevate the performance of government departments,” said Khalfan Belhoul, CEO of the Dubai Future Foundation, the host entity of Centre for the Fourth Industrial Revolution UAE.

You can find a copy of the Procurement in a Box toolkit here (PDF)

(Photo by Franck V. on Unsplash)

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The bill, passed on Wednesday, aims to accelerate the sweeping change of regulations across various fields to support the creation of such futuristic cities.

Addressing issues such as depopulation and an aging society will be the focus of the super cities. Technologies including big data and AI will be key to successfully tackling the challenging problems.

Large amounts of data will be collected and organised from across administrative organisations.

Local governments will be selected for the ambitious projects which will launch forums with the national government and private companies to take forward the plans.

Draft plans will be created from this deep public-private collaboration that will subsequently be submitted to the state government if approved by local residents.

As with many smart city plans, there are deep concerns about the collection of personal data and what it could mean for individual privacy. Local residents are sure to want assurance that any data collection is anonymous.

A similar bill was submitted to the Diet (Japan’s decision-making institution) last year. The bill was scrapped following calls from the ruling government to review it.

The revised bill was passed on Wednesday. Given the appetite for the project across the government; the plans are now expected to progress swiftly.

(Photo by Jezael Melgoza on Unsplash)

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If you recognise Xnor.ai, it’s likely because the company’s technology once powered the person-detection feature on Wyze’s popular cameras. Xnor.ai abruptly cancelled their contract with Wyze back in November – and now we know why.

In layman’s terms, edge computing means the processing is done on-device. On-device computation has significant advantages when it comes to reducing latency, improving performance, and retaining privacy.

https://www.youtube.com/watch?v=FG31XxX7ra8

Apple makes a big deal out of its privacy credentials. When people are becoming increasingly concerned about the data collection practices of competitors like Google, Amazon, and Microsoft, it’s a strategy that works.

Compared to all three of those cloud-focused competitors, many believe Apple has fallen behind when it comes to AI. With its Xnor.ai acquisition, Apple can boost its AI capabilities while saying that it’s protecting customers’ privacy by not sending it all up to the cloud like its rivals often do.

Xnor.ai started its journey as part of the nonprofit Allen Institute for AI. In 2017, the company spun off as its own entity headed by CEO Ali Farhadi.

While small change compared to what Apple acquired it for, Xnor.ai had already raised significant capital. In early 2017, it raised $2.7 million. In 2018, it raised $12 million. Both rounds were led by Madrona Venture Group.

Apple has been on something of an AI acquisition spree in recent years. In fact, a report by CBInsights last year found that Apple acquired more AI firms (20) than any other leading tech company in 2019. Google took second place with 14 acquisitions, followed by Microsoft with 10.

With the IoT becoming the next big thing, companies like Apple are looking to AI for ways to differentiate their products from competitors. Apple’s $200 million for Xnor.ai may seem a lot on paper, but it will almost certainly pay off in the long run.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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Wyze’s partner for the feature, Xnor.ai, made the decision to terminate their agreement.

In a post on its forums, Wyze explained its agreement with Xnor.ai included a clause allowing the contract to be terminated “at any moment without reason.”

A reason for the abrupt contract termination has not been provided. AI News has reached out to Xnor.ai for comment but is yet to receive a response.

“Person Detection will be temporarily removed from Wyze Cam beginning with a new firmware release planned for mid-January 2020, due to the unexpected termination of our agreement with our AI provider,” wrote Gwendolyn Evans, Social Media Manager at Wyze.

Xnor.ai specialises in edge-AI which means the AI runs directly on Wyze cameras instead of using the cloud and running on a server that charges recurring fees.

Wyze claims that the removal of the feature should only be temporary as the company is working on its own in-house version of the technology. The feature will remain free when it’s reintroduced but Wyze says it may use the cloud rather than run locally.

Person Detection was first added to the Wyze Cam v2 and Wyze Cam Pan back in July. The removal of the feature under half a year after its debut is unfortunate, to say the least.

One thing which may mean Wyze’s customers are more forgiving is the low cost of their smart cameras in comparison to rivals. The company’s entry-level camera is just $20, a bargain compared to Nest’s $199.

Wyze admits it has made “mistakes” and so perhaps the lesson for others is to learn from them and ensure contracts are robust.

Interested in hearing industry leaders discuss subjects like this? , , , AI &

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Goke believes technologies such as AI will be so transformative in the next five years, if you fell asleep today – and woke up in 2025 – you’d struggle to recognise electronic devices.

“The Americans among us, you all know the fairytale story of a man in the 18th century who falls asleep under a tree and wakes up 20 years into a completely different world,” says Goke. “Well, I bet you if you would go to sleep right now for just five years, and wake up in time for 2025, you will struggle to recognise many consumer electronic devices.”

Goke says you’ll notice less people talking about the IoT (Internet of Things) as an emerging tech because it’s already becoming adopted at a rapid pace.

“Today, nearly every single home appliance has become a truly digital device ready to take its place in our smart homes,” comments Goke. “People don’t really talk about the IoT anymore because it’s just there, it’s the new normal.”

Just a few years ago, Goke says AI was “not something tangible” and “more of a promise than a real offering”.

As the CEO of Messe Berlin, an organiser and host of IFA Berlin, Goke has seen trends evolve over many years. At this year’s IFA, Goke says you’ll see the impact of AI and 5G coming to life. There are certainly more exhibitors than ever on the show floor talking about how they’re now using AI.

“You will see how connectivity and artificial intelligence are coming totally integrated into our digital world,” comments Goke. “They are about to completely transform our lives. And they will be ubiquitous. We all will use these technologies quite naturally without a second thought.”

Dr Reinhard C. Zinkann, member of ZVEI’s board of directors, also spoke of the adoption of AI in everyday devices. According to Zinkann, 72 percent of robot vacuums now feature “smart” technology.

“Clearly, artificial intelligence has made its way into the home appliances sector,” says Zinkann. “Even if you could sometimes have the feeling that the term is used in a somewhat inflationary way.”

“But it’s true, home appliances offer very promising possibilities for artificial intelligence technologies. By using data analysis, pattern recognition, and clever algorithms, appliances can be individually and dynamically adapted to the needs of users.”

Goke believes one emerging technology that people should temper their expectations about this year is 5G.

“Our industry is talking a lot about the promises of 5G, the next-generation high-speed mobile network,” states Goke. “Yes, 5G is being rolled out, it has the power to transform the way we live and work. But it’s also time for a reality-check.”

“Right now, there are only a few real 5G products around. However, that will be changing rapidly. I believe that 2020 could be the first year at IFA when 5G is ready for launch.”

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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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The propeller-forward designs of most drones are inefficient and reduce flight time. Researchers from MIT, Dartmouth, and the University of Washington have proposed a new hybrid design which aims to combine the perks of both helicopters and fixed-wing planes.

In order to support the new design, a new AI system was developed to switch between hovering and gliding with a single flight controller.

Speaking to VentureBeat, MIT CSAIL graduate student and project lead Jie Xu said:

 “Our method allows non-experts to design a model, wait a few hours to compute its controller, and walk away with a customised, ready-to-fly drone.

The hope is that a platform like this could make more these more versatile ‘hybrid drones’ much more accessible to everyone.”

Existing fixed-wing drones require engineers to build different systems for hovering (like a helicopter) and flying horizontally (like a plane). Controllers are also needed to switch between.

Today’s control systems are designed around simulations, causing a discrepancy when used in actual hardware in real-world scenarios.

Using reinforcement learning, the researchers trained a model which can detect potential differences between the simulation and reality. The controller is then able to use this model to transition from hovering to flying, and back again, just by updating the drone’s target velocity.

OnShape, a popular CAD platform, is used to allow users to select potential drone parts from a data set. The proposed design’s performance can then be tested in a simulator.

“We expect that this proposed solution will find application in many other domains,” wrote the researchers in the paper. It’s easy to imagine the research one day being scaled up to people-carrying ‘air taxis’ and more.

The researchers will present their paper later this month at the Siggraph conference in Los Angeles.

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Drive.ai is full of skilled personnel after being founded in 2016 by a pioneering team of graduates from Stanford’s AI lab. With AI talent in short supply, it seems Apple is considering purchasing a whole company to get the skills on its side.

Apple would not get any of Drive.ai’s intellectual property as part of the deal, only the minds behind it. While it’s unclear what Apple is considering paying for the startup, the company was believed to be valued at ~$200 million during a VC round in 2017.

The fact Apple is scoping out talent from a driverless car firm shows Cupertino’s continued interest in the area.

Back in January, Apple pulled around 200 employees off its self-driving car project which made some people question whether Cupertino had the ability (and resolve) to break into such an established industry with a minefield of regulatory needs that vary from location-to-location.

A self-driving car being tested by Apple had a rear-end collision with another vehicle last year. Fortunately, unlike Uber’s self-driving car incident, there were no injuries.

During an interview with CNN last November, Apple CEO Tim Cook expressed that his company is focused on the software side of autonomous vehicles. Acquiring talent from Drive.ai is a surefire way of boosting Apple’s in-house expertise in applying machine learning to cars.

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In the keynote session, titled ‘What is the true adoption of AI’, Hogan provided real-world examples of how the technology is being used and enabled by Nvidia’s GPUs. But first, he highlighted the momentum we’re seeing in AI.

“Many governments have announced investments in AI and how they’re going to position themselves,” comments Hogan. “Countries around the world are starting to invest in very large infrastructures.”

The world’s most powerful supercomputers are powered by Nvidia GPUs. ORNL Summit, the current fastest, uses an incredible 27,648 GPUs to deliver over 144 petaflops of performance. Vast amounts of computational power is needed for AI which puts Nvidia in a great position to capitalise.

“The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before,” says Hogan. “You cannot train a neural network on a standard CPU cluster.”

Nvidia started off by creating graphics cards for gaming. While that’s still a big part of what the company does, Hogan says the company pivoted towards AI back in 2012.

A great deal of the presentation was spent on autonomous vehicles, which is unsurprising given the demand and Nvidia’s expertise in the field. Hogan highlights that you simply cannot train driverless cars using CPUs and provided a comparison in cost, size, and power consumption.

“A new type of computing is starting to evolve based around GPU architecture called ‘dense computing’ – the ability to build systems that are highly-powerful, huge amounts of computational scale, but actually contained within a very small configuration,” explains Hogan.

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market.

Nvidia has a machine called the DGX-2 which delivers two petaflops of performance. “That is one server that’s equivalent to 800 traditional servers in one box.”

Nvidia has a total of 370 autonomous vehicles which Hogan says covers most of the world’s automotive brands. Many of these are investing heavily and rushing to deliver at least ‘Level 2’ driverless cars in the 2020-21 timeframe.

“We have a fleet of autonomous cars,” says Hogan. “It’s not our intention to compete with Uber, Daimler or BMW, but the best way of us helping our customers enable that is by trying it ourselves.”

“All the work our customers do we’ve also done ourselves so we understand the challenges and what it takes to do this.”

Real-world impact

Hogan notes how AI is a “horizontal capability that sits across organisations” and is “an enabler for many, many things”. It’s certainly a challenge to come up with examples of industries that cannot be improved to some degree through AI.

Following autonomous cars, Nvidia sees the next mass scaling of AI happening in healthcare (which our dear readers already know, of course.)

Hogan provides the natural example of the UK’s National Health Service (NHS) which has vast amounts of patient data. Bringing this data together and having an AI make sense of it can unlock valuable information to improve healthcare.

AIs which can make sense of medical imaging on a par with, or even better, than some doctors are starting to become available. However, they are still 2D images that are alien to most people.

Hogan showed how AI is able to turn 2D imagery into 3D models of the organs which are easier to understand. In the GIF below, we see a radiograph of a heart being turned into a 3D model:

We’ve also heard about how AI is helping with the field of genomics, assisting in finding cures for human diseases. Nvidia GPUs are used for Oxford Nanopore’s MinIT handheld which enables DNA sequencing of things such as plants to be conducted in-the-field.

In a blog post last year, Nvidia explained how MinIT uses AI for basecalling:

“Nanopore sequencing measures tiny ionic currents that pass through nanoscale holes called nanopores. It detects signal changes when DNA passes through these holes. This captured signal produces raw data that requires signal processing to determine the order of DNA bases – known as the ‘sequence.’ This is called basecalling.

This analysis problem is a perfect match for AI, specifically recurrent neural networks. Compared with previous methods, RNNs allow for more accuracy in time-series data, which Oxford Nanopore’s sequencers are known for.”

Hogan notes how, in many respects, eCommerce paved the way for AI. Data collected for things such as advertising helps to train neural networks. In addition, eCommerce firms have consistently aimed to improve and optimise their algorithms for things such as recommendations to attract customers.

“All that data, all that Facebook information that we’ve created, has enabled us to train networks,” notes Hogan.

Brick-and-mortar retailers are also being improved by AI. Hogan gives the example of Walmart which is using AI to improve their demand forecasting and keep supply chains running smoothly.

In real-time, Walmart is able to see where potential supply challenges are and take action to avoid or minimise. The company is even able to see where weather conditions may cause issues.

Hogan says this has saved Walmart tens of billions of dollars. “This is just one example of how AI is making an impact today not just on the bottom line but also the overall performance of the business”.

Accenture is now detecting around 200 million cyber threats per day, claims Hogan. He notes how protecting against such a vast number of evolving threats is simply not possible without AI.

“It’s impossible to address that, look at it, prioritise it, and action it in any other way than applying AI,” comments Hogan. “AI is based around patterns – things that are different – and when to act and when not to.”

While often we hear about what AI could one day be used for, Hogan’s presentation was a fascinating insight into how Nvidia is seeing it making an impact today or in the not-so-distant future.

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