The club, through a partnership with talent identification platform AiScout, is enabling anybody above the age of 14 to record and upload themselves undertaking specific football drills to the AiScout mobile app for free. All videos uploaded to the app are analysed by AI technology to help Burnley’s scouts identify the most talented individuals.

Players identified as having potential will be invited to attend a formal academy trials day this year, with a chance of claiming a place in Burnley’s Category 1 Premier League Academy.

“This is a first opportunity for us to introduce new data-led technologies into the football club and promote Burnley to the wider football world by giving aspirational young players across the world an opportunity. We look forward to expanding the programme throughout 2021,” said Alan Pace, Chairman of Burnley FC.

“With the pandemic currently leading a suspension of youth football in Lancashire and across the UK, this trial represents an open and inclusive opportunity for football players to complete a set of drills in their own environment that could end with them being scouted by a Premier League club.”

Last year, AiScout received an investment from ALK Capital, which acquired an 84 per cent stake in Burnley for £200m in December 2020 through its sports investment arm, Velocity Sports Partners.

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Photoshop Camera will be powered through the artificial intelligence (AI) gathered through Adobe Sensei, its existing AI platform and framework. Photoshop Camera will allow users to capture, edit, add filters and share pictures straight from the app, and Adobe Sensei will automatically recommend edits to uploaded photos. Sensei’s platform will instantly recognize what type of photo was taken (portrait, landscape, selfie, food shot, etc.) and which technical settings could be improved.

“We built Photoshop Camera as a Sensei-first app on our journey to expand our focus to deliver creative tools, including Photoshop, for everyone. With Photoshop Camera and Photoshop Express, we’re increasing your choices for creating with the magic of Photoshop,” wrote Abhay Parasnis, executive vice president and chief technology officer, Adobe.

Exclusive to the app are lenses that were designed by celebrities and well-known influencers, which will continue to rollout as more personalities get involved with Adobe. To launch Photoshop Camera, Adobe has partnered with singer Billie Eilish to create her own collection of limited-edition lenses.

“Photoshop Camera is a major moment for us to broaden Adobe Creative Cloud as a creativity platform for all. We are committed to investing in accessible tools like Photoshop Camera built for the next generation of consumers and creatives. Innovation and pushing the limits of creative expression have always been core to Adobe’s DNA,” finished Parasnis.

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A Walmart in New York has been implementing new technology into its infrastructure, including artificial intelligence-enabled cameras, interactive displays and a state-of-the-art data center. The transformation will create the chain’s first Intelligent Retail Lab, or IRL, which uses artificial intelligence and machine learning to optimize the customer and employee experience.

“We’ve got 50,000 square feet of real retail space. The scope of what we can do operationally is so exciting,” said Mike Hanrahan, CEO of IRL. “Technology enables us to understand so much more – in real time – about our business. When you combine all the information we’re gathering in IRL with Walmart’s 50-plus years of expertise in running stores, you can create really powerful experiences that improve the lives of both our customers and associates.”

IRL includes a fully transparent data center consisting of sensors, cameras, processors, and enough cabling to scale Mt.Everest five times. Hanrahan said the data center’s first priority will be to use real-time information to help keep product inventory and availability consistent for customers.

“Customers can be confident about products being there, about the freshness of produce and meat. Those are the types of things that AI can really help with,” Hanrahan said.

Using cameras, sensors, and real-time analytics, the Walmart store will be able to detect and notify employees when an item is running low or out of stock, via an app. IRL’s technology will also be able to alert employees when carts need to be collected or more registers need to be opened.

Hanrahan said the choice to keep all technology viewable was made at the beginning of the project. Additionally, the IRL team made sure there was a “Welcome Center” and educational kiosks spread around the store, which explained what Walmart was doing with customer’s information. For a more fun effect, the team added giant plexiglass boards that mimic the movements of whoever is in front of it.

IRL will employee more than 100 people who will be able to test how the technology makes their workdays more efficient. Hanrahan hopes that by cutting down time spent on day-to- day tasks, employees can allocate more facetime to customers.

“We think it’s something our associates will be excited about,” Hanrahan said. “The technology has been built to improve associates’ jobs, to make their jobs more interesting, to help them alleviate some of the mundane tasks. AI can enhance their skillset in a very rapidly changing world.”

The post Walmart is opening its first experimental “smart” store, powered by AI appeared first on Mobile Marketing Magazine.

The social network allows users to choose a friend or family member to take charge of their account should they pass away. These so-called ‘legacy contacts’ are able to convert the deceased’s account into a memorialised one where their life can be remembered and celebrated.

The problem is that, prior to memorialisation, the accounts of deceased users will continue to pop up in recommendations and serve birthday reminders. So, Facebook is using AI to detect when a user has passed before they have been memorialised, though it hasn’t offered any information on how it is going to pull this off.

“If an account hasn’t yet been memorialised, we use AI to help keep it from showing up in places that might cause distress, like recommending that person be invited to events or sending a birthday reminder to their friends,” said Sheryl Sandberg, Facebook COO, in a blog post. “We’re working to get better and faster at this.”

The company has also made a couple of other changes to memorialisation. It’s introduced a ‘Tributes’ section, which separates posts made before memorialisation from those made after, and it’s giving legacy contacts full control over the moderation of posts within the section.

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COMB+ announces €65m fund to encourage AI startups to work in China
Chinese tech accelerator COMB+ has unveiled a new €65m (£57.4m) fund aimed at bringing international AI startups to China to help the country stay at the cutting edge of the key technology. As more and more firms aim to invest in AI and machine learning, China is hoping to attract talent from overseas to advance its own efforts and strengthen future trade ties.

COMB+ also runs the Sino Track accelerator program, which it launched last year, helping early-stage firms grow their presence in China. That fund, which has focused heavily on the Nordic region, has already seen 19 startups from the area graduate and receive investment. The new fund will target more advanced companies with proven business models in their local market who are looking to expand into China.

The fund is run by COMB+ and the Beijing Institute of Collaborative Innovation (BICI), and more than half of the €65m target has already been raised, with both government and corporate sources contributing. According to COMB+ CEO Leo Zhu, the fund is hoping to attract interest from partners in Finland while it raises the remaining cash, and possibly even tap into China-Finland government collaboration policies.

“We admire [Finland’s] advanced technology,” said Zhu. “Engineers would typically spend five to eight years to understand broader technology opportunities; we have a huge market in China and would like to take these advanced technologies there.”

Google makes first direct investment in Indian startup with Dunzo
Tech giant Google has picked up a minority stake in Dunzo, a hyper-local mobile-first concierge and delivery company based in Bengaluru, the firm’s first direct investment in a startup from India. The investment was made as part of the company’s Next Billion Users initiative, and was part of a $12m (£8.9m) Series B funding round that also saw investment from Blume Ventures and Aspada.

Founded in 2015, Dunzo is a chat-based task management app that enables users to create to-do lists through its app which are then fulfilled by workers. Tasks can cover everything from picking up groceries to basic paperwork, and the app also enables users to reimburse workers for expenses and track progress using messaging.

Google has reportedly taken a 33.8 per cent stake in the startup as part of the round, which also saw the firm’s valuation increase three-fold from its Series A round, taking its post-money capitalisation to $30m. The firm has recently expanded its services to Pune, and CEO Kabeer Biswas has announced plans to grow to five or six cities by the end of 2018, and scale up from 100,000 transactions a month right now, to 100,000 transactions a day.

Series C round brings in $19m for NGData
Customer experience management company NGData has announced the completion of a Series C funding round led by HPE Growth Capital which has brought in $19m (£14.2m) in new funding, bringing the firm’s total funding to just under $50m. The fund also saw participation from existing investors including Idinvest Partners, Pamica, SmartFin Capital and Nausicaa Ventures.

Lily, the company’s flagship customer data platform, is designed to help organisations improve customer experiences by combining data, generating insights and steering actions. The firm has primarily targeted banks, telecoms and media & publishing companies so far, but is looking to expand the reach of its automated, customer-centric solution.

“Consumer behaviour is changing, and companies are just starting to realise the value of customer data platforms and using them to drive meaningful experiences,” said Luc Burgelman, CEO of NGData. “For the past five years, NGData has been steadfast in helping marketers improve every touchpoint across the customer journey, and we’ve designed and continually improved Lily to deliver smarter, more informed customer experiences. This funding not only validates our success in achieving these goals, but it also supports our commitment to be the leading global customer data platform provider.”

Global payments marketplace Apexx grabs $4m in seed funding
Apexx, a marketplace for global payments that enables firms to integrate all their required payment suppliers into a single API, has secured $4m (£3m) in seed funding to extend its network of acquirers, payment providers and merchants, as well as continue its product development plans. The seed funding was led by Forward Partners, with support from MMC Ventures and Alliance Venture.

“We’re thrilled to see Apexx moving from strength to strength,” said Peter Keenan, co-founder and CEO at Apexx. “We’ve already launched the first single marketplace for global payments in the world, we have an experienced leadership team steering the company and a strong vision bringing transparency, efficiency and competition to the payments market. Investment from these leading VCs will bring the expertise and funding needed to help us accelerate the growth of the business.”

Apexx attracted interest from the fintech scene earlier this year when Valerie Soranno Keating, former CEO of Barclaycard, joined the board as non-executive director. According to the company, its technology enables customers to reduce the cost of cross-border payments, boost sales and manage multiple providers through a single integration point.

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“Artificial intelligence” and “machine learning” are two of the hottest buzzwords in the industry right now, but many professionals in our space are still working out what these technologies do and how they can be used to improve digital marketing.

This week, Oracle announced implementation of AI-driven capabilities across its Customer Experience Cloud. SalesForce recently launched joint artificial intelligence solutions in partnership with IBM’s Watson AI technology and others aren’t far behind as understanding and demand accelerates.

AI is already being leveraged by advertisers to do anything from creating chatbots to crunching massive data sets to drive hyper-targeted media buys. As CMOs explore new ways of improving return on digital ad spend, they must stay informed on the key attributes of this emerging technology as a proven solution to make our ecosystem more pure. Here are the finer points of artificial intelligence and machine learning necessary to understand how brands can leverage these tools to enhance their targeting and weed out fraudulent impressions.

So, what is the difference between artificial intelligence and machine learning?
Stanford’s computer science department defines artificial intelligence as “the science and engineering of making intelligent machines, especially intelligent computer programs.” In this context intelligence is the “computational” aspect of how we humans achieve our goals in the world.

Machine learning is a subset of AI that focuses on creating machines that “learn” without being explicitly programmed to do so. At their core, machine learning algorithms are designed to solve optimization problems, often to make some sort of prediction, such as how do I accurately predict which consumers have a high intent to purchase.

While machine learning was largely out of reach merely a decade ago, the rise of increasingly powerful CPUs to process the data and an almost infinite amount of cheap data storage has given us the power to parse previously unthinkable loads of information. As you might imagine, this ability is one that’s having a major impact on how brands and agencies approach digital advertising.

Machine learning helps brands buy the right impressions with incredible precision
Machine learning allows brands to take targeting to the next level by using algorithms to first analyze user behavior and then make predictions of which users are the “best”, which can be defined as highest intent to purchase or LTV, most likely to engage, or many other KPIs.

For example, a machine learning algorithm might process a training set of the site data of prior purchasers of widgets and then pinpoint a precise group of users as likely customers based on a range of detailed information, including how long the users spent reading widget-related content, what percentage of their browsing activity was devoted to widget review sites, whether they have interacted with an ad before, the type of interaction and so on. Already, marketing agencies like Publicis.Sapient are using these tools to automate more mundane functions to become more effective and efficient at their jobs and able to focus on more high-level work like strategy in order to achieve better results.

Machine learning is conquering the bots — and who knows what’s next
One of the most innovative applications of machine learning in marketing is bot detection. Bad actors are stealing billions of dollars from brands each year by using bots to generate fake impressions and clicks. These bots can range in complexity from General Invalid Traffic, such as automated browsers running within data centers, to Sophisticated Invalid Traffic, such as bots that copy user mouse movement and accurately simulate human behavior.

A common approach to detecting bots has been rules-based analysis. For instance, a simple rule could be flagging users that generate above a certain number of clicks each day. However, the flaw with rules based approaches are that black hats can always re-work their tacts to be just below the thresholds that would be flagged. Machine learning allows us to take a more sophisticated approach to fraud detection by analyzing how users browse with the internet and calling out anomalous behavior, without a necessarily pre-defined notion of what “anomalous” really is.

The goal of a bot on the internet is to steal ad revenue, whereas a human has much more benign goals, such as consuming content. Because of this bots have to browse in a way that is inherently non-human, and this anomalous behavior can be called out by learning algorithms can segregate the human from non-human activity.

Machine learning allows us to Identify and track nonhuman behavior
While machine learning allows us to identify groups of users which are browsing similarly, it takes additional human analysis to identify which patterns are anomalous and associated with nonhuman behavior. By combining this pattern recognition with a team of human experts capable of identifying bot behavior, ad-tech vendors are able to help brands and agencies catch on to fraudsters’ latest schemes in real time. In fact, this mixture of machine learning behavior clustering and human intelligence is how Forensiq discovered that brands had lost nearly $1 billion to black hats who were hijacking people’s phones to continuously run ads they couldn’t see.

Moving forward, the industry will continue to find innovative applications of machine learning technology. In the context of fraud detection, this is especially important as fraudsters continue to become more sophisticated and better able to hide in the noise of the vast amount of traffic on the internet.

By taking proactive steps to better acquaint themselves with this emerging technology, brand and agency-side marketers will put themselves in a position to make the most of whatever new innovations come down the pike. While the intelligence may be artificial, machine learning’s potential to transform digital advertising is very, very real.

The post How Machine Learning is Optimising Spending and Eliminating Fraud – Impact Radius appeared first on Mobile Marketing Magazine.

Halli is a startup in the truest sense of the word – it only announced its existence in May.

Details on Halli – including not just headcount and funding, but what exactly it does – are still a little hard to come by.

In its announcement of the deal. the company described itself as follows: “Halli Labs was founded with the goal of applying modern AI and ML techniques to old problems and domains?—?in order to help technology enable people to do whatever it is that they want to do, easier and better.” 

Don’t forget to enter the Effective Mobile Marketing Awards. The Early Bird deadline for submissions is 22 July. More details here.

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AI for Earth will see the company committing $2m to the project over the next fiscal year. It will open up access to AI training and resources for researchers and organisations working on agriculture, biodiversity, climate change and water. 

“The scale and speed of the changes we see in our physical and natural world require new solutions,” said Brad Smith, president and chief legal officer at Microsoft. “But the latest innovative technologies often come with a price tag and require computational expertise that puts them out of reach for many researchers and non-governmental organizations.”

Microsoft will also seek partners for lighthouse projects, three of which are already underway, involving smart mosquito traps, drone- and sensor-based agriculture, and land cover mapping.

“Our goal is to empower others in new and more impactful ways to help create a more sustainable future,” Smith said. “This program expands our commitments to democratizing AI and advancing sustainability around the globe. And it builds upon our experience in accelerating the pace of innovation bringing together philanthropic work, connectivity advances and more accessible technology around the world.”

The AI for Earth project will be led by Lucas Joppa, promoted to the role of chief environmental scientist.

Don’t forget to enter the Effective Mobile Marketing Awards. The Early Bird deadline for submissions is 22 July. More details here.

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The terms of the deal havent been disclosed – Samsung has been quiet on the acquisition as a whole – but TechCrunch reports that the price was somewhere under €40m (£36m).

Its not hard to imagine what Samsung might want with Innoetics technology.

Bixby, its voice-based virtual assistant, was developed after the $215m acquisition of Viv last October – and, according to reports last week, is currently being held back from an English-language launch due to difficulties in the translation from Korean.

Innoetics was founded in 2006, as a spin-off the Athena Research & Innovation Centre, and has offered its Speech Synthesis technmology through APIs to companies like Vodafone and Duolingo.

The company is discontinuing all current services, and will operate as an independent, wholly-owned subsidiary of Samsung, from its current office in Athens.

Don’t forget to enter the Effective Mobile Marketing Awards. The Early Bird deadline for submissions is 22 July. More details here.

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There has been an eight per cent increase in monthly unique users of all the assistants added together, up to 93.6m – though its worth noting that this might be larger than the actual total, as theres likely some crossover between the various assistants.

Despite Alexa being the new poster girl for the technology, the undisputed queen of virtual assistants is still Siri, with 41.4m monthly users to Alexas 2.6m. However, Siris user numbers are declining – down 15 per cent year-on-year – while Alexas have jumped by 225 per cent. 

Its a similar picture when it comes to individual usage of these assistants. The time that users spend with Siri is also falling – down from 39 minutes per month to 14 minutes – driven by a steep drop in monthly sessions, from 14 to just five. Meanwhile, monthly time with Alexa has more than doubled, to 18 minutes, and the number of session has nearly tripled, to 16.

Of course, its not just Apple and Amazon playing in this space. Samsung actually has the second biggest assistant in S Voice (23.2m monthly users) though this number is dropping – and S Voice is set to soon be replaced with Bixby.

Like Alexa, Microsofts Cortana still has a small userbase – 700,000 monthly users – but its on the rise.

Google saw the biggest growth, not least because of the breadth of its products which fall under the virtual assistant umbrella: Google Now, Allo, Home, and Text-to-Speech. The latter performed best, more than doubling its monthly users to 19.8m in May 2017.

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