New experiences in the stores of the future

Salmerón Uribes Marina    20 March, 2018

The digital transformation of brick-and-mortar stores and their integration with other channels is an inevitable process. It is also the best way to rise to the challenge that 100% digital ecommerce companies are posing to the retail sector. In this sense, Statista calculates that ecommerce will account for 14% of total retail sales in the USA by 2021.

In view of this increase, the evolution in the retail sector must encompass omnichannel (combination of online and offline) and digitalization. That is, online sales have to be combined with service in increasingly digital stores in order to improve customers’ shopping experience, as explained in a previous blogpost.

The evolution towards the customer as the core of technology 

Until just a few years ago, many of the top store technologies brought advantages for the stores but did not have much of an impact on customers. The computerization of points of sale, bar codes, and the adoption of more efficient lighting have improved many in-store processes and efficiency and have optimized revenues, but they have not significantly improved the shopping experience. Now, the most prominent new technologies are totally customer-centered and accompany them along their entire customer journey. So, what do these new in-store technologies consist of?

  • Digital shop windows: Digital signage has extremely important applications as an advertising strategy. One example is the impressive action with multiple videos that Samsung recently undertook in Plaza de Callao. However, it also provides intriguing solutions for both inside and outside the store so that customers can see selected products without them having to leave the displays, aisles, or warehouses.
  • Digital directories and interactive catalogues: Digital directories are located at the entrance to the store and show an interactive 3-D map of the store with the location of each product category. Likewise, interactive catalogues are distributed around the different sections in the store and allow customers to search for the availability of a given product and its location in the store.
  • Augmented reality applications for stores: The new capacities of smartphones, such as those that allow the ARKit of the latest version of iOS allow  applications to be developed in which augmented reality can make life easier for customers in the store. For example, it can provide the location of products by aisles or show products by pieces that have already been virtually assembled.
  • Beacons to interact with customers: Beacons are small, low-consumption devices which are scattered around the store and are capable of interacting with customers’ mobile devices. They provide the store with invaluable information on the customers’ location within the store and which areas are the densest and are sparking the most interest. Customers that authorize these interactions with beacons can receive personalized notices and deals when they draw near them.
  • Digital fitting rooms: Several retail companies have successfully implemented this technology. Currently, the majority consist of a touchscreen inside the fitting room which allows customers to choose clothing sizes and different colours, to see other products, or to ask store staff for help. But there are also developments that include augmented reality which virtually superimpose clothing onto the customers’ reflected image.  
  • Wearable and biometric payment: Clients can forget about carrying credit cards. Payment by NFC is becoming increasingly widespread, so customers can pay just by bringing their mobile devices or wearables such as smartwatches closer to the payment device. However, the next step is when devices are not even needed; all we will have to do is identify ourselves with a body part. In this vein, there are already payment systems via facial recognition in China.

There are also other solutions which make customers’ in-store experience more pleasant, such as background music like Spotmusic and  Wi-Fi solutions. They are all shaping the store of the future, whose core is not only productivity, efficiency, or cost-saving but also customers and new in-store experiences. 

#CyberSecurityPulse: PyeongChang Olympics: A New False Flag Attack?

ElevenPaths    20 March, 2018

A postmortem of the Olympic Destroyer malware used in the PyeongChang Olympics attack reveals a deliberate attempt by adversaries to plant a false flags when it comes to attribution, according to researchers. Days after the crippling attack on the backend networks tied to the Winter Olympic Games, a chorus of security experts attributed the attacks to everyone from Russia, Iran, China and groups such as Lazarus, the nation-state backed gang linked to North Korea. However, security experts now believe a skilled and mysterious threat actor behind the malware intended to sow confusion among those attempting to assign attribution to the attack. “Perhaps no other sophisticated malware has had so many attribution hypotheses put forward as the Olympic Destroyer,” said Vitaly Kamluk, researchers who co-authored a report released on the attacks. “Given how politicized cyberspace has recently become, the wrong attribution could lead to severe consequences and actors may start trying to manipulate the opinion of the security community in order to influence the geopolitical agenda.”

In the days proceeding the attack a steady stream of theories emerged that were later debunked and ruled inconclusive. “How the industry responded was a disaster,” Kamluk said. “There was too much finger pointing with no certainty.” Beyond the Lazurus false flag, researchers said Russian-speaking cyber espionage group Sofacy (also known as Fancy Bear and APT28) was also imprecisely implicated in the attack. Other bits of malware code linked Chines-affiliated cyber espionage groups APT3 (Gothic Panda), APT10 (MenuPass Group), and APT12 (IXESHE).


Actually, this is just one more example. According to Kamluk, time is a powerful tool for determining the attribution of an incident. True. However, in most cases we will not be able to wait indefinitely for decisions.

More information at ThreatPost

Top Stories

Cryptocurrency Firms Targeted in SEC Probe

The Securities and Exchange Commission sent subpoenas in recent weeks to dozens of tech companies and individuals who are involved in cryptocurrency, The Wall Street Journal reported Wednesday evening, citing anonymous sources. The targets of the subpoenas include companies that have launched initial coin offerings (ICOs), the cryptocurrency equivalent of IPOs, as well as their lawyers and advisers. The subpoenas reportedly include requests for information on how ICO sales and pre-sales are structured, the anonymous sources told WSJ. The SEC is also requesting the identities of the investors who bought digital tokens, The New York Times found. The SEC declined to comment.

More information at The Wall Street Journal

NSA Retreats From Targeted PCs If They’re Already Infected by Other APT Malware

Hacking tools leaked last year and believed to belong to the US National Security Agency (NSA) contain an utility for detecting the presence of malware developed by other cyberespionage groups. This utility, going by the codename of Territorial Dispute, is meant to alert NSA operators about the presence of other cyberespionage hacking groups on a compromised computer and allows an NSA operator to retreat from an infected machine and avoid further exposure of NSA hacking tools and operations to other nation-state attackers.

More information at Bleeping Computer

Rest of the Week´s News

Facebook Automatically Upgrading Links to HTTPS to Boost Security

Facebook announced on March 5, that it is turning on a new capability that will automatically direct users to an HTTPS secured version of a link target, if one is available. The feature known as HTTP Strict Transport Security (HSTS) preloading is being rolled out across facebook.com and Instagram. With HSTS preloading, a site link that a user posted as an un-encrypted HTTP link will automatically be re-directed to an encrypted HTTPS link for a given site.

More information at Eweek

Microsoft Fights Massive Cryptocoin Miner Malware Outbreak

Microsoft has blocked a rapidly spreading malware outbreak that could have infected nearly 500,000 Windows PCs within hours on March 6. The trojan, known as Dofoil or Smoke Loader, was designed to deliver a range of payload. However, in this case, it dropped a cryptocurrency miner on infected PCs, in order to earn those behind the trojan Electroneum coins from victims’ CPUs.

More information at ZDNet

Chinese APT Group TEMP.Periscope Targets US Engineering and Maritime Industries

Past attacks conducted by the group aimed research institutes, academic organizations, and private firms in the United States. FireEye researchers confirmed that the tactics, techniques, and procedures (TTPs) and the targets of the TEMP.Periscope overlap with ones both TEMP.Jumper and NanHaiShu APT groups.

More information at Fireeye

Further Reading

Plugins for Popular Text Editors Could Help Hackers Gain Elevated Privileges

More information at The Hacker News

Hackers Tried to Cause a Blast at a Saudi Petrochemical Plant

More information at Security Affairs

Experts Discovered Remotely Exploitable Buffer Overflow Vulnerability in MikroTik RouterOS

More information at Security Affairs

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Safer Societies using Facial Recognition

AI of Things    19 March, 2018
Facial recognition systems have been around for decades, and were first developed by the computer scientist Woodrow Bledsoe in 1964. Nowadays, such technology is a common part of our lives. For example, Face ID was one of the key features of the latest iPhone and Facebook help us tag our friends in photos with suggestions based on facial recognition. Whilst questions of privacy remain, the technology has matured in recent years thanks to advances in Big Data and Artificial Intelligence and in this blog we’ll explore some applications which bring added safety to our lives.

The term “facial recognition” refers to biometric technology that can be used to identify an individual. The basic process involves capturing a photo of the person and comparing it to a database photo. In order to find a match, algorithms are used to compare various points on the face, meaning it is a truly “Big Data” process. Now, the same Deep Learning algorithms that many AIs use can be applied, meaning that facial recognition is getting even smarter.

Two mounted surveillance cameras on a wall
Figure 1 : You may see policemen of the future wearing sunglasses with built-in surveillance capabilities.

    

Public Surveillance

The first area where facial recognition is already being applied is policing. In China, successful trials of “sunglasses” with facial recognition capabilities have meant that the technology is being tested in the wider Beijing area as well. The wearable technology is linked to a database of criminal records and if an individual’s face matches one of the list, an arrest can be made. You can catch a glimpse of the sunglasses in action in this video. Similar glasses have been used by police in the UK for large-scale events such as the Notting Hill Carnival. In events such as these, where there are large crowds of people, facial recognition comes into its own, since the human eye would have no change of scanning thousands of faces.

Smart Locks

A “Smart Lock” is a device which looks like a doorbell but has a camera built-in which, when using facial recognition or voice activation, can allow individuals to enter their home without needing a key.  AirBnb will use Latch smart locks to secure their own-branded apartments that open this spring. A smart lock would allow AirBnb to guarantee the identity of the individual staying in the room or house (currently it can only confirm the identity of who made the booking). These devices have been growing in popularity and sophistication recently. This fact was shown by the fact that Amazon recently acquired Ring, a smart lock making start-up, for an estimated $1bn. Amazon will likely integrate these locks with their Amazon Key service, which is the first “In-Home” delivery of its kind. You can watch a demonstration of a delivery using this on YouTube.
Camera interface with faces detected
Figure 2 : Facebook is starting to use facial recognition to detect photos of you where you haven’t been tagged.

    

Facebook Tagging

Facebook have been using facial recognition for a while now, in order to detect faces in photos and make suggestions of who to tag. However, the company quietly released an update last month (that many users didn’t notice) which takes the technology one step further. Now, Facebook will scan photos uploaded to the site to find ones that you are in, but aren’t tagged in. The idea is that this can help protect you from strangers uploading photos of you, and give back control of your online presence. What upset many users was not just that the notifiction of the change was easily overlooked, but that the default setting for the service is “on”. 

A Word on Privacy

Before closing, it’s worth highlighting the common questions of privacy. There exists a trade-off between the added security that facial technology can bring (in terms of surveillance etc) and the reduced amount of privacy that people have when their faces are being scanned (since permission isn’t required by law). In China, where the technology has seen most use and development, privacy laws are less strict, meaning that facial recognition can be used with more flexibility. In time, these questions will have to be tackled at a global level, in a similar way that data rights are being addressed with the incoming GDPR. For now, don’t forget to follow us on Twitter, LinkedIn and YouTube to keep up to date with all things LUCA.

3 daily services that rely on machine learning

AI of Things    16 March, 2018
Machine Learning [ML] has become a term everyone is accustomed to hearing about, yet it still feels very distant to many. Some people can be led to believe that only highly technological environments make use of it. Machine Learning however, does play a role in everyday life, whether people realize it or not. 
Below you can find examples of services using it for their everyday operations, to maximize their potential and improve their business. You may even find that you’re using one or even all of these services yourself on a daily basis, and in consequence benefitting from ML.

Video Streaming

If you’ve been a Netflix subscriber for some time now, you might start getting recommendations that are very on point, it would appear as if the service really knows you. All of a sudden you’re knee deep into a series you didn’t know existed two weeks ago. How can this be?
Netflix uses a series of complex algorithms and Machine Learning to identify audiences they call “taste groups”, to identify what is important within the data, and to recommend shows not only based on preference but on viewing activity. A simple way to begin to understand their process is through the terms explicit and implicit data. 
This means that if you give a thumbs up to Stranger Things (because how could you not?), you are giving a piece of data of exactly what you like, this is what we would call explicit data. Binge watching Chef’s Table nonstop lately? Can’t get enough of Peaky Blinders? Binge watching, how often you watch a certain series, and what you watched before and after are all examples of implicit data, what your viewing behavior says about your profile.

Food Delivery

One of the most important parts of ordering food is knowing when it will arrive, ensuring that you, the customer, won’t go hungry for too long. Food delivery companies have dedicated themselves to creating custom platforms, which allow them to make better use of the data that is generated through time, and with this, ensure that wait times are short, and drivers make the best use of their time.

Takeout food
Figure 1: Next time you order takeout, remember how ML helped get it there on time.

UberEats  for example, uses an internal internal “ML-as-a-service platform” as they call it, to give the most accurate ETD (estimated time of delivery).
Michaelangelo,created in 2015, helps the company make the best use out of machine learning internally and for each of their products. It creates models, makes and monitors predictions. This platform was designed to help data engineers and data scientists have a system they could use, as opposed to having to use different tools and have different teams building custom systems for specific projects. It has become a unified platform that all teams can use.  
Once an order is placed, data is collected based on time or order placement and the delivery location, historical features such as meal preparation time from the last week and average preparation time for the last hour are all used to determine how long the delivery will take.

Another food delivery company also relying on Machine Learning is Deliveroo, founded in the UK and operating across 12 countries. Dan Webb, VP of Engineering at Deliveroo has stated that they use data to give the best possible experience on their platform. One significant way in which data becomes crucial for riders to have the best routes, not only time-wise, but to ensure they are also earning the most they can out of every delivery made. Here at LUCA, one way optimal routes are detected is with LUCA Fleet. Time is as valuable as ever, and data in this sense, has become an asset to make the best of it.   
How does Deliveroo calculate all of this? That’s a job they have given to Frank. Deliveroo has created a dispatch engine called Frank, whose job is to give out the most efficient route combinations between orders and riders, and make use of the real-time data that is being collected. 

Transportation

Living in a big city means endless leisure, job opportunities and people but it can also have setbacks such as high volumes of traffic and long commutes. Ride sharing apps have become a lifeline for many people, not only to reduce the need for owning their own vehicle but for time and money saving purposes. 
Apps like Uber, who rely on Machine Learning for food delivery also rely on machine learning for their primary service, ride sharing. If you’re a seasoned Uber rider, you might be familiar with the term surge pricing. This means that your regular ride to work is now triple the cost, and your Monday is not off to the best start.
Uber application on iPhone
Figure 2: Uber relies on their Machine Learning platform to control surge pricing.
Jeff Schneider, the Engineering Lead at Uber Advanced Technologies explained that through Machine Learning, surge pricing can be predicted and controlled so it can ideally be avoided. Holidays for example, present a high demand for drivers and is a given; but ML really kicks into high gear during those unexpected moments, those days when nothing out of the ordinary happens and there is a rise in the demand. With these predictions already recorded, Uber can get that information out there and have their supply ready to prevent a rise in price. 
But what about those who have their own vehicle? How can Machine Learning help car owners? That’s precisely what the creators of CARFIT wanted to address. Both had experience with wearables, and had been working on an algorithm that would detect the exercise type of the wearer; when they quickly realized it could be applied to car ownership and maintenance.
Three elements compose the CARFIT platform: CARFIT Puls (sensor that detects vibrations) which goes attached to the steering wheel, CARFIT app and CARFIT vibration database. The great things about the vibration sensor is that it immediately sends data not only to the app, but also to the company that installed the device. By applying machine learning combined with the help of data scientists, patterns can be identified and compared to known issues in tires, brakes, shocks and other key elements, to check if there is a problem. This will predict maintenance needs, and give partners information about the vehicle. 
After going through the different services above, it can be observed that machine learning really is intertwined with day to day activities; that are not so distant after all. As companies begin to understand the power of data, and adapt internally to be able to fully embrace it, these examples above come to prove how early adopters and ones already on the data train, have been able to boost their sales and services through successful applications of Machine Learning.  

Virtual Reality: the next step for Data Visualizations

AI of Things    15 March, 2018
At LUCA, we’re passionate about the role that Big Data and technology can have in society, so last week on our blog we looked at 5 Medical Uses of Virtual Reality. As a company specializing in Big Data, a large part of what we do is creating visualization tools which our clients can use to extract value out of their data. In this post, we look at how Virtual Reality can take these visualizations one step further.  

Before we continue, it is worth understanding what we mean by a “Big Data Visualization”. Big Data, by definition, involves working with millions of data points. In fact, one of the “Four V’s” that characterize Big Data is “Volume”. Recent years have seen an explosion of data in all areas of life, and businesses have realized the potential of these large quantities. However, simply having the data is not enough, the value comes in analyzing it and extracting “Insights”. The term “Visualization” simply refers to the way in which this information is presented in an understandable, and often interactive, way. We work with our partner, Qlik, in order to provide high quality visualizations.
 
Now for the exciting part. As mentioned in the previous blog, Virtual Reality involves “creating environments that can be interacted with in a seemingly real or physical way“. These immersive environments have the potential to replace previous forms of Big Data visualizations such as timelines and infographics. 

 
A tablet computer showing various charts and numbers
Figure 1 : Infographics and pie charts can be replaced by VR visualizations.

   
Using Virtual Reality to aid data visualization is not a new development. In fact, the University of Washington Human Interface Technologies has been working in the area for over 20 years. However, virtual visualization can now benefit from recent developments in the fields of Artificial Intelligence, Machine Learning and Virtual Reality itself. Perhaps the company best known for merging Big Data and Virtual Reality is Virtualitics, whose VR platform claims to be “the first collaborative data exploration platform that merges Artificial Intelligence, Big Data and Mixed Reality.”

In a demonstration that you can watch on YouTube, one can see some of the amazing benefits that VR can bring to data visualization tools:

  • With a virtual visualization, multiple members of you team can collaborate at the same time, with data that updates in real time. This has particular useful applications for global companies where teams are often connecting from the other side of the world. Traditional ways of displaying data can sometimes be hard to explain, but when viewing them in virtual reality you can explain certain ideas more easily.
 
  • VR can also bring a new level of immersiveness to data analysis. As humans, we can only process a limited amount of data at once, and as more data is added, our “processors” begin to struggle. In a VR data visualization, users can reach out and touch, manipulate and move the data in order to understand what it is showing you.
 
  • The understanding of data is one of the area was VR really excels. As shown in the Virtualitics demo, data can be viewed in up to 10 dimensions, which means that user can see specific trends with greater ease. Additionally, machine learning can be used to analyze human interactions and expressions and thus change the graphics accordingly.
 
Woman wearing a VR headset
Figure 2 : Virtual Reality can bring a new immersiveness to understanding data.
 
 
 
     
Of course, these benefits apply to the principle of VR Data Visualizations, and not just the solutions of Virtualitics. Other companies such as DatavizVR are creating similar tools and it is likely that in the future more companies will enter the market. In the meantime, don’t forget to follow us on Twitter, LinkedIn and YouTube to keep up to date with all things LUCA.

 

Five keys of IoT in Industry 4.0

Beatriz Sanz Baños    14 March, 2018

In the midst of the explosion of IoT, Gartner stated that there will be around 20.4 billion connected devices by 2020; automation and connectivity have become the hallmarks of what is known as Industry 4.0. As we move towards the fourth Industrial Revolution, organizations are integrating the new technologies into their industrial processes in order to optimize their performance and improve their productivity. 

In the new connected age, Internet of Things has become the enabler of the digital transformation of organizations, their businesses and their processes. The implementation of the latest technical advances improves the traceability and automation of processes and fosters decision-making. Furthermore, the trends show that organizations will continue to focus on integrating technologies like the IoT in their production chains in order to make them safer and more efficient and to improve the quality of the service. Proof of this is that Industrial IoT market is expected to reach 195.47 billion dollars by 2022.   

Companies are beginning to make major investments aimed at innovating their processes. Predictive and prescriptive analyses in big data, cloud computing, 3-D printing and robotics will attract the bulk of organizations’ net investment. As mentioned above, we are moving towards an increasingly connected industry in which the Internet of Things is defining a new business model that is gaining more and more followers. Indeed, there are many advantages and benefits to applying the Internet of Things in the industrial ecosystem, but we can summarize the integration of the IoT into Industry 4.0 into five keys: 

  • It automates decision-making and captures information in real time: The sensorization of the IoT facilitates the automation of operations by optimizing processes and resources, providing valuable information in real time and facilitating decision-making. 
  • It lowers overall costs of ownership and improves business innovation: The implementation of IoT in the new Industry 4.0 allows for cost savings thanks to the automation of production chains, enabling machines to take charge of routine tasks and fostering human creativity. 
  • It optimizes the use of assets, generating better performance in processes: The integration of these new technical advances allows the organization’s assets to be used more efficiently, fostering improvements in the performance of productivity and improving the quality of the end products. 
  • It minimizes idle time in machines and assets: IoT lowers the amount of time that machines are idle, getting systems to be 100% available. By connecting machines to the Internet, organizations can monitor huge volumes of data, which enables them to predict machinery failures and thus lower maintenance costs and improve efficiency and availability. 
  • It improves the quality of the service while increasing the competitive advantage: IoT technology applied to organizations’ manufacturing systems optimizes industrial processes, enabling them to be more agile and efficient and contributing to improvements in the productivity of operations. 

As we showed in the most recent edition of the Mobile World Congress, the digital transformation of industry is one of Telefónica’s priority strategies, and Internet of Things is the epicentre of this process of digitalization of companies. In the fourth Industrial Revolution, IoT will play centre stage by spearheading a change in manufacturing systems in all industrial sectors and defining a new more efficient, safer and especially more connected business model: Industry 4.0.

5 medical uses of Virtual Reality

AI of Things    9 March, 2018
One of the technologies where Machine Learning plays an important, though not always very evident role, is Virtual Reality. Virtual Reality (or VR as it is often abbreviated to) is often in the news as a new form of entertainment. Recent releases on the Playstation VR platform have included Star Trek: Bridge Crew and Batman: Arkham VR, games which offer a truly immersive experience to users. VR uses Machine Learning algorithms in order to improve user interaction through powerful visualizations. The applications of VR are not limited to gaming and the same technology can be used for the betterment of society. In this blog post, we will see five uses of this technology in the world of medicine.

Before we explore the five exciting application of VR, it is important that we understand what this technology involves. According to the Oxford Dictionary, virtual reality can be defined as “the computer-generated simulation of a three-dimensional image or environment that can be interacted with in a seemingly real or physical way”. The generation of this image or environment is often presented via VR headsets such as the Oculus Rift. The key principle behind VR is that it alters our perception of the world, as explained by psychologist Carlos Salas at the recent Family 4.0 Conference in Madrid. As Salas said, “when we perceive differently, we think differently, and then act differently”. This idea is important to bear in mind as we now explore some of VR’s medical applications…

Training Doctors

Virtual Reality has many uses for patients, but it can also be extremely helpful for those professionals who work in the sector. When you were younger, you probably played the game “Operation”, and in many ways this first application of VR is like a high-tech version of this game. Historically, trainee doctors would learn how to carry out complex procedures through observations and videos, and then have just one chance to perform an actual operation on a cadaver. With VR, it is possible to practice a proceedure over and over again until proficiency is achieved. One company leading this technological advancement is Osso VR.

Physiotherapy

The second medical application of Virtual Reality is physical therapy, which refers to the area of medicine related to recovering mobility and function of limbs. This usually involves a long and largely repetitive process of repeating movements (such as raising an arm) in order to retrain the muscles the action uses. VR (of both the immersive and non-immersive kinds) can help in this by including these movements in real-life settings, and even games. This application is particular helpful for victims of strokes, who often struggle with daily activities such as using cutlery. SaeboVR claim to be “the world’s only virtual rehabilitation system exclusively focusing on ADL’s (activities of daily living)”.

Fighting Fears and Phobias

One of the most widely used applications of this technology is in tackling irrational phobias such as a fear of heights or the dark. Traditionally, systematic desensitization (also known as graded-exposure therapy) has been successfully used. This involves (as the name suggests) gradually introducing an individual to the fear in a control setting. Virtual Reality comes into its element here, since the simulations can be controled accurately. This use is not limited to phobias, but can also help those suffering from PTSD.

A bar with many drinks bottles.
Figure 2 : Virtual simulations of bars can help alcoholics tackle their addictions.

Tackling Addictions

As Carlos Salas highlighted in the talk mentioned previously, virtual reality can also be used to fight addictions. One of the key ways to achieve this is to place an addict (of alcohol or cigarettes for example), in virtual situations of temptation and use these scenarios to train them to say no. This form of treatment uses the theory of “cue reactivity“, which is the practice measuring someone’s reaction to a trigger that has been intentionally caused (for example, being offered a virtual cigarette). For an interesting discussion of this topic, check out this TED Talk.

Meditation to Combat Pain and Anxiety

As mentioned early, Virtual Reality creates a new perception of the world which inevitably can distract user from what is going on in the “real” world. This can be used as an alternative to anesthesia during painful operations (for example, burn victims) and also to tackle “phantom pain” (which arises when individuals lose limbs). At the University of Washington, research has shown that patients playing a VR game called SnowWorld reported feeling up to “50 per cent less pain than patients not playing the game“. In a similar vein, the DEEP app for the Oculus Rift help individuals to achieve meditative breathing which can reduce anxiety.

As you can see, the applications of Virtual Reality are varied and many areas of medicine can benefit. Of course, there are many other uses that haven’t been mentioned in this blog and here at LUCA, we are excited to see which other developments occur in the coming months. Don’t forget to follow us on Twitter, LinkedIn and YouTube to keep up to date with all things LUCA.

LUCA’s Sports Analytics Week in Movistar Centre Barcelona

AI of Things    8 March, 2018
The Mobile World Congress took place last week in Barcelona, and drawing upon this, Movistar Centre held a dynamic activity organized by LUCA, in collaboration with Movistar Team and Movistar Riders. The ad hoc activity allowed visitors to get into the athlete’s shoes for a moment; and ride the team’s bicycles, play a series of eSports and learn their metrics using Big Data like authentic professionals. Discover how the week went alongside LUCA and the Sports Analytics area in Barcelona.

During the week of Mobile World Congress, LUCA organized this dynamic action with the goal of showcasing how the application of data contributes in the improvement of the performance and results of sports teams. An action organized under the motto Sports Analytics and in collaboration with Movistar Team and Movistar Riders.

The action in collaboration with the cycling team received a great welcome, with more than 115 participants who not only took on the challenge and rode the official team bicycles, but got to know their levels of performance thanks to the insights obtained through data. The demo, which reflected variables like the speed and power of each participant, represented the stage of the Lakes of Covadonga with metrics obtained by Alejandro Valverde in La Vuelta España 2016.

An action that motivated all attendees at the center to participate, including Mikel Landa himself, professional cyclist of the Movistar Team, who was encouraged along with Chema Alonso, CDO of Telefónica, to participate and learn their metrics on the team’s bicycles. In addition, the participant with the best metrics obtained during the first day of the action, received as prize an official jersey of the Movistar Team signed by Mikel Landa.

Mikel Landa and Chema Alonso riding the bicycles
Figure 2: Chema Alonso and Mikel Landa, professional cyclist, participant of the LUCA activity at Movistar Centre
The application of data in eSports also gained a lot of traction and expectation among the crowd. This time, previously registered participants got to enjoy an online game of League of Legends with the professional gamers of Movistar Riders, while they also got to gain insights of their performance thanks to the demo designed ad hoc for it, and with which they could compare their own skills with that of the professionals. A way to get to know how the eSports professional team collaborated with LUCA to improve the potential of the physical and mental preparation of the players, through an analytical tool that provides unique insights on the players, competitions and rivals they face.

Additionally, some of them also got to test one of the headsets that the team users to monitor the brain waves of the players while they are competing. This allows stress and concentration levels to be tracked, for example, which external factors can influence their performance.

Players waiting to compete in League of Legends
Figure 3: Movistar Riders team live on the screen, and participants ready to compete on the computers
A week filled with sports at the Movistar Centre in Barcelona, where, once again, LUCA demonstrated how technology and the application of data allow us to improve different areas of our lives, such as sports, to make better decisions and improve the results.

If you want to find out more, check out the video below with all the details of the event:

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What connected intelligence can do in your home

Beatriz Sanz Baños    7 March, 2018

Lightbulbs that turn on and off remotely; washing machines that begin program 4 at 11:30; microwave ovens that heat up the food in the container five minutes before you get home so that your dinner is nice and hot just as you cross the threshold: these are just some of the applications that connected intelligence can do in our homes today.

And all of this is thanks to the fact that IoT is connecting more and more devices in our houses. The development of IoT all over the world has been exponential; according to estimates by Gartner, in 2017 we will reach 8.38 billion connected IoT units all over the world, and this year the figure will reach 11.196 billion. Furthermore, according to Machina Research, by 2025, there will be 27 billion connected objects. Companies are vying to move forward and ensure that, thanks to all of these connected devices, our life at home will be simpler and more comfortable.

Plus, this figure comes from new Internet of Things applications which seek to help us in our day-to-day lives.

Voice assistants: the new kings of the living room

The IoT inside the home has witnessed the arrival of the greatest advance in the past few years: the development of voice assistants. The new smart homes now have a clear nerve centre: these new devices, which harness artificial intelligence to allow us to manage our smart homes more efficiently and simply.

According to a report by Edison Research, 39 million Americans now have a voice assistant at home. But that’s not all: the adoption of smart speakers is already surpassing that of smartphones in the United States. It is even estimated that four years from now, more than 55% of American homes will have one. And logically, this is spreading to the rest of the world as well.

Major companies have worked on the development of these devices and applications, such as Amazon and Google, which already have on the market products like Alexa and Google Assistant. These two assistants allow users to shop online and interact in a much simpler, quicker way with the different connected devices in their homes. Thanks to the use of artificial intelligence, these developments have become very popular among users, so much so that according to a Google survey, 41% of users of smart speakers claim that they feel like they are talking to another person.

In the same vein, another new launch is Aura by Telefónica. Thanks to cognitive intelligence, it understands users better and interacts with other connected elements at home in a more natural way through the Movistar Home device. In this way, Aura helps you, for example, answer questions on the products and services you are using; manage and block access to devices on the Wi-fi router; request information on the content of a specific video and program a device to record it; or receive alerts when your data consumption is higher than usual, among other features.

Chatbots, another great ally for a comfortable connected home

But in addition to voice assistants, the experience that connected intelligence at home and the devices that have arisen from it bring to our homes doesn’t stop there. Another point worth bearing in mind are smart Chatbots. As Richard Tolcher, CTO of Action AI, says, these chatbots and the IoT fit together in a completely natural way. Therefore, we can see that the interactions with voice assistants allow them to accept increasingly complex requests, and yet they do so through interactions that are increasingly simple for users.

For example, Richard Tolcher explains that you could tell your voice assistant, “I work from home on Monday, so leave the heating on,” without a second thought; or even, “Could you tell me whether the red trousers I tried on earlier were manufactured following ethical standards? If they are, could you have them shipped to me at home?” This kind of interaction will be increasingly common as smart chatbots will also become our allies and operate based on information provided by the IoT.

Therefore, it’s not only that connected intelligence in our house can do more and more things, but that it can also do tasks that are increasingly complex without the interaction with users being anything out of the ordinary, instead simply through the most everyday conversations.

Audience Selection For Sponsored Data: Precision Is Power

AI of Things    7 March, 2018
Already a powerful mobile engagement mechanism, Sponsored Data is set to skyrocket thanks to the addition of precise consumer targeting.
Power only becomes truly effective when it’s well directed. Great power may have lifted the Apollo lunar missions into space – but it was precise targeting which took them to the moon. Down here on Earth, the same rule can be almost universally applied.

In the world of mobile marketing, sponsored data, which sees B2C brands cover the cost of mobile data generated by customers’ usage of their smartphone apps, has become a very powerful engagement mechanism. Top banking and retail brands have used sponsored data to great effect as a means of driving downloads and usage of their apps, with increases in installs and engagement ranging from 10% to 37%.

But downloads are just the lift-off phase – essential to an app reaching its destination, but not the destination itself. Instead, the goal for most apps is to drive key ongoing behaviors which are in some way beneficial to the app owner, often in terms of revenue generation or cost savings. This raises an important question for B2C brands which sponsor the data generated by their apps: Once the app has been downloaded, how can they derive ongoing return on their sponsored data investment?

One of the drawbacks with zero-rating apps is that brands are forced to approach it as a binary process; either all app installs are sponsored, or none of them are. So, if you have an e-commerce app in which 50% of your active users browse regularly but never make a purchase, much of the power of sponsored data (not to mention your budget) is disappearing into space. In other words, this is a situation where power is operating without direction.

The ability to identify and target specific consumers has been central to the engagement mechanisms so successfully employed by the likes of Amazon, Facebook, and Google, and sponsored data is no different. What app owners really need is to be able to direct their data sponsorship towards individuals and groups of users who are engaging in those key ongoing behaviors. This is what our customers have told us, and this is what has driven the development of our Audience Selection solution, which we launched this week.

In a first phase, a brand might choose to offer a period of data sponsorship with all new app downloads. But beyond this period, the ability to target specific users with Audience Selection offers a new level of flexibility. It can be offered only to app users who keep notifications switched on, for example, or who create an account, share demographic or personal data, or who have made some form of transaction within the last 30 days. Or brands can simply provide sponsored data only to those individuals who demonstrate demand by signing up for it.

However it is used, Audience Selection adds precision to the power of sponsored data. It allows app owners to identify and reward only their most valuable customers, while at the same time encouraging other users to earn sponsored data by engaging in high value behavior.
The brands and businesses embracing sponsored data today are increasingly dependent on effective segmentation and personalization in their core customer-facing activities. Sponsored data is on track to become an essential customer engagement mechanism, which means the same level of flexibility and dynamism is not only desirable but a fundamental requirement. Precision is the key to effective power.

Whatever the Internet memes say about landing among the stars, the reality is that, if you’re shooting for the moon, you want to hit the moon.
It’s not rocket science.

Content Written by David Nowicki, CMO and Head of Business Development at Datami and published through LinkedIn.