Patrick Buckley

Artificial intelligence and IoT technologies continue to revolutionise the way in which we live around the world. In today’s post we take a brief look at how technologies enhance the capabilities of security forces globally and help keep us all a little bit safer. Connected Cameras For a few decades now police forces around the world have used advanced connected camera systems to monitor areas with a high prevalence of crime, prevent incidences and track criminals. Around the world, as a direct consequence of this, crime rates have fallen. Surveillance has become increasingly reliable, sophisticated and effective. Let’s take the case of Montevideo, the capital city of Uruguay. For many years, the city has experienced increasingly high incidences of both petty and violent offences. In 2013, the local government launched a project with the aim of cutting down violent crime in the city. To achieve this, zones with the highest exposure to crime were isolated. This was done by analysing data on differential crime rates across the city zones and neighbourhoods. Connected IoT camera technology was installed strategically in each zone so as to maximise finite police resources. Over the following few years, the project was proven to be a resounding success, cutting crime in the targeted areas by an average of 20%. Machine Learning, the key to footage analysis Connected camera systems alone are pretty useless without the tools to properly analyse the footage gathered. Typically officers would spend many hours a day ciphering through hundreds of hours of footage in search of a desired profile or event. In recent years, police forces have started to apply Machine Learning algorithms when filtering through footage to exponentially increase efficiency and maximise valuable officer time. The New Orleans Police Department (NOPD) was the first law enforcement agency of its kind to adopt Machine Learning into their surveillance processes. In 2018, the department installed over 400 cameras across the city, as in Montevideo, these cameras are strategically located in crime hotspots around the city . The NOPD has employed the BriefCam solution to efficiently identify specific characteristics within a footage sample, or indeed a set of tapes. For example, if we know that a crime has been committed in a specific area by a man wearing a bright green shirt , an officer can tell the system to filter the footage to present only the frames which show people wearing such a shirt. The profile can be then tracked easily and their movements identified in a very short period of time, allowing for quick police action. Predictive Policing – The future? Thanks to Artificial Intelligence and Big Data forces can now even predict, with a fair degree of accuracy, the location of future crimes. Predpol is a revolutionary predictive policing model which was developed by a team of data scientists at UCLA. Law enforcement agencies can use this tool to understand the likely location of future criminal activity. The system uses historical input data regarding three main variables; the type of crime, the time it was committed and the site location. Using this data, the algorithm can then predict, in a dynamic way, the next moves of gangs and well known criminals. This technology goes way beyond the hot-spotting techniques described above. Its not about stationing officers where events have been, but where they are likely to be. Teams can be dynamically mobilised in an almost magical way. Every morning a new report is generated and every day teams mobilise in different locations. The company claims that this technology is "currently being used to help protect one out of every 33 people in the United States", as this technology continues to be developed and utilised around the world, it will undoubtedly become a crucial tool for any law enforcement agency. Conclusion The application of IoT technology in policing is nothing new. However, by combining IoT with Artificial Intelligence and Machine Learning, powerful tools have been created which help to dramatically enhance surveillance footage analyse and even predict new events. This technology is set to redefine the way in which policing is done around the world.

On July 30th 2020, the Atlas V-541 rocket, carrying the Perseverance rover launched from the Cape Canaveral Air Force Station, Florida. Six and a half months later, on February the 18th2021, the rover touched down on the surface of Mars. This marked the arrival of the most sophisticated exploration vehicle to ever land on the planet. The rover is equipped with a variety of instruments designed to detect and monitor signs of life. In today’s post, we consider some of the applications of Artificial Intelligence (AI) in helping us to better understand insights generated by exploration strategies. This all brings us closer finally answering the question –was there ever life on mars? AI driven advancements in sampling One of the many technological upgrades on the Perseverance rover is the Planetary Instrument for X-ray Lithochemistry, or PIXL device. This small device is equipped with X-ray technology which scans for interesting chemical matter embedded under the surface or within rocks on the planet. These chemicals may indicate evidence of fossilised ancient life. Using this technology, the rover can better assess the chemical properties of potential samples in order to identify the best, most interesting matter before excavation takes place. The Perseverance Rover also features a robotic arm equipped with a drill which is deigned to excavate material from within rocks. Once this has taken place, samples are then deposited on the surface of Mars in metal tubes. Only the most ‘chemically interesting’ samples will then be sent to earth during a future mission for further analysis. AI plays an important role in both the X-ray and drilling processes. In order for the X-Ray take place, the rover must first navigate itself towards interesting rock or sediment types. Camera and laser technology works together in tandem with AI to locate and remember these spots. This allows the rover to return at night, when temperatures are more stable to undergo these X-ray examinations. Due to the high fluctuations in daily temperatures on Mars, only at night can the rover collect accurate data. An established AI application The prospect of there being Ancient life on mars is nothing new. Since 2005 satellites such as the Mars Reconnaissance Satellite have been sending back images of craters that seem to indicate life. For example, craters with an apparent inflow and outflow channel indicate the ancient presence of a body of water. https://www.youtube.com/watch?v=bdHkgtLgcSY Images from the Mars Reconnaissance Satellite The issue that scientists are therefore presented with is locating images of such craters within an enormous data stock. The Reconnaissance Satellite has 3 cameras which have collected hundreds of thousands of images over the last 16 years. Lately NASA has trained an algorithm to sort through this massive image bank. After having fed the algorithm samples of 7,000 crater and non-crater images , AI can now accurately detect the existence of craters which may one day have supported life. With this information, scientises can better identify specific geographic areas on the planet appropriate for further examination, whether that be by the Perseverance Rover or as part of a future mission. Final thoughts AI has become a fundamental part of Mars exploration. It allows research processes to be carried out far more efficiently than before, allowing for more accurate and frequent insights that bring us closer to understanding potential patterns of ancient life on the planet. This technology will therefore become more integrated and useful in the exploration of Mars in the coming years and decades. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube

When Whitney Houston passed away in 2012, the world was shocked by the sudden and tragic news of her death. Fans gathered around the Beverly Hills hotel in Los Angeles not just to mourn the loss of a highly respected and talented artist, but also to come to terms with the fact that one of the greatest performers of the moment would never return to the stage. That was, until the powers of Artificial Intelligence (AI) and Computer Generated Imagery (CGI) came together to bring the star back to life for a revolutionary ‘hologram concert’ tour. This tour, 'An Evening with Whitney Houston' has been shown across the world from London to Los Angeles. https://www.youtube.com/watch?v=Kk6danErV0s A promotional video for the 'An Evening with Whitney' Hologram Tour Houston is not the only artist to have been digitally brought back to life. A Michael Jackson hologram shocked the world when it was featured at the Billboard Music Awards in 2014. Moreover, other late singers such as Amy Winehouse, Tupac and Roy Orbison have also been the subjects of Hologram recreations. So how does it work? It has not been revealed exactly how this so called hologram works, after all, the best magicians never reveal their secrets. It is, however, likely that AI plays a key role in creating and enhancing the images we see projected. The process starts by modelling a sequence of images based on the physical features and movements of a real-life human being. Of course, as Whitney herself is not available, a body double who matched the fundamental physical features of the star is used . These images are then digitally enhanced using Computer Generated Image technology (CGI). Here, AI and Machine Learning technologies play a key role. These technologies efficiently enhance the characteristics of the relayed div to make the hologram seem more like Whitney herself. Before the age of AI, this enhancement would have had to be done manually, frame by frame. For a two hour long set, this would have been an almost impossible task, taking many years to produce. Now, parallel simulations combined with Machine leaning algorithms learn to recognise the common body movements of the character. These movements are then relayed throughout the set, helping the ‘fake’ Whitney to express the body language and subtle expressions of the star herself. This digitally enhanced sequence is then projected by a 4K laser onto a flat surface to create the hologram effect that we see. According to its scientific definition, the end result is not actually a hologram as it does not rely on the reflection of an image through a transparent medium. Rather, It is a two- dimensional projection which appears to be three-dimesional thanks to the high quality nature of the digitally enhanced image. Does the Idea really have legs? Thanks to high quality digital projections and sophisticated CGI technology, so called 'hologram concerts' are becoming increasingly viable. The very fact that a worldwide tour has been organised is a testament to the technological advancements that AI and Machine learning permit. Whether or not fans can be convinced by what they see is yet to be seen. If it is successful, it very well might be the first of many hologram concerts to to take place in our increasingly digitalised world. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube

As we continue to battle the COVID-19 pandemic, the hospitality industry is looking to technology as a way to keep workers safe. Could robot waiters be the answer? In today’s post we bring you our perspective. The evolution of the Robot Waiter Robots have been used in hospitality settings for many years. The focus of the so called ‘robot revolution’ in this industry has been observed mainly in China. There, Robots have been serving bemused customers since 2006. Up until recently, the main value of this technology was in its novelty factor. In tech crazed regions of China, enthusiasts target establishments with robot waiters in order to experience this technology first hand. In the same way, cruise line Royal Caribbean International have installed robot bar tenders on a number of ships with the purpose of providing entertainment to holidaymakers. Aside from being a gimmick, employing robots in restaurants, bars and hotels makes a lot of financial sense. The cost of these systems can be as low as $500 USD/unit. This makes them value for money in China, a country where the average human waiter can expect to make at least twice that amount each month. Of course, even the most advanced systems can't replace the experience of interacting with a human. Perhaps this is why service robots are yet to boom outside of China. It is likely that the novelty of an emotionless machine would wear off faster in western countries who's culture promotes more social experiences. The Robot Waiter and the Pandemic Due to the pandemic, certain areas of our economy are experiencing a premature digital transformation. According to Transparency Market Research (TMR) , the robotics industry is projected to grow annually by 17.64% (CAGR) between now and 2024. The hospitality sector is not exempt from this transition. The sector is experiencing a shift in customer preference towards a more distanced, impersonal style of service that can only be provided by machines. Luxury in the service sector can be defined by the ability to provide the customer with what they desire at the time they want it. It is for this reason that we are increasingly seeing service robots being rolled out around the world, especially in luxury settings. Upmarket hotel chains such as Four Seasons, Marriott and Hilton Group have started to implement robot waiters in their establishments around the world. Systems powered by Artificial Intelligence are capable of answering customer enquiries, delivering room service and even enforcing social distancing rules. The benefits of the robot butler/waiter become even greater in a 'quarantine hotel' setting. Countries continue to impose mandatory hotel quarantine for international arrivals. Here, it makes total sense for robots to take the place of human workers in order to avoid contagion amongst staff and quarantining guests. Already hotels in Japan have rolled out this technology across a variety of locations. Final Thoughts The robot waiter is no longer just a gimmick. The COVID-19 pandemic has propelled this technology to the forefront of the minds of the hospitality sector.Whether or not this technology will survive in a post COVID-19 world is unclear and depends on the cultural importance placed on human interaction in different regions worldwide. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

From the way we shop, to the way we learn, the digital world in which we live is unrecognisable from the reality of a decade ago. One area which generates much discussion within the context of Artificial Intelligence (AI) is the topic of the labour market. In today’s post, we explain to you the likely impact that it will have on world of work and what this means for our society going forward. So what are we supposed to believe? On the one hand, AI sceptics may argue that continued innovation in technology will result in unemployment. As machines become proficient in roles previously occupied by human beings, unemployment will subsequently arise in certain sectors. On the other hand, we consider the argument of job creation, innovation and an improved work-life balance. So which is true? It is true that many positions are becoming digitally managed as industries undergo a digital transformation. It is also argued that machines are becoming increasingly emotionally intelligent. The threat of unemployment within lower-skilled sectors may therefore extend in due course to include customer-facing roles such as hotel receptionists, retail workers and secretaries. This argument is by no means the end of the story. Automation doesn’t have to be a negative thing for our society. In fact, the opposite is true. Whilst many jobs are indeed being lost to AI, even more positions are being created thanks to this booming industry. In 2018 The World Economic Forum’s Future Jobs Report estimated that AI will widen the job market by 58 million jobs by 2022. This is expected to be achieved through the creation of 133 million new skilled jobs globally. This is to be accompanied by the simultaneous elimination of 78 million lower-skilled positions. In short, AI isn’t taking our jobs, it's just converting them from lower-skilled to higher-skilled positions. Whilst we may no longer require humans to work as cleaners and rubbish pickers in the future, we increasingly will need more jobs to service a booming AI industry. Here we consider not just the software developers and the engineers, but also the salespeople, the marketers, the maintenance teams and logistics companies. These positions, whether they arise indirectly or directly from the AI industry, all depend on servicing machines that didn't exist just a few years ago. What does this mean for our Society? The socio-economic impact of this transition is hard to predict. It considerably depends on the level of social mobility experienced in our future global society. Its benefit will be felt increasingly as more young people around the world access quality higher level education that permits them to follow this transition towards a higher skilled role. These higher skilled positions require a more qualified workforce. Those who may previously have filled lower-skilled roles with low earning potential will instead find themselves working in higher-paid, more rewarding jobs. Thanks to AI, more people will experience enhanced career progression opportunity. Due to the diminishing need for humans to service medial jobs, we will all experience a better quality of life. AI even has the potential to redefine the way we live and work. Professionals may choose to work less as their salaries afford them more leisure time. People may devote more time to fulfilling personal goals. In the not too distant future we may not need to be enslaved to the 9-5. Machines can do all of those medial, administrative and frankly boring tasks which occupy most of our time. Final Thoughts As I see it, AI can only have a positive impact on the labour market. It is the tool that enables us to progress our society forward, to elevate the quality of the jobs which exist today. The extent to which society will benefit from this depends on the ability of the workforce to adjust to these more qualified positions. If society continues to go down this path of digitalisation, the very nature of how we work will be redefined for the better. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube

Cost overruns, delays, work-related accidents and expensive misunderstandings are everyday challenges which continue to plague the construction industry worldwide. In today's post, we explore how the construction industry can too benefit from undergoing a digital transformation, as we take a look at how technology can lead to efficiency gains in many daily procedures and protocols. Counting the Cost Everybody knows that cost overruns in construction projects are a painfully common occurrence. According to a 2015 survey published by the leading financial consultancy group KPMG, only 31% of all projects surveyed came within 10% of their original costing plan. Spiralling costs can happen for a variety of reasons, but as this report points out, poor project management tends to stand out as the main one. So how can solutions of the Internet of Things ( IoT) help keep a lid on costs? Firstly, Asset Tracking technology can help Project Managers keep track of the various components that make up a project, from the materials to the workforce. IoT devices can be used to help managers monitor deliveries. Managers can know the status of each material and can therefore act dynamically to organise the workforce accordingly. This maximises productivity and minimises delays. Say, for example, a project manager in London gets to know that a supply of bespoke doors produced in France has been delayed in transit at the border. With this information he may decide to instruct the workforce to install another component that day. This allows for the dynamic mobilisation of the workforce. Overtime this eliminates the deadweight loss associated with a stationary workforce. In the same way, IoT connected devices planted on the uniforms of construction workers can be used to measure their productivity throughout the day. Through movement monitoring data, managers can ensure that the correct amount of time off is exploited by each employee. This, if used correctly, could lead to a fairer and more productive career progression system. Managers could use this data to promote only the most productive workers to more senior roles. Subsequently, this would facilitate the development of only the most competent of teams. Keeping Workers Safe on Site In all sites, but especially on large scale projects, the location of those onsite must be known at all times. This can be achieved Through the utilisation of IoT connected devices on uniforms. As described above, the location of each employee can be easily identified in the event of an emergency such as an accident or dangerous event happening on site. In many countries around the world, construction firms have a legal obligation to safeguard the wellbeing of their employees. An employee tracking system would be a simple and effective way to manage this liability. The Future of AI in Construction – BIM Building Information Modelling (BIM) is a tool powered by Artificial Intelligence (AI) which allows all agents to monitor project progress. Whether they be the architect, project manager or chief engineer, the status of the project can be visually explored through a digitally enhanced computer-generated model. This solution gives real-time progress insights by allowing agents to input data and virtually simulate the building process. In this way, design flaws can be automatically picked up by algorithms which are programmed to understand potential hazards or impacts. These could come from changes in design, construction method or materials. Expensive and timely mistakes can therefore be prevented from occurring in real life. Final Thoughts As we enter a new era of digitalisation, construction companies around the world have the new-found ability to overcome some of the challenges associated with previous management practices. Principally, the centralised digital management of both materials and the workforce will lead to great efficiency gains, enhancing on site productivity and safety. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

With an estimated current global market value surpassing 406 billion USD, the fashion industry is one of the most significant yet vulnerable industries out there. In an ever-worsening socio-economic climate, analysts predict a grave year for fashion retailers, expecting consumer expenditure on fashion to fall by between 27% and 30% globally throughout 2021 as the world continues to battle through the COVID-19 pandemic. With this in mind, today, we bring you an insight into how technologies based in Big Data and Artificial Intelligence (AI) are giving many fashion retailers the edge, helping them stay afloat where others have gone under. Data Driven Supply Chain Management Inditex, a leading Spanish multinational fashion retailer with over 7,200 stores globally, has, for many years, been leveraging AI both in store and on the e-commerce side to shape product decisions. A data driven supply chain management system is used across all Inditex brands to support the 'fast fashion' model which underlines their business strategy. Upon releasing a new season of clothing, Inditex will send only a small quantity of each product to their stores and e-commerce channels in order to first gauge the customer response to each item before placing any significant order with the manufactures. Sales divs of each item are then recorded and internally processed. Machine Learning algorithms then automatically instruct systems to only order an appropriate amount of stock for each item at each individual location. In this way, the manufacturing process can be dynamically adjusted to only produce what is likely to sell, optimising revenue and minimising product waste. What’s Next in Fashion? Internal processes such as the fast fashion model described above, are not, by themselves sufficient to remain competitive in the cutthroat world of high street retailing. Predictive analytics based on competitor and market behaviour are becoming increasingly important to retailers as they dynamically make decisions on all aspects of their business, from pricing to product launch dates. Data firms partner with major brands, to offer insights into the state of the market, future demand and competitor behaviour patterns. Through compiling data on the top selling products of the moment across a variety of brands, algorithms can suggest which styles are likely to be successful going into the future, for example, the algorithm may suggest that a striped bathing suit is likely to be more successful this summer than a dotty one, based on consumer behaviour leading up to this point. Furthermore, by comparing pricing data with demand patterns for individual products or collections, algorithms can predict at which price point a certain collection is likely to be successful for each individual brand. These insights allows designers and retailers to stay ahead of the curve and plan future collections based on hard data insights. Final Thoughts Data driven decisions are becoming increasingly valuable as fashion retailers are tasked with understanding their consumer's wants at a time of rapid socio-economic change and uncertainty. AI technology allows for a more dynamic design and manufacturing process and insights allow brands to identify and take advantage of relevant trends. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

Throughout the COVID-19 pandemic, video-conferencing has become the backbone of both our work and social lives. Today, on #WorldHugDay, we take a look at some of the ways in which AI (Artificial Intelligence) will help to more efficiently connect us virtually in the future. Almost a year after most of the western world was plunged into a state of lockdown, it’s hard for most of us to imagine life without the constant bleeping of the team’s application on our phones or the ever so frequent occurrence of having to remind a co-worker that they had accidentally muted their microphone. As innovative and advanced as this current technology may be, the future possibilities of further technological advancements in video-conferencing platforms are becoming increasingly visible thanks to the continuous evolution and advancement of AI based technologies. Sorry, you froze! There’s nothing more annoying than a ‘laggy’ or low-quality video stream when you're trying to catch up with friends or take part in a meeting. It’s a daily problem for most of us without a high-speed internet connection, but this bothersome reality of the virtual lifestyle will soon be a thing of the past. So called ‘AI video compression technology’ completely reinvents the way in which video-chat platforms work and is currently being incorporated into video-conference platforms. How does it work? By collecting data on the facial features of users such as the eyes, nose and mouth, this AI powered technology creates a virtual avatar which, when combined with the organic video image, produces a much higher quality stream for users. At the same time, this technology dramatically reduces bandwidth consumption. The result is a much more seamless user experience, allowing everybody to enjoy high quality video streams regardless of their bandwidth capacity, making video-conferencing possible in remote areas with weak network connections. This technology also has the ability to adjust camera angles, make users appear more engaged by diverting eye contact towards the screen and potentially even mask imperfections on the skin such as zits and eye-bags. NVIDIA MAXINE is an example of such a pioneering solution that offers integrated AI frameworks to video conferencing developers. Can you translate please? As we become accustomed to working remotely and depending on video- conference technology as a primary way of doing business, developers are starting to incorporate conversational AI frameworks into their products. Video-conferencing platforms of the future will incorporate tools such as a digital assistant function which can inform users of relevant information such as of the weather and offer real time translations whilst on call. Clearly, this will be extremely helpful for those who wish to engage in international conversations both in a business and leisure context. Conversational AI frameworks also have the capacity to identify different voice tones, allowing the platform to recognise the voice of the main speaker and mute all noise from the surrounding environment, making it far easier for people to hold a virtual conversation in busy public spaces, or indeed at home with noisy animals or children around. Final Thoughts. Video-conferencing platforms are a vital tool for many of us as we go about our daily lives during the COVID-19 pandemic. This has incentivised developers to push the boundaries of existing platforms and apply AI within current technology to achieve increased functionality. Thanks to this innovation in AI, someday soon, perhaps without even realising it, we will be communicating with our digitally produced avatars as we ignore the screaming children in the background of our online interview. The future is in sight. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

From chocolate bars to lollipops, gumdrops to Haribos, the confectionary industry is now worth an estimated 210$ billion worldwide. With the industry experiencing strong growth, it is no surprise that confectionary producers are also beginning to implement IoT (Internet of Things) technologies in their production processes in order to achieve efficient outcomes and take further steps forward towards the digital transformation. Why is IoT technology especially useful in this industry? Due to the nature of candies, especially chocolate-based confectionary, slight variations in environmental factors such as temperature and moisture level can affect the quality of the final product. This is especially true when we talk about smaller products, such as Hershey’s Twizzlers or Rees’s Peanut Butter Cups, that may have been easily disdivd by temperature fluctuations. By lining production lines with multiple IoT connected sensors, and linking these with temperature control systems, candy manufacturers are able to maintain a constant environment in their production line. Furthermore, these same sensors are able to check the weight and shape of all pieces, thus allowing manufacturers to reduce waste and provide a better quality, more consistent final product to the consumer. A current Use Case Back in 2016, Hershey, a leading American confectionary manufacturer, started to implement IoT technologies in their production line with the aim of optimising production output by more effectively controlling environmental factors. Currently, Hershey have tested this technology on the Twizzlers production line, installing 22 sensors in the holding tank which deliver 60 million data points. In this case, Microsoft Azure algorithms are used to process these insights. Further benefits The benefits don’t end there! This technology also means that manufactures can continue to innovate products without being restricted by product size. A more controlled environment allows for smaller pieces to be manufactured that would have been deemed previously too ‘environmentally sensitive’. Although this could be bad news for consumers who may soon observe reduced sizes of candy products, it is great news for manufacturers as it allows them to experience dramatic cost savings. In the case of Hersheys, the company estimate that a 1% reduction in the size of each Twizzler would lead to a dramatic cost saving of $500,000 each year! This technology may also allow manufacturers to experiment with different ingredients and offer vegan/healthier product alternatives, allowing us to all become a little healthier whilst quenching our desire for a sweet treat! In summary, Today’s candy factories may not be run by Willy Wonka and his faithful band of Oompa Loompas, but the magic of IoT technology is transforming production processes worldwide beyond my wildest imagination. Thanks to the use of IoT connected sensors, producers can experience dramatic cost savings and consumers receive a more homogenous and higher quality product, even if it may be little bit smaller in size! As this technology is increasingly adopted within the industry, IoT technology will also allow manufacturers to experiment with recipes and offer a wider variety of products. To keep up to date with Telefónica’s Internet of Things area, visit our web site or follow us on Twitter, LinkedIn and YouTube

As Christmas approaches this year, we have all been gifted the great news that the Pfizer/BioNTech vaccine has shown to be both safe and effective in creating an immune response to COVID-19. Recently it has been approved for use both in the United States and the United Kingdom, with selected high risk British citizens becoming the first in the world to access the vaccine during early December. In this post we briefly explain how Artificial Intelligence (AI) and Machine Learning technologies continue to play an increasingly important role in the development of vaccines. How do vaccines actually work? Vaccines create an immune response by exposing the patient to inactive, harmless virus particles known as proteins. Once the human body has been exposed to a virus, in an inactive form, it will develop antibodies. It is these antibodies which protect cells from becoming infected and, ultimately, prevent the patient from getting sick. Once these antibodies have been triggered once, the same immune response will be triggered every time the patient is exposed to the virus, allowing the patient to become immune. The role of AI. When described in brief, the process of formulating a vaccine seems straight forward; simply identify the virus, extract inactive proteins that generate the immune response and you have a vaccine! Unfortunately, the reality is far more complicated. For an immune response to be activated, specific parts of the virus have to be exposed to antibodies. The challenge therefore is being able to identify these specific parts and understand their properties. Once these properties have been identified, scientists can extract the correct viral proteins that will trigger the best immune response. AI is becoming an increasingly useful tool in this process. As the COVID-19 pandemic started to grip the world back in January 2020, researches from the University of Stanford started to use Machine Learning solutions to identify proteins to include in a potential vaccine. Firstly, proteins of the SARS-CoV-2 virus were profiled, this is the virus which triggers COVID-19. Once the protein data had been collected, it was compared with data collected by researchers over many years on typical viral properties which trigger the antibodies to recognise common properties. Once this data has been collected on a large scale, scientists are able to predict which viral proteins will trigger an immune response. This process would have taken far longer without the use of this technology and many of the insights gathered could not have been spotted by the human eye. This technology allowed researchers to pass accurate insights and predictions to vaccine developers dynamically and quickly, allowing pharmaceutical companies to expedite the development of their vaccines without compromising on quality and safety. This technology is currently limited by the lack of data to refer to. As AI & Machine Learning tools are increasingly used in vaccine development, more data will be collected, and scientists will have a deeper understanding of the viral protein properties which generate the best immune response. Conclusion. Vaccine development is an extremely complex and intricate process. Although the technology is still in its early days, Machine Learning tools have already contributed to the successful development of vaccines. As we continue to use Machine Learning in vaccine development, the availability and quality of the data on which it relies will improve. As the data becomes increasingly insightful, Machine Learning tools will become increasingly useful in vaccine development. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

As Artificial Intelligence (AI) continues to shape the world around us, in today’s post we explore the impact of AI on commercial Real Estate investment. To what extent is AI the key to investment success on the property market? The Applications of AI on Real Estate Investment Real estate investment can be highly lucrative if it is done right. But investors must be aware that, like any market, property markets do suffer from a degree of volatility and are vulnerable to demand and supply-side ‘shocks’. A clear example of such as ‘shock’ is the COVID-19 pandemic which saw net Real Estate investment volume drop dramatically in certain global markets in the first quarter of 2020, with the Spanish market alone suffering from a 40% reduction in sales. This, of course, will come as no surprise due to the inevitable decrease in wealth and access to finance arising from the complex financial side effects of state intervention. As the CBRE (a leading commercial Real Estate services and investment firm) predicts, investment volume in the global Real Estate market will fall by 38% in 2020. The important question facing investors now is whether markets will crash or boom across the world as a result of the pandemic. In answering this question, AI and Big Data can provide helpful insights. Data trends can help us predict the future state of Real Estate markets around the world to an increasingly accurate extent. Skyline AI, a New York based property investment company, offers commercial investors the opportunity to make investment decisions with the help of unique software that compiles and analyses data on a broad set of market indicators. These indicators include interest rates, property data and stock market trends, to predict the future value of property investments in specific areas at specific times. Skyline AI’s algorithms are also able to monitor potential off-market investment opportunities and predict when they will come to market. This gives investors the edge when it comes to accessing lucrative deals. Other commercial property investment companies focus their analysis on social data such as neighbourhood crime rates, school ratings and accessibility of public transport to offer a similar analysis to both commercial investors and regular households who wish to make an informed property buying decisions. So is AI the future of Real Estate? Not exactly. Whilst AI can offer increased insights into the future state of property markets, and can be an extremely valuable tool for commercial investors, algorithms can only predict to a certain degree of accuracy the effects of shock events on markets, no algorithm could have predicted COVID-19. Markets are simply too unpredictable, and human intuition and evaluation of shock events remain fundamental. AI insights are only really valuable if combined with the expertise of analysts who may be able to predict more accurately the impact of ‘new events’ on markets. We must also remember that humans are far better at selling than any bot or algorithm. The best salesman may be able to persuade even the most prudent investor to purchase a property. Conclusion The commercial Real Estate investment market has been and continues to be revolutionised by AI-driven insights. Investment companies are able to use Big Data to justify and influence purchasing decisions, almost guaranteeing a good investment return. However, we must remember that AI generated insights cannot always predict surprise shocks and they must be combined with analysts experience to provide a more accurate picture of future valuations. Furthermore, AI will never be able to replicate the selling powers of a human being. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

As we all look forward to returning to live events in a post-pandemic world, in today’s post we share with you the latest exciting innovation in smart stadiums, the implementation of in-stadium 5G coverage. 5G Is set to be a game-changer for fans at home and in the stadium, allowing for enhanced IoT connectivity, faster internet speeds and better quality live streams for those unable to make it to the match. This is no distant dream! In 2019, Telefónica teamed up with FC Barcelona to deliver Europe’s first 5G connected stadium at Camp Nou and many football clubs and stadiums worldwide are following suit. In the US alone, there are 13 NFL stadiums connected by a 5G network powered by Varizon. Powering the passion for the game Stadiums have always been more than simply physical spaces, they can be defined better by the atmosphere created in them as fans and players come together to celebrate their love of the game. Promoting this passion that spectators have is the main driver behind sports clubs seeking 5G coverage in their stadiums. The atmosphere of camaraderie is promoted by allowing fans to connect easily to social media platforms. Previously, low bandwidth and high latency often prevented game goers from accessing the internet quickly or to any usable extent. This is due to the extremely high levels of traffic within the stadium. Now, thanks to the upgraded network, fans will be able to tweet, share and react to game updates in real-time, allowing for a more interactive game experience for those both inside and outside the stadium. Enhanced IoT Connectivity Over the last few years, many football clubs have invested in IoT technologies in their stadiums to improve the game experience for fans and allow for better crowd management. Back in 2018, Telefonica teamed up Atletico Madrid to deliver the world’s first smart stadium at Civitas Metropolitano , implementing enhanced IoT powered security management systems, smart scoreboards and a wrap-around IoT powered LED lighting system. By upgrading network coverage within the stadium, sports clubs are laying the infrastructure for the next generation of IoT technology powered by 5G. A 5G network can support far more connected devices than previous network generations, paving the way for even more innovations to be introduced in the future such as security cameras, crowd control technology and smart information displays. And for those stuck at home… As we are currently unable to go to live games due to the Covid-19 pandemic, it has become more important than ever for sports clubs and broadcasters to deliver the best live streaming capabilities possible. Thanks to the increased capabilities of IoT connected devices, the quality of live streams has increased dramatically. According to FC Barcelona, 5G technology allows for the live streaming of 4K 360º footage, allowing for fans to experience the game in real time virtual reality, totally redefining how we watch games from home. Conclusion We cannot underestimate the power of 5G to revolutionise the way in which we experience live sporting events. As more stadiums implement this technology, we will soon take for granted the ability to connect to social media easily whilst in the stadium and remotely view games in 360º 4k ultra-high definition. Further to this, the capabilities of IoT devices within stadiums are significantly enhanced thanks to 5G, bringing never before seen innovations to stadiums around the world. To keep up to date with Telefónica’s Internet of Things area, visit our web site or follow us on Twitter, LinkedIn and YouTube

Governments know that a functional and efficient transport system is key to economic growth and social development. Well run transport infrastructure unlocks the productive potential of an economy. Naturally, a more mobile workforce will spend less time commuting and more time working, thus allowing the population to produce a higher level of economic output. As governments roll out transport infrastructure projects, they must choose technologies which will stand the test of time. New transport infrastructure must have the capacity to adapt to increased future demand due to growing populations, be efficient and environmentally sustainable. The Smart Train is the answer to all of this! The need for Automation In Train Operations - ATO If you want to go somewhere quickly and happen to be In a global capital, your best bet is usually to take the Metro. These systems are crucial to residents who depend on them to commute and go about their daily lives. Usually metro systems run frequently and efficiently worldwide, but human error and staff disorganisation or industrial action can lead to rail accidents, delays in services and reductions in frequencies. This is why we need ATO sysems. The arrival of ATO systems (Automation of Train Operation), powered partly by artificial intelligence (AI) technology, removes the need for the train driver and therefore elimates the costs associated with human error. It is these efficiency gains, however slight, that allow for the necessary increases in service frequency and capacity to adapt to population growth in some of the world’s busiest cities. ATO technology, in itself, is nothing new. According to the European Parliamentary Research Service (EPRS), in 2018 the world was home to 1 000 km of the automated metro lines in 41 cities around the world. Amongst the most well-known ATO systems include the Singapore Mass Rapid Transit Line and the terminal connector at Dubai International Airport (DXB). Current AI applications within ATO. As I write this post in late 2020, the full power of AI in Automatic Train Operations has not been fully realised. At present the main applications of AI across the industry are focussed on administrative processes such as arrival and departure board management, intelligent scheduling (based on demand insights provided by Big Data) and security monitoring systems. In many cases, AI technology can assist in the process of train acceleration/deceleration as the onboard computer recognises stop locations and speed limits. AI technology can even provide speed optimisation insights. For example, The European Train Control System (ETCS) is an automatic monitoring system that informs train drivers in participating EU networks of speed restrictions and protocols. A Future Application of AI in Automatic Train Operations – Smart Sensing Systems are currently being developed to sense imperfections on the railway line and of the train itself. So called ‘Smart Sensing’ technology will soon be installed inside train wheels to detect material fatigue of infrastructure and predict maintenance needs. This not only allows for enhanced rail safety but it also allows train operators to plan service and infrastructure upgrades in a cost-effective and timely way, making sure that service capacity remains sufficient at all times. Conclusion The train is here to stay and the smart train is getting smarter! Governments prioritise rail infrastructure projects due to the long-term sustainability and ability to accommodate the mobility needs of a growing population. Currently, artificial intelligence plays a key role in the running of rail networks mainly in an administrative capacity. Smart sensing of trains and rail networks is the next AI powered industry break through! LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

In today’s post, I will share with you how Waste Management is set to be revolutionised by Big Data and IoT (Internet of Things) technology. The Social Importance of Waste Management Waste management is increasingly becoming a priority for governments around the world. As populations continue to grow exponentially, urban areas become increasingly over-crowded and levels of public litter inevitably increase. It is estimated by the UK based consultancy MapleCroft that 2.3 billion tons of waste is produced every year globally. This is expected to increase to 3.4 billion tons by 2050. It is therefore important that we become aware of waste patterns in order to optimise management strategies and sustainably manage populations. The Smart Bin IoT connected devices and Big Data technology are set to play a critical role in the future of waste management. Currently, the most well-known example of Smart Waste Management infrastructure is the Smart Bin. The Smart Bin is a connected device, equipped with IoT powered sensors which monitor bin usage and encourage sustainable waste management. Route Optimisation As urban environments and populations continue to grow, public space littering and bin overflow continues to be an issue which plagues cities around the world. By monitoring bin overflow through the use of sensors, the Smart Bin sends signals to monitoring systems operated by local councils and waste management companies. This means that bin collection routes can be organised to optimise bin capacity and ultimately reduce the level of street litter. Other benefits of informed route planning include reduced fuel costs for waste managemnet companies and decreased working hours of employees. This technology has already been rolled out in many cities such as Singapore, Dubai and Hong Kong. In most cases, this technology is developed by private sector companies such as Sensoneo. Encouraging Recycling Smart Bin technology can also be used to inform members of the public of the importance of recycling. Sensors which detect human contact initiate information displays promoting pedestrians to recycle correctly. Further to this, by collecting data on how many times bins are emptied, households and councils can become more informed on waste , allowing for a greater understanding of how one may be able to cute waste output and contribute towards sustainable outcomes. As we become increasingly socially aware of the importance of recycling and cutting down on food waste, households will increasingly value the insights that a Smart Bin in the home will provide. Equally, there is potential for government’s and councils to use this insight to monitor recycling habits and apply pressure to those who refuse to recycle through, for example, government-imposed restrictions or fines. Unlocking the power of Data Analytics in Waste Management. As we start to see the Smart Bin concept being rolled out in towns and cities worldwide, waste-management data will become increasingly influential to local councils and governments. The possibilities for this data to be used for social good are never-ending. For example, governments could be able to target specific geographic areas or social cohorts with educational campaigns regarding the importance of recycling. Councils will be able to more accurately predict waste levels on a national scale and respond accordingly with infrastructure upgrades. When this data is combined on a global scale, the global community will be able to track recycling habits and progress towards a future of sustainable waste management. Conclusion There is great potential for the Smart Bin to revolutionise the way in which we manage our waste on a global scale. IoT and Big Data technology is already advanced enough to provide valuable insights to local councils and governments. This technology is already being rolled out in public spaces around the world. There is great potential for smart waste-management systems to provide further insights, especially if they are incorporated into the domestic waste-management market. To keep up to date with Telefónica’s Internet of Things area, visit our web site or follow us on Twitter, LinkedIn y YouTube

Increasingly, energy management is becoming a topic of great social importance. In today’s post, I will explain how IoT (Internet of Things) together with technology Artificial Intelligence (AI), is improving energy management solutions for both the energy provider and the consumer. Industrial Consumers - Building Management Systems Industrial consumers are often faced with an ultimatum. How can they drive forward growth strategies without having to comprise on their commitment to environmental sustainability? The answer is by implementing Smart Building Management Systems. Large industrial consumers may struggle to monitor network wide energy consumption trends due to their large scale of operations and complex structure. IoT technology offers large enterprises the ability to monitor energy usage easily and efficiently. Smart Building Management Systems use IoT powered sensors to collect data across complex infrastructure networks. Using AI capabilities, this data is analysed automatically allowing inefficacies in usage patterns to be easily identified and targeted. This real-time reporting allows for smarter energy management decisions, reduced energy bills and a lower carbon footprint for the organisation. IoT powered sensors also have the capability to coordinate with existing energy systems and supply networks. Energy usage can be adjusted automatically according to environmental conditions. This could be factors such as room occupancy, temperature and level of natural light. In this way, building occupants may not even notice a reduction in energy consumption in their environment. As IoT powered smart Building Management Systems are rolled out across extensive networks the potential reductions in network overall energy consumption is enormous, benefiting the enterprise both socially and finically. Efficient Grid Management The use of IoT sensors along distribution channels allows energy providers to easily monitor demand and consumption patterns. This allows providers to easily adjust supply along power lines. This can be done dynamically in accordance with real-time demand. The risk of oversupply in networks is therefore reduced, resulting in a decrease in energy wastage. Due to the real-time nature of IoT sensor reporting, providers can understand in great detail the consumption habits of consumers. This information can be understood geographically, helping providers to plan targeted network expansions and upgrades, leading to a higher quality network infrastructure. AI also plays a key role here. It is not feasible for technicians to manually adjust power requirements as often as demand changes. Through the compilation of consumption data, smart grid management systems can automatically adjust voltage along power lines. This allows suppliers to predict consumption changes so the grid is always prepared to supply the energy requirements of customers. Smart Meters, the future for domestic consumers IoT powered smart meters constantly collect energy consumption data and send it to both to service providers and customers. This allows suppliers to understand, in great detail, the consumption habits of their consumers. Suppliers can therefore adjust network capacities accordingly. For the domestic consumer, sophisticated integrated IoT systems allow the consumer to understand the energy consumption of every device in the home. This helps to identify power-hungry appliances, reducing energy wastage. It is also helpful to the domestic consumer to understand, through detailed reporting, how a household may be able to save money on energy bills by changing consumption habits. Conclusion The use of IoT sensors and AI is the future of energy management. Large industrial consumers have the most to gain here. A network-wide smart energy management solution will provide lucrative savings for any large organisation, whilst allowing them to fulfil promises of environmentally sustainable practice. Domestic consumers also win through lower energy bills. IoT technology allows energy providers to be far more dynamic and flexible in their supply planning. To keep up to date with Telefónica’s Internet of Things area, visit our web site or follow us on Twitter, LinkedIn y YouTube

It would be fair to say that 2020 has not been a great year for retailers around the world. As many high street stores have been forced to temporarily close their doors due to government-imposed restrictions, many retailers have had to turn to e-commerce to maintain revenues. In this piece, I will outline the three main ways in which Big Data is vital for the success of e-commerce. According to a recent study carried out by the United Nations Conference of Trade and Development, e-commerce activity has increased across most industries in the wake of the COVID-19 outbreak. Figure 1: % of active online shoppers conducting at least one online purchase every 2 months (UNCTAD and NetComm Suisse eCommerce Association) Product Personalisation Product personalisation in e-commerce is the process of displaying appropriate content based on the profile of the individual consumer. This anonymous profile is generated by an algorithm through the compilation of data based on, for example, previous searches and purchase history. Imagine you enter a store and the employee already knows what you like the most, what suits your budget and what you have been excited about buying Marketing consultants Epsilon published a report In January 2018 stating that 80% of consumers are willing to pay more for a personalised shopping experience. This is the case as it saves the consumer valuable time and effort in seeking out desired products. Product personalisation makes sure that the offer of sale is relevant to the wants of the consumer, efficiently inducing sales and boosting the revenue of retailers. Optimised Pricing Pricing is, perhaps, the most important factor which induces a sale. Whilst traditionally, a retailer would price goods based on common ‘cost + mark-up’ model, Big Data allows for a much more tailored and targeted approach. Through the use of Big Data extensions, retailers can constantly track the pricing patterns of competitors and adjust their pricing strategy accordingly, always making sure that their pricing aligns appropriately with their specific vision and business model. This is especially beneficial to those retailers who target the price-sensitive consumer as they can maintain a competitive position in the market without having to manually track the pricing strategies of competitors. Targeted Marketing Traditionally, targeting the correct consumer is the hardest part of the marketing process. Once the appropriate segment of the market has been identified, a retailer must employ strategies appropriate to the specific type of consumer. For example, a social-media based marketing strategy on Instagram unlikely to be effective when targeting the Baby Boomer, as this cohort is statistically less likely to use the platform. Whilst this concept is obvious, Big Data extensions allow the retailer to understand on a more intricate level and dynamic basis the types of consumers that are interested in a particular product, allowing for a more considered, appropriate and targeted marketing strategy to be implemented. Conclusion Big Data technology in e-commerce is increasingly important and influential to retailers as consumers engage more in e-commerce activity. Retailers must offer the consumer a personalised online experience to optimise sales. Big Data extensions allow for a far more considered pricing and marketing strategy, leading to revenue optimisation and enhanced visibility to the targeted consumer. To keep up to date with LUCA visit our website, subscribe to LUCA Data Speaks or follow us on Twitter, LinkedIn or YouTube .

In 2020 remote learning platforms have become essential for students around the world as a result the COVID-19 pandemic. Many of these platforms incorporate Artificial Intelligence (AI) technology which continues to improve the outcomes of students and educators. In this piece, I will share with you the three main ways AI continues to revolutionise the way in which students learn. Enhanced personal development The main benefit of AI in education is that it allows for students to experience personalised learning experiences. The Personalisation of learning acts as a catalyst for individual growth and development. Through the compilation of personal performance data, platforms can adjust material suggested to the user in accordance with their individual ability. This not only allows for high performing students to develop a more advanced understanding, but it also allows for less able students to consolidate information at an appropriate pace, maximising the potential of each student. This concept is illustrated by the language learning app ‘Duolingo’ which tailors its suggested material to each user depending on their pre-existing knowledge. Given the positive impact Personalised Learning has on personal development, it is no surprise that education tools which incorporate AI have experienced exponential growth in recent years. Duolingo saw its number of uses increase from just 125,000 people in 2012 to over 30 million in 2019. Improved Engagement Amongst Students It is often argued that students are becoming increasingly uninspired by existing learning techniques. This is, in part, due to the lack of interaction and personal attention offered in a traditional classroom setting. Interactive so-called ‘Classroom Management Tools’ aim to motivate students by offering an increased level of personal attention that cannot be feasibly given by a class teacher. Market leader, ‘Class Craft’, is a virtual learning platform which uses the principles of a typical computer game as a framework for lessons, rewarding good behaviour and performance with game points. Simultaneously, Class Craft is able to track each pupil’s ability in crucial skills such as critical thinking and empathy, allowing teachers to understand more about each pupil whilst encouraging the engagement of students. Assessment Automation leads to increased learning opportunities According to a study carried out in January 2020 by leading management consultancy McKinsey and Company, teachers spend just 49% of their working hours interacting directly with their students. The rest of their time is spent carrying out administrative and evaluative tasks. As AI technologies develop, there is an enhanced potential for automation in these tasks, allowing the teacher to spend more time interacting directly with the students. This, ultimately will give students more opportunities to learn. Online multiple-choice assessments are increasingly being used by institutions of all academic levels to cut down on the marking burden, but it is clear that there is a lot of potential for innovation here as emotional intelligence capabilities evolve. Conclusion The power of AI in education cannot be underestimated. Although we can clearly see the potential for personalised learning platforms to increase the performance and engagement of students, we must also consider the impact of AI on the role of educators. If the power of AI can be fully harnessed to significantly reduce the workload of teachers, increased learning opportunities for students will be a direct and significant consequence. To stay up to date with LUCA, visit our Webpage, subscribe to LUCA Data Speaks and follow us on Twitter, LinkedIn o YouTube.