This material is prepared for the needs of Output 1 of the SMART project and is based on the market study conducted by the Cluster Sofia Knowledge City in 2019, supported by some of the members of the SMART project team at that time.

By smart city technologies are meant those technologies that refer to the concept of a smart city, most often these are digital and/or data-based technologies that are applicable in the real conditions of the city and contribute to the city's coping with public problems or challenges. In smart cities, these technologies are used to develop "critical infrastructure" in the following areas: transport, water and waste management, construction, energy, security, education, health, and urban management.
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The following areas of application of these technologies are presented below. All of them are known cumulatively in most of the smart cities around the world, which are at different stages in the process of transformation. This list does not represent all areas of application at all and is only an introduction for a better understanding of the way and reasons for the penetration of these technologies.

• Autonomous vehicles - vehicles equipped with sensors and software to work alone; full self-management capability (level 4) is achieved when human intervention is not expected to take control at any time.
• Bicycle sharing - bicycles for public use, either in docking centers or as freely used, to provide an alternative to riding, public transport, and private bicycle ownership. This option can cover the first mile / last mile segment when public transport does not take a door-to-door journey.
• Car sharing - access to short-term use of cars without full ownership; can be bidirectional (station-based), unidirectional (free-floating), spot-to-spot, or partial.
• Congestion pricing - fees for using a personal car in certain areas, during peak demand, or both.
• Demand-based micro-transit - sharing services with fixed routes, fixed stops, or both, often complementing existing public transit routes. The algorithms use a historical search to determine routes, vehicle size, and travel frequency. May include seat reservation options.
• Payment by digital public transport - digital and contactless payment systems in public transport, which allow prepayment and faster upload. Includes smart cards and mobile payments.
• Electronic call (private and combined) - the real-time ordering of point-to-point transportation via a mobile device. Unified e-ringing involves the dynamic connection of individual journeys with compatible routes to increase vehicle utilization (ie local real-time search optimization).
• Integrated multimodal information - real-time information on price, time, and availability of transport options in many modes.
• Intelligent road signals - improving overall traffic by dynamically optimizing traffic lights and speed limits, leading to higher average road speeds and less frequent stopping and returning. Includes preferential light technology that prioritizes emergency vehicles, public buses, or both.
• Consolidation of the parcel load - online matching of the demand for supplies with the available supply of freight capacity. By making maximum use of vehicles, fewer trucks make more deliveries.
• Predictable maintenance of transport infrastructure - sensory monitoring of the condition of public transport and related infrastructure (such as rails, roads, and bridges) so that predictive maintenance can be performed before accidents and disruptions occur.
• Real-time public transport information - real-time arrival and departure information for modes of public transport, including informal bus systems.
• Real-time road navigation - real-time navigation tools for selecting driving routes, with signals for construction, detours, traffic jams, and accidents. This is especially true for those who drive alone or in a car.
• Smart mailboxes - boxes in a place where people can pick up packages using individual access codes sent to their mobile devices.
• Smart parking - systems that direct drivers directly to the available spaces; may affect demand through variable charges.

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• Leak detection and control - remote monitoring of the condition of the pipes with the help of sensors and control of the pump pressure to reduce or prevent water leakage. Early identification of leaks can lead to follow-up by relevant city departments and utilities.
• Smart irrigation - optimizing irrigation by analysing information such as local weather, soil conditions, plant species, etc. to eliminate unnecessary watering.
• Monitoring of water consumption - feedback (via a mobile application, e-mail, text, etc.) on the water consumption of the occupant in order to raise awareness and reduce consumption. Smart water meters allow utilities to measure consumption remotely, reducing labour costs for a manual meter reading. It also allows for dynamic pricing.
• Water quality monitoring - real-time water quality monitoring (in networks, rivers, oceans, etc.) through signals delivered to the public through channels such as a mobile application, e-mail, text or website. This warns the public to avoid consumption or contact with polluted water and to make cities and utilities follow the problem immediately.
• Digital tracking and payment for waste disposal - digital payment systems according to the volume of generated waste; includes feedback (via mobile app, email, text, etc.) provided to users to raise awareness and reduce waste.
• Optimization of the waste collection route - use of sensors in the waste containers to measure the volume of waste and direct the routes of waste trucks. This application restricts the travel of garbage trucks to bins with a small amount of waste.

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• Building automation systems - systems that optimize the use of energy and water in commercial and public buildings by using sensors and analysis to manually or automatically eliminate inefficiencies. Includes optimized lighting and HVAC, as well as features such as access/security control and parking information.
• Home energy automation systems - optimization of energy consumption from the home by using intelligent thermostats, programmable and remotely controlled electronic devices (smart home), and control of backup electricity.
• Tracking energy consumption in the home - tracking the consumption of electricity in homes with feedback provided to the consumer through a mobile application, e-mail, or text to raise consumer awareness and promote their protection. It also allows utilities to remotely measure electricity use.

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• Supply automation systems - various types of smart grid technologies, including FDIR, M&D, Volt / Var, and substation automation, to optimize energy efficiency and grid stability.
• Dynamic electricity pricing - dynamic adjustment of electricity prices to reduce electricity consumption and reduce electricity generation costs. By reducing peak consumption, cities can reduce the number of power plants that operate during peak hours.
• Intelligent street lamps - connected and equipped with sensors energy-saving street lights (including LED), which optimize brightness and reduce maintenance needs. Smart street lights can be equipped with speakers, shot sensors, and other features to improve functionality

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• Body cameras - audio, video or photographic recording systems commonly used by police officers to record incidents and police operations.
• Crowd management - technology for monitoring and, where necessary, guiding crowds to ensure safety.
• Data-based building inspections - use of data and analysis to focus inspections on the buildings with the highest potential risks (e.g. prioritization of commercial buildings for fire code inspections and homes for lead inspections).
• Disaster Early Warning Systems - technology designed to anticipate and mitigate the effects of natural disasters such as hurricanes, earthquakes, floods and forest fires.
• Emergency response optimization - the use of analyses and technologies to optimize the processing of emergency calls and field operations, such as the strategic deployment of emergency vehicles.
• Shot Detection - Acoustic surveillance technology that includes audio sensors to detect, locate and alert police agencies to real-time shooting incidents.
• Home security systems - security systems that monitor homes and alert users, emergency services, or both, to unusual activity.
• Personal alarm applications - applications that alert you to an emergency by alerting the Emergency Center, loved ones, or both. Devices (such as personal protective equipment, crash detectors, and fall warning systems) can transmit location and voice data.
• Predictable control - the use of big data and analysis (including social media monitoring) to predict more accurately where and when crimes are likely to occur. These systems are used to deploy police patrols and prevent prevention.
• Real-time crime mapping - a technology used by law enforcement to map, visualize and analyse crime incident models. Information gathering and intelligence serves as a management tool for the efficient allocation of resources and accountability among employees.
• Intelligent surveillance - intelligent monitoring to detect anomalies based on visual emissions, including face recognition, intelligent closed-circuit television systems and registration number recognition.

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• Personalized learning - the use of data from students to identify people who need extra attention or resources; the potential for adapting the learning environment for individual students.
• Online retraining programs - lifelong learning opportunities provided in digital format, especially to help people who are unemployed or at risk of becoming unemployed to acquire new skills.
• Local e-career centers - online platforms for publishing open positions and profiles of candidates; can use algorithms to match compatible candidates with available jobs.
• Reduce job search time and increase net new employment.

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• Public health interventions based on maternal and child health data - use of analyses to target highly targeted health interventions for at-risk groups (in this case, identification of pregnant and new mothers to conduct educational campaigns for and postnatal care).
• Public-based health interventions to improve sanitation and hygiene - use of analyses to target highly targeted interventions, such as understanding where to increase rainfall absorption capacity or collecting data on sewage leaks systems.
• Urgent aid alerts - technologies that alert passers-by trained in CPR so that victims of cardiac arrest receive prompt and urgent care.
• Monitoring of infectious diseases - collection, analysis and response to prevent the spread of infectious and epidemic diseases. Includes awareness and vaccination campaigns (eg for HIV / AIDS).
• Integrated patient flow management systems - real-time hardware and software solutions that provide visibility to where patients are in the system to improve hospital operations and coordinate use at the city or multi-site level.
• Lifestyle clothing - portable devices that collect data on lifestyle and activity indicators and inform the user; they can promote exercise or other aspects of a healthy lifestyle.
• Online care search and planning - tools that support the selection of providers and providers with financial and clinical transparency.
• Real-time air quality information - real-time sensors to detect and monitor the presence of air pollution (outdoor, indoor, or both). Individuals can view the information online or on a personal device and decide to change their behaviour accordingly.
• Remote patient monitoring - collection and transmission of patient data for analysis and intervention by the healthcare provider elsewhere (eg monitoring of vital signs or blood sugar). Includes drug adherence technologies that help patients take medications as recommended by their healthcare provider.
• Telemedicine - virtual interaction of the patient and the doctor through audio-visual technology

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• Obtaining licenses and permits for business digitally - a digitalized process (as an online portal) for companies to obtain licenses and permits for operation.
• Digital Tax Submission - a business channel to perform online tax filing.
• Obtaining permits for the use of land and buildings by digital means - digitalization and automation of the application process for permitting the use of land and construction, reducing the time for approval, and increasing transparency.
• Open database for the cadastre - a complete database for the plots in the city, open to the public; allows for a more efficient land market by creating transparency of available land and reducing the cost of registering plots.
• Peer-to-peer accommodation platforms - digital markets where individual owners can list and rent properties for short-term accommodation.
• Digital civil services - digitalization of state administrative services aimed at citizens, such as filing income tax, registering cars or applying for unemployment benefits.
• Local applications of civic engagement - public engagement in urban issues through digital applications. It may include reporting problems and maintenance needs (for example, reporting broken street lamps through an application), providing information on policy decisions, participating in digital urban initiatives (such as open data hackathons), and interacting with city authorities and social services departments. networks,
• Local communication platforms - websites or mobile applications that help people connect and potentially meet other people in their community. It can be used to find people with similar interests and hobbies, to connect with neighbours, etc

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The SMART project is aimed at developing new competencies of the SMEs managers to manage deep-tech businesses in one very fast-growing market - the smart cities. The penetration of so-called smart city technologies results in the creation of new markets and requirements for new skills. On the basis for the achievement of this purpose is laid the map of digital smart disruptions.
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This short article explains some important terms and the approach implemented in this project.
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The picture is worth a thousand words - Fred R. Barnard.
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The map of digital and smart disruptions provides an easy for understanding and learning overview and analysis of how digital and disruptive technologies (innovations) do shape the smart city concept. The map includes the results generated by the focus group and corrected later by the partners with the results from the research on the technologies advances, level of change and impact, performance, purpose, and fit.

The map is a two-dimensional cross factorial analysis matrix made with different instruments in diverse forms (Fig.1). 

The ultimate purpose of a map is to improve the scenario planning of businesses and the cities in the process of their transformation into smart cities and to point out the opportunities for involvement of the businesses in the process. So, this can be treated as a sample of an opportunity map for every city in the process of urban management and for every company in the process of innovation management.

In the case of the project SMART, the map will present the results of the studies and research of the project partners in a systematic and simplified way, based on:

1. the conclusion and definition of the smart city’ technologies that might be a source for disruptive innovations in the main city’s areas. Every identified technology can be studied in regard to the market, product/service, delivery methods, production model and business model.
   
   
2. the conclusion and presentation of what the main smart city’s areas are, where the process of transformation is carried out, what their needs are, barriers and stimulations for smart technologies implementations;
   
   
3. the level of penetration of the smart city’ technologies into the smart city’s areas - the less level of penetration the less-differentiated business opportunities can be generated for applications in the near future. On the other hand, more deep-tech smart city applications are possible in the cities with a higher level of penetration of such technologies;
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4. the opportunities for the businesses and impact of these technologies and their application for the business growth and the cities transformation. The map has a digital format and encompasses all potential companies including start-ups and corporates which work or would like to move to the smart city segment.

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The definition of the disruptive innovation given by Clayton Christensen is the innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market-leading firms, products, and alliances. The following benefits and changes coming from the disruptive innovations are identified:

• everything that could be digital will become digital, technology will be embedded widely in products and services in a near term;
• customers look for high levels of personalization and individual approach and service;
• subscription-based business models and pay-per-click are rising;
• shortened product life cycle - on the one hand, the pace at which new products are adopted (number of years until x % penetration has been reached) increases. On the other hand, product life cycles are shortening. With technology products even to three or six months. For SMEs that invest and develop digital innovations the revenue stream is generated by products launched within the previous years.
• the use is replacing possessions - very often customers prefer to use the product, rent it or pay only for the time if using it.

Following this definition, it is easy to presume that in general the “disruptive smart city technology” is any kind of emerging, advanced & digital (but not only) technology that can generate disruptive innovations creating benefits and many changes in the context of an urban (territorial) area and if this process is well managed it should result in a better life of the citizen.

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The mapping is a structured process, focused on a topic or construct of interest, involving input from many participants, that produces an interpretable pictorial view (concept map) of their ideas and concepts and how these are interrelated. The mapping helps partners to think more effectively as a group without losing their individuality. It helps the project group to manage the complexity of the vision on the smart city disruptive technologies without trivializing them or losing detail.

The mapping process is one of the portfolios of many other similar methods that management and social scientists have developed like brainstorming, brainwriting, nominal group techniques, focus groups, affinity mapping, Delphi techniques, facet theory, and qualitative text analysis. The mapping process is focused on the major shifts from the business perspective and how these changes will affect the growth.

It uses focus groups to understand and analyse the impact of smart city technologies and their application for business growth. The trends, types of technology, and levels of transformation are included in studying within the process. Consequently, the project partners have to study a framework of the following five dimensions of these technologies:

• market - customer segments, citizens’ participation, needs, behaviours, trends;
• products and services - user experience, brands, product features, functionalities;
• delivery methods - supply chains, delivery models for online and offline business;
• production model - co-creation, co-development, production technologies, facilities, software, hardware, HR, etc.;
• business model - how revenue and cost models look like, what partnerships are developed, how public-private partnerships work, and what best practices are in place in terms of innovation.

The study includes the level of impact and change of the emerging technologies - so, what does exist now and what is the current maturity level (according to Gartner).
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The process is placed in a focus group that is facilitated by the leader of the mapping process. A mapping process in a such group involves five steps that can take place in a period of time, planned for the output and depending on the project development situation.
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1. The first step is preparation of the focus group. There are three things to be done here. The leader of the mapping process identifies who the participants will be in the focus group. It is a relatively small group of participants from the stakeholders involved (8-10 persons). Then, the leader works with the participants to develop the focus for the project. In this case, the group is focused on defining what the smart city disruption technologies are and on choosing what challenges (problems) areas to map (prognosis) in all of the outcomes (impacts) they might expect to be seen as a result.
   
   
2. In the second step (generation step) the participants in the focus group develop a large set of statements that address this focus. They generate statements that describe the advanced technologies that are currently used in smart city areas and mark what is their level of penetration. They also generate statements describing specific outcomes that might occur as a result of the implementation of these technologies. A wide variety of methods can be used to accomplish this including traditional brainstorming, and so on.
   
   The group will generate finally many and diverse statements for possible current and future applications of smart disruptive technologies in smart cities, answering the following questions:
   - What of the city’s challenges (problems) do you know as already resolved based on the defined in the first stage smart city disruptive technologies?
   - What other challenges (problems) could be resolved with the smart city disruptive technologies in the cities of future?
   
   
3. In the third, the structuring step, the participants select and sort the statements into groups (clusters) of similar ones, and every participant rate each of the statements on some scale for their relative importance and level of penetration of the technologies in the respective areas, from the 1-to-5 scale. In this stage, the group can use the results from facts finding and studies that are already in place done by the project partners. The participants in the group can receive, use, and interpret the gathered reports, analyses, and the open access papers on emerging & smart disruptive technologies and their use before the final evaluation (scoring).
   
   
4. The fourth, the representation stage is where the analysis is done - this is the process of taking the sort and rating input and “representing” it in a matrix form. Every group of similar statements and received scoring on the level of penetration will be located in a proper and logically defined field of the matrix. So, every area in the result will be filled or empty. The empty fields will have to be analysed in-depth and the marked field will have a group of statements that well describe the current and future situation of the smart city disruptive technologies and their possible impact.
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5. Finally, the utilization and interpretation step involves the use of the map to help address the original focus. On the programme side, the maps can be used as a visual framework to prove the results of the analyses and the reports that the partners have generated or for their possible improvements. The map can be used for developing measures and displaying results and for further analyses and planning when a scenario approach is needed. The interpretation is easy. The empty or low-level evaluated areas, if they are not resulting in a lack of competences and information, will generate opportunities for the business to proactive product/services development, investing in R&D and for upskilling new talents.

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Author
KISMC

This article is animated by the interim results of an ongoing Erasmus+ project named Smart technologies by design (Smart by Design).

The project is being carried out by the following partners:

• KISMC - an innovation management organization that is focused on developing competences in innovation, creativity and entrepreneurship
• GAIA - a cluster & BSO that unites companies from the knowledge and applied technologies industries and supports policy and deployment of ICT, Engineering and Electronics in Basque Country
• DEUSTO - leading university in Spain specialized in educating & training in the innovation and entrepreneurship, design thinking and IoT & Smart city solutions through its Business School and the Faculty of Engineering
• United Academics - Foundation in Netherlands that promotes, supports and maintains open-access library and publishing that results in faster scientific communication, wider influences of scientific knowledge on the industry, government, and education
• ARIES - industrial cluster and BSO that contributes to designing & implementing the smart city strategy of Cluj in Romania, supports digital transformation and creates digital innovation hubs
• ULSIT - Bulgarian state university of bibliography and information technology.  
  
  

In the next several articles the project partners will present their findings and deliverables generated and developed during the project execution. Our attention will be focused on the gap between the citizen's (smart cities') needs, the technological solutions which can cover these needs, and the knowledge and skills of citizens, cities and service providers of using these technologies for a better life.
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Disruptive technologies have the potential to transform the way cities currently operate and they are at the core of nearly all upcoming smart city’s solutions. After a decade of experimentation, smart cities are entering a new phase. Although digital solutions are only one of the tools needed to make a city great, they are the most powerful and cost-effective additions seen in many years.

According to the McKinsey Global Institute, digital solutions could improve some quality-of-life indicators by as much as 30 percent. Real-time crime mapping, for instance, utilizes statistical analysis to highlight patterns, while predictive policing goes a step further, anticipating crime to head off incidents before they occur. Another example of these solutions is the Internet of Things sensors on existing infrastructure systems which can help crews fix problems before they turn into breakdowns and delays.
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If we examine our history, we realize that we live in constant change. Humans have faced all sorts of changes, economic, political, climatic, technological. In the different industrial revolutions seized, adaptations made by humans can be perceived, along the 1st industrial revolution, railway arrived with the steam engines which enabled transportation of goods causing all the farming, demographic and transport revolution.

The city of the future must meet the needs of its residents. Yet in surveying residents of 25 major cities, McKinsey finds that a fifth of those cities falls short of delivering satisfaction. Respondents cited numerous inadequacies: crime, congestion, fire emergency response, waste management, active mobility options, police security, lack of basic utilities, public transit, as well as poor quality of housing and government services. Given the fierce competition for talent across cities, dissatisfied urbanites are likely to vote with their feet and leave for more attractive environments. (Source: Thriving amid turbulence: Imagining the cities of the future; Capital Projects & Infrastructure, Public Sector October 2018, McKinsey & Company).

In order to not lag behind due to inactivity, the city leaders must know how to use the newest and most advanced technologies, which is progressing faster than expected.
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We have realized during the implementation of the project and as a result of the study that the most important and those with enough predictive potential to transform the cities into smart cities are the following technologies (technology areas):

1. Artificial Intelligence
2. Data analytics
3. Cloud Computing
4. Internet of Things
5. Cyber physical systems
6. Smart sensors
7. Collaborative robotics
8. Cybersecurity
9. Blockchain
10. Augmented reality
11. Virtual reality

We start with the first area - Artificial Intelligence (AI) and below you can find the features, functionalities, existing platforms, and standards, which are recognized by us as the most important from the point of view of upcoming evolving and disruptive solutions in the smart cities.

Artificial Intelligence (AI) refers to the technology which aims to create intelligent machines that react and work like humans. Some of the skills for the computers with AI are the speech recognition, learning (information acquisition and patterns to use it), reasoning (using rules to reach definitive conclusions), self-correction and problems solving.

This technology allows machines to learn from past experiences, adjust to new inputs and perform tasks like humans. Nowadays, the AI refers to a big range of concepts, from robotic process automation to the current more sophisticated robots. It has evolved due to the huge amount of available data or the increase of speed, size and variety of data that companies are collecting. The AI is also able to realize tasks such as the identification of patterns in data in a much more efficient way than humans, which allows companies to extract better conclusions from the information.

 The Artificial Intelligence can be categorized in different ways and types of activities that they perform . But basically, the tasks that a computer based on AI can carry out, can go from developed very concrete tasks (faint AI) to other systems provided with human cognitive skills (strong AI), being able to give answers to problems or tasks that previously were not assigned or expected to them.
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In recent years, AI has grown capabilities thanks to the advance of some technologies, such as: 

• Improved machine learning (ML) techniques
• Availability of massive amounts of training data
• Unprecedented computing power
• Mobile connectivity

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Platforms for AI are hardware architectures or software frameworks that allow the software to run. Even if there are many different types of platforms related to this technology, some of the most relevant are: 

• Microsoft Azure Machine Learning
• Google Cloud Prediction API
• TensorFlow
• Infosys Nia
• Wipro Homes
• API.AI
• Premonition
• Rainbird         
• Ayasdi
• MindMeld
• Wit
• Vital A.I
• KAI
• Receptiviti
• Rage-A
• Infrrd

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The European Commission has launched the Communication COM (2018) Artificial Intelligence for Europe which establishes a new initiative for Europe about AI. This recognizes the need of the standardization as an answer to the challenges of this key technology, especially in terms of security, trust and ethical considerations. CEN and CENELEC support the fulfillment of the European legislation with harmonized standards.

In relation to international organizations, the ISO has a technical committee which is working on the development of standards in Artificial Intelligence, which is the ISO/IEC JTC 1/SC 42. There are currently two published ISO standards under this working group, and there are some more which are being developed.

Published standards:

• ISO/IEC TR 20547-2:2018: Information technology - Big data reference architecture - Part 2: Use cases and derived requirements;
• ISO/IEC TR 20547-5:2018: Information technology - Big data reference architecture - Part 5: Standards roadmap;

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Artificial Intelligence is a term which has existed for many years, but due to the development that current technologies are having, it has been recently spread to different application sectors and environments. Some of them are:

• Entertainment: videogames, apps, sports betting;
• Recommendations in music, videos or movies;
• Self-driving cars like Google driverless car, Tesla’s autopilot: self-parking, collision detection, blind spot monitoring, voice recognition or navigation;
• Chat-bots for online customer service;
• Banking and finance: analysing market data, manage finances, offer suggestions;
• Manufacturing: assembling plants;
• eCommerce and digital marketing;
• Home applications: learning behaviours and patterns;
• Electronics: thermostats or smart lights;
• Workplace communication;
• Healthcare: diagnosis and treatments, virtual nurses;
• Cybersecurity;
• Logistics and supply chain;
• Online retail stores;
• Smartphones: virtual personal assistants (Siri, Cortana or Google Now)

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​As one of the main technologies which will help to evolve our ways to work and interact with technology, the trend is that the AI will keep growing and becoming more normalized in our society. So, platforms, standards, and applications will keep incrementing. 

Apparently, the future of the AI resides on a deeper personalization, innovation in voice AI and a better view of the customer. Even if there are plenty of platforms for AI, not many of them are deploying voice interfaces. Some applications for mobile devices have already implemented it in daily life and it will offer opportunities for companies. The tendency will be to add voice control to their AI for a better experience with their customers. Some of the most famous firms (Google, Amazon...) are integrating voice assistants as part of their services.
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The AI will help for a bigger personalization of services. Customers will be able to express preferences to the biggest brands and acquire much more personalized services or products. So, future platforms will shift towards a new type of services. 

The European Commission foresees that Artificial Intelligence will impact the commitment of some European guidelines. It will have effects in several sectors in which standardization is very relevant: smart manufacturing, robotics, autonomous driving, virtual reality, health sector, visual recognition, data analysis and management, domestic tools or cybersecurity. In all those sectors there are already essential standards which will need to be updated in order to add this new technology.

Currently, there are four standards under development from the working group ISO/IEC JTC 1/SC 42:

• ISO/IEC AWI TR 20547-1: Information technology -- Big data reference architecture Part 1: Framework and application process;
• ISO/IEC DIS 20547-3:  Information technology -- Big data reference architecture -- Part 3: Reference architecture;
• ISO/IEC AWI 22989: Artificial Intelligence Concepts and Terminology
• ISO/IEC AWI 23053: Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML).

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Even if Artificial Intelligence is becoming more common in our daily life, there are many more applications foreseen or improved such as:

• Automated transport: fully self-driving cars, buses or trains;
• Cyborg technology: enhancing natural abilities, amputated parts;
• Climate change: identifying trends and use information to come up with solutions and natural disasters;
• Dangerous jobs: bomb defusing, toxic substances, intense heat, difficult access places, prevent human harm;
• Robot as friends: in the future, robots will be able to understand and feel emotions;
• Elder care: helping with everyday life providing more independence;
• Journalism: it is expected that the AI will be able to write articles or reports which don’t require a very deep knowledge.

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