We present here the second edition of our research aimed at establishing an operational definition of artificial intelligence (AI), to which we refer to in the activities of AI Watch. This edition builds on the first report, published in February 2020, and complements it with several recent developments. Since then, the European Commission has proposed a regulatory framework on artificial intelligence (AI Act) that establishes a legal definition of AI, which we incorporate in the current review. In addition to this legal definition, an operational definition is still needed to better delineate the boundaries and analysis of the AI Watch AI landscape. The proposed AI Watch operational definition consists of an iterative method providing a concise taxonomy and list of keywords that characterise the core domains of the AI research field, complemented by transversal topics such as AI applications or ethical and philosophical considerations - in line with the wider monitoring objective of AI Watch. The AI taxonomy is designed to inform the AI Watch AI landscape analysis and is also expected to cover applications of AI in closely related technological domains, such as robotics (in a broader sense), neuroscience or internet of things. The literature considered for the qualitative analysis of existing definitions and taxonomies has been enlarged to include recently published reports from the three complementary perspectives considered in this work: policy, research and industry. Therefore, the collection of definitions published between 1955 and 2021 and the summary of the main features of the concept of AI appearing in the relevant literature is another valuable output of this work. Finally, alternative approaches to study AI are also briefly presented in this new edition of the report. These include the classification of AI according to: families of algorithms and the theoretical models behind them; cognitive abilities reproduced by AI; functions performed by AI. Applications of AI may be grouped also according to other dimensions, like the economic sector in which such applications are found, or their business functions. These approaches, complementary to the taxonomy used for the analysis of the AI Watch international landscape, are useful to gain a wider understanding of the AI domain, and suitable to be used in studies related to these dimensions.

The brief presents the results of the AI worldwide ecosystem analysis for the period 2009-2020, by applying the Techno-Economic ecoSystem (TES) analytical approach. The TES approach allows to map the AI worldwide ecosystem by considering the main AI-related industrial, innovation and research activities, and all the economic players that are involved in them (i.e. firms, research institutes, governmental institutions). The brief analyses the position of the EU in the international context, via-a-vis the United States, China, and other main players in the landscape, in terms of size of the AI ecosystem, specialisation in AI areas, AI firms and AI R&D capacities. It follows with an in-depth analysis of the EU ecosystem, with a section devoted to the impact of EC-funded projects on the EU AI ecosystem.

Artificial Intelligence (AI) is spreading throughout our economies and societies in multiple ways, but the absence of standardized classifications prevents us from obtaining a measure of its pervasiveness. AI is not a part of a specific sector, but r a t h er a transversal technology because the fields in which it is applied do not have precise boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon by investigating economic players’ involvement in industrial activities aimed to supply AI-related goods and services, and AI-related R&D processes in the form of patents and publications. In order t o conduct this extensive analysis, we use a complex systems approach, which identifies the core dimensions that should be considered. Therefore, by considering the geographic location of the involved players and their organisation types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview of the worldwide presence of AI players, (ii) analyze the demographic structure of AI firms (ii) investigate the patterns in which AI technological subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, compositio n , and topology of the AI R&D collaboration network. Based on a unique data collection of multiple micro-based data sources and supported by a methodological framework for the analysis of techno-economic segments (TES), we capture the state of AI in th e worldwide landscape in the period 2009–2018. As expected, we find that major roles are played by the US, China, and the EU28. Nevertheless, by measuring the system and describing different aspects of the AI landscape, we unveil elements that provide new, crucial information to support more conscious discussions in the process of policy design and implementation. This work contributes to AI Watch, the European Commission knowledge service to monitor the development, uptake and impact of AI for Europe.

This work aims at supporting policy initiatives to ensure the availability in the EU27 of an adequate education offer of advanced digital skills in the domains of artificial intelligence (AI), high performance computing (HPC), cybersecurity (CS) and data science (DS). The study investigates the education offer provided in the EU27 and six additional countries: the United Kingdom, Norway, and Switzerland in Europe, Canada and United States in America, and Australia, with a focus on the characteristics of the detected programmes. It analyses the number of programmes offered in these domains, considering the distinction based on programme’s scope or depth with which education programmes address the technological domain (broad and specialised), programme’s level (bachelor programmes, master programmes and short courses), as long as the education fields in which these programmes are taught (e.g. Information and communication technologies, Engineering, manufacturing and construction, Business, administration and law), and the content areas covered by the programmes. The analysis is conducted for each technological domain separately, first addressing the features of the overall education offer detected in the countries covered by the study, and followed by an in-depth analysis of the situation in the EU27. Among the many results that this work provides, those associated to the most relevant insights can be listed as follows. First of all, the main role in the offer of advanced technological skills is held by the US, which leads in terms of number of programs provided in almost all combinations of technological domain, scope and level. Secondly, another important player is the UK, with a very consistent offer of bachelor and master degree programs (in both cases, the UK’s share is around 25% of the total offer detected). The consequences of the Brexit have, therefore, to be considered and faced also in terms of the education offer of advanced technological skills in the EU27. Thirdly, the role of the EU27 is notable but more varying (depending on the combination of domain, scope and level of programmes) than that of the UK. Regarding more specific aspects related to the EU27 offer, we detect a good amount of programmes offered in the domain of DS. As this domain is found out to be remarkably associated to the field of education of Business, Administration and Law, this is a positive finding suggesting a good supply of competences that are suitable to economic activities of various types. Therefore, what observed for the EU27 suggests a good alignment between the offer and the demand of DS-related skills. In the EU27 we observe a large share of programmes belonging simultaneously to both DS and AI. Considering the relatively high offer in DS, and the fact that AI is currently a techno-economic domain that is attracting a lot of attention and of private and public resources, a consistent connection between these two domains can be considered as an important key to favour synergies and future economic growth. Additionally, we find DS programmes quite widespread among the fields of education, which may facilitate the role of DS as a vehicle to further introduce AI, HPC and CS in the fields of education barely addressing these technological domains. We also observe a relatively large offer of AI master degree programmes in the EU27, which is an important finding given the role of this education level in the provision of competences for the workforce. Finally, it is important to note that we detect potential elements of weakness in the EU27’s education offer related to CS. These competences are increasingly crucial to prevent and fight cyber-related incidents, concerning both private and public spheres. Therefore, the detection of a relatively modest CS education offer (in comparison to other geographic areas) is a point that deserves attention. Many other findings are described throughout this report, but what discussed in this abstract has to be retained as the most relevant content aimed at supporting EU policies.

In order to investigate the extent to which the education offer of advanced digital skills in Europe matches labour market needs, this study estimates the supply and demand of university places for studies covering the technological domains of Artificial Intelligence (AI), High Performance Computing (HPC), Cybersecurity (CS) and Data Science (DS), in the EU27, United Kingdom and Norway. The difference between demand and supply of tertiary education places (Bachelor and Master or equivalent level) in the mentioned technological domains is referred in this report as unmet students' demand of places, or unmet demand. Demanded places, available places and unmet demand are estimated for the following dimensions: (a) the tertiary education level in which this demand is observed: Bachelor and Master or equivalent programmes; (b) the programme’s scope, or depth with which education programmes address the technological domain: broad and specialised; and (c) the main fields of education where this tuition is offered: Business Administration and Law; Natural sciences and Mathematics; Information and Communication Technology (ICT); and Engineering, Manufacturing and Construction, with the remaining fields grouped together in a fifth category. From these estimations, it is concluded that the number of available places in the EU27, at Bachelor level, reaches 587,000 for studies with AI content, 106,000 places offered in HPC, 307,000 places in CS and 444,000 places offered in the domain of DS. At Master level this demand is comparatively lower, except for the DS domain, were it equals the offer at bachelor level. DS outnumbers AI in demand of places at Master level, with 602,000 and 535,000 demanded places, respectively. The unmet demand for AI, HPC, CS and DS in EU27 at MSc level is approximately 150,000, 33,000, 59,000 and 167,000 places, respectively. At BSc level, the unmet demand reaches 273,000, 53,000, 159,000 and 213,000 places, respectively. Another finding is that the unmet demand for broad academic programmes is higher than for specialised programmes of all technological domains and education levels (Bachelor and Master). Higher availability of places for AI, HPC, CS and DS domains is found for academic programmes taught in the ICT field of education, both at Bachelor and Master levels. For Bachelor studies, Germany and Finland are estimated as the countries with the highest unmet demand in AI, HPC, CS and DS, either with a broad or specialised scope. United Kingdom is the only studied country offering places for all fields of education and technological domains at Bachelor level and Master level. For Master studies, this is also found in Germany, Ireland, France and Portugal.

Artificial intelligence (AI) is playing a major role in the new paradigm shift occurring across the technological landscape. After a series of alternate seasons starting in the 60s, AI is now experiencing a new spring. Nevertheless, although it is spreading throughout our economies and societies in multiple ways, the absence of standardised classifications prevents us from obtaining a measure of its pervasiveness. In addition, AI cannot be identified as part of a specific sector, but rather as a transversal technology because the fields in which it is applied do not have precise boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon by investigating economic agents’ involvement in industrial activities aimed to supply AI-related goods and services, and AI-related R&D processes in the form of patents and publications. In order to conduct this extensive analysis, we use a complex systems approach through the agent-artifact space model, which identifies the core dimensions that should be considered. Therefore, by considering the geographic location of the involved agents and their organisation types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview of the worldwide presence of agents, (ii) investigate the patterns in which AI technological subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, composition, and topology of the AI R&D collaboration network. Based on a unique data collection of multiple micro-based data sources and supported by a methodological framework for the analysis of techno-economic segments (TES), we capture the state of AI in the worldwide landscape in the period 2009–2018. As expected, we find that major roles are played by the US, China, and the EU28. Nevertheless, by measuring the system, we unveil elements that provide new, crucial information to support more conscious discussions in the process of policy design and implementation.

In this work we represent a techno-economic complex system based on the agent-artifact space theoretical model. The objective is to structure a methodology to statistically investigate the presence of hierarchical order, as an emerging property of this system. To analyse the agent-artifact space, two statistical methodologies are initially employed. The first is a community detection method, employed with the objective to detect groups of agents that are likely to intensively exchange information within the considered complex system. The second is a natural language processing method, the LDA topic model, employed with the objective of identifying types of artifacts as technological subdomains through textual information that describes the activities of agents. After this initial part, we address the investigation of the structure of the agent-artifact space by estimating the involvement of each community in the detected topics. This is effectuated by means of a statistic that considers the information flow percentage of agents, the fractional count of activities, and the probability of agents’ activities to belong to topics. We then estimate the hierarchical order of the topics’ distribution in communities, by computing its nestedness temperature, which is adopted by studies on ecological systems. This statistic’s significance is finally evaluated with z -scores based on homogeneous systems. The case study is a system consisted of economic agents (e.g. firms, universities, governmental institutions) patenting in the technological domain of photonics. The analysis is effectuated over five time spans in the period 2000–2014. The observed values of nestedness temperature are proved statistically significant, which suggests that hierarchical order is an emerging property of the agent-artifact space.

The digital transformation of the economy and society has intensified the need for digitally skilled labour force. Recent studies inform about expected increased demand, and skill shortages in the Information and Communication Technologies (ICT) sector, with a widening gap between supply and demand of ICT specialists. The need for accurate data on the number of ICT specialists in employment becomes more pertinent due to the development of policy initiatives aimed at increasing digital skills. Eurostat and the OECD define ICT specialists and propose a statistical definition using the International Standard Classification of Occupations (ISCO) 2008. Based on the Labour Force Survey, Eurostat provides an estimated 8.9 million persons working as ICT specialists in 2018 in the EU. This indicator annually feeds the Digital Economy and Society Indicator, a composite indicator that assesses the digital performance of EU Member States. This paper shows that this value underestimates the actual number of ICT specialists and proposes a more accurate method for the estimation. The list of ICT occupations includes both 3-digit (3d) and 4-digit (4d) codes. The number of EU Member States not reporting 4d data was 12 in 2011 and 6 in 2018. Therefore, the direct implementation of the definition is not possible, and a method is needed to estimate the missing 4d values and produce complete estimates for the EU. Eurostat developed an estimation method based on education data (EF method) to provide estimates for ICT in employment. This paper proposes the Ratio method for the estimation of missing data, compares its performance with the EF method, and produces estimates on ICT specialists in employment for 2004-2018, for the EU and its Member States. The results show that the Ratio method provides more accurate estimates than the EF method. We test the performance with two error measures by means of a cross-validation algorithm; in both cases, all six variants of the Ratio method tested reduce the error of the EF method between 35% and 55% when measured on countries reporting 4d data. The new proposed method estimates 9.2 million ICT specialists are working in the EU in 2018, 2% above the value with the old estimation method (the difference reached 26% in 2004). At country level, for countries with missing data, the new method implies an average increase of 34% in 2004-2010 and 17% in 2011-2018 with respect to the estimate with the current method. According to our estimations, the number of ICT specialists in employment followed an increasing trend over the two analysed periods (2004-2010 and 2011-2018), with an overall increase of 19% in the first period, and of 35% in the second one. The share over total employment also increases, with a grow from 3.2% in 2011 to 4.0% in 2018. These results are in line with other studies that show that the ICT sector was more resilient to the economic crisis that started in 2008 than the whole economy.

This report analyses and compares countries and regions in the evolving international industrial and research landscape of Artificial Intelligence (AI). The evidence presented is based on a unique database covering the years 2009-2018. The database has been specifically built from a multitude of sources to provide scientific evidence and monitor the AI landscape worldwide. Companies, universities, research institutes and governmental authorities with an active role in AI are identified and analysed in an aggregated fashion. The report presents a wide variety of indicators, allowing us to expand our knowledge on issues such as: the size of the AI ecosystem globally and at country level; which are the main global competitors of the EU; what is the level of industrial involvement per country; what are the firms’ demographics, profiling of economic agents according to their strengths in innovation and take-up of AI, including their patenting performance; and the degree of internal and external collaborations between EU and non-EU firms and research institutions. The analysis of the AI activities developed by agents in the studied territories provides interesting insights on their areas of specialisation, highlighting the strengths of the EU and its Member States in the global landscape. Each section offers a focus on EU Member States.

1 This work presents an operational definition that facilitates a common understanding of AI and its measurability, paving the way for a transparent and comparable monitoring activity in the context of AI Watch, the European Commission’s knowledge service to monitor the development, uptake and impact of artificial intelligence (AI) for Europe. The operational definition is constituted by a taxonomy and a list of related keywords. The method that we propose is iterative, considering that AI is a dynamic field, so its definition should be updated over time to capture the rapid AI evolution. The method consists of the following steps: (i) qualitative analysis of AI definitions and subdomains emanating from reports with academic, industrial and policy perspectives, (ii) selection of definition as starting point , taxonomy formation, and identification of representative keywords in AI with a natural language processing method, and (iii) taxonomy and keywords validation. This results in a unique taxonomy that represents and interconnects all the AI subdomains from political, research and industrial perspective and enables the efficient mapping of the AI landscape of economic agents across different technological areas.

This document presents a sectoral analysis of AI in health and healthcare for AI Watch, the knowledge service of the European Commission monitoring the development, uptake and impact of Artificial Intelligence for Europe. Its main aim is to act as a benchmark for future editions of the report to be able to assess the changes in uptake and impact of AI in healthcare over time, in line with the mission of AI Watch. The report recognises that we are still at an early stage in the adoption of AI and that AI offers many opportunities in the short term for improved efficiency in administrative and operational processes and in the medium-long term for clinical applications, patients’ care, and increased citizen empowerment. At the same time, AI applications in this sensitive sector raise many ethical and societal issues and shaping the direction of development so that we can maximise the benefits whilst reducing the risks is a key issue. In the global context, Europe is well positioned with a strong research base and excellent health data, which is the pre-requisite for the development of beneficial AI applications. Where Europe is less well placed is in translating research and innovation into industrial applications and in venture capital funding able to support innovative companies to set themselves up and scale up once successful. There are however noticeable exception as the case of the BioNTech that is leading the development of one of the COVID-19 vaccines. It should also be noted that in AI-enabled health start-ups, many of them are in the area of drug discovery, i.e. the domain of BioNTech. Investment in education and training of the healthcare workforce as well as creating environments for multidisciplinary exchange of knowledge between software developers and health practitioners are other key areas. The report recognizes that there are many important policy developments already in the making that will shape future directions, including the European Strategy for Data which is setting up a common dataspace for health, a riskbased regulatory framework for AI to be put in place by the end of 2020, and the forthcoming launch of the Horizon Europe programme as well the Digital Europe Programme with large investments in AI, computing infrastructure, cybersecurity and training. The COVID-19 crisis has also acted as a booster to the adoption of AI in health and the digital transition of business, research, education and public administration. Furthermore, the unprecedented investments of the Recovery Plan agreed in July 2020 may fuel development in digital technologies and health beyond expectation. We are therefore at the junction of a potentially extraordinary period of change which we will be able to measure in future years against the baseline set by this report.

This report proposes an operational definition of artificial intelligence to be adopted in the context of AI Watch, the Commission knowledge service to monitor the development, uptake and impact of artificial intelligence for Europe. The definition, which will be used as a basis for the AI Watch monitoring activity, is established by means of a flexible scientific methodology that allows regular revision. The operational definition is constituted by a concise taxonomy and a list of keywords that characterise the core domains of the AI research field, and transversal topics such as applications of the former or ethical and philosophical considerations, in line with the wider monitoring objective of AI Watch. The AI taxonomy is designed to inform the AI landscape analysis and will expectedly detect AI applications in neighbour technological domains such as robotics (in a broader sense), neuroscience or internet of things. The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry. A valuable contribution of this work is the collection of definitions developed between 1955 and 2019, and the summarisation of the main features of the concept of artificial intelligence as reflected in the relevant literature.

This report analyses the worldwide landscape of the Earth observation ecosystem to identify opportunities, synergies, and obstacles that need to be addressed to foster the development of a vibrant space data economy in Europe. The report uses the Techno-Economic Segment (TES) analytical approach to provide a holistic view of the EO and geospatial ecosystem in Europe and worldwide through the identification of players and key clusters of activities. It also takes into consideration the potential flows of knowledge resulting from shared activities, locations and technological fields. The approach adopts a micro-based perspective considering a wide range of both horizontal and segment specific data sources. The outcome is a compelling characterisation of the key features of this very dynamic ecosystem. The TES EO ecosystem shows a very diverse global landscape with three distinguished global hubs, namely EU28, China and the US, as possible incubators for EO-linked innovation. Those hubs have the largest number of players in case of R&D and well as in case of industry. Nevertheless, the distribution of EO activities and concentration of those activities look quite different in the three leading macro areas. As far as the R&D activities are considered, the EU28 has the highest overall number of players involved in the all types of R&D activities, but scores quite low if only the patents are taken into account. Out of the three big players, the US has the smallest number of players involved in the overall EO R&D and stable position in number of patenting. In case of China, the largest number of R&D activities is concentrated in hands of relatively few players. In conclusion, the findings of this report confirm a general expectation about the growth in the EO downstream segment. However, up to 2017 the growth has not been staggering. Since 2017, there have been continuous policy efforts to increase the uptake of EO data in order to enable market growth.

The aim of this study is to capture a technology’s pathway by identifying emerging subdomains in a complex system of economic processes. The objective is to uncover indirect latent relations among agents interacting in a specific techno-economic segment (TES). A methodology, including an “Extract-Transform-Load” (ETL) process preceding the two steps aimed for analysis, is developed to analyse a TES regarding R&D economic processes of the photonics technology. In the first step, economic relevant R&D activities (EU funded projects and patents) are analysed through a multilayer network (MLN) of agents, considering their interactions in three dimensions, which represent occurred and latent relationships: co-participations in economic activities, common geographical location provenance, common use of technological terms. Then communities are detected (Infomap Algorithm for MLN), and their ongoing within and between connections are studied, as potential factors that affect the entire structured technological ecosystem. In the second step, technological subdomains associated with method-oriented and application-oriented activities are identified through topic modelling. Using the MLN structure, the textual information of the corpus of documents describing the aforementioned economic R&D activities is associated to agents, and the topic model (Latent Dirichlet Allocation) uncovers additional potential semantic connections among them. Subsequently, the results of the MLN community detection and of the topic modelling based on the descriptions of economic activities are considered. Hence, the latent relations of agents are mapped.

The Techno-Economics Segment (TES) analytical approach aims to offer a timely representation of an integrated and very dynamic technological domain not captured by official statistics or standard classifications. Domains of that type, such as photonics and artificial intelligence (AI), are rapidly evolving and expected to play a key role in the digital transformation, enabling further developments. They are therefore policy relevant and it is important to have available a methodology and tools suitable to map their geographic presence, technological development, economic impact, and overall evolution. The TES approach was developed by the JRC. It provides quantitative analyses in a micro-based perspective. AI has become an area of strategic importance with potential to be a key driver of economic development. The Commission announced in April 2018 a European strategy on AI in its communication Artificial Intelligence for Europe, COM(2018)237, and in December a Coordinated Action Plan, COM(2018)795. In order to provide quantitative evidences for monitoring AI technologies in the worldwide economies, the TES approach is applied to AI in the present study. The general aim of this work is to provide an analysis of the AI techno-economic complex system, addressing the following three fundamental research questions: (i) Which are the economic players involved in the research and development as well as in the production and commercialisation of AI goods and services? And where are they located? (ii) Which specific technological areas (under the large umbrella of AI) have these players been working at? (iii) How is the network resulting from their collaboration shaped and what collaborations have they been developing? This report addresses these research questions throughout its different sections, providing both an overview of the AI landscape and a deep understanding of the structure of the socio-economic system, offering useful insights for possible policy initiatives. This is even more relevant and challenging as the considered technologies are consolidating and introducing deep changes in the economy and the society. From this perspective, the goal of this report is to draw a detailed map of the considered ecosystem, and to analyse it in a multidimensional way, while keeping the policy perspective in mind. The period considered in our analysis covers from 2009 to 2018. We detected close to 58,000 relevant documents and, identified 34,000 players worldwide involved in AI-related economic processes. We collected and processed information regarding these players to set up a basis from which the exploration of the ecosystem can take multiple directions depending on the targeted objective. In this report, we present indicators regarding three dimensions of analysis: (i) the worldwide landscape overview, (ii) the involvement of players in specific AI technological sub-domains, and (iii) the activities and the collaborations in AI R&D processes. These are just some of the dimensions that can be investigated with the TES approach. We are currently including and analysing additional ones.