Artificial Intelligence Innovation Research Articles

Network anomaly detection based on keyword embedding log

Log anomaly detection is an important and challenging task in the field of Artificial Intelligence for IT Operations (AIOps). Logs that record important runtime information are widely used for troubleshooting purposes. There have been many studies that use log data to construct deep learning methods for detecting system anomalies, which are usually based on log parsing. However, they ignore the effect of keywords that are promising for system status analysis. Here, we propose KELog (Keyword Embedding Log), a novel log anomaly detection approach that utilizes keyword information. We build a keyword library by keyword information extraction and fuse them into log representations. In this way, KELog can raise the reliability of anomaly detection. The experimental results on a real-world log dataset of a communications operator show that the F1 score of our proposed KELog method achieves a maximum increase of 0.341 compared with the commonly used machine learning algorithms (PCA, SVM, Invaiant Mining) and a maximum increase of 0.039 compared with deep learning algorithms (DeepLog, LogBERT) respectively. In 2021, ITU launched the second ITU AI/ML in 5G Challenge. We used KELog to participate in the thematic track of the Artificial Intelligence Innovation and Application Competition in the China Division, and won first place with a full F1 score.

Exploring the Top-Priority Innovation Types and Their Reasons

This is a foresight study to explore the top priorities of innovation types and the reasons behind them with respect to artificial intelligence (AI), big data, and the Internet of Things (IoT). This study set up two research strategies. One of the research strategies is to make the research design and methods fit with this study’s intellectual queries. Another strategy is to use the triangulations of method, analysis, data source, and researcher. This study selected expert panels, the Delphi technique, and interviews. In the collection of the qualitative and quantitative data from 23 experts through the Delphi surveys, it organized respectively the qualitative and quantitative data analysis. This study conducted the two main data analyses–Delphi results and interview data. Service innovation of AI and process innovation of IoT are chosen as a top-priority-innovation type. Marketing innovation of big data, as non-technological innovation, is selected as a top-priority innovation type. Through the interviews with 17 experts, for each of the pairs, all the experts said that the three technologies can have greater technological capabilities going beyond the existing capacities of relevant technologies. AI as hyper-intelligence can help to provide more customized or sophisticated converging offerings, the regulation of various non-standardized services and service provisions through the interaction between AI and customers or employees. The technological capacity of big data and the need of customer preferences can lead marketing innovation. IoT can create the new or improved process of the manufacturing, production, and supply chain areas through hyper-connectivity in terms of quality, quantity, speed, and coverage of information.

Differentiating artificial intelligence activity clusters in Australia

We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions by activity in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district (CBD) locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers and investors can use these results to learn about the current landscape of AI business and innovation activities in Australia, identify potential growth opportunities in AI capabilities and to guide future policy and business decisions.

P2.12-03 External Validation of a Novel CT-Based Prognostic Radiomic Signature in Patients with Metastatic NSCLC in SWOG S0819 Phase III Randomized Trial

There is an unmet need in elucidating effective correlative biomarkers for individualized selection of therapeutic regimens in patients with a diagnosis of advanced NSCLC. Recent innovations in artificial intelligence (AI) can enhance the role of CT imaging by taking advantage of previously unutilized information through the ability to identify imaging biomarkers through quantitative analysis that is objective, reproducible, and rapidly obtainable. Our group has previously leveraged this approach by applying AI in patients with NSCLC treated with nivolumab in CheckMate017, CheckMate026, and CheckMate063.

Automatic recognition of parasitic products in stool examination using object detection approach.

BackgroundObject detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low-end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4-Tiny model, for automatic recognition of intestinal parasitic products in stools.MethodsTo identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets.ResultsThe non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963.ConclusionThis study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.

Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study.

BackgroundArtificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care–related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated.ObjectiveThis study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety.MethodsA total of 26 participants completed 2 therapy sessions—an introductory (session 1) and a problem-solving (session 2)—with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants’ perspectives regarding Lumen use.ResultsParticipants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen’s on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions.ConclusionsOn the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services.

Artificial Intelligence Innovation and Privacy Law: The Moderating Role of Culture

With the advent of artificial intelligence (AI) centred around a logic of commercial surveillance and data capitalism, scholars are increasingly being called upon to consider the implications of privacy law. We respond by exploring the relationship between privacy law and AI innovation and how that relationship between privacy law and AI innovation varies across countries with different culture. Employing a unique dataset of AI patents, we perform a difference-in-differences analysis by exploiting the introduction of General Data Protection Regulation (GDPR). Our results show that compared with the GDPR-unaffected countries, the GDPR-affected countries exhibited a lower AI innovation rate after the GDPR introduction. Further, we find this negative effect is weaker in GDPR-affected countries that have higher levels of individualism, masculinity, and power distance, but stronger with higher levels of uncertainty avoidance. The main contribution of our study is introducing the role of privacy law and its contingencies vis à vis national culture that constitute drivers of AI innovation.

Public views on ethical issues in healthcare artificial intelligence: protocol for a scoping review

BackgroundIn recent years, innovations in artificial intelligence (AI) have led to the development of new healthcare AI (HCAI) technologies. Whilst some of these technologies show promise for improving the patient experience, ethicists have warned that AI can introduce and exacerbate harms and wrongs in healthcare. It is important that HCAI reflects the values that are important to people. However, involving patients and publics in research about AI ethics remains challenging due to relatively limited awareness of HCAI technologies. This scoping review aims to map how the existing literature on publics’ views on HCAI addresses key issues in AI ethics and governance.MethodsWe developed a search query to conduct a comprehensive search of PubMed, Scopus, Web of Science, CINAHL, and Academic Search Complete from January 2010 onwards. We will include primary research studies which document publics’ or patients’ views on machine learning HCAI technologies. A coding framework has been designed and will be used capture qualitative and quantitative data from the articles. Two reviewers will code a proportion of the included articles and any discrepancies will be discussed amongst the team, with changes made to the coding framework accordingly. Final results will be reported quantitatively and qualitatively, examining how each AI ethics issue has been addressed by the included studies.DiscussionConsulting publics and patients about the ethics of HCAI technologies and innovations can offer important insights to those seeking to implement HCAI ethically and legitimately. This review will explore how ethical issues are addressed in literature examining publics’ and patients’ views on HCAI, with the aim of determining the extent to which publics’ views on HCAI ethics have been addressed in existing research. This has the potential to support the development of implementation processes and regulation for HCAI that incorporates publics’ values and perspectives.

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

AbstractResearch SummaryResearchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development.Managerial SummaryText‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research.

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation.

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.

An Epistemological Analysis of the Social and Humanitarian Significance of Artificial Intelligence Innovations in Context of Artificial General Intelligence

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI achievements, especially regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues.

Legal concerns in health-related artificial intelligence: a scoping review protocol

BackgroundMedical innovations offer tremendous hope. Yet, similar innovations in governance (law, policy, ethics) are likely necessary if society is to realize medical innovations’ fruits and avoid their pitfalls. As innovations in artificial intelligence (AI) advance at a rapid pace, scholars across multiple disciplines are articulating concerns in health-related AI that likely require legal responses to ensure the requisite balance. These scholarly perspectives may provide critical insights into the most pressing challenges that will help shape and advance future regulatory reforms. Yet, to the best of our knowledge, there is no comprehensive summary of the literature examining legal concerns in relation to health-related AI. We thus aim to summarize and map the literature examining legal concerns in health-related AI using a scoping review approach.MethodsThe scoping review framework developed by (J Soc Res Methodol 8:19-32, 2005) and extended by (Implement Sci 5:69, 2010) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guided our protocol development. In close consultation with trained librarians, we will develop a highly sensitive search for MEDLINE® (OVID) and adapt it for multiple databases designed to comprehensively capture texts in law, medicine, nursing, pharmacy, other healthcare professions (e.g., dentistry, nutrition), public health, computer science, and engineering. English- and French-language records will be included if they examine health-related AI, describe or prioritize a legal concern in health-related AI or propose a solution thereto, and were published in 2012 or later. Eligibility assessment will be conducted independently and in duplicate at all review stages. Coded data will be analyzed along themes and stratified across discipline-specific literatures.DiscussionThis first-of-its-kind scoping review will summarize available literature examining, documenting, or prioritizing legal concerns in health-related AI to advance law and policy reform(s). The review may also reveal discipline-specific concerns, priorities, and proposed solutions to the concerns. It will thereby identify priority areas that should be the focus of future reforms and regulatory options available to stakeholders in reform processes.Trial registrationThis protocol was submitted to the Open Science Foundation registration database. See https://osf.io/zav7w.

Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum.

Artificial intelligence (AI) is on course to become a mainstay in the patient’s room, physician’s office, and the surgical suite. Current advancements in health care technology might put future physicians in an insufficiently equipped position to deal with the advancements and challenges brought about by AI and machine learning solutions. Physicians will be tasked regularly with clinical decision-making with the assistance of AI-driven predictions. Present-day physicians are not trained to incorporate the suggestions of such predictions on a regular basis nor are they knowledgeable in an ethical approach to incorporating AI in their practice and evolving standards of care. Medical schools do not currently incorporate AI in their curriculum due to several factors, including the lack of faculty expertise, the lack of evidence to support the growing desire by students to learn about AI, or the lack of Liaison Committee on Medical Education’s guidance on AI in medical education. Medical schools should incorporate AI in the curriculum as a longitudinal thread in current subjects. Current students should understand the breadth of AI tools, the framework of engineering and designing AI solutions to clinical issues, and the role of data in the development of AI innovations. Study cases in the curriculum should include an AI recommendation that may present critical decision-making challenges. Finally, the ethical implications of AI in medicine must be at the forefront of any comprehensive medical education.

Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities

Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.

Artificial Intelligence Innovation Related Factors Affecting Organizational Performance

Organisations paid more attentionto the innovations of artificial intelligence (AI)technology to improve the organizational performance. Hence, using AI-related innovations to support the organizationrequires understanding of factors affecting organizational performance. Thus, this paper presents the development of PLS-SEMmodelof AI-related innovations factors that affect the organisational performance. The study identified 21innovation AI-related factors that were clustered into four groups namely process innovation; management capabilities; personal expertise and organization structure.The model comprised of fourexogenous constructs of the innovationfactors and one endogenous construct of organisational performance. The data used to develop the model was derived from 384valid responses of a questionnaire survey amongst the employees of three government organizations in Dubai, which are Dubai Police, Dubai Electricity & Water Authority Dewa, and Emirates Integrated Telecommunications Company. The survey adopted simple random sampling technique in respondents’ selection. The model was developed in SmartPLS software and was evaluated at the measurement and structural components of the model. It was found that the modelhas achieved its goodness-of-fit, GoF criteria of0.596which indicates that the model has substantial validating power. The hypothesis testing results found that three out of four relationships are significant which are having t-value and p-value above the cut-off values. The significant relationships are organization structure, personal expertise and process innovation. However, the unsignificant relationship ismanagement capabilities affecting the organisational performance. This is due to the characteristics of the collected data which is not strong enough to establish significant relationship as what have been hypothesized.The findings are contributions to any parties that involved in the application of AI innovation to improve organisational performance.

Artificial intelligence and poverty alleviation: Emerging innovations and their implications for management education and sustainable development

Artificial Intelligence (AI) could help humanity achieve the ambitious goal of eradicating extreme poverty. The United Nations (UN) General Assembly, in its declaration Transforming Our World: The 2030 Agenda for Sustainable Development, boldly proclaimed the aspirations of the nations of the world to “end poverty in all its forms everywhere.” No Poverty is the first of the 17 UN Sustainable Development Goals (SDGs). However, humanity is not on track to accomplish this ambitious goal; the COVID-19 pandemic has increased joblessness and economic disruption pushing additional millions into extreme poverty and rolling back the gains in poverty alleviation over the past three decades. This paper briefly explores the theoretics of poverty, recent trends in management education, and emerging innovations in AI. It provides some examples of AI implementation in the agriculture sector and draws inferences from these diverse but coalescing transformations in AI, management education, and sustainable development.

Innovation of Artificial Intelligence and Digital Media in Environmental Art Design

With the current upsurge of innovation and entrepreneurship in China, this paper objectively analyzes the importance of designers’ professional quality and scientific awareness in response to the need for the integration and development of innovative skills and artistic spirit in environmental design. This paper emphasizes that the level of intelligent operation of science and technology should be improved in design, the rigor of intelligent utilization in creative expression should be improved, and the problem of evaluation criteria of artistic design value should be solved based on professional development. This paper gives the design scheme of intelligent monitoring module based on Lora technology and designs the hardware circuit of the intelligent monitoring module with MSP430F2272 as the core. It includes a microcontroller circuit, a Lora module interface circuit, an input signal detection module, a control output module, and the like. This paper focuses on the content, representation, and implementation of the associated configuration table, and compares four analog input signals with variable thresholds. The sensor detects 800Lux, and the error is 500Lux, which is higher than errmax, so it outputs -10 brightening every time to generate analog input logic. The 4 digital inputs are combined with the analog input logic to generate output control signals by associating operations.

Exploring Physician Perspectives on Using Real-world Care Data for the Development of Artificial Intelligence-Based Technologies in Health Care: Qualitative Study.

BackgroundDevelopment of artificial intelligence (AI)–based technologies in health care is proceeding rapidly. The sharing and release of real-world data are key practical issues surrounding the implementation of AI solutions into existing clinical practice. However, data derived from daily patient care are necessary for initial training, and continued data supply is needed for the ongoing training, validation, and improvement of AI-based solutions. Data may need to be shared across multiple institutions and settings for the widespread implementation and high-quality use of these solutions. To date, solutions have not been widely implemented in Germany to meet the challenge of providing a sufficient data volume for the development of AI-based technologies for research and third-party entities. The Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions (pAItient) project aims to meet this challenge by creating a large data pool that feeds on the donation of data derived from daily patient care. Prior to building this data pool, physician perspectives regarding data donation for AI-based solutions should be studied.ObjectiveThis study explores physician perspectives on providing and using real-world care data for the development of AI-based solutions in health care in Germany.MethodsAs a part of the requirements analysis preceding the pAItient project, this qualitative study explored physician perspectives and expectations regarding the use of data derived from daily patient care in AI-based solutions. Semistructured, guide-based, and problem-centered interviews were audiorecorded, deidentified, transcribed verbatim, and analyzed inductively in a thematically structured approach.ResultsInterviews (N=8) with a mean duration of 24 (SD 7.8) minutes were conducted with 6 general practitioners and 2 hospital-based physicians. The mean participant age was 54 (SD 14.1; range 30-74) years, with an average experience as a physician of 25 (SD 13.9; range 1-45) years. Self-rated affinity toward modern information technology varied from very high to low (5-point Likert scale: mean 3.75, SD 1.1). All participants reported they would support the development of AI-based solutions in research contexts by donating deidentified data derived from daily patient care if subsequent data use was made transparent to them and their patients and the benefits for patient care were clear. Contributing to care optimization and efficiency were cited as motivation for potential data donation. Concerns regarding workflow integration (time and effort), appropriate deidentification, and the involvement of third-party entities with economic interests were discussed. The donation of data in reference to psychosomatic treatment needs was viewed critically.ConclusionsThe interviewed physicians reported they would agree to use real-world care data to support the development of AI-based solutions with a clear benefit for daily patient care. Joint ventures with third-party entities were viewed critically and should focus on care optimization and patient benefits rather than financial interests.

Artificial intelligence in learning and development: a systematic literature review

PurposeThe presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach.Design/methodology/approachThe presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis.FindingsThe result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners.Originality/valueIn the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.

Investing in AI for social good: an analysis of European national strategies

Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of society through the lens of their national AI strategies. In particular, we aim to investigate how European countries are investing in AI and to what extent the stated plans contribute to the good of people and society as a whole. Our contribution consists of three parts: (i) a conceptualization of AI for social good highlighting the role of AI policy, in particular, the one put forward by the European Commission (EC); (ii) a qualitative analysis of 15 European national strategies mapping investment plans and suggesting their relation to the social good (iii) a reflection on the current status of investments in socially good AI and possible steps to move forward. Our study suggests that while European national strategies incorporate money allocations in the sphere of AI for social good (e.g. education), there is a broader variety of underestimated actions (e.g. multidisciplinary approach in STEM curricula and dialogue among stakeholders) that can boost the European commitment to sustainable and responsible AI innovation.