Investment In Artificial Intelligence Research Articles

Venture capital investments in artificial intelligence

AbstractArtificial intelligence (AI) technologies have significantly attracted the attention of institutional investors over the last decade. However, previous literature has not deeply explored the characteristics of venture capital (VC) investments in AI ventures. In this study, we explore whether and to what extent investments in AI ventures differ from those in similar non-AI ventures, and whether they are moderated by venture-level, country-level, and investor-level factors. We test our hypotheses on a sample of 5235 investments in 2689 AI ventures and 9215 investments in 4373 non-AI ventures belonging to the Industry 4.0 domain, observed from 2000 to 2019. We find that the amount invested in AI ventures is significantly lower than non-AI ones: this negative relationship is, however, moderated by a venture’s development stage, VC investor’s experience and the AI development level of the country in which the invested venture operates.

ENHANCING ENERGY EFFICIENCY WITH AI: A REVIEW OF MACHINE LEARNING MODELS IN ELECTRICITY DEMAND FORECASTING

This study presents a comprehensive review of the impact of artificial intelligence (AI) and machine learning (ML) on enhancing energy efficiency, particularly in the context of electricity demand forecasting. The review systematically explores the paradigm shift brought about by the emergence of AI in energy efficiency, focusing on the role of AI in electricity demand forecasting and the historical evolution of forecasting techniques. A critical analysis of various ML models is conducted, examining their theoretical underpinnings, selection criteria, and performance in diverse scenarios. Key insights reveal that ML models, especially those incorporating deep learning and big data analytics, significantly outperform traditional forecasting methods in accuracy and adaptability. These models are adept at handling complex, nonlinear relationships and large datasets, making them particularly effective in the dynamic and increasingly renewable-focused energy markets. The review also highlights the importance of selecting appropriate ML models based on criteria such as accuracy, adaptability to forecasting periods, data handling capabilities, and environmental impact considerations. The study further delves into the technological, economic, and environmental impacts of ML in energy efficiency. It underscores the potential of ML to drive innov4eations in energy forecasting, contributing to more sustainable and efficient energy management. However, challenges such as data privacy, cybersecurity, and the need for skilled professionals are identified as critical areas requiring attention. Strategic recommendations are provided for practitioners and policymakers, emphasizing the need for investment in AI and ML training, development of supportive regulatory frameworks, and fostering collaborations across sectors. The review concludes with a future outlook, suggesting directions for future research in AI and energy efficiency, particularly in developing robust and scalable ML models that can integrate with renewable energy sources and smart grid technologies. This study serves as a valuable resource for researchers, practitioners, and policymakers engaged in the field of energy efficiency and AI-driven forecasting.
 Keywords: Machine Learning, Energy Efficiency, Demand Forecasting, Artificial Intelligence.

Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes

This research article presents a novel approach to examining the utilization of artificial intelligence (AI) in sustainable supply chains (SSCs) by applying social network analysis (SNA). In contrast to previous literature that primarily relied on bibliometric analysis, this study investigates the intricate relationships among AI applications in SSCs using SNA. The primary objective is to promote collaborative research and offer valuable insights into influential authors, international co-authorship networks, collaborations among scientific institutions, and research categories identified through keyword analysis.Data were collected from the Web of Science (WOS) database to conduct this research, enabling a comprehensive co-authorship network analysis. This network revealed the collaboration patterns of 1400 authors and identified the most significant authors in this field using centrality measures and the TOPSIS technique.The study also explores institutional co-authorship networks, highlighting India's National Institute of Technology (NIT) as the most active institution. By considering geographical proximity and research specialization, the institutional network was divided into distinct clusters. A keyword analysis and thematic map also shed light on Sustainability and Supply Chain Management themes, revealing their connections with AI methodologies. Based on the thematic map, E-waste, Green supply chain management (GSCM), and Green innovation themes are potential future trends. The analysis of the country network showcased varying levels of development and scientific output, with China leading the field of AI applications in SSC research. Countries were further classified into clusters based on their investment in AI for sustainable supply chains, facilitating knowledge exchange and collaboration.The outcomes of this research contribute significantly to the field by utilizing social network analysis to explore co-authorship networks, leading to a comprehensive understanding of collaborative dynamics and knowledge dissemination. The insights gained from this study can inform decision-making, investments, and strategic collaborations in the domain of AI applications for sustainable supply chains, ultimately driving progress toward sustainability objectives.

Artificial Intelligence (AI)-Powered Virtual Assistants and their Effect on Human Productivity and Laziness: Study on Students of Delhi-NCR (India) & Fujairah (UAE)

This study investigates the impact of artificial intelligence (AI) on decision-making, laziness, and privacy concerns among university students of Delhi NCR (India) and Fujairah (UAE). As AI technologies are increasingly adopted in various sectors, including education, to tackle contemporary challenges, there is a growing investment in AI, projected to reach USD 253.82 million between 2021 and 2025. However, while researchers and institutions worldwide praise the positive role of AI, this study sheds light on the concerns associated with its implementation. This study utilizes a qualitative methodology employing PLS-Smart for data analysis. The primary data was collected from 315 students representing various universities in Delhi NCR (India) and Fujairah (UAE). The sample was drawn using purposive sampling techniques from the population. The findings of the data analysis demonstrate that artificial intelligence (AI) has a significant impact on human decision-making, laziness, and security and privacy concerns. The results indicate that 68.9% of human laziness, 68.6% of personal privacy and security issues, and 27.7% of the loss of decision-making can be attributed to the influence of AI in Delhi and Fujairah. Notably, human laziness emerges as the most affected area due to artificial intelligence..

Do we really need a new theory to understand over-parameterization?

This century saw an unprecedented increase of public and private investments in Artificial Intelligence (AI) and especially in (Deep) Machine Learning (ML). This led to breakthroughs in their practical ability to solve complex real-world problems impacting research and society at large. Instead, our ability to understand the fundamental mechanism behind these breakthroughs has slowed down because of their increased complexity, while in the past breakthroughs often emerged from foundational research. This questioned researchers about the necessity for a new theoretical framework able to help researchers catch up on this lag. One of the still not well understood mechanisms is the so-called over-parametrization, namely the ability of certain models to increase their generalization performance (reduce test error) when the number of parameters is above the interpolating threshold (zero training error). In this paper we will show that this phenomenon can be better understood using both known theories (surveying them in the process) and empirical evidences for both shallow and deep learning algorithms.

Tracking Online Interest in Artificial Intelligence in Ophthalmology Using Google Trends

ABSTRACT Purpose To examine trends in internet search queries related to artificial intelligence (AI) in ophthalmology and determine the correlation between online interest in AI, capital investment in AI, and peer-reviewed indexed publications regarding AI and ophthalmology. Methods Online search trends for “AI retina”, “AI eye”, and “AI healthcare” were obtained via Google Trends from 2016 to 2022 on a relative interest scale in 1-week intervals. Global venture financing of AI- and machine learning (ML)-focused companies in healthcare was tracked from 2010 to 2019 from the consulting company, Klynveld Peat Marwick Goerdeler (KPMG), and the technology market intelligence company, CB Insights. Citation count from pubmed.gov was determined using the search query “artificial intelligence retina” from 2012 to 2021. Results An increasingly linear growth in online search trends for “AI retina”, “AI eye”, and “AI healthcare” keyword searches was observed between 2016 and 2022. Global venture financing of AI and ML companies in healthcare also increased exponentially over the same time frame. There was an exponential increase in citations with nearly a 10-fold increase as reported by PubMed from 2015 onwards for the “artificial intelligence retina” search query. There was a significant and positive correlation between online search trends and investment trends (correlation coefficients of 0.98–0.99 and p-values <0.05) and between online search trends and citation count trends (correlation coefficients of 0.98–0.99 and p-values <0.05). Conclusions These results demonstrate that the applications of AI and ML in ophthalmology are increasingly being investigated, financed, and formally researched, suggesting a prominent role for AI-derived tools in ophthalmology clinical practice in the near future.

Digital Technology Innovation: Mythical Claims about Regulatory Efficacy

This paper examines myths envisaging a “balancing” of commercial, state and citizen interests through a market-led adjustment process underpinned by regulatory interventions in digital technology markets. It considers how these myths operate to downplay or conceal a persistent commitment to digital innovation that privileges the interests of technology companies and states. The key assumptions of the prevailing imaginary of technology innovation—emphasising investment in artificial intelligence and commercial datafication with harms mitigated by regulatory “guardrails”—are contrasted with an alternative imaginary that pays attention to uneven power relations and their consequences for the protection of citizens’ fundamental rights. Drawing upon Gramsci’s insights into the role of myth, the paper emphasises the need to examine the implementation of regulation and its outcomes if we are to understand how myth operates by concealing outcomes that leave the prevailing imaginary robustly intact. In arguing that myth suppresses possibilities for resistance to the capitalist exploitation of digital technology innovation, the conclusion discusses why a reframing of digital technology innovation processes and their regulation is essential if digital systems are to operate in ways that are consistent with human autonomy, dignity and democracy.

AI-tocracy

Abstract Recent scholarship has suggested that artificial intelligence (AI) technology and autocratic regimes may be mutually reinforcing. We test for a mutually reinforcing relationship in the context of facial-recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China since the early 2010s. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial-recognition AI as a new technology of political control, and increased AI procurement indeed suppresses subsequent unrest. We show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets and are more likely to export their products; noncontracted AI firms do not experience detectable negative spillovers. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.

ESG quality management: Тhe role of digital business transformation

The article explores the prospects for the most complete elucidation of the potential of Russian business products for improving their quality through the ESG quality management in the context of digital business transformation based on artificial intelligence and big data. Econometric modeling of the dependence of the quality management effectiveness in business is carried out by using the method of regression analysis. Two alternative models are constructed and compared. The first model is compiled in isolation from the effectiveness of ESG management. The second model is compiled together with the effectiveness of ESG management and the active use of artificial intelligence and big data in business. The prospects for increasing the effectiveness of quality management in Russian business along with maximizing the effectiveness of ESG management are evaluated based on the best international experience and the obtained regression models. Comparison of the results in the alternative models allowed the authors to justify the advantages of ESG quality management based on artificial intelligence and big data. The results include the development of a forward-thinking approach to ESG quality management in the context of digital business transformation. This approach provides a number of advantages for ESG quality management including firstly, broader opportunities for obtaining state (tax, subsidiary) support for the introduction of artificial intelligence and big data the flexibility to raise funding through a combination of sustainable investments in ESG and investments in artificial intelligence and big data. Secondly, more stable consumer preferences, more sustainable competitive advantages, and higher and more permanent loyalty. Thirdly, reliance on dynamically developing and increasingly accessible technologies for intellectual support in making managerial decisions based on artificial intelligence and big data (technologies are being introduced into the activities of the business itself).

The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review.

Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93-0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56-236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.

ФІНАНСУВАННЯ ТА ІНВЕСТУВАННЯ РОЗВИТКУ ТЕХНОЛОГІЙ СТВОРЕННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ В УКРАЇНІ

Today, the race between the top countries in the field of artificial intelligence continues in the world. The leaders, of course, are the USA and China. Everyone else is catching up. Among those who are catching up, including the European Union. The EU recently presented a strategy for the development of artificial intelligence (AI). The EU White Paper on Artificial Intelligence is a European approach to excellence and trust. The purpose of the article is to determine the main points of the strategy: artificial intelligence is one of the main factors in achieving the goals of sustainable development; creation of a European space, a single data market, to unlock unused data and allow its free movement within the EU and across sectors; creation of European data pools, allowing to create trust within the EU. The subject of research is modern trends in the development of artificial intelligence, changes in financial and economic systems. The research hypothesis is the influence of artificial intelligence on the trends of socio-economic development at different levels. Methods used in the research: comparison, comparison, systematic and analytical approaches, generalization and classification, dialectical and formal logic. Presenting main material. The EU should significantly increase its investments in AI — focusing on public-private partnerships will allow to mobilize private and public investments; launch the European Investment Fund in the amount of 100 million euros in 2020 to finance startups; unification of various competence and skills development centers to increase competitiveness; adopt a program for the development of artificial intelligence to carry out public procurement of AI systems. The originality and practical significance of the obtained results is a call for the creation of a strong European regulatory framework for the development of AI-based products and services with a high degree of trust; special regime for AI with a high level of risk if its technologies are applied in the field of health care, transport, energy, the public sector and for the use of AI that creates legal or significant consequences for the rights of individuals or legal entities, as well as technologies that can have a risk of injury, death or significant material/non-material damage. Conclusions. Society must actively and adequately respond to modern world challenges such as "Industry 4.0", "Society 5.0", sustainable and safe development ("Goals of the SR" - 17 solutions proposed by the UN), promptly respond to military and terrorist threats, cyber security, systemic crises and so on.

Five Big Ideas About AI

Massive investments in artificial intelligence (AI) have sparked a renewed debate over its impact on how we live, learn, and work. The last few years have also seen a burst of critical sociology about AI, pushing the conversation toward a deeper understanding of structural and intersectional inequalities. Here, we offer five big ideas that highlight what is distinctive about the emerging sociology of AI.

Quality 4.0 – an evolution of Six Sigma DMAIC

Purpose Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application. Design/methodology/approach The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR2) – an ad hoc seven-step problem-solving approach. Findings The IADLPR2 approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology. Practical implications From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI. Originality/value An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR2. Each of the steps is used to solve a real General Motors’ case study.

Collaborative Social Media Marketing in Small Scale Business Using Artificial Intelligence

Technological progress causes structural changes in company strategies and business models, while effectively meeting customer and improving the cognitive ability of organizations. Through this advanced technology firm has to create a knowledge-based environment, which acts as a significant source for competency in the industry. In business environment, Artificial Intelligence (AI) has emerged as advanced technology to promote marketing practices through social media. However, adoption of artificial intelligence is relatively difficult to small business units rather than large firms. Small businesses need to focus on Artificial Intelligence-based social media marketing and investments in AI. Previous research has focused on implementing artificial intelligence in marketing, but very few studies focused on the AI-based social media marketing for small businesses that gives scope for present study. Therefore, present paper emphasized on AI-based social media marketing, challenges, and benefits for small businesses and explores the possibility of implementing AI.

Artificial intelligence in the context of the transhumanism concept: an ethical approach

The author reveals the features of artificial intelligence, reflecting on the problem of machine achieving an ability to act and think within the framework of ethics. This is all the more important since investments in artificial intelligence (AI) systems are constantly growing, AI is becoming a strategically important area for all developed countries, and the impact of AI systems on society is rapidly increasing - if two decades ago, the field of machine ethics seemed interesting only to a small number of scientists as a field for theoretical research, today questions about methods and approaches to computability and algorithmization of moral judgments are becoming an essential applied area of knowledge.

The Impact of Artificial Intelligence on Agricultural Labour in Europe

Artificial Intelligence (AI) is set to redefine how farming occurs. Throughout history examples of technological advances have shown that less labour has been required on a farm while at the same time increasing the output of food production. However, an interesting observation is that as technology has improved the farming process and replaced workers, it has opened a new avenue known as diversification. This paper focuses specifically on the impact that AI will have on the future of farming in the European sector. The literature brings to light common trends that technological innovations have always decreased the number of workers required in the farming process while at the same time maximising efficiency. AI will also follow the same trend, however, instead of eliminating workers in the farming process soon, the present observations show that farmers will still require workers to work alongside AI. The reason for increased investments in AI is due to research data showing a decline in population growth in Europe and the struggling profitability of farmers. Thereby analysts believe that a labour shortage will occur, and industries will struggle to fill those skills requirements. A qualitative summary was done on artificial intelligence technologies’ development impact on the labour of the agricultural sector of Europe.

A Human in the Loop: AI won't Surpass Human Intelligence Anytime Soon

We are well into the third wave of major investment in artificial intelligence. So it's a fine time to take a historical perspective on the current success of AI. In the 1960s, the early AI researchers often breathlessly predicted that human-level intelligent machines were only 10 years away. That form of AI was based on logical reasoning with symbols, and was carried out with what today seem like ludicrously slow digital computers. Those same researchers considered and rejected neural networks. • In the 1980s, AI's second age was based on two technologies: rule-based expert systems—a more heuristic form of symbol-based logical reasoning—and a resurgence in neural networks triggered by the emergence of new training algorithms. Again, there were breathless predictions about the end of human dominance in intelligence.

Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics

PurposeThis paper aims to analyze if and to what extent mechanical artificial intelligence (AI)-embedded in hotel service robots-influences customers’ evaluation of AI-enabled hotel service interactions. This study deploys online reviews (ORs) analytics to understand if the presence of mechanical AI-related text in ORs influences customers’ OR valence across 19 leading international hotels that have integrated mechanical AI – in the guise of service robots – into their operations.Design/methodology/approachFirst, the authors identified the 19 leading hotels across three continents that have pioneered the adoption of service robots. Second, by deploying big data techniques, the authors gathered the entire population of ORs hosted on TripAdvisor (almost 50,000 ORs) and generated OR analytics. Subsequently, the authors used ordered logistic regressions analyses to understand if and to what extent AI-enabled hospitality service interactions are evaluated by service customers.FindingsThe presence of mechanical AI-related text (text related to service robots) in ORs influences positively electronic word-of-mouth (e-WOM) valence. Hotel guests writing ORs explicitly mentioning their interactions with the service robots are more prone to associate high online ratings to their ORs. The presence of the robot’s proper name (e.g., Alina, Wally) in the OR moderates positively the positive effect of mechanical AI-related text on ORs ratings.Research limitations/implicationsHospitality practitioners should evaluate the possibility to introduce service robots into their operations and develop tailored strategies to name their robots (such as using human-like and short names). Moreover, hotel managers should communicate more explicitly their initiatives and investments in AI, monitor AI-related e-WOM and invest in educating their non-tech-savvy customers to understand and appreciate AI technology. Platform developers might create a robotic tag to be attached to ORs mentioning service robots to signal the presence of this specific element and might design and develop an additional service attribute that might be tentatively named “service robots.”Originality/valueThe current study represents the first attempt to understand if and to what extent mechanical AI in the guise of hotel service robots influences customers’ evaluation of AI-enabled hospitality service interactions.

The Implications of Diverse Human Moral Foundations for the Ethicality of Artificial Intelligence

Organizations are making massive investments in artificial intelligence (AI), and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. But realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, developing and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave “ethically”. But “behaving ethically” means different things to different people. How should organizations using AI—and the AI itself—handle ethical dilemmas where humans cannot agree on the right course of action? Though a variety of ethical frameworks have been suggested, these approaches do not adequately answer how people make ethical evaluations of AI systems or how to incorporate the fundamental disagreements that people have regarding what is and is not ethical behavior. Drawing on moral foundations theory, we theorize that a person will perceive an organization’s use of AI and AI decisions as ethical to the extent that those decisions resonate with the person’s moral foundations. Since people hold diverse moral foundations, this highlights the crucial need to address individual moral differences in the development, application, and evaluation of AI systems. Our approach addresses several unresolved issues existing in the current state of the ethical AI literature. It suggests potential solutions to handling conflict in moral judgment and highlights future directions to augment ethical AI frameworks by incorporating individual differences in moral judgment.

Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care.

Digitalization of healthcare delivery is rapidly fostering development of precision medicine. Multiple digital technologies, known as telehealth or eHealth tools, are guiding individualized diagnosis and treatment for patients, and can contribute significantly to the objectives of precision medicine. From a basis of “one-size-fits-all” healthcare, precision medicine provides a paradigm shift to deliver a more nuanced and personalized approach. Genomic medicine utilizing new technologies can provide precision analysis of causative mutations, with personalized understanding of mechanisms and effective therapy. Education is fundamental to the telehealth process, with artificial intelligence (AI) enhancing learning for healthcare professionals and empowering patients to contribute to their care. The Gulf Cooperation Council (GCC) region is rapidly implementing telehealth strategies at all levels and a workshop was convened to discuss aspirations of precision medicine in the context of pediatric endocrinology, including diabetes and growth disorders, with this paper based on those discussions. GCC regional investment in AI, bioinformatics and genomic medicine, is rapidly providing healthcare benefits. However, embracing precision medicine is presenting some major new design, installation and skills challenges. Genomic medicine is enabling precision and personalization of diagnosis and therapy of endocrine conditions. Digital education and communication tools in the field of endocrinology include chatbots, interactive robots and augmented reality. Obesity and diabetes are a major challenge in the GCC region and eHealth tools are increasingly being used for management of care. With regard to growth failure, digital technologies for growth hormone (GH) administration are being shown to enhance adherence and response outcomes. While technical innovations become more affordable with increasing adoption, we should be aware of sustainability, design and implementation costs, training of HCPs and prediction of overall healthcare benefits, which are essential for precision medicine to develop and for its objectives to be achieved.