Intelligence In Society Research Articles

The effect of knowledge acquisition from social media on competitive intelligence process: the mediating role of absorptive capacity

PurposeWhile existing literature offers valuable insights into social media knowledge, absorptive capacity, and competitive intelligence, it does not fully address the integrated activities involved in generating valuable knowledge. This study aims to address this gap by investigating how high-tech companies leverage knowledge acquired from social media, mediated through absorptive capacity, to generate valuable competitive intelligence.Design/methodology/approachA total of 270 valid responses were collected from high-tech company managers through an online survey. PROCESS Model 4, employing 5,000 bootstrap samples and 95% confidence intervals, was used to assess the constructs of interest. Using the Knowledge-Based View, this approach tested the mediating role of companies' absorptive capacity in the association between knowledge acquisition from social media and competitive intelligence processes.FindingsThe results indicate that knowledge acquisition from social media, both directly and indirectly (through absorptive capacity), positively influences competitive intelligence processes, including collection and analysis.Practical implicationsAcquiring knowledge from social media does not necessarily lead to superior intelligence in companies. This study helps managers focus on enhancing companies' absorptive capacity for more effective competitive intelligence processes.Originality/valueThis study investigates the role of absorptive capacity in terms of the connection between acquiring knowledge from social media and competitive intelligence. The findings have significant theoretical and managerial implications, providing valuable insights that could help high-tech company managers improve intelligence generation in their companies.

Prospects of Computational Intelligence in Society: Human-Centric Solutions, Challenges, and Research Areas

The future of society could be shaped by artificial intelligence (AI), which is a part of soft computing that deals with probabilistic reasoning, neural networks, fuzzy logic, and evolutionary algorithms. This paper focuses on issues that are faced by soft computing as it moves towards human-centric solutions, outlines the challenges for soft computing, and provides possible research directions for development. Currently, there is an increasing emphasis on human-centered solutions in various fields, such as the healthcare industry, transportation sector, financial institutions, and education systems in the world today. Using soft computing techniques in this way comes with its own advantages, especially when managing unclearness or ambiguity or unsureness existing within humans' decision-making processes. Soft computing based on using neural networks may help to develop computer systems capable of faking human behavior and preferences as they evolve over time by using fuzzy logic and evolutionary algorithms. This human-centric approach helps improve the user experience while creating more symbiotic ties between technology and society, leading to examples such as personalized medicine treatment plans, among others. Soft computing’s future in society will be, to a large extent, determined by the provision of human-friendly AI while addressing the ethical, legal, and social challenges of its applications. Rights on AI represent humanity and shall be accountable. Collaborating with interdisciplinary scientists and carrying out frontier studies on developing human-friendly AI systems from ethical, legal, and social perspectives and welfare has the potential to have an influence on community and policy-maker decisions, facilitate a suitable climate for soft computing and AI in society, leverage it for empowering individuals, communities, and societies, and to improve the quality of human life while respecting human values and dignity. It also paves the way for society to be more inclusive and equitable.

Do Generative Artificial Intelligence Company Strategies of ‘Moving Fast and Breaking Things’ in Civil Society Cancel Their Social Licence to Operate? A Nurse’s Evaluation of Chatbot Impacts

A rapid expansion of the computer technology industry, particularly in the field of artificial intelligence, has ignited a global concern that warrants our immediate action. As nurses, our professional values frameworks compel us to protect public health and address national and global health issues. When industry activities adversely affect the social wellbeing of civil society and social institutions, it is important to evaluate them against their industry’s ‘social license to operate, which is a measure of public trust, credibility, and the legitimacy of their industrial and corporate citizenship status. The central question is, do computer technology companies continue to have a social license to operate in civil society? Nurses are encouraged to evaluate the computer technology industry’s recent ‘generative artificial intelligence’ chatbot activities against its tacit undertaking to be good corporate citizens in return for social acceptance of their operations and behaviour. An evidence-based overview of chatbot impacts on societies, environmental sustainability and human rights provide a basis for evaluation. Basic computer technology terminology and relevant concepts are explained. This article is a direct call to action for clinical nurses and those involved in research, education, management, and policy. We have a duty to critically assess the claims made by chatbot technology vendors in both practice and social contexts. If these vendors integrate chatbot technologies with existing machine learning used in nursing and healthcare technologies it could result in detrimental effects beyond user control. By influencing decisions on technology adoption, we can ensure the implementation of safeguards, protect patient safety and social well-being, and uphold the integrity of nursing values. A closing discussion of impacts of computer industry trust deficits on healthcare and research reflects the author’s concerns and conclusions about the central question. Readers may draw other conclusions and perhaps use the issues and evidence presented here to stimulate further investigations.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Hybrid Inverse Weed Optimization Algorithm with Math-Flame Optimization Algorithm

In this work, two Meta-Heuristic Algorithms were hybridized, the first is the Invers Weed optimization algorithm (IWO), which is a passing multiple algorithm, and the second is the Moth-flame Optimization Algorithm (MFO). Which depend in their behavior on the intelligence of the swarm and the intelligence of society, and they have unique characteristics that exceed the characteristics of the intelligence of other swarms because they are efficient in achieving the right balance between exploration and exploitation. So the new algorithm improves the initial population that is randomly generated, A process of hybridization was made between the IWO and MFO Algorithm to call The new hybrid algorithm (IWOMFO). The new hybrid algorithm was used for 16 high-scaling optimization functions with different community sizes and 250 repetitions. The Algorithm showed access to optimal solutions by achieving the value Minority () for most of these functions and the results of this algorithm are compared with the basic algorithms IWO, MFO

My career path at a medical artificial intelligence company, working as a physician outside of clinical practice

My career path at a medical artificial intelligence company, working as a physician outside of clinical practice

Benefits of artificial intelligence in companies

The impact generated by artificial intelligence (AI) on companies radically transforms current business dynamics. In this work, we sought to identify the benefits of the implementation of this revolutionary technology in companies. Its development and execution achieves notable improvements in operational efficiency, decision making, continuous innovation and service personalization, the latter being a crucial factor for customer satisfaction. To identify these benefits, different research documents were used, where it was established, that artificial intelligence is fundamental to the success of a company, because it not only generates benefits with the automation and optimization of routine tasks, but also achieves the most efficient use. of resources, generating a significant reduction in operating costs

Impact of Artificial Intelligence in Companies Marketing Strategies

In today's digitally driven marketplace, personalized marketing has emerged as a pivotal strategy for businesses aiming to engage customers on a deeper level and foster long-term relationships. With the advent of Artificial Intelligence (AI), personalized marketing has reached unprecedented levels of customization and effectiveness. This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction. The research explores various facets of personalized marketing, including data collection, analysis, and implementation of tailored campaigns. Through a comprehensive literature review, this thesis examines the theoretical underpinnings of personalized marketing and the evolution of AI technologies in this domain. Moreover, empirical studies and case analyses are conducted to illustrate real-world applications and outcomes of AI-driven personalized marketing strategies across diverse industries. Key themes investigated include the role of machine learning algorithms in deciphering consumer behavior patterns, the ethical considerations surrounding data privacy and consent, and the implications of AI-generated content on brand-consumer interactions.

Attitudes towards and expectations on the role of artificial intelligence in the classroom among digitally skilled Finnish K-12 mathematics teachers

The growing impact and importance of artificial intelligence in society has led to an increasing interest for the potential of artificial intelligence as an educational tool in schools to aid both students and teachers. In this study we investigate digitally skilled K-12 mathematics teachers’ (N=85) attitudes towards and expectations on the role of artificial intelligence in the classroom. The study was done by conducting and analyzing the results of a web-based survey among Swedish and Finnish speaking mathematics teachers using a mixed methods strategy. The Will, Skill and Tool framework was used for the analysis. The survey was done before the introduction of ChatGPT-3. The results indicate that the teachers’ attitudes toward AI tools in school are characterized by interest, openness, and awareness. Teachers have a balanced view on the possibilities and risks of AI use in school. However, the teachers also stress that there is a risk that AI tools will shift the focus from learning key mathematical skills towards learning and interaction with the AI tools themselves. The research concluded that the K-12 mathematics teachers surveyed have broad experience with digital tools and will likely become early adopters of AI tools in the classroom.

Features of using artificial intelligence in companies’marketing communications

The use of artificial intelligence (hereinafter referred to as Al) is one of the leading trends in marketing and advertising in recent years. With its help, it is possible to solve tasks of varying complexity: from generating ideas for promotion to creating advertising creatives. The research problem is that in order to work effectively, companies need to understand which neural networks can most optimally solve the tasks of marketing activities. To do this, it is necessary to define a list of such neural networks. The authors consider the use of Al in marketing communications and analyse neural networks that can be used in business. The study purpose is to form a relevant list of neural networks for the implementation of marketing activities of the company. The most popular neural networks, their functionality and features were studied. The domestic and foreign experience analysis of using neural networks in the marketing activities of companies is conducted. Recommendations for more efficient use of neural networks are proposed. Based on the conducted research, a relevant list of neural networks for the implementation of the company’s marketing activities was determined. The methodological basis of the article was formed of publications on the problems of using neural networks in business and marketing sphere. The works of domestic and foreign researchers were studied. The following theoretical research methods were used in the article: analysis, synthesis, problematisation. The content analysis was chosen as an empirical method.

UPOTREBA VEŠTAČKE INTELIGENCIJE U RAČUNOVODSTVU – ETIČKI ASPEKTI

Artificial intelligence (AI) represents a broad term for intelligent technologies capable of performing tasks that typically require human intelligence. In the accounting sector, it is already being applied in various applications, such as process automation, data analysis, and decision-making. It is anticipated that the use of artificial intelligence in this sector will continue to expand in the future, as technology progresses and becomes increasingly accessible. With the increased adoption of artificial intelligence in society, including the accounting sector, various ethical issues and challenges arise. These issues may relate to data privacy, fairness of algorithms, transparency in decision-making, responsibility for the consequences of artificial intelligence implementation, and similar concerns. These dilemmas are particularly relevant in the context of accounting, where accuracy, reliability, and adherence to rules play a crucial role. Two key ethical questions have been identified in relation to the application of artificial intelligence in accounting: integrity and responsibility. Considering these aspects is crucial for the development of ethical guidelines and practices that will ensure responsible and transparent use of artificial intelligence in accounting.

BUSINESS INTELLIGENCE IMPLEMENTATION ROADMAP FOR HOSPITALITY AND TOURISM INDUSTRY: EXPLORATY WORK

Hospitality and tourism are sectors that generate a large amount of data, from hotel bookings, flights and customer information to data on tourist destinations and tourist preferences. The use of business intelligence in the hospitality and tourism industry enables companies to intelligently analyse this data and make informed decisions to improve their services, optimise operational efficiency, and enhance the customer experience. This research addresses the adoption of business intelligence in hospitality and tourism companies and, in the first phase, aims to identify and characterise the strategic objectives underlying decision-making in hospitality and tourism companies. After reviewing the literature, it was found that there are some studies on the use of business intelligence in companies in the hospitality and tourism sectors, although there is no roadmap to serve as a reference for the implementation of a business intelligence system in companies in these sectors. The objective of this research is to present the methodology of a roadmap proposal for the implementation of business intelligence systems in companies in the hospitality and tourism sectors, which will allow them to improve their ability to analyse and evaluate the data and information available in the different systems and platforms.

Artificial Intelligence in Society

Artificial Intelligence in Society

Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study.

Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative). Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34-78.2), specificity of 34.5% (95% CI: 17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases. The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.

Review of Using Technologies of Artificial Intelligence in Companies

Artificial intelligence (AI) has become increasingly prevalent in business as companies adopt machine algorithms that can learn and improve over time. These AI-powered solutions are used in various business areas, including operations, analytics, product personalisation, marketing, sales, customer service, and human resource (HR). AI can help companies automate mundane tasks, make smarter decisions based on data and insights, and provide capabilities for smoother customer experiences, better customer service, and increased efficiency. AI has also allowed companies to enhance the quality of their digital services, optimise supply chain processes, and gain access to real-time insights and analytics. Companies can use AI to reduce lead times, generate new customer insights, improve customer service, and create meaningful customer experiences. This paper aims to address the gap in knowledge on incorporating AI into business strategy by conducting a critical literature review, synthesising current approaches and frameworks, highlighting potential benefits, challenges, and opportunities, and discussing future research directions.

THE ROLE OF "THINK TANKS" IN THE USA INFORMATION WARFARE SYSTEM

Aim. To investigate the activities of the U.S.’ “think tanks” in Russia in terms of the intelligence functions they perform to misinform the enemy.Methodology. By implementing historical sources analysis, content analysis and case studies, the author investigated the U.S.’ think tanks activities and the history of their participation in the informational and psychological warfare against Russia during the SMO in Ukraine in 2022; the intelligence product offered by “think tanks” was also examined in the study.Results. The study established that private intelligence companies operate under the legend of “think tanks” in USA. They have a significant informational and psychological influence both on a potential enemy (misinform the enemy, induce the enemy to act in a certain manner) and on the domestic and foreign policies of the United States itself.Research implications. The findings of the study can become the basis for the development of practical recommendations for the Russian authorities on building communication and business relations with the Western think tanks.

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.

Defensive Medicine in an Era of Ever-Expanding Technology

The practice of defensive medicine is set in a medical world of expanding exponentials, including artificial intelligence, nanotechnology, quantum computing, and robotics. The increases in knowledge in medicine are occurring at an exponential pace. In recent times, a successful healthcare company still could be run without being an artificial intelligence company. Those days are gone. We live in a post–information world in which applied knowledge arises from understanding, and, in fact, mastery, of context. One can apply medical knowledge well if one understands the context. Mastery of knowledge and context in medicine will have the potential to reduce defensive medicine.

Artificial Intelligence: The Significance of Tesla Bot

The rising humanoid robot industry has brought significant interest from the public and treasure seekers who see this industry as the future. Tesla, a leading motor manufacturer and artificial intelligence company, has also joined the competition of humanoid robots by introducing “Optimus.” The service robotics industry, which “Optimus” focuses on, is fast-growing, with approximately 119% growth over the last four years. Compared with its competitors, Tesla has a few advantages. First of all, it has abundant experience in the field as it has accumulated countless data on human behavior through the beta that its Tesla cars have provided over the years. In addition, Tesla has more capital than its competitors. As of September 10th, 2021, Tesla has a market capital of 939 billion dollars. However, Tesla is also known for its overconfidence in its products, as it usually exaggerates the delivery date and the capability of its products. Another obstacle that Tesla faces comes from the industry itself. Admittedly, it is not a mature industry without much experience that Tesla can learn from. As a result, like many of its competitors, Tesla has to develop all of its technologies in-house, leading to the high prices of humanoid robots on the market. However, suppose Tesla is able to develop a complete version of “Optimus” faster and better than its competitors. In that case, it is very likely for Tesla to become the leader in the humanoid robot industry.

Ethics of Artificial Intelligence in Society

ABSTRACT: Every day, artificial intelligence (AI) is becoming more prevalent as new technologies are presented to the public with the intent of integrating them into society. However, these systems are not perfect and are known to cause failures that impact a multitude of people. The purpose of this study is to explore how ethical guidelines are followed by AI when it is being designed and implemented in society. Three ethics theories, along with nine ethical principles of AI, and the Agent, Deed, Consequence (ADC) model were investigated to analyze failures involving AI. When a system fails to follow the models listed, a set of refined ethical principles are created. By analyzing the failures, an understanding of how similar incidents may be prevented was gained. Additionally, the importance of ethics being a part of AI programming was demonstrated, followed by recommendations for the future incorporation of ethics into AI. The term “failure” is specifically used throughout the paper because of the nature in which the events involving AI occur. The events are not necessarily “accidents” since the AI was intended to act in certain ways, but the events are also not “malfunctions” because the AI examples were not internally compromised. For these reasons, the much broader term “failure” is used. KEYWORDS: Ethics; Artificial Intelligence; Agent-Deed-Consequence (ADC) Model; Principles of Artificial Intelligence; Virtue Ethics; Deontology; Consequentialism; AI Systems