Era Of Artificial Intelligence Research Articles

Fast Exploring Literature by Language Machine Learning for Perovskite Solar Cell Materials Design

Making computers automatically extract latent scientific knowledge from literature is highly desired for future materials and chemical research in the artificial intelligence era. Herein, the natural language processing (NLP)‐based machine learning technique to build language models and automatically extract hidden information regarding perovskite solar cell (PSC) materials from 29 060 publications is employed. The concept that there are light‐absorbing materials, electron‐transporting materials, and hole‐transporting materials in PSCs is successfully learned by the NLP‐based machine learning model without a time‐consuming human expert training process. The NLP model highlights a hole‐transporting material that receives insufficient attention in the literature, which is then elaborated via density functional theory calculations to provide an atomistic view of the perovskite/hole‐transporting layer heterostructures and their optoelectronic properties. Finally, the above results are confirmed by device experiments. The present study demonstrates the viability of NLP as a universal machine learning tool to extract useful information from existing publications.

Medicine in the 21st Century: How Not to Lose the Art of Healing?

In our time of advanced technologies, the Internet, and the enormous acceleration of all areas of humanitarian activity, medical scientists make outstanding discoveries and innovations that revolutionize diagnostic approaches and treatment options, improve patient care, and enrich the way to a healthier world. Is there still a place for the art of medicine in the era of innovations and artificial intelligence? Although modern technology in the field of medicine with computerization of every aspect (including electronic medical records, laboratory data entry, images, patients' monitoring systems, etc.), young physicians, while embracing that innovation, should be trained in the development of clinical thinking and the establishment of personalized connections to the patients. Young physicians should be taught to adopt the ability to see, hear, and feel a patient's problems, formulate optimal management, and be empathetic to the patient's emotions and suffering. Communication skills are a key to successful medical practice and a part of "the Introduction to Clinic" course in almost all medical schools, but a substantial lack of time in clinical settings creates new challenges for communication between medical students, clinical teachers, and patients in medical schools. In many areas around the world, including Ukraine, this problem has been aggravated by wars, epidemics, pandemics, and natural disasters. At the same time, developing clinical observational skills remains an integral part of medical education and the basis of medical art. The art of a careful physical exam should not be lost behind a computer screen. Dnipro State Medical University has traditions of integrating centuries-old world historical experiences of teaching the art of healing into the philosophical concept of forming the critical clinical thinking of medical students. Our medical school has incorporated "diagnosing the canvas" into its curriculum. A thematic gallery of diagnostic symptoms and signs from great artists of all times and peoples has been created, expanding the young physicians' educational horizons in clinical thinking, empathy, and mercy.

Hepatocellular carcinoma surveillance after HBsAg seroclearance

Hepatitis B surface antigen (HBsAg) seroclearance is considered the functional cure and the optimal treatment endpoint for chronic hepatitis B (CHB). Patients with CHB who cleared HBsAg generally have a favorable clinical course with minimal risk of developing hepatocellular carcinoma (HCC) or cirrhotic complications. Nevertheless, a minority of patients still develop HCC despite HBsAg seroclearance. While patients with liver cirrhosis are still recommended for HCC surveillance, whether other non-cirrhotic patients who achieved HBsAg seroclearance should remain on HCC surveillance remains unclear. This review provides an overview of the incidence of HBsAg seroclearance, the factors associated with the occurrence of HBsAg seroclearance, the durability of HBsAg seroclearance, the risk of developing HCC after HBsAg seroclearance, the risk factors associated with HCC development after HBsAg seroclearance, the role of HCC risk scores, and the implications on HCC surveillance. Existing HCC risk scores have a reasonably good performance in patients after HBsAg seroclearance. In the era of artificial intelligence, future HCC risk prediction models based on artificial intelligence and longitudinal clinical data may further improve the prediction accuracy to establish a foundation of a risk score-based HCC surveillance strategy. As different novel hepatitis B virus (HBV) antiviral agents aiming at HBsAg seroclearance are under active development, new knowledge is anticipated on the natural history and HCC risk prediction of patients treated with new HBV drugs.

Problems of bias and discrimination of human capital in artificial intelligence systems

The article is devoted to the study of the genesis of understanding the causes of bias and discrimination of human capital from traditional forms to the era of digitalisation and artificial intelligence (hereinafter referred to as AI). In this article, the authors continue their case studies in the field of studying the ethics of AI, advantages and risks of its widespread distribution and use. The main purpose of writing this work is to study the question of how the use of AI and algorithmic solutions aggravates the problem of bias and discrimination of human capital which is inevitably associated with the emergence of AI. The authors of the article present a brief overview of the retrospective, discover new modern forms of discrimination generated by the action of AI and subject them to open discussion, offering their vision of solving issues on neutralisation the risks caused by the use of AI technologies in relation to certain groups of workers. The authors consider in the article the manifestations of bias and discrimination in society in general and in the field of human resource management in particular, identify possible threats of discrimination as a result of the spread of AI and the consequences of these threats.

Impact of next-generation Artificial General Intelligence (AGI) on international relations

The new generation of artificial intelligence technologies occurrence represented by ChatGPT is the beginning of the Artificial General Intelligence (AGI) era. In contrast to the impact of weak AI technologies on international relations discussed in the previous era, AGI is no longer just a tool in the traditional sense. The human-computer integration facilitated by AGI technology will rapidly and deeply penetrate into all areas of social policy and economy. It also may change the mode of social production and economic development, influence international relations and geopolitics by transforming the comprehensive state capacity, and simultaneously force new challenges in global governance. The acknowledgement of a new generation of AGI technological qualitative changes’ impact on future changes in international relations is no longer the fantasy; the underlying AGI changes in labour productivity will certainly lead to a disruptive reconfiguration of future international relations.

Emerging opportunities of using large language models for translation between drug molecules and indications

A drug molecule is a substance that changes an organism’s mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI.

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.

Augmenting CNS Tumor Surgery: The Era of Artificial Intelligence

Abstract not available Bang. J Neurosurgery 2023; 12(2): 1

Breast cancer in the era of generative artificial intelligence: Assistant tools for clinical doctors based on ChatGPT.

Breast cancer in the era of generative artificial intelligence: Assistant tools for clinical doctors based on ChatGPT.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception. This article is protected by copyright. All rights reserved.

Analytical study of hashtag # at social media platforms in the era of artificial intelligence

The increasing influence of social networking sites, today instead of the traditional use of communication technology, changes are being seen in certain signs and symbols in the language every day. Among all these symbols, hashtag is at the forefront as a symbol of artificial intelligence. hashtags (#) have become a new way to express your views in an organized manner in the media. The ideas of the Capitol Hill movement in America and the farmers' movement in Delhi were spread through hashtags. hashtag (#) Control is not the answer hashtag (#) Self-control is the answer. This is based on the trend of hastag and its usage at various platforms.

COVID-19 in the Era of Artificial Intelligence: Existing Technologies and A Strategic Model for Mitigating Future Pandemics

COVID-19 in the Era of Artificial Intelligence: Existing Technologies and A Strategic Model for Mitigating Future Pandemics

Foundation Models, Generative AI, and Large Language Models

We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patient education, drafting responses to patient questions and emails, medical record summarization, and medical research facilitation, there are concerns about the tools' readiness for use within the healthcare domain and acceptance by the current workforce. The goal of this article is to provide nurses with an understanding of the currently available foundation models and artificial intelligence tools, enabling them to evaluate the need for such tools and assess how they can impact current clinical practice. This will help nurses efficiently assess, implement, and evaluate these tools to ensure these technologies are ethically and effectively integrated into healthcare systems, while also rigorously monitoring their performance and impact on patient care.

A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology

A Mixed Methods Pilot Study to Evaluate User Engagement with MedMicroMaps: A Novel Interactive E-learning Tool for Medical Microbiology

생성형 인공지능과 교육에 관한 언론보도 토픽 분석

Objectives The purpose of this study is to analyze topics of media reports regarding generative artificial intellignece and education. Methods For this purpose, topic model, one of the text mining techniques, was used. First, we collected newspaper article data on ‘Generative Artificial Intelligence and Education’ through the Big Kinds service and then performed pre-processing and morphological analysis. Based on this, 30 keywords were analyzed. By measuring the number of topics and deriving the top 10 words for each topic, 13 topics were presented and explained. Results The 13 topics presented through the research results are as follows. Researchers focus on education that fosters artificial intelligence leadership, Gyeonggi GPT and fair art education, digital finance professional training, problems and responses to digital democracy, reliability and ethical use of artificial intelligence, artificial intelligence used in creation in various fields, training programs between Gyeonggi-do and State University of New York, regional cooperation model for artificial intelligence convergence education, artificial intelligence edutech support project and core talent training, Metaverse Lab support project, strengthening employee artificial intelligence capabilities for use in local government administrative work, Busan Metropolitan Office of Education's digital program attracting attention from overseas and changes in cultural arts education methods in the era of artificial intelligence were presented. Conclusions Based on the topics presented in this study, we proposed ways to apply and activate generative artificial intelligence in education. The five measures are strengthening teacher capacity for generative artificial intelligence, establishing guidelines for the development and use of artificial intelligence, learner capacity to use generative artificial intelligence correctly, developing and disseminating generative artificial intelligence teaching cases, and based on spontaneity. This is support from the Artificial Intelligence Teacher Research Group.

Navigating merits and limits on the current perspectives and ethical challenges in the utilization of artificial intelligence in psychiatry – An exploratory mixed methods study

BackgroundThe integration of Artificial Intelligence (AI) in psychiatry presents opportunities for enhancing patient care but raises significant ethical concerns and challenges in clinical application. Addressing these challenges necessitates an informed and ethically aware psychiatric workforce capable of integrating AI into practice responsibly. MethodsA mixed-methods study was conducted to assess the outcomes of the “CONNECT with AI” - (Collaborative Opportunity to Navigate and Negotiate Ethical Challenges and Trials with Artificial Intelligence) workshop, aimed at exploring AI's ethical implications and applications in psychiatry. This workshop featured presentations, discussions, and scenario analyses focusing on AI's role in mental health care. Pre- and post-workshop questionnaires and focus group discussions evaluated participants' perspectives, and ethical understanding regarding AI in psychiatry. ResultsParticipants exhibited a cautious optimism towards AI, recognizing its potential to augment mental health care while expressing concerns over ethical usage, patient-doctor relationships, and AI's practical application in patient care. The workshop significantly improved participants' ethical understanding, highlighting a substantial knowledge gap and the need for further education in AI among psychiatrists. ConclusionThe study underscores the necessity of continuous education and ethical guideline development for psychiatrists in the era of AI, emphasizing collaborative efforts in AI system design to ensure they meet clinical needs ethically and effectively. Future initiatives should aim to broaden psychiatrists' exposure to AI, fostering a deeper understanding and integration of AI technologies in psychiatric practice.

Hospitality labor leakage and dynamic turnover behaviors in the age of artificial intelligence and robotics

Purpose This study aims to examine the impact of artificial intelligence (AI) adoption on job insecurity and its subsequent effect on turnover intentions within the hotel industry. It investigated how AI-induced job insecurity affects the likelihood of employees considering leaving their current hotel jobs for other hotels or for opportunities outside the hotel sector, mediated by feelings of job stress and insecurity. Design/methodology/approach Quantitative data analysis used 259 responses from frontline hotel employees. Confirmatory factor analysis was used to explore the factor structure and assess model fit indices. Structural equation modeling was then applied to test the hypotheses. Findings Findings reveal that AI awareness has a positive impact on job stress and insecurity. Moreover, job insecurity is found to positively affect turnover intentions, with a notably stronger effect observed for turnover intentions toward non-hotel companies. Additionally, the influence of social capital as a moderator on the relationship between job insecurity and turnover intention varies depending on the specific dimensions of turnover intention. Research limitations/implications This study contributes to enhancing both theoretical frameworks and empirical insights into turnover dynamics within the hotel sector. However, future research should take into account employees’ positions, roles, organizations and career levels by examining these factors in relation to technology awareness, job stress, job insecurity and turnover intention. Originality/value This study initially focuses on the phenomenon of dynamic turnover issues within the hospitality sector, offering empirical and practical perspectives on effectively integrating new technologies and managing human resources amidst the automation and AI era.

Perceptions of 21st-century digital skills and agency among design sprint participants in Laurea UAS, Finland

This explorative study investigated students’ (N=16) perceptions before and after the study unit Digital Analytics and Consumer Insights. The studies were conducted as an intensive hybrid five-day design sprint, a variant of project- and problem-based learning. An online questionnaire with a 5-point Likert scale was used for data collection. The findings indicate that the intervention improved perceptions of most studied digital “hard skills” (8/11 claims). Out of twelve 21st-century “soft skills” claims, perceptions were high initially and improved significantly for critical thinking and systematic problem-solving claims during the design sprint. The agency scores showed a slight improvement but no significant difference. Face-to-face groups would be willing to recommend the sprint method more for peers than online groups.  In the era of global turbulence and artificial intelligence, in addition to hard skills, soft skills like communication, teamwork, problem-solving and project management are in demand by employers. According to LinkedIn data in 2/2024, adaptability is the most demanded skill. In addition to traditional subjects, the pedagogical methods in higher education should better support the development of 21st-century skills.

De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods

AbstractIncreasingly, automatic face recognition algorithms have become necessary with the development and extensive use of face recognition technology, particularly in the era of machine learning and artificial intelligence. However, the presence of unconstrained environmental conditions degrades the quality of acquired face images and may deteriorate the performance of many classical face recognition algorithms. Due to this backdrop, many researchers have given considerable attention to image restoration and enhancement mechanisms, but with minimal focus on occlusion-related and multiple-constrained problems. Although occlusion robust face recognition modules, via sparse representation have been explored, they require a large number of features to achieve correct computations and to maximize robustness to occlusions. Therefore, such an approach may become deficient in the presence of random occlusions of relatively moderate magnitude. This study assesses the robustness of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transformation for preprocessing and city block distance for classification (DWT-PCA/SVD-L1) face recognition module to image degradations due to random occlusions of varying magnitudes (10% and 20%) in test images acquired with varying expressions. Numerical evaluation of the performance of the DWT-PCA/SVD-L1 face recognition module showed that the use of the de-occluded faces for recognition enhanced significantly the performance of the study recognition module at each level (10% and 20%) of occlusion. The algorithm attained the highest recognition rate of 85.94% and 78.65% at 10% and 20% occlusions respectively, when the MICE de-occluded face images were used for recognition. With the exception of Entropy where MICE de-occluded face images attained the highest average value, the MICE and RegEM result in images of similar quality as measured by their Absolute mean brightness error (AMBE) and peak signal to noise ratio (PSNR). The study therefore recommends MICE as a suitable imputation mechanism for de-occlusion of face images acquired under varying expressions.

My AI students: Evaluating the proficiency of three AI chatbots in <i>completeness</i> and <i>accuracy</i>

A new era of artificial intelligence (AI) has begun, which can radically alter how humans interact with and profit from technology. The confluence of chat interfaces with large language models lets humans write a natural language inquiry and receive a natural language response from a machine. This experimental design study tests the capabilities of three popular AI chatbot services referred to as my AI students: Microsoft Bing, Google Bard, and OpenAI ChatGPT on <i>completeness</i> and <i>accuracy</i>. A Likert scale was used to rate c<i>ompleteness </i>and <i>accuracy,</i> respectively, a three-point and five-point. Descriptive statistics and non-parametric tests were used to compare marks and scale ratings. The results show that AI chatbots were awarded a score of 80.0% overall. However, they struggled with answering questions from the higher Bloom’s taxonomic levels. The median <i>completeness</i> was 3.00 with a mean of 2.75 and the median <i>accuracy</i> was 5.00 with a mean of 4.48 across all Bloom’s taxonomy questions (n=128). Overall, the<i> completeness</i> of the solution was rated mostly incomplete due to limited response (76.2%), while <i>accuracy</i> was rated mostly correct (83.3%). In some cases, generative text was found to be verbose and disembodied, lacking perspective and coherency. Microsoft Bing ranked first among the three AI text generative tools in providing correct answers (92.0%). The Kruskal-Wallis test revealed a significant difference in <i>completeness </i>(asymp. sig.=0.037, p<0.05) and <i>accuracy</i> (asymp. sig.=0.006, p<0.05) among the three AI chatbots. A series of Mann and Whitney tests were carried out showing no significance between AI chatbots for <i>completeness</i> (all p-values>0.015 and 0<r<0.2), while a significant difference was found for <i>accuracy</i> between Google Bard and Microsoft Bing (asymp. sig.=0.002, p<0.05, r=0.3 medium effect). The findings suggest that while AI chatbots can generate comprehensive and correct responses, they may have limits when dealing with more complicated cognitive tasks.