Future Of Artificial Intelligence Research Articles

Neuromorphic Cognitive Learning Systems: The Future of Artificial Intelligence?

Neuromorphic Cognitive Learning Systems: The Future of Artificial Intelligence?

Artificial intelligence and future of secondary education in delta state: Implications for educational administration

This study investigated Artificial Intelligence (AI) and the future of secondary education in Delta State, with a particular focus on its implications for educational administration. The purpose of this study was to assess the benefits and challenges of AI in future of secondary education in Delta State. To address this, three research questions and hypotheses were raised and formulated. Using the quantitative research approach of the ex-post-facto research design. Stratified sampling method was used to sample 191 school administrators, representing 40% of the population of 477 government schools in Delta State. A carefully designed, 24-item questionnaire was used to collect information from respondents. Mean, standard deviation, graphical representation, and t-test were employed to answer research questions and formulate hypotheses at a significance level of 0.05. Findings reveal that implementing AI in education offers prospective advantages. Ethical considerations arising from AI integration encompass potential biases in decision-making, among others. We also discovered that effective AI implementation is associated with critical issues. The findings shed light on the perceptions and beliefs of school administrators regarding the integration of Artificial Intelligence (AI) in education. There is an urgent need to develop and implement a comprehensive state-wide plan for the integration of AI in education.

Against the singularity hypothesis

AbstractThe singularity hypothesis is a hypothesis about the future of artificial intelligence on which self-improving artificial agents will quickly become orders of magnitude more intelligent than the average human. Despite the ambitiousness of its claims, the singularity hypothesis has been defended at length by leading philosophers and artificial intelligence researchers. In this paper, I argue that the singularity hypothesis rests on undersupported growth assumptions. I show how leading philosophical defenses of the singularity hypothesis fail to overcome the case for skepticism. I conclude by drawing out philosophical and policy implications of this discussion.

New Scientific Field for Modelling Complex Dynamical Systems: The Cybernetics Artificial Intelligence (CAI)

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a “catastrophic Winter” for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.

Digital proficiency: assessing knowledge, attitudes, and skills in digital transformation, health literacy, and artificial intelligence among university nursing students

BackgroundImplementing digital transformation and artificial intelligence (AI) in education and practice necessitates understanding nursing students’ attitudes and behaviors as end-users toward current and future digital and AI applications.PurposeThis study aimed to assess the perceived knowledge, attitudes, and skills of nursing students regarding digital transformation, as well as their digital health literacy (DHL) and attitudes toward AI. Furthermore, we investigated the potential correlations among these variables.MethodsA descriptive correlational design was employed in a Saudi nursing college utilizing a convenience sample of 266 nursing students. A structured questionnaire consisting of six sections was used, covering personal information, knowledge, skills and attitudes toward digital transformation, digital skills, DHL, and attitudes toward AI. Descriptive statistics and Pearson correlation were employed for data analysis.ResultsNursing students exhibited good knowledge of and positive attitudes toward digital transformation services. They possessed strong digital skills, and their DHL and positive attitude toward AI were commendable. Overall, the findings indicated significant positive correlations between knowledge of digital transformation services and all the digital variables measured (p = < 0.05). Senior students reported greater digital knowledge and a positive attitude toward AI.ConclusionThe study recommends an innovative undergraduate curriculum that integrates opportunities for hands-on experience with digital healthcare technologies to enhance their digital literacy and skills.

Using machine learning to increase access to and engagement with trauma-focused interventions for posttraumatic stress disorder.

Post-traumatic stress disorder (PTSD) poses a global public health challenge. Evidence-based psychotherapies (EBPs) for PTSD reduce symptoms and improve functioning (Forbes etal., Guilford Press, 2020, 3). However, a number of barriers to access and engagement with these interventions prevail. As a result, the use of EBPs in community settings remains disappointingly low (Charney etal., Psychological Trauma: Theory, Research, Practice, and Policy, 11, 2019, 793; Richards etal., Community Mental Health Journal, 53, 2017, 215), and not all patients who receive an EBP for PTSD benefit optimally (Asmundson etal., Cognitive Behaviour Therapy, 48, 2019, 1). Advancements in artificial intelligence (AI) have introduced new possibilities for increasinfg access to and quality of mental health interventions. The present paper reviews key barriers to accessing and engaging in EBPs for PTSD, discusses current applications of AI in PTSD treatment and provides recommendations for future AI integrations aimed at reducing barriers to access and engagement. We propose that AI may be utilized to (1) assess treatment fidelity; (2) elucidate novel predictors of treatment dropout and outcomes; and (3) facilitate patient engagement with the tasks of therapy, including therapy practice. Potential avenues for technological advancements are also considered.

Exploring Motivators for Trust in the Dichotomy of Human—AI Trust Dynamics

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

Future of artificial intelligence in plastic surgery: Toward the development of specialty-specific large language models

Future of artificial intelligence in plastic surgery: Toward the development of specialty-specific large language models

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

Crafting explainable artificial intelligence through active inference: A model for transparent introspection and decision-making

This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.

The future of artificial intelligence in perioperative nursing

The future of artificial intelligence in perioperative nursing

New Era’s of Artificial Intelligence in Pharmaceutical Industries

Artificial Intelligence (AI) is the future of pharmaceutical industries. We make our tasks easier with help of Artificial Intelligence in future. With help of Artificial Intelligence we can also increase in production in pharmaceutical industry, can be save of dangerous and risky works in the production or manufacturing. Artificial Intelligence can drugs designing in future and discover new drugs and determine the chemical structure of drugs. Artificial Intelligence is very important role play in clinical research. For the pharmacological action drugs are works with the target protein. Than this target proteins are show the pharmacological action and Artificial Intelligence is help in determination of target protein and Artificial Intelligence can easier the drug discovery related work. Artificial Intelligence will used in the marketing such as the patient or customer related information or data collection and deposition. Creation of essential and specialized advertisement for increase product Sell. Different type application will in pharmaceutical industry of Artificial intelligence. And AI will change the pharmaceutical industry or drug associated work and that is come new revolution in pharmacy. Many types AI robots are invented in various pharmacy fields for the help of human being in manufacturing or production in pharmaceutical industry. Artificial Intelligence will advantages and disadvantage for the human beings. This review aims that drug delivery nanosystems design, characterization, and production stand to benefit greatly from artificial intelligence (AI). Furthermore, the ability to perform reverse engineering and ongoing system optimisation is becoming possible with the help of big data.

Artificial intelligence in kidney transplant pathology

Artificial intelligence (AI) systems have showed promising results in digital pathology, including digital nephropathology and specifically also kidney transplant pathology. Summarize the current state of research and limitations in the field of AI in kidney transplant pathology diagnostics and provide afuture outlook. Literature search in PubMed and Web of Science using the search terms "deep learning", "transplant", and "kidney". Based on these results and studies cited in the identified literature, aselection was made of studies that have ahistopathological focus and use AI to improve kidney transplant diagnostics. Many studies have already made important contributions, particularly to the automation of the quantification of some histopathological lesions in nephropathology. This likely can be extended to automatically quantify all relevant lesions for akidney transplant, such as Banff lesions. Important limitations and challenges exist in the collection of representative data sets and the updates of Banff classification, making large-scale studies challenging. The already positive study results make future AI support in kidney transplant pathology appear likely.

"Better than my professor?" How to develop artificial intelligence tools for higher education.

Artificial Intelligence (AI) tools are currently designed and tested in many fields to improve humans' ability to make decisions. One of these fields is higher education. For example, AI-based chatbots ("conversational pedagogical agents") could engage in conversations with students in order to provide timely feedback and responses to questions while the learning process is taking place and to collect data to personalize the delivery of course materials. However, many existent tools are able to perform tasks that human professionals (educators, tutors, professors) could perform, just in a timelier manner. While discussing the possible implementation of AI-based tools in our university's educational programs, we reviewed the current literature and identified a number of capabilities that future AI solutions may feature, in order to improve higher education processes, with a focus on distance higher education. Specifically, we suggest that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner. Future research should explore high-level opportunities represented by AI for higher education, including their effects on learning outcomes and the quality of the learning experience as a whole.

A common data model for the standardization of intensive care unit medication features.

Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n = 43) and clinical practice-related (n = 63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Learner Experiences of Mobile Apps and Artificial Intelligence to Support Additional Language Learning in Education

This study examines learners’ experiences and the use of language learning applications (“apps”) as a primary source of second or additional language learning (“L2”) instruction and assessment in higher education. It purviews the integration of artificial intelligence (AI)-powered features that support technology-enhanced language learning experiences. Principles of pedagogy, heutagogy, and self-determination theory are used to inform the appropriate design and application of AI to support language learning. We examine the congruence between learner's goals with perceived outcomes following a 4-week language learning intervention using an app. A survey of n = 151 adult learners across two Canadian universities revealed: (a) apps are perceived as an engaging, convenient, and structured approach to early stages of L2 learning and (b) the integration of AI for conversation-based simulations or speech recognition would enable more adaptive, personalized L2 learning experiences. The authors discuss implications for future developments and AI uptake for language learning apps.

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management.

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.

Navigating the Complexity of Generative AI Adoption in Software Engineering

This paper explores the adoption of Generative Artificial Intelligence (AI) tools within the domain of software engineering, focusing on the influencing factors at the individual, technological, and social levels. We applied a convergent mixed-methods approach to offer a comprehensive understanding of AI adoption dynamics. We initially conducted a questionnaire survey with 100 software engineers, drawing upon the Technology Acceptance Model (TAM), the Diffusion of Innovation Theory (DOI), and the Social Cognitive Theory (SCT) as guiding theoretical frameworks. Employing the Gioia Methodology, we derived a theoretical model of AI adoption in software engineering: the Human-AI Collaboration and Adaptation Framework (HACAF). This model was then validated using Partial Least Squares – Structural Equation Modeling (PLS-SEM) based on data from 183 software engineers. Findings indicate that at this early stage of AI integration, the compatibility of AI tools within existing development workflows predominantly drives their adoption, challenging conventional technology acceptance theories. The impact of perceived usefulness, social factors, and personal innovativeness seems less pronounced than expected. The study provides crucial insights for future AI tool design and offers a framework for developing effective organizational implementation strategies.

A Novel Validated Real-World Dataset for the Diagnosis of Multiclass Serous Effusion Cytology according to the International System and Ground-Truth Validation Data

Introduction: The application of artificial intelligence (AI) algorithms in serous fluid cytology is lacking due to the deficiency in standardized publicly available datasets. Here, we develop a novel public serous effusion cytology dataset. Furthermore, we apply AI algorithms on it to test its diagnostic utility and safety in clinical practice. Methods: The work is divided into three phases. Phase 1 entails building the dataset based on the multitiered evidence-based classification system proposed by the International System (TIS) of serous fluid cytology along with ground-truth tissue diagnosis for malignancy. To ensure reliable results of future AI research on this dataset, we carefully consider all the steps of the preparation and staining from a real-world cytopathology perspective. In phase 2, we pay special consideration to the image acquisition pipeline to ensure image integrity. Then we utilize the power of transfer learning using the convolutional layers of the VGG16 deep learning model for feature extraction. Finally, in phase 3, we apply the random forest classifier on the constructed dataset. Results: The dataset comprises 3,731 images distributed among the four TIS diagnostic categories. The model achieves 74% accuracy in this multiclass classification problem. Using a one-versus-all classifier, the fallout rate for images that are misclassified as negative for malignancy despite being a higher risk diagnosis is 0.13. Most of these misclassified images (77%) belong to the atypia of undetermined significance category in concordance with real-life statistics. Conclusion: This is the first and largest publicly available serous fluid cytology dataset based on a standardized diagnostic system. It is also the first dataset to include various types of effusions and pericardial fluid specimens. In addition, it is the first dataset to include the diagnostically challenging atypical categories. AI algorithms applied on this novel dataset show reliable results that can be incorporated into actual clinical practice with minimal risk of missing a diagnosis of malignancy. This work provides a foundation for researchers to develop and test further AI algorithms for the diagnosis of serous effusions.