Artificial Intelligence Revolution Research Articles

Artificial Intelligence (AI) Challenges and Opportunities in Translation : An African Experience

The Artificial Intelligence (AI) revolution has become a reality in today’s world and its importance for linguistics was recognized very early. Despite its unprecedent surge and integration into various academic fields including language teaching and translation, surprisingly, little work has been done by scholars in advancing discussions on the profound impact of the AI on the diversity of widely available languages in both developed and developing world. Africa is linguistically diverse continent with about one third of the world’s languages that are vastly underrepresented in the global digital data pool. AI translation machine is supported in only 25 languages out of over 2000 languages in the continent. The paper deploys homomorphism model of AI theory to interrogate the natural language data drawn the African languages to present the current and future challenges, opportunities and potential for developing AI algorithms that could fit neatly into the translation of the African languages. Most of the discussions in the paper focuses on the seven patterns of the AI, the usage and implementation of AI algorithms in the translation science. The research findings show some of the complexities of the African languages in which their syntactic categories have multiple corresponding semantic objects. Unlike English, the findings also reveal that syntactic operation in the African languages do not always have one corresponding semantic operation as postulated by the homomorphism model of AI theory. the study contributes to scholarly literature by stressing the limits and opportunities that relate to using AI in translation science and supplying input from NLP algorithms practitioners to expand the AI applicability operation in the translation science.

Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Artificial intelligence and social accountability in the Canadian health care landscape: A rapid literature review.

Situated within a larger project entitled "Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence," this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy. The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.

Adapting legal horizons in reshaping intellectual property law for the artificial intelligence revolution

Adapting legal horizons in reshaping intellectual property law for the artificial intelligence revolution

Analyzing the Influential Factors of Valuation: A Case Study on Taiwan Semiconductor Manufacturing Company

During the Artificial Intelligence (AI) revolution, the semiconductor industry saw unprecedented demand, driven mostly by the need for more powerful computational technology. This study examines the value impact of AI breakthroughs on Taiwan Semiconductor Manufacturing Company (TSMC) using a rigorous Discounted Cash Flow (DCF) analysis. With AI technologies largely driving demand in the semiconductor business, this study adapts classic financial modeling approaches to capture the subtle implications of technical advancements on firm valuation. Using substantial financial data from TSMC, the research modifies growth rates to reflect AI-driven market potential, while also incorporating cautious forecasts to allow for industry volatility. The findings indicate that the market may undervalue TSMC, implying an upbeat forecast for the firm in the middle of the AI boom. The research contributes to financial academic knowledge by illustrating the use of DCF to evaluate technology influences on business values, providing important insights for investors and corporate strategists. Future refinements to the DCF model are proposed, with an emphasis on incorporating dynamic market data and policy effect research to improve investment assessments in technology.

Implications of artificial intelligence

The artificial intelligence (AI) revolution is underway. AI has become omnipresent in medicine, and its broad capabilities have permitted significant developments in many subspecialties including spine surgery. With AI, spine surgeons have been able to perform advanced computational analytics on vast amounts of data, allowing for solutions in each step of patient care from preoperative evaluation and planning, intraoperative execution, and postoperative evaluation. The tremendous potential of AI in medicine is clear and exciting. However, as the utility of AI in clinical practice expands, the medicolegal implications of this technology are poorly understood. In this chapter, we explore the existing technology, ethical considerations, legal challenges, and risk management strategies as it relates to AI in the field of spine surgery.

Artificial Intelligence in Higher Education : A Literature Snapshot

In Artificial Intelligence, “Artificial” means objects that are produced by human beings, and “Intel ligence” is the capability to form tactics to achieve goals by interacting with huge information. Artificial Intelligence (AI) is evolving rapidly in higher education, and various AI applications have been developed to solve some of the most pressing problems that higher education field currently face. The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned Higher education is crucial for producing ethical citizens and profess ionals globally. The introduction of generative AI (GenAI), such as ChatGPT, has posed opportunities and challenges to the traditional model of education. However, the current conversations primarily focus on policy development and assessment, with limited research on the future of higher education.AI - powered language tools (AILTs) are commonly used by university students, yet there is a limited understanding of how students utilize and perceive these tools in everyday academic communication practice. Following the very recent launch of the ChatGPT, chatbot, numerous comments and speculations were posted concerning the potential aspects of society that are expected to benefit from this AI revolution. This paper addresses some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society.AI education aims to teach AI concepts, essential knowledge, and skills related to the fundamental ideas in AI. The affordances of artificial intelligence (AI) have not been totally utilized in education. To effectively integrate AI into education, teachers’ AI - specific technological and pedagogical knowledge is important.

A glimpse into the future: Integrating artificial intelligence for precision HER2‐positive breast cancer management

AbstractBreast cancer (BC), specifically HER2‐positives subtype, has a poor prognosis. Nevertheless, the development of anti‐HER2 therapy yielded satisfactory outcomes. Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti‐HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. In the field of medicine, AI is an emerging modality that is gaining momentum for facilitating cancer diagnosis and treatment, particularly in the effective management of breast cancer. This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites. Advancements and practical applications of these models were also evaluated. Additionally, we examined existing obstacles that AI encounters for anti‐HER2 therapy. We also proposed future directions for integrating AI in assessing and managing anti‐HER2 therapy. The findings of this study offer valuable insights into the evaluation of AI‐based anti‐HER2 therapy, emphasizing key concepts and obstacles that, if addressed, could facilitate the integration of AI‐assisted anti‐HER2 therapy. The integration of AI has the potential to enhance the precision and customization of screening and treatment protocols for HER2+ breast cancer.

The Revolution of Artificial Intelligence: Enhancing Digital Literacy of Prospective Economics Teachers

The Revolution of Artificial Intelligence: Enhancing Digital Literacy of Prospective Economics Teachers

Ready or not, the artificial intelligence revolution is here.

Ready or not, the artificial intelligence revolution is here.

AI Revolution in Banking Recruitment: Enhancing Efficiency and Objectivity

This paper examines the profound impact of Artificial Intelligence (AI) on recruitment and selection processes within the banking sector. Through the integration of AI technologies, traditional practices are undergoing a paradigm shift, resulting in heightened efficiency, objectivity, and effectiveness in talent acquisition. The utilization of AI facilitates automated resume screening, precise candidate matching, predictive analytics, and bias reduction, while also incorporating innovative tools such as chatbots for initial engagement. Furthermore, AI enables comprehensive analyses of employee retention and skills gap, empowering organizations to optimize workforce management strategies. This abstract highlight the transformative role of AI in reshaping recruitment practices within the dynamic landscape of the banking sector.

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension.

This review explores the transformative role of artificial intelligence (AI) in hypertension care, summarizing and analyzing published works from the last three years in this field. Hypertension contributes to a significant healthcare burden both at an individual and global level. We focus on five key areas: risk prediction, diagnosis, education, monitoring, and management of hypertension, supplemented with a brief look into the works on hypertensive disease of pregnancy. For each area, we discuss the advantages and disadvantages of integrating AI. While AI, in its current rudimentary form, cannot replace sound clinical judgment, it can still enhance faster diagnosis, education, prevention, and management. The integration of AI in healthcare is poised to revolutionize hypertension care, although careful implementation and ongoing research are essential to mitigate risks.

AI Revolution in Exit Polls: Enhancing Accuracy, Efficiency, and Inclusivity

Exit polls have long been integral to understanding electoral dynamics, offering insights into voter behavior and preferences. Traditional exit poll methodologies, involving manual data collection and in-person interviews, face significant challenges in accuracy, sample size, and inclusivity. This paper explores the transformative impact of Artificial Intelligence (AI) on the exit polling process. By leveraging neural networks for enhanced sampling, employing automated Voice-AI for data collection, and utilizing Natural Language Processing (NLP) for real-time data analysis, AI significantly improves the efficiency, accuracy, and inclusivity of exit polls. The paper discusses the technical aspects of implementing AI-driven exit polls, including the architecture of neural networks, real-time data processing systems, and machine learning models for prediction. Additionally, it addresses the advantages of AI, such as cost-effectiveness and reduced human biases, while acknowledging challenges like algorithmic bias, privacy concerns, and the complexities of accurately interpreting voter behavior. As AI continues to evolve, it promises to further enhance the reliability and comprehensiveness of exit polls, providing valuable tools for understanding public opinion in democratic processes

Historical evolution of accounting and its implications with the Artificial Intelligence revolution

Accounting has evolved significantly over the centuries, from its rudimentary origins to the adoption of advanced technologies such as artificial intelligence (AI). This article explores this historical trajectory and examines how new technologies are transforming accounting practices. Initially focused on fiscal compliance and asset control, accounting has adapted to meet the complex demands of modern economies. The methodology of this study was based on a bibliographic review, utilizing scientific articles and theses. The results indicate that AI increases the accuracy and efficiency of accounting processes by automating repetitive tasks and enhancing data analysis, allowing accountants to take on more strategic roles. However, AI also presents challenges in terms of data privacy and security, highlighting the importance of an ethical approach and continuous professional development.

The Artificial Intelligence Revolution in Accounting and Auditing: Opportunities, Challenges, and Future Research Directions

The Artificial Intelligence Revolution in Accounting and Auditing: Opportunities, Challenges, and Future Research Directions

THE AI REVOLUTION IN IT EDUCATION

Artificial intelligence (AI) is rapidly transforming the IT landscape, demanding a paradigm shift in IT education. This paper explores the impact of AI on the educational process for aspiring IT specialists. The goals of transformation. Shifting focus of the curriculum is transitioning from a purely technical focus to preparing students for human-AI collaboration. Develop essential skills in data science, machine learning, and understanding the ethical implications of AI become central to the IT education framework. Orientation on lifelong learning for create adaptability and continuous learning skills to navigate the evolving field. Forming soft skills in communication, collaboration, and creativity gain prominence alongside technical expertise. The benefits are job market preparedness with graduates equipped by AI skills professionals become more competitive in the workforce. Enhanced problem-solving with understanding and applying AI tools improves students’ ability to analyze problems and develop innovative solutions. Increased Efficiency by leverage AI to automate tasks, optimize workflows, and achieve greater productivity. Fostering innovation with Integrating AI into the curriculum encourages students to explore new applications of AI technology. AI revolution create some challenges. Educational institutions need to adapt their curriculum and train faculty to effectively deliver AI-related content. Ensuring all students have equal access to the resources required for learning AI is crucial. Integrating discussions about AI ethics prepares students to use AI responsibly. The results are the integration of artificial intelligence (AI) in IT education is transforming the landscape of learning and teaching. This revolution is reshaping how students engage with content, how educators deliver instruction, and how institutions operate. Here are the key areas where AI is making a significant impact in IT education.

Impact of Artificial Intelligence in Business: What Uganda needs to do to survive the Artificial Intelligence Revolution

Impact of Artificial Intelligence in Business: What Uganda needs to do to survive the Artificial Intelligence Revolution

Revolution of Artificial Intelligence in Computational Chemistry Breakthroughs

Revolution of Artificial Intelligence in Computational Chemistry Breakthroughs

How can Artificial Intelligence Techniques Effectively Enhance Credit Card Fraud Detection Systems?

The boom in Artificial Intelligence (AI) revolution is significantly transforming our everyday lives. Every aspect of technology, process, and system implements AI to provide a superior user experience, enhanced machine capabilities and advanced problem-solving and research capabilities. However, due to this technological revolution, rise in fraud has become an enormous challenge in the digital economy. This research paper aims to test different Machine Learning (ML) models to explore real-time fraud detection capabilities accurately, particularly for credit card fraud prevention systems. Technologies like online bank transfers and smartphone payments based on credit accounts are major contributors to fraudulent transactions. AI/ML models have proven to be industry-disruptors, robust, significantly faster and produce more accurate results. These advantages have led to launch of successful companies like OpenAI. This paper uses model metrics such as Supervised, Unsupervised, and Ensemble methods to improve the detection of unauthorized transactions for fraud detection. Models will be ranked for performance using accuracy metrics, F-1, and Area Under Curve (AUC) scores. While zero false rates are not yet achievable, this study aims to reach a reasonably low level by selecting an appropriate model.

‘Quantum of information’ functionality as a measure of subjectivity beyond the capabilities of deep learning

The potential of conscious artificial intelligence (AI), with its functional systems that surpass automation and rely on elements of understanding, is a beacon of hope in the AI revolution. The shift from automation to conscious AI, once replaced with machine understanding, offers a future where AI can comprehend without needing to experience, thereby revolutionizing the field of AI. In this context, the proposed Dynamic Organicity Theory of consciousness (DOT) stands out as a promising and novel approach for building artificial consciousness that is more like the brain with physiological nonlocality and diachronicity of self-referential causal closure. However, deep learning algorithms utilize "black box" techniques such as “dirty hooks” to make the algorithms operational by discovering arbitrary functions from a trained set of dirty data rather than prioritizing models of consciousness that accurately represent intentionality as intentions-in-action. The limitations of the “black box” approach in deep learning algorithms present a significant challenge as quantum information biology, or intrinsic information, is associated with subjective physicalism and cannot be predicted with Turing computation. This paper suggests that deep learning algorithms effectively decode labeled datasets but not dirty data due to unlearnable noise, and encoding intrinsic information is beyond the capabilities of deep learning. New models based on DOT are necessary to decode intrinsic information by understanding meaning and reducing uncertainty. The process of “encoding” entails functional interactions as evolving informational holons, forming informational channels in functionality space of time consciousness. The “quantum of information” functionality is the motivity of (negentropic) action as change in functionality through thermodynamic constraints that reduce informational redundancy (also referred to as intentionality) in informational pathways. It denotes a measure of epistemic subjectivity towards machine understanding beyond the capabilities of deep learning.