Artificial Intelligence Innovation Research Articles

Machine Learning and AI Innovations with Python: Trends and Future Directions

This comprehensive article examines the current state and future directions of machine learning and artificial intelligence innovations using Python. It provides an in-depth analysis of recent advancements in popular Python-based libraries such as TensorFlow, PyTorch, and scikit-learn, highlighting key features and performance improvements. The article explores emerging machine learning techniques, including federated learning, few-shot learning, and explainable AI, discussing their principles, Python implementations, and real-world applications. Furthermore, the article investigates future trends in AI development with Python, considering potential technological advancements, new libraries and frameworks, and emerging research areas such as quantum machine learning and neurosymbolic AI. It also addresses the challenges and opportunities facing Python in the evolving landscape of AI, including performance optimization, ethical considerations, and the increasing demand for production-ready AI solutions. By synthesizing current developments and future prospects, this article offers valuable insights for researchers, developers, and organizations seeking to leverage Python's capabilities in the rapidly advancing field of artificial intelligence and machine learning.

Can urban digital intelligence transformation promote corporate green innovation? Evidence from China

Can urban digital intelligence transformation (DIT) facilitate corporate green innovation (CGI)? This research uses the staggered difference-in-differences (DID) approach to study how urban DIT, represented by the Artificial Intelligence Innovation Development Pilot Zones (AIIDPZs), affects CGI. Regression results indicate that urban DIT promotes both the quantity and quality of CGI. Mechanisms analysis reveals that urban DIT promotes CGI by facilitating digital transformation, increasing firms’ investment in R&D, and facilitating information sharing. Heterogeneity analysis shows that urban DIT has a more significant effect on promoting green innovation in non-SOEs, industries with lower pollution levels, and enterprises in regions with high intellectual property protection (IPP). This study offers new perspectives on the role of urn promoting green development and provides empirical support for policy-making.

Harnessing Artificial Intelligence to Transform Secondary Education in India: Innovations, Challenges, and Future Directions

India's National Education Policy (NEP) 2020 envisions a comprehensive education system that is inclusive, fair, and geared towards lifelong learning. Artificial Intelligence (AI) has a pivotal role in achieving these goals by enhancing personalized learning experiences, streamlining assessments, and promoting inclusivity within secondary education. This paper explores the integration of AI in Indian secondary education, analyzing its alignment with NEP 2020 objectives. It highlights AI innovations, such as adaptive learning platforms and automated grading systems, while also addressing challenges like data privacy, infrastructure limitations, and the need for teacher training. Through a review of recent literature and case studies, this paper offers recommendations for the responsible adoption of AI in India, underscoring the importance of policy support, ethical standards, and regional infrastructure development.

Navigating the Privacy Paradox : Balancing AI Advancement and Data Protection in the Digital Age

The rapid advancement of artificial intelligence (AI) technologies has ushered in an era of unprecedented data utilization, raising critical concerns about individual privacy. This article examines the complex interplay between AI development and data protection, exploring the challenges of balancing the need for large-scale data processing with the imperative to safeguard personal information. We analyze current regulatory frameworks, including GDPR and CCPA, and their efficacy in addressing AI-specific privacy issues. The article also evaluates technical measures such as differential privacy, federated learning, privacy-preserving machine learning algorithms, and organizational best practices for responsible data management. By synthesizing computer science, law, and ethics perspectives, we propose a multidisciplinary approach to fostering AI innovation while robustly protecting individual privacy rights. Our findings underscore the need for adaptive regulations, enhanced technical safeguards, and increased collaboration between stakeholders to navigate the evolving landscape of AI and privacy in the digital age.

Legal Challenges in Regulating Artificial Intelligence: A Comparative Study of Privacy and Data Protection Laws

This article explores the legal challenges in regulating artificial intelligence (AI) with a focus on privacy and data protection laws through a qualitative, comparative study. By employing a library research methodology, the study analyzes existing literature and legal frameworks across multiple jurisdictions to identify common trends, gaps, and inconsistencies in AI regulation. It investigates how various countries, including the European Union, the United States, and emerging economies, have developed or are developing legal instruments to address privacy concerns raised by AI technologies. The study highlights key issues such as the tension between technological innovation and regulatory constraints, the adequacy of current legal standards in addressing AI-specific risks, and the role of international cooperation in harmonizing data protection laws. The comparative analysis reveals significant divergences in how privacy is protected across different regions, particularly in the application of principles like consent, transparency, and accountability. Moreover, the article identifies critical gaps in existing frameworks, including the lack of clarity in AI accountability, data sovereignty, and enforcement mechanisms. The findings underscore the need for a more coherent and globally coordinated approach to regulating AI that balances privacy protection with technological advancement. Ultimately, the study calls for reforms that ensure robust privacy safeguards while fostering innovation in AI.

Artificial intelligence in management education: transformative potential and challenges

Purpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.

Use of Artificial Intelligence Technologies in Visual Design

Artificial intelligence (AI) refers to computer systems crafted to replicate human cognitive functions such as learning, perception, and problem-solving. Recently, AI technologies have gained widespread popularity across various domains including healthcare and finance. They have also significantly impacted visual design. This study investigates the burgeoning influence and utilization of AI technologies in visual design, delving into their effects and applications. It scrutinizes how AI innovations transform visual design processes, influence creativity, and shape design practices. The research emphasizes the emergence of AI-powered design tools, their impact on designer creativity, and the potential advantages and drawbacks they pose to creative processes. Furthermore, it examines the ethical and practical dimensions inherent in integrating AI into design practices. By thoroughly assessing AI's impact on visual design, the study aims to prognosticate future technological advancements in this realm.

The Role of AI Technology in Digital Media Technology Course Teaching

With the rapid development of Internet technology and the widespread use of smart terminals, we have entered the Internet era. The continuous innovation of artificial intelligence (AI) technology has enriched people's work and life and has changed how people receive information. Unlike traditional media such as newspapers, magazines, and television, digital media provides a more efficient way to access information. In the 21st-century knowledge economy, digital media has become a core industry. Under such circumstances, universities across the country have successively introduced related courses, with the Digital Media Technology program being one of the most representative. Due to the diverse fields and wide application range of digital media technology, its social functions have been fully demonstrated during its development. Therefore, integrating AI technology into the teaching of digital media technology courses can help adjust teaching goals, optimize teaching methods, and gradually enhance students' initiative. This paper aims to explore the specific application of AI technology in digital media technology course teaching to provide useful references for educators.

A policy framework on AI usage in developing countries and its impact

The rapid growth of Artificial Intelligence (AI) presents both significant opportunities and challenges for developing countries. A well-structured policy framework is crucial to maximize the benefits of AI while mitigating its risks. This review proposes a comprehensive AI policy framework tailored to developing countries, emphasizing the need for robust infrastructure, capacity building, ethical governance, and economic incentives. Key elements include the development of digital infrastructure, education and training programs to enhance AI literacy, and ethical guidelines to ensure fairness and transparency in AI applications. Data governance and privacy protections are critical, particularly in countries where regulatory frameworks are underdeveloped. Furthermore, international cooperation is highlighted as a necessity for aligning local policies with global AI standards, facilitating cross-border data sharing, and ensuring equitable access to AI innovations. The potential impact of AI on economic growth, job creation, healthcare, education, and public service delivery is profound, yet challenges such as workforce displacement, increased inequality, and the digital divide must be carefully managed. The proposed framework addresses these challenges, providing strategies to overcome barriers to AI adoption, including financial constraints, governance issues, and unequal access to technology. Moreover, it stresses the importance of fostering public-private partnerships and ensuring that AI development is inclusive, benefiting all segments of society. By implementing a comprehensive AI policy framework, developing countries can harness AI’s transformative power to drive sustainable development, improve social outcomes, and strengthen their economic standing in the global landscape. This review concludes by recommending continuous policy evaluation and adaptation to keep pace with AI's rapid evolution.

Application and Innovation of Artificial Intelligence in Forensic Medicine

As the fourth industrial revolution, artificial intelligence is reshaping many industries. It has become a new cornerstone of digital conversion, and it is no exception in the field of forensic medicine.This paper mainly discusses how artificial intelligence can solve the problems related to forensic medicine.How to use artificial intelligence technology to develop into an auxiliary tool in the field of forensic medicine.This paper summarizes the exploratory data analysis, statistical modeling and machine learning in artificial intelligence, extracts insights and knowledge from the data, and applies them to various fields of forensic medicine.Artificial intelligence will improve the accuracy and efficiency of forensic work: it can automate some tasks and improve the quality of evidence. The comprehensive analysis results show that the artificial intelligence proposed in this paper will be an important auxiliary in the three directions of forensic medicine.

Use of Artificial Intelligence in Fashion Sales Techniques

This article discusses the prominent elements of artificial intelligence innovation in fashion sales techniques and details artificial intelligence-supported sales strategies such as virtual reality, augmented reality, virtual try-on rooms, smart mirrors, artificial intelligence-supported style consultants, visual search technologies and chatbot. This study was written with the compilation method. The compilation method is written with the aim of collecting the literature written so far on the research subjects from a scientific perspective and making a collective contribution to the literature. While virtual reality and augmented reality offer customers interactive shopping experiences and the opportunity to try products in their own environment without going to the store, virtual trial rooms and smart mirrors; Studies have shown that it has the potential to make online and face-to-face shopping easier, faster and more interactive. It has been stated that visual search technologies enable customers to easily find the products in the style they want, and artificial intelligence-supported style consultants and chatbots play an important role in optimizing the shopping experience by making personalized suggestions to customers. New and innovative solutions can be produced by conducting studies that encourage interdisciplinary collaboration between fashion designers, software developers and academicians. In this period when we live intertwined with technology, artificial intelligence-focused modules can be added to academic curricula for students studying in the field of design, and these modules will provide benefits for students to gain knowledge and skills on how to integrate artificial intelligence technologies into fashion design. The effective use of artificial intelligence in fashion sales techniques will provide a competitive advantage against competitors in the industry.

Preliminary Investigation of Context-Aware AAC with Automated Just-in-Time Cloze Phrase Response Options for Social Participation from Children on the Autism Spectrum.

Social participation for emerging symbolic communicators on the autism spectrum is often restricted. This is due in part to the time and effort required for both children and partners to use traditional augmentative and alternative communication (AAC) technologies during fast-paced social routines. Innovations in artificial intelligence provide the potential for context-aware AAC technology that can provide just-in-time (JIT) communication options based on linguistic input from partners to minimize the time and effort needed to use AAC technologies for social participation. This preliminary study used an alternating treatment design to compare the effects of a context-aware AAC prototype with automated cloze phrase response options to traditional AAC for supporting three young children who were emerging symbolic communicators on the autism spectrum in participating within a social routine. Visual analysis and effect size estimates suggest the context-aware AAC condition resulted in increases in linguistic participation, vocal approximations, and visual attention for all three children. While this study was only an initial exploration and results are preliminary, context-aware AAC technologies have the potential to enhance participation and communication outcomes for young emerging symbolic communicators on the autism spectrum and more research is needed.

Contactless Service: Artificial Intelligence Applications of Airports

Airports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; they utilize two technologies: face detection and recognition. The study examines how passengers' performance expectancy and emotions affect satisfaction and willingness to adopt the AI service of passengers via familiarity. Based on the AI device, it uses acceptance and socio-technical systems perspectives; participants who adopted the AI service in airports in Beijing, Taipei, and Singapore who have the potential to use AI service usage experience more than three times; 387 completed questionnaires. A satisfaction model was analyzed using the partial least squares approach; this study used purposed sampling to collect data based on the passenger volume analysis by those airports, and 387 completed questionnaires were analyzed. With the rise of AI innovation services in airports, passengers can quickly complete boarding procedures through AI innovative services. The research findings point out that passengers' performance expectancy and emotions positively affect familiarity with AI services for passengers. AI innovations provide a service change driver for and have the potential to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently, and airports will collaborate with airlines to serve passengers with AI.

PACE: Participatory AI for Community Engagement

Public sector leverages artificial intelligence (AI) to enhance the efficiency, transparency, and accountability of civic operations and public services. This includes initiatives such as predictive waste management, facial recognition for identification, and advanced tools in the criminal justice system. While public-sector AI can improve efficiency and accountability, it also has the potential to perpetuate biases, infringe on privacy, and marginalize vulnerable groups. Responsible AI (RAI) research aims to address these concerns by focusing on fairness and equity through participatory AI. We invite researchers, community members, and public sector workers to collaborate on designing, developing, and deploying RAI systems that enhance public sector accountability and transparency. Key topics include raising awareness of AI's impact on the public sector, improving access to AI auditing tools, building public engagement capacity, fostering early community involvement to align AI innovations with public needs, and promoting accessible and inclusive participation in AI development. The workshop will feature two keynotes, two short paper sessions, and three discussion-oriented activities. Our goal is to create a platform for exchanging ideas and developing strategies to design community-engaged RAI systems while mitigating the potential harms of AI and maximizing its benefits in the public sector.

Artificial intelligence innovation and corporate environmental investment: A contingent view

Existing research has begun to focus on the negative impacts caused by artificial intelligence (AI), but few studies have examined the influence of AI on corporate environmental activities. This study, using 15,779 observations from 3,097 Chinese listed firms from 2008 to 2022, finds that corporate AI innovation has a significant negative impact on CEI. Additionally, we discover that firm size strengthens the negative relationship between AI innovation and CEI, while foreign direct investment (FDI) intensity weakens this negative relationship. This study extends the research on AI in the field of corporate environmentally friendly behavior.

Convergence and Innovation of Artificial Intelligence in Corporate Strategic Planning: Opportunities, Challenges and Future Research Directions

In the contemporary digital era, the swift evolution of Artificial Intelligence (AI) has not only revolutionized various sectors but has also become an indispensable asset in corporate strategic planning, decision-making processes, and operational efficiency. This paper delves into the multifaceted applications of AI across a myriad of industries, providing a comprehensive analysis of its integration into corporate strategy and its profound impact on business operations. This study adopts a comprehensive research methodology that merges qualitative case studies with quantitative data analysis to scrutinize the practical use of AI in strategic planning. It delves into the interdependent relationship between AI and conventional digital frameworks, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The research aims to illuminate how the fusion of these technologies can substantially enhance corporate competencies and optimize operational workflows. The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges. Ethical considerations, legal ramifications, and societal implications must be carefully navigated. Additionally, the study reveals a notable void in existing literature concerning the enduring strategic ramifications of AI and its effects on corporate culture and talent management practices. This gap highlights the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.

Artificial Intelligence & the Capacity for Discrimination: The Imperative Need for Frameworks, Diverse Teams & Human Accountability

The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.

Policy Text AI Mining Study —— A Comparative Analysis Based on U.S., U.K., and Chinese Policies

Artificial Intelligence (AI) has become a key strategic area to enhance national competitiveness globally. We adopts a textual analysis methodology, based on the three-dimensional framework of policy tools and the LDA thematic model, to conduct a systematic analysis and comparative study of the AI policy texts of the United States, the United Kingdom, and China. The study aims to reveal the policy stance, strategic planning, and challenges and opportunities faced by the three countries in the field of AI. Through the analysis, the study identifies the major themes in the policy texts of each country and maps them to the three dimensions of supply, demand, and environment of policy tools. The study finds that the US AI policy focuses on international cooperation, fair civil rights, and innovation ecology; the UK emphasizes ethical governance and legal regulation; and China highlights innovation-driven and smart economy development. In addition, all three countries emphasize building a stable, predictable and innovation-supportive policy environment. The study puts forward a series of comprehensive recommendations, including strengthening international cooperation, balancing innovation and regulation, optimizing a supportive innovation ecosystem, improving the legal system, focusing on talent cultivation, and building an AI innovation alliance. These recommendations aim to promote the healthy development of global AI technologies and maximize their positive contributions to the economy and society.

Transforming banking: Examining the role of AI technology innovation in boosting banks financial performance

With the ongoing advancement of technology, artificial intelligence is increasingly being integrated into banking and finance, with the transformative potential to reshape the financial performance of economies worldwide. This research investigates the dynamic relationship between banking and finance artificial intelligence technology innovation and banks' financial performance across 20 countries using the feasible generalized least squares and the generalized method of moments techniques. The results show that banking and finance artificial intelligence technology innovation positively impacts banks' return on assets, highlighting its role in enhancing financial performance. The interaction term between artificial intelligence innovation and economic growth emphasizes their collaborative positive impact on financial performance. Mediation analysis highlights information and communication technology development's role in transforming artificial intelligence innovation into improved financial outcomes. Considering lagged effects, initial innovation surges correlate with improved financial performance, but prolonged exposure leads to diminishing returns. Moreover, the findings indicate that non-performing loans negatively affect financial performance, underscoring the importance of asset quality. Additionally, regulatory capital and economic growth are positively associated with financial performance, while government regulations exhibit a negative impact. This study highlights the essential role of artificial intelligence technology innovation in banking and finance, emphasizing the need to consider economic and technological factors for maximizing its benefits in enhancing financial performance. Policy recommendations include promoting an artificial intelligence innovation ecosystem, adapting regulatory frameworks, and investing in information and communication technology infrastructure to harness artificial intelligence innovation's benefits while addressing associated challenges.

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks.

The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts. This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems. We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses. In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.