Artificial Intelligence Research Research Articles

Responsible research in artificial intelligence: lessons from the past

Responsible research in artificial intelligence: lessons from the past

Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.

Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light's properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

Epistemic integration and social segregation of AI in neuroscience

In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn’t really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.

How should artificial intelligence be used in Australian health care? Recommendations from a citizens’ jury

AbstractObjectiveTo support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care.Study designCitizens’ jury, deliberating the question: “Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?”Setting, participantsThirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March – 2 April 2023): fifteen days online and three days face‐to‐face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face‐to‐face meeting.Main outcome measuresJury recommendations, with reasons.ResultsThe jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients’ rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication.ConclusionsThe deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

Challenges as catalysts: how Waymo’s Open Dataset Challenges shape AI development

AbstractArtificial intelligence (AI) and machine learning (ML) are becoming increasingly significant areas of research for scholars in science and technology studies (STS) and media studies. In March 2020, Waymo, Google/Alphabet’s autonomous vehicle project, introduced the ‘Open Dataset Virtual Challenge’, an annual competition leveraging their Waymo Open Dataset. This freely accessible dataset comprises annotated autonomous vehicle data from their own Waymo vehicles. Yearly, Waymo has continued to host iterations of this challenge, inviting teams of computer scientists to tackle evolving machine learning and vision problems using Google's data and tools. This article analyses these challenges, situating them within the context of the ‘Grand Challenges’ of artificial intelligence (AI), which aimed to foster accountable and commercially viable advancements in the late 1980s. Through two exploratory workshops, we adopted a ‘technographic’ approach to examine the pivotal role of challenges in the development and political economy of AI. Serving as an organising principle for the AI innovation ecosystem, the challenge connects companies and external collaborators, driving advancements in specific machine vision domains. By exploring six key themes—interface methods, incrementalism, metrics, AI vernacular, applied domains, and competitive advantages—the article illustrates the role of these challenges in shaping AI research and development. By unpacking the dynamic interaction between data, computation, and labour, these challenges serve as catalysts propelling advancements towards self-driving technologies. The study reveals how challenges have historically and presently shaped the evolving landscape of self-driving and AI technologies.

The use of artificial intelligence in scientific research with integrity and ethics

This paper addresses the evolution of Artificial Intelligence (AI) in scientific research and the ethical and integrity challenges that arise with its integration. AI has become an indispensable tool for researchers, accelerating discoveries and optimizing processes. However, using these algorithms raises concerns about bias, transparency, and accountability. The ability of machines to learn and create knowledge challenges the paradigms of authorship and credibility, putting integrity and ethics under new scrutiny. The discussion emphasizes robust ethical governance, collaboration among stakeholders, ongoing education, and the creation of transparent and auditable algorithms. It further highlights the importance of maintaining ethics and integrity at the heart of AI research to ensure its advancement benefits humanity fairly and responsibly, emphasizing the need for a holistic approach involving education, transparency, accountability, and active participation of multiple stakeholders. Finally, it reiterates that as we embark on this new era of AI-driven discovery, we must embrace both the opportunities and the ethical challenges it presents, ensuring that the use of AI in scientific research continues to benefit humanity by promoting knowledge and well-being.

Entropic Artificial Intelligence and Knowledge Transfer

An overview of entropy applications to illustrate a few possible uses of entropy in knowledge transmission and artificial intelligence. Artificial intelligence uses entropy as a fundamental concept in many diverse applications, including reinforcement learning, data compression, and decision-making. It assists artificial intelligence models in producing wellinformed forecasts and judgments by assessing uncertainty and information content. As such, the purpose of this work is to highlight the importance of entropy and draw the attention of the artificial intelligence research community to it as a potent tool for advancing artificial intelligence. This work also addresses the importance of knowledge transfer (KT), especially intergenerational KT (IGT), in knowledge management. Knowledge entropy (KE) is a concept that is used to measure the complexity of knowledge distribution within an organisation and evaluate the effectiveness of KT activities. Furthermore, the KT model—which is predicated on the ideas of information content and tacitness—is presented. It blends techniques for customisation and codification. A few challenging open problems are presented along with future study options

Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis

BackgroundThe field of Artificial Intelligence (AI) holds transformative potential in medicine. However, the lack of universal reporting guidelines poses challenges in ensuring the validity and reproducibility of published research studies in this field.MethodsBased on a systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics, 26 reporting guidelines published between 2009 and 2023 were included in this analysis. Guidelines were stratified by breadth (general or specific to medical fields), underlying consensus quality, and target research phase (preclinical, translational, clinical) and subsequently analyzed regarding the overlap and variations in guideline items.ResultsAI reporting guidelines for medical research vary with respect to the quality of the underlying consensus process, breadth, and target research phase. Some guideline items such as reporting of study design and model performance recur across guidelines, whereas other items are specific to particular fields and research stages.ConclusionsOur analysis highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines. Overall, this comprehensive overview could help researchers and public stakeholders reinforce quality standards for increased reliability, reproducibility, clinical validity, and public trust in AI research in healthcare. This could facilitate the safe, effective, and ethical translation of AI methods into clinical applications that will ultimately improve patient outcomes.

Study of Road-based Versus Pattern-based Game Strategies in Connect6

Computer game is an important branch in the field of artificial intelligence research nowadays, and the in-depth study of computer game can not only promote the development of game theory, but also contribute to the development of artificial intelligence. In this paper, for the game search strategy of connect6, according to the two most widely used search strategies and valuation function design, one is the valuation strategy based on the road, and the other is the valuation method based on the chess type, under the PVS search algorithm, the principle and implementation of these two search strategies are introduced in detail, and at the same time, the advantages and disadvantages of the two methods are compared, so as to provide the decision-making policy and the design of valuation function for the game of connect6 a more reasonable reference. Through comparison, it can be seen that in hexachrome, due to the complexity of the evaluation function and game strategy based on chess shapes, it requires the game programme to have a strong recognition of chess shapes, and the complexity of accurately evaluating the advantages and disadvantages of the position is higher. The road-based game strategy has better accuracy in evaluating and predicting the game, and can effectively reduce the game failure due to positional judgement errors.

Analyze and Evaluate the Method Used as Well as the Fallout of OpenAI's Board of Directors Decision to Remove Sam Altman as CEO of the Company

Formed in December of 2015 by such leading lights as Elon Musk and Sam Altman, OpenAI has become a major player on the AI research stage. The OpenAI is committed to developing and Artist, researching investing in creating a technology beneficial for all of mankind. As such, the organization's primary visionaries are working creatively on artificial General Intelligence This latter type refers to systems which would be able perform better than humans at economically significant tasks. OpenAI and my future This study focuses on the course of this endeavor to develop artificial general intelligence, but more important than that is emphasis which Security matters the most fundamental consideration in infusing ethical principles into AI technologies is safety. The most recent in the line--GPT 3.5 -- has been offering impressive abilities related to a wide range of language processing tasks and is reflected by improvements in fields like computer-assisted translation services, automated content generation applications or even virtual assistants. Not only do the GPT models promise a transformative change for natural language processing, their announcement and reception--both within the community of artificial intelligence researchers, as well by people in various sectors of technology industry -- deserve an academic examination.

Artificial intelligence and natural product research.

Artificial intelligence and natural product research.

Artificial intelligence in career development: a scoping review

ABSTRACT Artificial intelligence (AI) has received more scholarly attention over the past few years, evidenced by the increasing volume of literature on AI. However, most of our current knowledge about AI, including its applications, has been derived from studies situated in the context of automation, medicine, and business. This article is a response to acall for expanding AI research to understand its implications for people and their career development. The goal is to provide an updated, holistic understanding of existing research on AI in career development. Through a scoping review of 101 journal publications, this article offered three insights. First, AI has a double-edged sword effect on career development. While AI technology helps streamline career development processes in organisations, it can also disrupt a workplace by causing job insecurity. Secondly, with the unintended consequences, integrating AI into organisational practices can backfire, leading employees to explore alternate careers. Lastly, infusing AI into career development programmes has caused some ethical concerns. With increasing applications of AI, humans are now expected to trust technology-based output, even when such output contradicts their own knowledge and experience. Such blind trust in AI may hinder career progression by providing inaccurate information and advice to the workforce.

Convergence analysis of artificial intelligence research capacity: Are the less developed catching up with the developed ones?

AbstractThis study examines whether less developed countries are catching up with developed ones using the log t convergence technique (LCT) and the dynamic spatial ordered probit (DSOP) model. The findings revealed that first, there is no overall convergence in AI research capacity. Second, club clustering analysis showed convergence in four of the five groups of countries on AI research output and in three of the four groups on AI patent grants. Third, the countries are experiencing a slow divergence process in AI research capacity. Fourth, the region, income group and cluster of the countries are influencing the convergence process.

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks.

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

Descriptive and Predictive Analysis of Artificial Intelligence Research and Innovation in Health

Descriptive and Predictive Analysis of Artificial Intelligence Research and Innovation in Health

The shift of Artificial Intelligence research from academia to industry: implications and possible future directions

The shift of Artificial Intelligence research from academia to industry: implications and possible future directions

Deep reinforcement learning algorithm based on multi-agent parallelism and its application in game environment

Deep reinforcement learning has become a prominent area of research in artificial intelligence in recent years. Its application in solving complex tasks and game environments has garnered significant attention. This study aims to develop a deep reinforcement learning algorithm based on multi-agent parallelism to enhance intelligent decision-making in game environments. The algorithm combines a deep Q-network with a multi-agent cooperation strategy. Through parallel training of multiple agents, the learning process is accelerated, and decision accuracy is improved. The experimental results indicated that the Actor-Critic algorithm, when combined with precision rate, recall rate, and average fitness of multi-agent parallel, achieves a relatively high accuracy rate index, which stabilizes above 0.95. The recall rate index was also above 0.8, and the average fitness was in a relatively high range. The research shows that the deep reinforcement learning algorithm based on multi-agent parallelism performs better and is more effective in game environments. It can learn the optimal strategy faster and obtain higher rewards. This not only provides a new technical means for game development but also offers a useful reference for the application of multi-agent systems in complex environments.

Artificial intelligence in the battle against COVID-19: A comprehensive review

The COVID-19 pandemic has precipitated a global crisis, affecting all facets of human life. The rapid spread of the virus necessitated urgent responses from the healthcare sector, with artificial intelligence (AI) taking center stage as a pivotal tool in this fight. This paper provides a comprehensive review of the multifaceted role of AI during the pandemic, spanning from early detection and diagnosis to treatment, management, and the development of vaccines. We delve into the ethical and societal implications of deploying AI in such critical scenarios, discussing data privacy, algorithmic bias, and accessibility. The paper also presents various case studies, highlighting country-specific implementations and the dichotomy of success stories and failures. Furthermore, we explore the future directions of AI in healthcare, emphasizing emerging technologies and policy recommendations that could shape post-pandemic health-care systems. The conclusion synthesizes these insights, reflecting on the lessons learned and the prospective landscape of AI in global health. This paper aims to serve as a cornerstone for policymakers, health-care providers, and AI researchers, guiding the responsible and effective integration of AI in future health-care strategies.