Artificial intelligence potential for net zero sustainability: Current evidence and prospects

oai:repository.uel.ac.uk:8xqzxThis comprehensive review explores the nexus between AI and the pursuit of net-zero emissions, highlighting the potential of AI in driving sustainable development and combating climate change. The paper examines various threads within this field, including AI applications for net zero, AI-driven solutions and innovations, challenges and ethical considerations, opportunities for collaboration and partnerships, capacity building and education, policy and regulatory support, investment and funding, as well as scalability and replicability of AI solutions. Key findings emphasize the enabling role of AI in optimizing energy systems, enhancing climate modelling and prediction, improving sustainability in various sectors such as transportation, agriculture, and waste management, and enabling effective emissions monitoring and tracking. The review also highlights challenges related to data availability, quality, privacy, energy consumption, bias, fairness, human-AI collaboration, and governance. Opportunities for collaboration, capacity building, policy support, investment, and scalability are identified as key drivers for future research and implementation. Ultimately, this review underscores the transformative potential of AI in achieving a sustainable, net-zero future and provides insights for policymakers, researchers, and practitioners engaged in climate change mitigation and adaptation

Roadmap to competitive and socially responsible artificial intelligence

The roadmap to competitive and socially responsible artificial intelligence (AI) offers an overview of AI governance drivers and tasks. It is intended for organizations using or planning to use information systems that include AI functionalities, such as machine learning, natural language processing, and computer vision. Responsible AI is still an emerging topic, but legal and stakeholder requirements for AI systems to comply with societally agreed standards are growing. In particular, the European Union’s proposed Artificial Intelligence Act is set to introduce new rules for AI systems used in high-risk application domains. However, beyond binding legislation, soft governance, such as guidelines and ethics principles, already seeks to differentiate between socially responsible and irresponsible AI development and use practices. The roadmap report begins by laying out its target group, instructions, and structure and then moves on to definitions. Next, we introduce the institutionalization of AI as a necessary background to the consideration of AI governance. The main roadmap section includes a visual representation and explanation of the six key drivers of competitive and socially responsible AI: 1) Movement from AI ethics principles to AI governance 2) Responsible AI commercialization potential and challenges 3) AI standardization 4) Automation of AI governance 5) Responsible AI business ecosystems 6) Stakeholder pressure for responsible AI The roadmap is followed by a future research agenda highlighting five emerging research areas: 1) operational governance mechanisms for complex AI systems, 2) connections to corporate sustainability, 3) automation of AI governance, 4) future of responsible AI ecosystems, and 5) sociotechnical activities to implement responsible AI. Researchers and research funding bodies play a key role in advancing competitive and socially responsible AI by deepening these knowledge areas. Advancing socially responsible AI is important because the benefits of AI technologies can be reaped only if organizations and individuals can trust the technologies to operate fairly, transparently, and according to socially defined rules. This roadmap was developed by the Artificial Intelligence Governance and Auditing (AIGA) co-innovation project funded by Business Finland during the years 2020 to 2022. The roadmap was cocreated by researchers, company practitioners, and other AIGA project stakeholders

Bringing Artificial Intelligence to Research Analytics Research Highlighter-MatchMaker at SUNY UAlbany

Research analytics has become an indispensable part in today’s higher education institutions’ organizational structure. It not only provides a mechanism or platform to present research awards and expenditures data to internal and external data users, but also serves a superior tool that enables data-driven strategic decision makings with more reliable, valid, and in-depth analysis. The development of Artificial Intelligence techniques could push the boundary of Research Analytics to an even higher level by incorporating the abundance of data scattered across the university. We propose to present one attempt adopted by University at Albany, State University of New York (UAlbany) to incorporate AI techniques to Research Analytics: a project called Research Highlighter-MatchMaker. The Research Highlighter-MatchMaker project aims to identify research clusters at UAlbany, recommend funding opportunities for faculty/researcher, and suggest potential collaborations. Based on AI and Big Language Models, the project will identify core research clusters by UAlbany faculty and researchers and suggest potential collaborations for multidisciplinary projects across the campus. Moreover, the project aims to recommend more suitable and relevant funding opportunities to different faculty and researchers based on their research track records including their grant proposal abstracts and publication abstracts, with a potential to incorporate more research data associated with researchers such as technology transfer agreements and compliance documents in the future. One major strength of this self-established project is that it takes advantage of AI techniques to provide more personalized and tailored matches for faculty and researchers by considering their research track records on file, instead of providing limited recommendations based on only keyword match that is adopted by many current products on the market. Our purpose is to offer personalized suggestions to faculty and researchers with well-matched funding opportunities, save their time and effort on searching and screening a long list of funding opportunities from databases on their own, and avoid the occurrence of missing good funding opportunities that they could have successfully obtained

Does artificial intelligence have a role in the IVF clinic?

Funding: K R D is supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). K D is supported by the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1 and EP/R004854/1).Lay summary The success of IVF has remained stagnant for a decade. The focus of a great deal of research is to improve on the current ~30% success rate of IVF. Artificial intelligence (AI), or machines that mimic human intelligence, has been gaining traction for its potential to improve outcomes in medicine, such as cancer diagnosis from medical images. In this commentary, we discuss whether AI has the potential to improve fertility outcomes in the IVF clinic. Based on existing research, we examine the potential of adopting AI within multiple facets of an IVF cycle, including egg/sperm and embryo selection, as well as formulation of an IVF treatment regimen. We discuss both the potential benefits and concerns of the patient and clinician in adopting AI in the clinic. We outline hurdles that need to be overcome prior to implementation. We conclude that AI has an important future in improving IVF success.Publisher PDFPeer reviewe

Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?

The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.This research was in part funded by IDIS (Instituto de Investigación Sanitaria de Santiago). Partial funding for open access charge: Universidad de Málag

Catalyzing Equity in STEM Teams: Harnessing Generative AI for Inclusion and Diversity

Collaboration is key to STEM, where multidisciplinary team research can solve complex problems. However, inequality in STEM fields hinders their full potential, due to persistent psychological barriers in underrepresented students' experience. This paper documents teamwork in STEM and explores the transformative potential of computational modeling and generative AI in promoting STEM-team diversity and inclusion. Leveraging generative AI, this paper outlines two primary areas for advancing diversity, equity, and inclusion. First, formalizing collaboration assessment with inclusive analytics can capture fine-grained learner behavior. Second, adaptive, personalized AI systems can support diversity and inclusion in STEM teams. Four policy recommendations highlight AI's capacity: formalized collaborative skill assessment, inclusive analytics, funding for socio-cognitive research, human-AI teaming for inclusion training. Researchers, educators, policymakers can build an equitable STEM ecosystem. This roadmap advances AI-enhanced collaboration, offering a vision for the future of STEM where diverse voices are actively encouraged and heard within collaborative scientific endeavors.Comment: 21 pages, 0 figure, to be published in Policy Insights from Behavioral and Brain Science

HOW TO DEFINE AN ORGANIZATION’S MATURITY FOR ADOPTING ARTIFICIAL INTELLIGENCE SOLUTIONS

Artificial intelligence (AI) is the most anticipated technology to date. Proven to increase value on a multitude of business enterprises, nations and private organization are racing to develop and adopt the most sophisticated AI solutions. Even as funding for development surges, few organizations comprehend how to properly implement AI solutions. The following thesis investigates the requirements for an organization to adopt AI solutions. Additionally, it provides a tool for evaluating maturity and readiness for adopting AI solutions. The investigation was conducted in a large organization, data was collected with semi-structured themed interviews and analyzed by grounded theory research methodology. The study involved eight (8) respondents from the investigated organization and three (3) experts from renowned consulting organizations. The literature review was conducted by studying the research of acknowledged articles and advocated literature from the AI experts and community. The study concluded the most significant enablers for organizations to adopt AI solutions are: availability of the training data, workforce abilities and experience, organizational status on current AI solutions, as well as mentality and future ambitions. The results implicate that the organizations should focus on development of their workforce abilities and experience on modern AI tools and methods. Moreover, the organization must be able to collect, transfer and store the data in the means of quality and quantity. Furthermore, the data availability and usability must be well assessed. Therefore, employees can access data effortlessly and process data according to the data privacy and security policies

Malaria detection using Deep Convolution Neural Network

The latest WHO report showed that the number of malaria cases climbed to 219 million last year, two million higher than last year. The global efforts to fight malaria have hit a plateau and the most significant underlying reason is international funding has declined. Malaria, which is spread to people through the bites of infected female mosquitoes, occurs in 91 countries but about 90% of the cases and deaths are in sub-Saharan Africa. The disease killed 4,35,000 people last year, the majority of them children under five in Africa. AI-backed technology has revolutionized malaria detection in some regions of Africa and the future impact of such work can be revolutionary. The malaria Cell Image Data-set is taken from the official NIH Website NIH data. The aim of the collection of the dataset was to reduce the burden for microscopists in resource-constrained regions and improve diagnostic accuracy using an AI-based algorithm to detect and segment the red blood cells. The goal of this work is to show that the state of the art accuracy can be obtained even by using 2 layer convolution network and show a new baseline in Malaria detection efforts using AI