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This paper delves into the intricate relationship between Artificial Intelligence (AI) and the art ecosystem, emphasizing the need for a decolonizing approach in the face of AI's growing influence. It argues that the development of AI is not just a technological leap but also a significant cultural and societal moment, akin to the advent of moving images that Walter Benjamin famously analyzed. The paper examines how AI, particularly in its current oligarchical and corporate-driven form, perpetuates and magnifies the existing social inequalities, thereby necessitating a critical and radical rethinking of its role in society and the arts. At the heart of the discussion is the concept of AI as a broad term encompassing various forms of machine intelligence, from natural language processing to computer vision. The paper criticizes the dominant anthropocentric view of intelligence and creativity, proposing a more inclusive approach that considers the diverse forms of intelligence present in other species and potentially in AI itself. It underscores the role of AI in shaping the art ecosystem, not just in the creative process but also in gatekeeping and decision-making. The paper proposes a framework for decolonizing AI in the art ecosystem, focusing on four key tasks: recognizing access as a form of power, understanding and addressing biases inherent in AI, assessing the impact of AI on marginalized communities, and challenging dominant narratives and epistemologies to create space for alternative voices and perspectives. It emphasizes the need for artists and the art community to engage actively with AI, shaping its development towards more equitable and just outcomes. In conclusion, the paper calls for a radical reimagination of AI's role in society and the arts, advocating for a future where AI is not just about technological advancement but also about fostering a more inclusive, equitable, and creatively diverse world. It invites artists, thinkers, and innovators to join in this journey of reimagining and reshaping the future of AI and the art ecosystem. [ABSTRACT FROM AUTHOR]

In the United States, recent changes to the National Statistical System have amplified the geographic-demographic resolution trade-off. That is, when working with demographic and economic data from the American Community Survey, as one zooms in geographically one loses resolution demographically due to very large margins of error. In this paper, we present a solution to this problem in the form of an AI based open and reproducible geodemographic classification system for the United States using small area estimates from the American Community Survey (ACS). We employ a partitioning clustering algorithm to a range of socio-economic, demographic, and built environment variables. Our approach utilizes an open source software pipeline that ensures adaptability to future data updates. A key innovation is the integration of GPT4, a state-of-the-art large language model, to generate intuitive cluster descriptions and names. This represents a novel application of natural language processing in geodemographic research and showcases the potential for human-AI collaboration within the geospatial domain. [ABSTRACT FROM AUTHOR]

Prominent works in the space of Natural Language Processing (NLP) have long attempted to create new innovative models by improving upon previous model training approaches, altering model architecture, and developing more in-depth datasets to better their performance. However, with the quickly advancing field of NLP comes increased greenhouse gas emissions, posing concerns over the environmental damage caused by training LLMs. Gaining a comprehensive understanding of the various costs, particularly those pertaining to environmental aspects, that are associated with artificial intelligence serves as the foundational basis for ensuring safe AI models. Currently, investigations into the CO2 emissions of AI models remain an emerging area of research, and as such, we evaluate the CO2 emissions of well-known large language models, which have an especially high carbon footprint due to their significant amount of model parameters. We argue for the training of LLMs in a way that is responsible and sustainable by suggesting measures for reducing carbon emissions. Furthermore, we discuss how the choice of hardware affects CO2 emissions by contrasting the CO2 emissions during model training for two widely used GPUs. Based on our results, we present the benefits and drawbacks of our proposed solutions and make the argument for the possibility of training more environmentally safe AI models without sacrificing their robustness and performance. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the effectiveness of using natural language processing (NLP) artificial intelligence (AI) in enterprise resources planning (ERP) to identify specialized job candidates in real-time big data--globally across the internet. The central problem was that companies using traditional methods for recruiting remote specialists were missing good candidates because the skilled employees may not be looking for a job yet they may be receptive to an offer. The auxiliary problem was too much data on the internet for human resources management (HRM) staff to make sense of to find the best-fitting candidate. Thus, the research question was: could NLP AI identify good candidates for ERP remote specialist jobs using internet real-time big data? Job criteria were developed using machine learning to identify key skills from existing staff in a case study company. The skills were transformed into ERP remote specialists hiring criteria. The NLP AI software was activated to find the best candidate. The HRM staff at the case study company evaluated the effectiveness of the candidate selected by the NLP AI. The case study company set 70% as the acceptable mean evaluation score. ANOVA was used to determine if HRM staff agreed about their evaluation scores. A Z-test was used to determine if the NLP AI was faster than the mean time needed for HRM to select ERP candidates. The results were that the NLP AI outperformed the humans by a factor of almost 8 h. All HRM staff agreed that the NLP AI was effective in selecting a candidate to match the hiring criteria. The proposed approach might facilitate the research and development of big data, data analytics, NLP AI, and HRM process improvement. [ABSTRACT FROM AUTHOR]

We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist's report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists. [ABSTRACT FROM AUTHOR]

Over the last decade, there has been great research interest in the application of artificial intelligence (AI) in various fields, such as medicine, finance, and law. Recently, there has been a research focus on the application of AI in education, where it has great potential. Therefore, a systematic review of the literature on AI in education is therefore necessary. This article considers its usage and applications in Latin American higher education institutions. After identifying the studies dedicated to educational innovations brought about by the application of AI techniques, this review examines AI applications in three educational processes: learning, teaching, and administration. Each study is analyzed for the AI techniques used, such as machine learning, deep learning, and natural language processing, the AI tools and algorithms that are applied, and the main education topic. The results reveal that the main AI applications in education are: predictive modelling, intelligent analytics, assistive technology, automatic content analysis, and image analytics. It is further demonstrated that AI applications help to address important education issues (e.g., detecting students at risk of dropping out) and thereby contribute to ensuring quality education. Finally, the article presents the lessons learned from the review concerning the application of AI technologies in higher education in the Latin American context. [ABSTRACT FROM AUTHOR]