MATILDA: Inclusive Data Science Pipelines Design through Computational Creativity

The digital revolution made available vast amounts of data both in industry and in the research landscape. The ability to manipulate and extract knowledge and value from this data represents a new profession called the Data Scientist: expected to be the most visible job in future years. The EDISON project has been established in order to support universities, research centers, industry and research infrastructure organisations to cope with the potential shortfall of Data Scientists, to define the framework of competences as well as the body of knowledge for this profession. In this paper the EDISON team describes how it intends to nurture the profession of Data Scientist to cope with the expected increase in demand. The strategy proposed is based on both the analysis of the demand side (industries, research centers and research infrastructure organisations) and the supply side (Universities and training centers) bridging between the providers and employers by cooperating on the establishment of a Competence Framework and a Body of Knowledge for the Data Scientist Professional. The project will exploit piloting initiatives in cooperation with pioneer universities and also involve external experts as evangelists.

The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher and not objectively at all. This article provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers (1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and (2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system and may be employed by the researcher to both better understand the efficacy of their system and its impact and to incorporate feedback into the system. Here we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition and can be used to demonstrate the impact and novelty of particular approaches.

Computational creativity has gained in the recent years momentum as an established interdisciplinary field, whose aim is to investigate, design and foster artificial intelligence. This article analyses whether artificial outputs can appear to as being creative and assesses thus computational creativity from the standpoint of the receiver. While taking Boden’s distinction between several forms of creativity as a starting point, I argue that creativity implies besides surprise and value an additional essential feature, namely the creative intention, not only from the standpoint of the creator but also of the receiver. Following the notion that I propose, creative intention cannot be identified to the mere absence of randomness, but implies the possibility of interpreting the intention, as well as volition and the expression of personal lived experiences. Subsequently I reflect on the possibility of attributing creativity to computer programs while being aware of their lack of creative intention. I argue that the best solution for this purpose is considering computer programs as co-creative partners, which can be credited as contributors to the creative process. In my conclusive remarks, I assess the implications of this possibility for the notion of creative subject. More precisely, I raise the question whether the latter should necessarily be linked to the notion of creativity, and hence, whether co-creative artificial programs should necessarily be considered as creative subjects.

The Fourth industrial revolution to be successfully implemented requires higher levels of creativity and innovation in the collaboration, design and composition of its cyber-physical building blocks. Powering industry 4.0 by creativity and innovation triggers efforts for better understanding of human creativity concepts and its intersection with cyber-physical systems in industry 4.0. Since computational creativity is an emerging field of research within AI that focuses on theoretical and practical issues in the study of creativity, the goal of this paper is to motivate a discussion about the application of computational creativity to design cyber physical production systems, which are at the core of industry 4.0. The fundamental research question in the paper is about the applicability of computational creativity in an industry 4.0 context.

Creativity has become a crucial topic in several disciplines such as computational creativity. Co-creativity is a rapidly growing research field in designing creative systems and artificial intelligence (AI) agents. Co-creative systems can use neuroscience to detect temporal activations to both control aspects of the user interaction and validate creativity theories. This work briefly introduces co-creative and computational creativity, presents the recent works of co-creative agent. It also introduces my future study of investigating divergent and convergent thinking in developing co-creative systems, reports my progress in research and doctoral program

This paper describes a thesis exploring how computer programs can collaborate as equals in the artistic creative process. The proposed system, CoCo Sketch, encodes some rudimentary stylistic rules of abstract sketching and music theory to contribute supplemental lines and music while the user sketches. We describe a three-part research method that includes defining rudimentary stylistic rules for abstract line drawing, exploring the interaction design for artistic improvisation with a computer, and evaluating how CoCo Sketch affects the artistic creative process. We report on the initial results of early investigations into artistic style that describe cognitive, perceptual, and behavioral processes used in abstract artists making.

This narrative review introduces key concepts in artificial intelligence (AI), data science, and wearable devices aimed at headache clinicians and researchers. PubMed and IEEEXplore were searched to identify relevant studies, and these were reviewed systematically. We identified six primary research topics. First, the most common application of AI and data science is in the diagnosis of headache disorders, with reported accuracies of up to 90%. Second, AI and data science are used for predicting headache disease trajectories and forecasting attacks. Third, prediction of treatment effects and data-driven individualization of treatment prescription demonstrate promising results, with accuracies ranging from 40% to 83%. Fourth, AI and data science can uncover hidden information within headache datasets, offering clinicians deeper insights. Fifth, wearables, combined with AI and data science, can improve remote monitoring and migraine management. Lastly, user experience studies indicate strong interest from both clinicians and patients in adopting these technologies. The potential applications of AI, data science, and wearable device technologies in headache research are vast. However, many studies are small pilot studies, and models often suffer from poor performance, limited reporting, and lack of external validation, which impede generalizability and clinical implementation.

Data science is defined as the collection of basic principles that support and lead the extraction of information and knowledge from data (Provost & Fawcett, 2013). It is an interdisciplinary field as it requires mathematics, statistics, computer sciences, natural sciences, journalists, sociology, psychology and other disciplines to afford knowledge from data (Igual & Seguí, 2017).

This paper focuses on explicit attempts at developing artificial intelligence in the production of art that generate outcomes similar to, or even technically superseding, the works of human artists. We aim at revealing the underlying discourses that equate art production with transformation of information, artists with input/output systems, and artistic creativity with an unlimited and autonomous generation of art-like outcomes. As a point of departure, we begin from an exposition of Margaret Boden’s account of creativity and proceed by examining different arguments to the effect that computers can be truly creative, primarily those offered by Boden (2004, 2010). We question what the assumptions, operative in the discourse on artificial or computational creativity, entail. AI-agents can produce creative outcomes because they implement our best models of creativity. By implementing these models, however, AI-agents evidence a particular understanding of what art is and what constitutes artistic production. This understanding does not fully conform to how contemporary artistic practices are perceived and valued. As a result, we argue, better models to frame artistic AI and computational creativity are needed to fully appreciate the developments in this field and their articulation within the existing art world.

Heterogeneous catalysis has a wide range of applications in chemical manufacturing and sustainable technologies. It uses solid catalysis to enable efficient chemical transformations. Traditional research on active sites and reaction mechanisms relies heavily on experiments and computational methods, such as density functional theory calculations. However, the volume of scientific literature and data is growing fast. This rapid growth has made it increasingly difficult to capture, process, and act on emerging insights systematically. Recently, large language models (LLMs) have emerged as powerful tools to support various stages in catalysis research. Their ability to understand and generate natural language helps them extract useful information from vast amounts of text, assist in catalyst design, aid in planning experiments, and clarify complex descriptors. In this advanced review, we first analyze recent progress in applying LLMs to heterogeneous catalysis, focusing on four key areas: literature mining and knowledge extraction, catalyst design and screening, experiment automation and workflow optimization, and the interpretation of high‐dimensional descriptors. We then highlight the challenges in this field despite these advances, most notably the need for domain‐specific fine‐tuning and the improvement of molecular representation. We conclude by discussing future opportunities for integrating LLMs with complementary machine learning approaches and expert‐in‐the‐loop systems, toward accelerating the rational discovery of next‐generation catalysts. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Chemoinformatics Theoretical and Physical Chemistry > Reaction Dynamics and Kinetics

Large cohort study gained its popularity in biomedical research and demonstrated its application in exploring disease etiology and pathogenesis, improving the prognosis of disease, as well as reducing the burden of diseases. Data science is an interdisciplinary field that uses scientific methods from computer science and statistics to extract insights or knowledge from data in a specific domain. The results from the combination of the two would provide new evidence for developing the strategies and measures on disease prevention and control. This review included a brief introduction of data science, descriptions on characteristics of large cohort data according to the development of the study design, and application of data science at each stage of a large cohort study, as well as prospected the application of data science in the future large cohort studies.

Now a day Data science is emerging field , before data science we had statisticians. These statisticians are skilled person who are evaluate records and organizations hired them to research their standard overall performance and income. Data science is a booming field of study which has a multidimensional scope for all organizations and industries. Data Science has lots of scientific methods which includes statistical techniques, machine learning, artificial intelligence all together we all know from earlier time the mathematics can solve the once complex problems. It gives various information on emerging trends and patterns in a specific model . Data science provides with various methods to analyzed data, and make predictions on the data. The basic objective of this paper is to explore the techniques of data science , tools which are available as an open source for data science and various tools associated with it..

Introduction/Main Objectives: This research explores the impact of Artificial Intelligence (AI) on human creativity, by asking whether AI acts as a tool to support human creativity or as a substitute. Background Problems: AI has opened up great opportunities to enrich the creative process in various fields, but has raised concerns about the potential replacement of human creativity. Novelty: This research makes a unique contribution by exploring the interaction between AI and human creativity, offering new insights into how their synergy can drive innovation while highlighting the importance of considering the ethical and social implications of these technological developments. Research Methods: This research uses a qualitative approach through a literature review, analyzing relevant literature from the last ten years (2014-2024) that includes scholarly journals, articles, and e-books. Data was analyzed descriptively to understand the impact of AI on human creativity and sustainability in business practices. Finding/Results: Collaboration between AI and humans is proven to support sustainable business practices through more efficient and environmentally friendly innovations, and strengthen long-term competitiveness without compromising creative and human values. However, oversight is needed to ensure AI does not replace the unique essence of human creativity, which is emotional, intuitive and based on personal experience. Conclusion: This study emphasizes the importance of harmonious collaboration between AI and humans, where AI acts as a supportive tool that enriches creative ideas without compromising the essential value of human creativity. The findings provide deep insights into the interaction between AI and humans in the context of creativity and its implications for business sustainability and the future of creative work.

This research explores the transformative synergy arising from the convergence of data science and artificial intelligence (AI), propelling the boundaries of insights to unprecedented levels. As data science and AI realms interlace, a paradigm shift unfolds, transcending conventional analytical approaches. This study delves into the novel methodologies and applications stemming from this fusion, shedding light on how it reshapes the landscape of information discovery.The investigation scrutinizes advanced machine learning algorithms, deep neural networks, and cognitive computing models within the context of data science applications. By intertwining these technologies, the research unveils the emergence of intelligent systems capable of deciphering intricate patterns in vast datasets and generating anticipatory insights that transcend traditional analytical boundaries. The synergy goes beyond predictive prowess, delving into prescriptive analytics, enabling proactive decision-making and strategic foresight.Ethical considerations and interpretability of AI-driven data science models are addressed, emphasizing the need for responsible and transparent deployment in diverse fields. Through case studies and practical examples, this research demonstrates the tangible impact of this fusion on industries such as healthcare, finance, and beyond. Ultimately, "Beyond the Horizon" presents a compelling narrative of how data science and AI fusion propels us into an era of unparalleled insights, reshaping our approach to knowledge extraction and decision-making.

The inclusion of artificial intelligence (AI) and machine learning (ML) in biomedical engineering opens new frontiers of innovation and better decision‐making and allows the art to cross new thresholds in healthcare technologies. This review focuses on the primary contributions of AI and ML to the advancement of biomedical engineering, particularly in the areas of diagnostic tools, predictive analytics, and personalized medicine. This will further allow us to identify possible the state‐of‐the‐art solutions by using new frameworks for applications including medical imaging, wearables, and biomanufacturing. Also, it reflects on how ethics in AI and ML for biomedical challenges address important issues such as bias, privacy, and accountability. It also underlines how different opportunities and challenges can be opened or addressed by the integration of AI‐driven systems in biomedical workflows: engineering, clinicians, and data scientists have to cooperate. Emerging technologies, including but not limited to deep learning, natural language processing, and reinforcement learning, are discussed for their potential to alter biomedical research and clinical practice. The work concludes with a look at the future of biomedical engineering, where AI and ML have brought a domain of synergy into innovation, better patient outcomes, and impactful advancement. It thus also provides a prescription for the need for ethics in the adoption of AI, together with collaborative efforts toward maximum transformative technology.