AI in Research

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https://africarxiv.ubuntunet.net/handle/1/1629
The use of AI to advance scientific research, improve data analysis, and enable discoveries across disciplines, including the acceleration of drug discovery and climate modeling.

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    Artificial Intelligence: The Game Changer in Scientific Research
    (2024-08-08) Ilegbusi, Paul
    Artificial Intelligence (AI) has revolutionised scientific research by enhancing data analysis, accelerating research processes, and improving accuracy. AI's applications span various fields, including biomedicine, environmental science, physics, and materials science. This paper explores AI's transformative impact on scientific research, highlighting its role, applications, challenges, and future prospects. AI tools, such as Explain Paper, Paper Digest, and Chatdoc, facilitate research by summarizing papers, explaining complex concepts, and assisting with literature reviews. Despite AI's benefits, challenges persist, including data privacy and security concerns, bias, and transparency issues. To address these challenges, the paper emphasizes the need for ethical guidelines, robust security measures, and interpretable AI models. The future of AI in scientific research holds promise, with emerging trends and technologies, interdisciplinary innovations, and collaborative platforms driving progress. The paper concludes by highlighting the importance of addressing AI's challenges to ensure its beneficial impact on science and society.
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    The integration of Artificial intelligence (AI) in literature review and its potentials to revolutionize scientific knowledge acquisition
    (2024-04-28) Ilegbusi, Paul
    This presentation discusses the role of artificial intelligence (AI) in enhancing the literature review process and its potential to transform scientific knowledge acquisition. The presentation highlights the importance of literature review in research and the challenges associated with the traditional manual approach. The presentation emphasizes that integrating AI in literature review can significantly improve efficiency, accuracy, and reduce bias. AI-powered tools can automate various aspects of the literature review process, including search, selection, analysis, and synthesis of relevant literature. The benefits of AI in literature review include increased efficiency, improved coverage of literature, and the ability to identify gaps in knowledge and uncover new research questions. The presentation also provides a comprehensive list of AI tools that can be used in literature review, such as Cramly.ai, Quillbot, GPT-minus 1, ChatGPT, Samwell.ai, and many others. These tools offer functionalities such as rewriting, paraphrasing, summarizing, understanding literature, and extracting key information from articles. The future of AI in literature review is promising, with emerging trends such as deep learning models and knowledge graphs. These trends have the potential to enhance the accuracy and comprehensiveness of literature reviews. In conclusion, the integration of AI in literature review has the potential to revolutionize scientific knowledge acquisition by improving efficiency, accuracy, and coverage of literature. By combining AI with human expertise, researchers can unlock new insights and accelerate scientific progress in various fields.
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    Increasing Discovery in Research, Design, and Other Processes with Artificial General Intelligence and General Collective Intelligence
    (2020-12-17) Williams, Andy
    Any system with repeatable behavior can potentially be defined with the minimal set of functions that might be composed to represent the entirety of that behavior. The states accessible through these functions then forms a “functional state space” through which the system moves. Since functional states spaces can be used to represent every problem domain from physics, to communications, to business operations, to the human cognition itself, a general approach to not only research but design and all other processes of discovery that is applicable to all domains can potentially be defined to radically increase capacity for discovery in each domain.
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    Trustworthy Machine Information Behaviour and Open Research Repositories
    (2023-06-14) Simango, Samuel
    The perception regarding the nature of repository users is changing as a result technological advancements. This is reflected in arguments contending that machines should be recognised as users of library services. Such arguments are based on the view that library collections such as open repositories should be considered as data that can be used by artificially intelligent machine users. These arguments raise questions regarding the concept of trust as they do not actually address attributes that machines users must possess in order for them to be considered trustworthy. This research develops a conceptual framework for understanding the parameters within which trustworthy machine information behavior can emerge. This outcome is achieved by applying machine ethics to a modified version of Wilson's general theory of information behavior that incorporates elements of machine learning. The results indicate that the level of trust placed in machines users is dependent on the algorithms and software used for programming such AI systems as well as the actions of humans who make use of such machine users. In order for any semblance of trust in machine users of open repositories to exist, the machine information behavior employed by the machine users should adhere to certain ethical principles.