[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127274-en":3,"doc-seo-127274-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127274,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Artificial Intelligence-Augmented Machine Learning for Autonomous Scientific Discovery in Interdisciplinary Research","Artificial intelligence combined with machine learning can dramatically accelerate progress across multiple scientific domains, yet existing workflows still rely heavily on human experts for hypothesis construction and model refinement. The paper proposes an autonomous transformation in which AI-augmented ML agents independently hypothesize, simulate, interpret results, and optimize models. Applications are motivated by genomics, environmental science, and quantum chemistry, enabling faster multidisciplinary discovery through digital scientists that classify mutations, forecast climate, and identify new chemicals. Key concerns include explainability, data bias, ethical implications, and collaborative research sandboxes.","Artificial Intelligence-Augmented Machine Learning for Autonomous Scientific Discovery in  \nInterdisciplinary Research  \nMd Mehedi Hassan  \nSchool Of IT, Washington University of Science and Technology  \n[mehedi61@gmail.com](mehedi61@gmail.com)  \nSyed Nurul Islam  \nSchool Of IT, Washington University of Science and Technology  \n[snislam.student@wust.edu](snislam.student@wust.edu)  \nRakshya Sharma  \nWorld Health Organisation  \n[sharmarakshya7@gmail.com](sharmarakshya7@gmail.com)  \nSaila Nasrin  \nSchool Of IT, Daffodil International University  \n[sailanasrin92@gmail.com](sailanasrin92@gmail.com)  \nAmit Banwari Gupta  \nSchool Of IT, Washington University of Science and Technology  \n[amit.gupta@wust.edu](amit.gupta@wust.edu)  \nAbstract  \nWhen Artificial Intelligence (AI) and Machine Learning (ML) are applied together, they vastly accelerated the process of improvement of numerous fields of science. However, despite the existence of existing systems, even now people have tobe involved in the process of hypothesis formulation and adjustment of the models to a great extent. In this paper, a vision of fully autonomous transformation of the AI-augmented ML systems is extrapolated, with the agent being free to hypothesize, simulate, interpret the outcome, and optimize the models independently of the existential guiding presence of human. The trends in genomics, environmental sciences, and quantum chemistry have prompted the creation of the described framework, which is the accomplishment of a new breed of intelligent digital scientists who could increase the speed and reach of the multi-disciplinary research many times over.  \nThe conceptualization of AI agent usage in the study is methodologies in such a way as to take into account the fact that the agents do not participate in scientific discovery as status quo tools or accessories but also as active explorers of issues. Such agents take advantage of representational capacities of the generative models and the reinforcement learning and neural-symbolic systems in response to vast stretches of complex data to infer underlying patterns and run experiments. As illustrated in the case scenarios, it is possible to use this AI system to automatically classify genetic mutations, forecast climate and discover new chemical substances. Visual representations, i.e., process diagrams, performance graphs, and application maps are used to express technical exposition.  \nOther constrains associated with this article are explain ability of the knowledge that the AI produces, risk of biases in  \ndata-driven research, and ethical implications of making the discovery system completely autonomous. Finally, it offers visioning with the indication that the global AI research sandboxes are established in such a way that digital scientists can collaborate across fields and with guided mentoring. The work triggers the threshold of the paradigm shift since the AI-augmented ML systems will no longer be the assistant role in focus, as this role will shift to being the core of future development of interdisciplinary science.  \nKeywords: AI-Augmented Machine Learning, Autonomous Scientific Discovery, Interdisciplinary Research, Digital Scientist Framework, Simulation-Driven Hypothesis Generation  \n1. Introduction  \nHuman intuition and experimentation together with  \ninterpretation have been significant even long before scientific discoveries are significant. Despite the fact that computational tools and machine learning (ML) have enhanced a wide range of disciplines, such as genome sequencing and climate forecast, they have mostly served an auxiliary purpose, where the machine learning systems have assisted the researcher rather than coming up with new knowledge. Now there is anew opportunity to dismantle the centuries-old boundaries of science with the help of a combination of AI and advanced ML approaches. In this paper, I would like to support the idea that the future of research is the further elaboration of m","cbCaiubTIkXoDk4w","https://ap.wps.com/l/cbCaiubTIkXoDk4w","pdf",593152,1,16,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the paper address in current AI-ML scientific workflows?\",\"answer\":\"Existing systems still require substantial human involvement for hypothesis formulation and for adjusting models, limiting full autonomy in scientific discovery.\"},{\"question\":\"How does the proposed approach achieve autonomous scientific discovery?\",\"answer\":\"Agents freely hypothesize, run simulations, interpret outcomes, and optimize models independently, using generative representations, reinforcement learning, and neural-symbolic reasoning.\"},{\"question\":\"What are the main application scenarios mentioned?\",\"answer\":\"The paper describes using the system to classify genetic mutations, forecast climate, and discover new chemical substances across domains like genomics, environmental sciences, and quantum chemistry.\"}]","Artificial Intelligence-Augmented Machine Learning for Autonomous Scientific Discovery in Interdisciplinary Research | 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problem does the paper address in current AI-ML scientific workflows?","Question",{"text":75,"@type":76},"Existing systems still require substantial human involvement for hypothesis formulation and for adjusting models, limiting full autonomy in scientific discovery.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach achieve autonomous scientific discovery?",{"text":80,"@type":76},"Agents freely hypothesize, run simulations, interpret outcomes, and optimize models independently, using generative representations, reinforcement learning, and neural-symbolic reasoning.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main application scenarios mentioned?",{"text":84,"@type":76},"The paper describes using the system to classify genetic mutations, forecast climate, and discover new chemical substances across domains like genomics, environmental sciences, and quantum 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