Seven questions are examined: the erosion of the intergenerational transmission of scientific competence; the growing opacity of AI-generated theories; the collapse of peer evaluation under a flood of machine-generated output; the unproven capacity of AI for paradigm-shifting discovery; the capture of the scientific agenda by political and industrial actors; the compounding of systematic errors in closed-loop pipelines; and the structural bifurcation of the global research community into incommensurable tiers.
Abstract
Artificial intelligence is transforming scientific research - not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself. This transition constitutes, in the most precise sense of the term, the industrialization of research: a shift from a craft model, in which knowledge, method, and judgment are embedded in the researcher, to a pipeline model, in which these steps are decomposed, automated, and supervised. The US Department of Energy's Genesis Mission is the most ambitious current instantiation of this shift, but the fundamental questions it raises extend far beyond any single program. This essay examines seven such questions: the erosion of the intergenerational transmission of scientific competence; the growing opacity of AI-generated theories; the collapse of peer evaluation under a flood of machine-generated output; the unproven capacity of AI for paradigm-shifting discovery; the capture of the scientific agenda by political and industrial actors; the compounding of systematic errors in closed-loop pipelines; and the structural bifurcation of the global research community into incommensurable tiers. These concerns do not constitute an argument against AI-driven science - whose demonstrated potential is real and significant. They constitute the conditions under which that potential can be responsibly pursued.
The advancement of AI in science raises broader questions concerning the division of cognitive labour between human researchers and machines, and a typology of AI systems used in research is proposed, ranging from specialised scientific AI through scientific AI assistants and agents to hybrid experimental systems that combine computation and physical experimentation.
P. Jedlička· Teorie vědy / Theory of Scie...· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
An integrative, stage-based analysis of how contemporary AI systems—particularly large language models, retrieval-augmented systems, and tool-using agents—are reshaping the research lifecycle, from the earliest formulation of a research question through data analysis, manuscript preparation, peer review, and post-publication dissemination is offered.
Dr. Gagandeep Singh, Ravi Ranjan, Anirudh Gupta, Harinakshi Aravind Shetty· International Journal of Adv...· 0 citations
Artificial intelligence has evolved from specialized tools to comprehensive, goal-oriented systems that can learn, adapt, and soon even reason. New models that can write code, produce art, forecast market movements, and support decisions in all spheres of society are being developed every week. But this book covers more than just technology. Power exists. Ethics are a factor. It has to do with intelligence, the nature of future work, and people in general. It is up to the pioneers, leaders, and curious thinkers who must now lead us through the most disruptive time in our history.
Dr. Jörg Storm
is an international keynote speaker, podcast host, and lecturer at several academic institutions. He holds a Ph.D. in Economics and is recognized for turning technological complexity into crisp, board-level decisions and measurable business outcomes.
Artificial intelligence (AI) has moved within a single decade from a specialised computational method to a general-purpose research instrument that now touches almost every stage of the scholarly lifecycle, from problem formulation and literature synthesis to experimentation, analysis, writing and peer review. This paper asks three connected questions: where AI produces genuine innovation in research rather than incremental automation; where it delivers measurable efficiency; and what conditions of responsible use must hold for those gains to be epistemically and ethically legitimate. The study adopts a structured narrative review of peer-reviewed literature, policy instruments and editorial guidance published between 2016 and 2026, and synthesises the evidence through thematic coding into a conceptual model. We find that innovation gains cluster in domains where AI compresses very large combinatorial search spaces—protein structure prediction, molecular and materials screening, and simulation surrogates—while efficiency gains are widest in language-intensive and data-curation work, where they are also most weakly audited. The dominant risks are epistemic rather than merely procedural: fabricated citations, homogenisation of research questions, and an illusion of explanatory depth in which fluent output is mistaken for understanding. We argue that disclosure statements alone are an insufficient governance response, and propose the Innovation–Efficiency–Responsibility (IER) framework, comprising three pillars, twelve operational levers and a five-level institutional maturity scale. Implications are drawn for universities, funders, publishers and policymakers, with particular attention to resource-constrained institutions in India and comparable settings.
Ritesh Kandari, Dr. Vinod Kumar Kanakapura Channan, Dr. Amita Garg, Ravi Ranjan· International Journal of Adv...· 0 citations
Drawing upon recent academic publications and reports, this paper systematically examines current developments in artificial intelligence (AI) through a five-stage framework encompassing technology, applications, expectations and reality, risks and safety, and control and governance. Based on this analysis, the paper identifies key challenges and proposes future directions for AI research and development.
Following the emergence of ChatGPT, large language models (LLMs) have rapidly proliferated. Their core component, the transformer, has expanded beyond natural language processing into diverse domains such as computer vision (Vision Transformer, ViT) and multivariate time-series analysis (Time Series Transformer, TST). Furthermore, LLMs are evolving into agent-based systems and are being applied to the automation of scientific research, as exemplified by Google’s Co-Scientist, AlphaEvolve, and AlphaGenome. These developments have significantly heightened expectations regarding the transformative potential of AI across society.
However, a gap remains between these expectations and the current state of technology, as structural issues such as data bias and hallucination continue to pose substantial risks. In particular, hallucination is interpreted as a phenomenon arising from the model’s tendency to maximize expected evaluation outcomes, and it is identified as a critical challenge for ensuring AI safety.
Accordingly, the safe deployment of AI requires effective monitoring of model behavior and improved interpretability of chain-of-thought (CoT) reasoning processes, red-teaming activities at both macro- and micro-levels, and the establishment of international governance frameworks, including those in the healthcare domain such as guidelines from the World Health Organization (WHO).
In conclusion, while AI is driving profound changes not only in science and technology but also across society as a whole, addressing technical challenges— such as mitigating hallucination, preventing catastrophic forgetting in continual learning, and improving data efficiency—must be accompanied by the development of control and governance systems aligned with human values. In particular, international governance initiatives are needed to reduce disparities between countries and address polarization at the global level.
Sang-Hoon Oh· Liberal Arts Innovation Cent...· 0 citations
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