A survey of the past and future of AI Scientists: machines capable of automating science, which have the potential to transform science and create a new form of science that will create a new form of science and transform the world.
Abstract
We present a survey of the past and future of AI Scientists: machines capable of automating science. AI Scientists can originate hypotheses, deduce their consequences, design and execute experiments, interpret their results, and revise their beliefs. Such systems are integrated scientific agents, connected to the literature, formal knowledge, mathematical models, simulations, data-analysis systems and physical laboratories. Adam was the first machine to make novel scientific discoveries through cycles of hypothesis formation and physical experimentation. Eve established the architecture of the modern self-driving laboratory. Foundation models, autonomous agents and laboratory robotics now make it possible to build systems far more general than either Adam or Eve. The central problem is no longer whether individual components of science can be automated. They can. The problem is integration. AI Scientists must combine neural learning with logic, probability, mathematics, causal reasoning, simulation, experimental design, robotics and formal scientific records. AI Scientists have the potential to transform science: to make science faster, cheaper, more systematic and more reproducible. AI Scientists could investigate systems too complicated for unaided human science, and enable thousands of AI scientists to work together on single problems. The Nobel Turing Challenge sets the goal of developing by 2050 AI systems capable of automating Nobel-quality discoveries. Progress is ahead of schedule. When we succeed it will create a new form of science and transform the world.
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
This paper examines the growing role of AI in scientific discovery. It first surveys the rapid rise of AI capabilities, especially in reasoning, abstraction, planning, and long-horizon task execution, before turning to scientometric evidence of AI’s diffusion across the sciences. It then proposes a typology of AI systems used in research, ranging from specialised scientific AI through scientific AI assistants and agents to hybrid experimental systems that combine computation and physical experimentation. On this basis, it offers a selective overview of recent achievements in mathematics and computer science, physics, chemistry, the life sciences, and the behavioural and social sciences. It argues that, despite these advances, current systems remain constrained by important technical, epistemic, and institutional limitations, and that their growing use introduces both near-term and longer-term risks. The conclusion further suggests that the advancement of AI in science raises broader questions concerning the division of cognitive labour between human researchers and machines.
P. Jedlička· Teorie vědy / Theory of Scie...· 0 citations
Abstract Artificial intelligence (AI) is transitioning from a research aid to a scientific discovery agent. The new paradigm of “AI + Science” (AI for Science, AI4S) – the intelligent science paradigm (or the fifth paradigm of science) – characterized by the deep integration of artificial intelligence into the entire process of scientific discovery, is rapidly emerging and becoming a “new agent” for intelligent and autonomous execution of scientific discovery and technological invention as well as a key force in reshaping the human knowledge production system and the global landscape of technological competition. The intervention of AI in the field of knowledge production is an unprecedented major event in the history of human civilization, indicating that another type of knowledge production subject besides humans has officially made its debut. In order to seize the benefits brought by this scientific paradigm revolution, major science and technology powers – including the United States, the United Kingdom, and Japan – and regions have unanimously introduced “AI+Science” strategies during October and November 2025, striving to seek new technological hegemony in the new era of technological revolution. This study systematically reviews the “AI+Science” strategies from major science and technology powers and regions, analyzes their strategic objectives, core components, and key policy measures, while also offers insights and recommendations.
Zhiqiang Zhang, Yawei Shao· Bulletin of the Chinese Acad...· 0 citations
Artificial intelligence has altered the way computer science research is planned, carried out, and judged. What began in the early twentieth century as a body of formal theory about computation and reasoning matured, over several decades, into a practical discipline: early decision trees and rule-based systems prepared the ground for the statistical learning algorithms in use today (Taulli T). Machine learning, and within it deep learning, now anchors work in natural language processing, computer vision, and robotics, where multi-layered neural networks extract structure from data volumes no manual analysis could cover (Aslay F). Two consequences of this shift frame the present review. The first is methodological. Learning-based models let researchers process large datasets quickly and surface regularities that complexity previously hid from view; in areas ranging from medical diagnostics to autonomous systems, this has translated into measurable gains in accuracy and speed of discovery. The second consequence is organizational. Because AI methods travel well across domains, computer scientists increasingly work alongside specialists in health, finance, and environmental science. These collaborations do more than export algorithms; they feed domain requirements back into algorithm design, producing tools that answer to real problems such as climate modeling and public health surveillance. Integration of this kind is not free of cost. It brings technical hurdles — data quality, reproducibility, computational expense — and it raises ethical questions about bias, transparency, and accountability that the discipline is still learning to answer. Frameworks that establish ethical standards are needed both to preserve public trust and to comply with emerging regulation. The sections that follow trace the historical development of AI within computer science, review the principal technique families and their applications, and close with an original discussion of cross-cutting patterns, ethical obligations, and likely future directions.
Elayaraja Subbaiah, Manykandaprebou Vaitinadin, E. Kesavan· International Journal of Sci...· 0 citations
Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.
Mengru Wang, Junfeng Fang, Shuofei Qiao et al.· 0 citations
Scientific discovery has advanced through successive transformations in the organization of knowledge. Observation and experimentation established the empirical foundations of science. Theory made it possible to derive general principles from particular phenomena. Computation extended inquiry into systems beyond direct observation, while data-intensive methods opened new spaces of pattern and prediction. Science now confronts a different frontier. The central challenge is no longer simply to produce more information, but to organize expanding knowledge, reasoning, and evidence into a coherent process of discovery. Here, we introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence. BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery. It connects persistent knowledge, collective reasoning, empirical validation, and human judgment within a continuous research lifecycle, transforming fragmented activities into a cumulative process of inquiry, criticism, and revision. The central premise of BLAZE is that scientific intelligence does not arise from computation alone. It emerges from the sustained interaction among knowledge, hypotheses, experiments, and collective verification. By organizing humans and machines within a shared scientific process, BLAZE makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility. Socialized scientific intelligence may provide a foundation for the next era of science. Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.
Xinjie Yao, Xingxin Xu, Xiyuan Gao et al.· 0 citations