Aug 2026· Artificial Intelligence & Environment· 0 citations· 6 references
TL;DR
This editorial traces key technological milestones across seven decades, from early symbolic reasoning to today's agentic systems capable of autonomously generating hypotheses and accelerating discovery, and examines promising future directions.
Abstract
The 70th anniversary of the 1956 Dartmouth Summer Research Project offers an occasion to celebrate artificial intelligence's extraordinary trajectory from a bold hypothesis about machine simulation of intelligence to a transformative force reshaping science, medicine, and environmental stewardship. This editorial traces key technological milestones across seven decades, from early symbolic reasoning to today's agentic systems capable of autonomously generating hypotheses and accelerating discovery. It then examines promising future directions, with particular emphasis on AI's rapidly expanding role in scientific research from protein structure prediction to AI co-scientists that collaborate with researchers in real time. Far from a source of apprehension, AI stands as one of the most powerful tools ever devised for expanding the frontiers of human knowledge, provided we guide its development with wisdom, responsibility, and a steadfast commitment to human flourishing.
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
In 2026, three landmark Nature papers collectively announced that artificial intelligence has crossed a threshold, moving from a laboratory instrument to an autonomous research agent. The Empirical Research Assistant (ERA) writes expert-level scientific software across multiple domains. The AI Scientist executes the entire research lifecycle from hypothesis to peer-reviewed manuscript. MIRA navigates clinical workflows with diagnostic accuracy exceeding that of physicians. Together, these systems signal a profound paradigm shift, albeit one laden with technical fragility, epistemic ambiguity, and unresolved ethical tensions. This article reviews the breakthroughs, dissects the persistent limitations (including hallucinations, silent errors, and reproducibility failures), and argues that the real revolution lies not in replacement of humans but in reconfiguring how human curiosity and machine scalability co-evolve.
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
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.
Digital computers have reshaped scientific practice, moving working scientific knowledge from printed texts into algorithms, simulations, and models. Advances in artificial intelligence (AI) are now accelerating that shift, progressing science in areas from protein folding to climate modelling, and raising the prospect of a further transformation in how science is done. With growing hype around the field, there is a risk that inflated claims about AI’s potential obscure both its current limitations and its longer-term possibilities. This paper explores how AI contributes to science, introducing a framework organised around task capabilities, scientific workflow integration, and domain constraints. It uses that framework to open wider questions about the role of AI in scientific discovery. These include: Is scientific knowledge constructed and used by AI agents considered scientific understanding if it is impenetrable to humans, or does scientific understanding refer to an activity that is intrinsically human? What technical advances are needed to move AI beyond pattern matching toward causal reasoning? And what institutional changes are needed to support responsible AI adoption? How researchers and policymakers engage with these questions will shape whether AI accelerates progress within existing scientific paradigms or catalyses the generation of new forms of scientific knowledge. This paper marks the opening of a call for papers from RSS Data Science and AI, which invites contributions that take up these and related questions from multiple perspectives.
Kyle Cranmer, Neil D. Lawrence, Jessica K Montgomery et al.· Robotics: Science and System...· 0 citations
Overall, it is found that the use of coding agents in scientific computing holds great promise for accelerating scientific research and increasing the reliability of critical systems, but that outstanding concerns remain.
Jeremiah H. Li, Alex Rubinsteyn, Sergey Feldman et al.· bioRxiv· 0 citations
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