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...
P. Jedlička· Teorie vědy / Theory of Scie...· 0 citations
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 machi...
Alexander Taktakidze· Longevity Horizon· 1 citation
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.
This paper explores how AI contributes to science, introducing a framework organised around task capabilities, scientific workflow integration, and domain constraints, and uses that framework to open wider questions about the role of AI in scientific discovery.
Kyle Cranmer, Neil D. Lawrence, J. 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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