AI scientist

Pope Leo has publicly dismissed suggestions that worries about the risks of artificial intelligence are exaggerated, saying such concerns are not 'fake news'. The remarks add the Vatican's moral weight to the global debate over AI safety, reinforcing calls from scientists and regulators for serious attention to the technology's potential harms and for safeguards in its development.

A New York Times piece examines how scientists can influence public opinion on the risks of artificial intelligence, reportedly through protests and public engagement. It suggests researchers are becoming active voices in the debate over AI safety, rather than leaving the conversation to companies and regulators.

Anthropic is facing scrutiny over claims that its AI systems achieved a significant breakthrough in biology. CNET asked independent scientists to assess the claim, and researchers are now debating how real and how meaningful the reported advance actually is. The discussion highlights growing caution about AI companies' big scientific promises and the difficulty of verifying such claims from outside.

A new study is probing whether artificial intelligence systems could experience pain, one of the hardest questions in debates over machine consciousness. The research feeds into a wider argument among scientists and ethicists about whether large language models have any form of subjective experience, and what that would mean for how such systems should be treated and regulated.

MIT Technology Review examines the question of when an AI system can truly be credited with making a scientific discovery. As AI tools increasingly generate hypotheses, run experiments and produce research results, scientists and philosophers are debating what threshold of autonomy and understanding would justify calling a discovery AI's own rather than that of its human collaborators.

Scientists say artificial intelligence could help bring the woolly mammoth back from extinction, according to a report. AI is being used to analyze the mammoth genome and accelerate efforts to edit elephant DNA so the Ice Age giant's traits could be recreated. The idea of de-extincting the mammoth has drawn both excitement and skepticism about whether such a creature can truly be brought back.

Anthropic is facing pushback over a claimed biology breakthrough made using its Claude AI system, after a scientist said he had already achieved the same result. The dispute raises questions over whether the AI truly produced something novel or merely rediscovered existing work, a recurring issue in AI-assisted scientific claims.

An NPR report says AI assessors conclude that current science cannot yet support the safety measures the public wants from artificial intelligence. The finding highlights a gap between expectations for AI safeguards and what research can actually verify, renewing debate among scientists and policymakers over how to regulate systems whose risks remain poorly understood.

Anthropic says its Claude AI helped identify a previously unknown gene-editing enzyme, a claim that would be notable if verified. The company itself acknowledges the finding has not been independently confirmed or validated through peer review, so scientists are treating the announcement with caution until laboratory evidence backs it up.

Researchers report that artificial intelligence can distinguish between aged and young stem cells, a finding highlighted in a new science news release. If confirmed, the method could help scientists understand how ageing affects regenerative capacity and support therapies that rely on healthy stem cells. Details of the study team and the underlying research remain limited in the available information.

New research reported by The Scientist suggests bacteria can learn and store memories in ways that resemble artificial neural networks. The findings point to memory-like behaviour in single-celled organisms without nervous systems, drawing comparisons between microbial adaptation and machine learning models. Scientists say the work could reshape understanding of how simple life forms process information and inspire new computing approaches.

Billionaire investor Mark Cuban asked Elon Musk's chatbot Grok which risks are most likely to cause human extinction within 25 years. According to reports, the AI responded that artificial intelligence is roughly 3,000 times more likely than climate change to be the cause. The claim has drawn attention because both figures are speculative model outputs rather than established scientific estimates, and climate scientists generally rank climate change among the most serious long-term global threats.

An article examining the environmental impact and greenwashing of artificial intelligence includes a fact check of Donald Trump's climate remarks. It explains that his references to people warning "we'll all be dead in twelve years" stem from media coverage of a 2018 United Nations Intergovernmental Panel on Climate Change report, rather than scientists' actual statements. Readers are sharing it as a useful example of climate misinformation being corrected within broader tech-environment reporting.

Data centers are consuming growing amounts of energy, drawing attention to the environmental footprint of the AI and cloud computing boom. Other stories gaining traction include scientists discovering a new amoeba species and an effort to relocate turtles to a new habitat, part of a broader science news roundup drawing reader interest.

The Bulletin of the Atomic Scientists argues that artificial intelligence does not need to reach 'superintelligence' or act out of malice to spark a nuclear conflict. Ordinary system errors, misinterpretation of data, or human overreliance on automated systems could be enough to set off escalation. The warning adds to ongoing debate about integrating AI into military command and control.

Researchers report that an AI system has, for the first time, autonomously generated and validated new biological discoveries, carrying out the full research cycle from hypothesis to confirmation without human intervention. The development is being covered widely by science news outlets and is drawing attention to both the promise of automated scientific research and concerns about oversight and reliability.

The Bulletin of the Atomic Scientists argues that alarmist claims about artificial intelligence destroying humanity rest on weak science, and that this doomerism ultimately benefits major AI companies. By framing existential risk as the central issue, the argument goes, firms can steer debate away from present-day harms, regulation and accountability while appearing responsible.

Researchers have introduced a physics-grounded AI framework designed to make predictions about new materials more testable, according to Phys.org. The approach is intended to anchor machine-learning forecasts in physical principles so that scientists can verify them against real experiments rather than treating model outputs as black boxes, a persistent problem in AI-driven materials discovery.

Researcher Schwartz has released BootLoops 1.0, an open-source harness designed to run large language models in scientific research workflows. The tool is intended to help scientists apply LLMs to experimental and analytical tasks in a reproducible way. Coverage so far is limited to software news, and details on the project's features, licensing and adoption remain sparse pending wider testing by the research community.

An artificial intelligence system can now design scientific experiments, carry them out in a physical laboratory, and learn from the results without human intervention. Reports describe it as a step toward automated scientific discovery, with the AI closing the loop between hypothesis, experimentation and analysis. Observers are debating what autonomous research could mean for the pace of science and the future role of human researchers.

An AI-driven system has been developed that can propose biological research questions, design experiments to answer them, and direct robotic equipment to carry out the tests with minimal human input. The development is being shared widely among science and technology readers, with commentary focusing on what autonomous 'AI scientists' could mean for the speed and scale of biological research.

A Nature paper describes a method for embedding watermarks into proteins designed by artificial intelligence without disrupting their biological function. The technique would let scientists mark AI-designed sequences so their origin can be verified, addressing growing concerns about accountability and safety in computational protein design. Researchers say such watermarking could help distinguish machine-generated biomolecules from natural ones as AI tools become widely used in biotechnology.

The SETI Institute is hosting a talk by scientist Dr. Vishal Gajjar with ScienceKind titled 'The Great Radio Silence in the Age of AI'. The discussion will examine whether humanity would actually recognize an extraterrestrial signal if one arrived, how potential signals could escape traditional search methods, and how artificial intelligence is being used to rethink the search for alien life.

Computer scientists have long studied how to keep powerful automated systems under human control, and the classic 1983 film WarGames is being used as a reference point in debates about artificial intelligence safety. The Conversation examines what decades of research in computer science say about controlling AI, and why those lessons matter as modern systems grow more capable and autonomous.

Anthropic announced that its artificial intelligence identified a previously unknown enzyme system resembling CRISPR, the gene-editing tool that transformed biology. The claim suggests AI can uncover novel biological mechanisms, not just analyze existing data. Details on the discovery, its validation, and practical uses remain limited so far, and scientists will be watching for independent confirmation.

Google has published 'AI in Science: Early Insights', a 42-page report by researchers including Mihai Codreanu, Alex Imas, Juan Mateos-Garcia, Joseph Emmens, Evalyne Muiruri, Arthur Turrell and Julian Jacobs. It looks at how heavily scientists are using AI in their work and how the outputs of that research can in turn be reused to train future AI models, creating a feedback loop between science and machine learning.

Astrophysicist Neil deGrasse Tyson says plans to put AI chips and data centers in space face 'many challenges', according to The Hill. His comments come as tech companies increasingly float orbital data centers as a way to handle the enormous power and cooling demands of artificial intelligence, and the remarks add a skeptical scientific voice to that debate.

Researchers have introduced GenAI-Net, a generative AI framework designed to automate the design of biomolecular networks. Published by Science, the work applies generative models to biological systems, potentially speeding up how scientists construct and test molecular interaction networks for research and biotechnology. Attention is focused on what this could mean for synthetic biology and AI-driven scientific discovery.