A reflection on ML4PS 2025, where researchers in physics and machine learning grappled with the role AI should play in scientific discovery, and what it would take to move from process acceleration toward deeper scientific insight.

FirstPrinciples presented an early demo of its AI Physicist at the Open Science Fair, held this year at CERN. The event sparked conversations on trust, openness, and the role of AI in research, underscoring how collaboration will shape the future of discovery.

Chain-of-thought reasoning has become a rare interface between human and machine logic. But a new paper warns that the window may be closing.

Physicist Neil Johnson has mapped the exact moment AI can flip from accurate to false, and he says understanding the physics could be the key to safer systems.

At the intersection of artificial intelligence and fundamental physics, the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) is preparing scientists to learn, think and understand at the deepest levels. In its latest week-long Summer School program, students explored frontier challenges through lectures, tutorials, and collaborative hackathons, testing how AI can shape the future of physics, and how physics can push the boundaries of AI.

Z.ai's new open-source model and Harmonic's math-native chatbot highlight contrasting strategies for AI reasoning, while a new wave of increasingly specialized benchmarks invites us to rethink how we measure progress.

From prompt injection to physics simulators and open reasoning models, recent news shows that AI isn’t just accelerating science, it’s reshaping how it works. The question now, is will it deepen inquiry, or erode the principles on which credibility in science is built?

She’s one of the internet’s sharpest scientific voices: equal parts physicist, critic, and communicator. In this candid conversation, Sabine Hossenfelder reflects on AI, the flood of low-impact theory papers, and how a scientific culture ripe for reform could finally be ready for change.

Hidden prompts buried in preprints show how easily large language models (LLMs) can be manipulated, exposing a deep vulnerability in science’s quality-control system. As artificial intelligence (AI) becomes part of the scientific review process, traceability and transparency must become new norms, not afterthoughts.

Large language models have advanced dramatically in recent years, yet when physicists gave them an undergraduate-level test, even the best models were only correct on around one out of every three questions. The new PhysUniBench benchmark exposes how far AI still has to go in mastering fundamental science.

From rule-based ‘expert systems’ to neural networks, AI has long chased the dream of scientific reasoning. But while today’s models are good at pattern matching and can generate code or summarize academic papers, they struggle with the heart of scientific discovery: structured reasoning.

Scientists are using artificial intelligence and machine learning to tackle dark matter and other cosmic puzzles.

Could a new startup harness the laws of physics to build a computer chip that lessens the energy demands of artificial intelligence?

A machine learning convert discusses the synergies between AI and physics, and the institute devoted to that fruitful intersection.