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Inexpensive air quality monitors may open up pollution monitoring to the wider public. What can the physics of measurement science tell us about how devices need to perform, and how can standardization help?

As artificial intelligence (AI) makes increasingly impressive contributions to science, scientists increasingly want to understand how AI reaches its conclusions. Matthew D. Schwartz discusses what it means to understand AI and whether such a goal is achievable — or even needed.

Venkatesh Narayanamurti and Jeffrey Y. Tsao discuss lessons learned from the success of the great 20th-century industrial research labs and warn against three common misconceptions about the nature and nurture of research.

In 2002, an experiment with ultracold atoms emulated a textbook condensed-matter physics phenomenon: the phase transition from a superfluid to a Mott insulator. Two decades later, Immanuel Bloch and Markus Greiner ponder how far quantum simulation with ultracold atoms has come.

The ‘Wigner’s friend’ thought experiment illustrates the puzzling nature of quantum measurement. Časlav Brukner discusses how recent results suggest that in quantum theory the objectivity of measurement outcomes is relative to observation and observer.

Will quantum computers someday give super-polynomial speedups for machine learning on classical data? Answering this question is challenging. Ewin Tang explains how dequantizing algorithms can uncover when there is no quantum speedup and perhaps help explore analogies between quantum and classical linear algebra.

The International Union for Pure and Applied Physics (IUPAP) celebrates its centenary this year, but its beginnings were far from easy. Roberto Lalli and Jaume Navarro reflect on IUPAP’s evolving role in promoting international cooperation.

Light–matter interactions are already used to induce new states in condensed-matter systems — such as in Floquet engineering. Combining these ideas with the vectorial properties of structured light promises to further expand the toolbox for optical control of quantum properties of matter.

Getting the most from power-law-type data can be challenging. James Sethna points out some of the pitfalls in studying power laws arising from emergent scale invariance, as well as important opportunities.

Past and present chairs of the Division of Particles and Fields of the American Physical Society explain how the high-energy physics community in the US decides the priorities for research through regular planning exercises that started 40 years ago at Snowmass, Colorado.

Twenty years ago, the particle physics community launched Indico, an open-source software package for handling all aspects of meetings. This is brief guide to what Indico can do, and how the wider physics community could benefit from adopting it.

Over the past decade machine learning has made significant advances in approximating density functionals, but whether this signals the end of human-designed functionals remains to be seen.

The development of time-resolved, multiscale and multi-modal X-ray imaging techniques at advanced light sources raises challenges on the data processing end — but image processing methods from other research areas will help.

Topological defects play an important role in biology, as shown by a growing body of evidence. Aleksandra Ardaševa and Amin Doostmohammadi survey the new research directions that are opening.

Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.

Wigner crystals — ordered arrays of electrons — have been recently found in various 2D materials, but the first studies of these crystals in 2D electron systems (2DESs) date back from the 1980s. Mansour Shayegan gives a brief history of Wigner crystals and highlights future prospects.

Quantum machine learning may provide powerful tools for data analysis in high-energy physics. Sau Lan Wu and Shinjae Yoo describe how the potential of these tools is starting to be tested and what has been understood thus far.

Machine learning methods relying on synthetic data are starting to be used in mathematics and theoretical physics. Michael R. Douglas discusses recent advances and ponders on the impact these methods will have in science.

Although participating in outreach activities has many benefits for early-career researchers, outreach programmes are not always structured in a way that helps them participate. Three physicists explain why this motivated them to start a spin-off company dedicated to outreach.