New review highlights strategies to overcome bacterial resistance in phage therapy
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Updates every hour. Last Updated: 4-Apr-2026 11:15 ET (4-Apr-2026 15:15 GMT/UTC)
Customers are 32% more likely to buy a product after reading a review summary generated by a chatbot than after reading the original review written by a human. That’s because large language models introduce bias, in this case a positive framing, in summaries. That, in turn, affects users’ behavior.
These are the findings of the first study to show evidence that cognitive biases introduced by large language models, or LLMs, have real consequences on users’ decision making.
Kyoto, Japan -- Quantum materials and superconductors are difficult enough to understand on their own. Unconventional superconductors, which cannot be explained within the framework of standard theory, take the enigma to an entirely new level.
A typical example of unconventional superconductivity is strontium ruthenate, SRO214, the superconductive properties of which were discovered by a research team that included Yoshiteru Maeno, who is currently at the Toyota Riken - Kyoto University Research Center.
It has long been believed that this material exhibits spin-triplet superconductivity, in which electron pairs retain magnet-like properties and can transport quantum information without electrical resistance. However, results from recent nuclear magnetic resonance -- NMR -- experiments have overturned previous conclusions, prompting the need for independent verification using other techniques.
Guidance based on Artificial Intelligence (AI) may be uniquely placed to foster biases in humans, leading to less effective decision making say researchers, who found that people with a positive view of AI may be at higher risk of being misled by AI tools.
The study entitled “Examining Human Reliance on Artificial Intelligence in Decision Making” is published in Scientific Reports.
Lead author Dr Sophie Nightingale of Lancaster University said: “Understanding human reliance on AI is critical given controversial reports of AI inaccuracy and bias. Furthermore, the erroneous belief that using technology removes biases may lead to overreliance on AI.”
Turbulence can be found everywhere, from stirring in a teacup to currents in the planetary atmosphere. Predicting such flows is difficult, especially when only incomplete information is available. Now, researchers from Japan and the UK have shown that, in two-dimensional turbulent flows, observing only large-scale motion is sufficient to reconstruct the full flow. Their findings contribute to a deeper understanding of fluid dynamics, with implications for data-driven weather forecasting.