AI learns biological variability to develop a high-performance serum-free culture medium
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Sep-2025 02:11 ET (10-Sep-2025 06:11 GMT/UTC)
Creating stable isolated spins is crucial for quantum-spin applications, like quantum bits or qubits, sensors, and single-atom catalysts. Currently, most approaches include engineering isolated spins on noble metal surfaces, whose abundant conduction electrons can also disturb their spin state. In a new study, researchers successfully demonstrated for the first time isolated spins on an insulating magnesium oxide film, laid over a ferromagnetic iron substrate, suggesting a new pathway towards realizing qubits using conventional thin-film techniques.
A review published in National Science Review highlights recent progress at the intersection of machine learning and quantum science, focusing on how AI techniques are revolutionizing the estimation and control of complex quantum systems.
When it comes to adopting artificial intelligence in high-stakes settings like hospitals and airplanes, good AI performance and a brief worker training on the technology is not sufficient to ensure systems will run smoothly and patients and passengers will be safe, a new study suggests. Instead, algorithms and the people who use them in the most safety-critical organizations must be evaluated simultaneously to get an accurate view of AI’s effects on human decision making, researchers say. The team also contends these evaluations should assess how people respond to good, mediocre and poor technology performance to put the AI-human interaction to a meaningful test – and to expose the level of risk linked to mistakes.