Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence ...

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper publishing April 22 in the Cell Press journal ...

Designing AI systems that move from prototype to production demands more than good models — it requires proven architectural patterns. From decoupling training and inference to using feature stores, ...

How can systems be designed to guard against unexpected downtime? How does system reliability impact product development? In what ways can artificial intelligence and machine learning improve system ...

How deep learning enhances rule-based machine vision in quality and process control inspection applications. How edge learning compares to deep learning in machine-vision applications. Which ...

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trials is rapidly evolving alongside the broader field. For example, AI-driven adaptive trial designs may ...

As part of "shift left" to incorporate security discussions earlier in the software development life cycle, organizations are beginning to look at threat modeling to identify security flaws in ...

What are some of the key considerations when designing a vision system? What are the questions prospective customers should ask when appraising whether a vision application is feasible, or whether it ...

What if the skills you choose to learn today could determine your career trajectory in 2025? The field of machine learning is evolving at a breakneck pace, and with it comes a growing demand for ...