While precision seems critical for science, researchers from the U.S. Department of Energy's (DOE) Brookhaven National Laboratory and Texas A&M University are embracing uncertainty, using it to ...

This process is costly, time-consuming, and has a low success rate. KAIST researchers have developed an AI model that, using only information about the target protein, can design optimal drug ...

Heavy machinery is entering a new phase where hydraulics, electronics and embedded software are engineered as one integrated system. Using model-based systems engineering (MBSE) as a framework to ...

Neo-1 is the first model to unify de novo molecular generation and atomic-level structure prediction in a single model, by generating latent representations of whole molecules instead of predicting ...

Autodesk has acquired Datum360 in a move it says will allow more connections of data across its authoring tools, such as Revit, and other construction project management tools. Terms of the deal were ...

Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and ...

Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects. What is ...

The company remains tight-lipped on how it uses customer content to train its own AI model, which can generate layered designs that are easier to edit. Canva has built its own foundational AI model ...

Transforming an initial idea into a concept design is a complex process. It requires understanding project requirements like context, program, budget, and functionality and rapidly iterating—usually ...