Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

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 ...

Validates the performance of AI infrastructure by emulating real-world workloads Evaluates how new algorithms, components, and protocols improve the performance of AI training Adjusts and optimizes ...

Traditional drug development methods involve identifying a target protein (e.g., a cancer cell receptor) that causes disease, and then searching through countless molecular candidates (potential drugs ...

Silent schema drift is a common source of failure. When fields change meaning without traceability, explanations become ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

AI systems are only as fair and safe as the data they’re built on. While conversations about AI ethics often focus on model architecture, algorithmic transparency or deployment oversight, fairness and ...

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 ...

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 ...