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 ...

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 ...

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

New workload demands are turning data handling into a system-level design challenge rather than a back-end afterthought.

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 ...

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 ...

Building Information Modeling (BIM) has revolutionized design and construction by streamlining collaboration, improving efficiency, and reducing costs. However, as contractors and designers continue ...

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.