Understand why testing must evolve beyond deterministic checks to assess fairness, accountability, resilience and ...

A simple rule of thumb: In general, AI is best reserved for well-defined, repetitive tasks. This includes anything that ...

For years, the network fabric inside data centers were built for relatively predictable traffic flows. Testing this ...

Meta (META) researchers have raised doubts about one of the most widely used tests for artificial intelligence models. The warning suggests that some of the world’s top systems may not be as capable ...

Testing APIs and applications was challenging in the early devops days. As teams sought to advance their CI/CD pipelines and support continuous deployment, test automation platforms gained popularity, ...

The semiconductor industry is increasingly turning to artificial intelligence as the solution for increasing complexity in test analytics, hoping algorithms can tame the growing flood of production ...

A less visible dependency is emerging as a critical constraint of AI infrastructure: the resilience of the data and storage ...

A new community-driven initiative evaluates large language models using Italian-native tasks, with AI translation among the ...

There is a phenomenon in artificial intelligence and deep learning called model collapse. Model collapse is the slow erosion of a generative AI system grounded in reality as it learns more and more ...