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.

Labeling adversary activity with ATT&CK techniques is a tried-and-true method for classifying behavior. But it rarely tells defenders how those behaviors are executed in real environments.

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When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

The University of Texas at San Antonio (UTSA) has both rights and responsibilities for the retention of research or other data acquired or developed as a result of a grant, contract, cooperative ...

The latest trends in software development from the Computer Weekly Application Developer Network. This is a guest post written by Jacob Rank in his role as senior director of product management at ...