Choosing an AI model is no longer about “best model wins.” Instead, the right choice is the one that meets accuracy targets, fits latency and cost budgets, respects compliance boundaries and ...

MIT introduces Self-Distillation Fine-Tuning to reduce catastrophic forgetting; it uses student-teacher demonstrations and needs 2.5x compute.

Researchers warn model collapse when AI trains on AI text; “photocopying a photocopy” leads to less varied outputs.

Researchers have developed a new explainable artificial intelligence (AI) model to reduce bias and enhance trust and accuracy in machine learning-generated decision-making and knowledge organization.