Interpretability is the science of how neural networks work internally, and how modifying their inner mechanisms can shape their behavior--e.g., adjusting a reasoning model's internal concepts to ...

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...

Third, Fava concludes, excessive reliance on checklist criteria is likely to “impoverish the clinical process” and clinical ...

Less than a year after holding that generic machine-learning patents are abstract in Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit ...

Scientists at Hopkins, University of Florida simulate and predict human behavior during wildfire evacuation, allowing for improved planning and safety ...

Brainteasers are more than casual puzzles as they are structured in such a way that they serve as mental exercises. These ...

ElevenLabs CEO argued at Web Summit Qatar that voice is the next interface for AI, as OpenAI, Google, and Apple push ...

Interpretation is the discipline through which molecular datasets reveal their significance. As the life sciences enter a new era defined by data richness and technological capacity, interpretive ...

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks.

In this video, I show a smothering method to manage unwanted weeds and grasses in vegetable garden beds. At the same time, I ...

Traditional fault detection and diagnosis (FDD) pipelines often depend on handcrafted features, narrow domain models, and ...

If the FDA follows through with the proposed guidelines, and they are not fatally twisted by pressure from the medical ...