Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...

In my experience teaching students in grades K–12, what strikes me most is both the power of retrieval practice and how much it depends on context: what students are learning, how learning was built, ...

Fixed-Dimensional Encoding (FDE) solves a fundamental problem in modern search systems: how to efficiently search through billions of documents when each document is represented by hundreds of vectors ...

State dependent memory (SDM) occurs when memory retrieval varies with the individual's psychological and physiological state at encoding and recall. Growing evidence shows that internal states shape ...

One of the main aims of memory research is to devise strategies that would support effective learning. So far, the golden standard of learning guidelines is supposed to involve repeated retrieval ...

Abstract: Problems such as background interference and various sizes and types of marine ships still exist in the field of SAR ship image segmentation and retrieval. In this paper, we present a deep ...

Suppressing irrelevant information in working memory enhances the long-term formation of subsequent memories. Through behavioral and neuroimaging experiments, we demonstrate that intentionally ...

Challenging assumptions about infant memory, a novel functional magnetic resonance imaging (fMRI) study shows that babies as young as 12 months old can encode memories, researchers report. The ...