The LLM group showed "weaker memory traces, reduced self-monitoring and fragmented authorship," the study authors wrote. That ...

Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their privacy concerns.

Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While ...

In recent years, with the public availability of AI tools, more people have become aware of how closely the inner workings of ...

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on ...

The evolution of web search engines offers an instructive example, showing how knowledge can be extracted from unstructured sources and refined over time into a structured, interconnected graph.

To address these gaps, the researchers from the University of Notre Dame and Amazon introduce Knowledge Graph Enhanced Language Agents (KGLA), a framework that enriches language agents with the ...

Knowledge graphs , which are essential for internet searches and machine learning, use a graph structure to link various pieces of knowledge and link data to perform knowledge exploration and ...

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess ...

Best Practices in What’s Ahead in AI, Machine Learning, and Knowledge Graphs Understanding, Knowing, and Connecting via Knowledge Graphs Knowledge graphs grant us new and different ways of visualizing ...

How do I find what I want to find?’ whether I’m using all that data to drive some AI or machine learning processes or whether I’m using it for reporting or whatever.” It’s a “virtuous cycle” between ...