A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

Let’s be real. Brokers and shippers sometimes dangle freight that runs a little heavy. Maybe it’s “just” 2,000 pounds over. Maybe it’s right at the legal limit, but you know the scale could tip you ...

How do you design a study that captures human experience as it unfolds in real time? In this episode, Under the Cortex explores the Experience Sampling Method (ESM), a powerful approach for studying ...

Abstract: DC microgrids (MGs) have emerged as an alternative interconnection method for DC-type loads and distributed energy resources (DERs). Owing to the vulnerability of grid-connected converters ...

Function calling lets an LLM act as a bridge between natural-language prompts and real-world code or APIs. Instead of simply generating text, the model decides when to invoke a predefined function, ...

ABSTRACT: The introduction of automation and recent technological advancements have changed the textile industry in different ways. Intelligent types of machinery have been developed in different ...

Structural equation modeling (SEM) is a widely used statistical method in social science. However, many published articles employing SEM appear to contradict its underlying principles and assumptions, ...

A new approach to the local and global explanation based on selecting a convex hull constructed for the finite number of points around an explained instance is proposed. The convex hull allows us to ...

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