LLM tagged posts

Will artificial intelligence ever be able to reason, learn, and solve problems at levels comparable to humans? Experts at the University of California San Diego believe the answer is yes—and that such artificial general intelligence has already arrived. This debate is tackled by four faculty members spanning humanities, social sciences, and data science in a recently published Comment invited by Nature.

Computer scientist Alan Turing first posed this question in his landmark 1950 paper, though he didn’t use the term artificial general intelligence (AGI). His “imitation game,” now known as the Turing Test, asked whether a machine could pass as human in text-based conversation with humans. Seventy-five years later, that future is here.

Over the p...

Additive manufacturing has revolutionized manufacturing by enabling customized, cost-effective products with minimal waste. However, with the majority of 3D printers operating on open-loop systems, they are notoriously prone to failure...

AI might know where you’re going before you do. Researchers at Northeastern University used large language models, the kind of advanced artificial intelligence normally designed to process and generate language, to predict human movement.

How RHYTHM predicts human movement
RHYTHM, their innovative tool, “can revolutionize the forecasting of human movements,” forecasting “where you’re going to be in the next 30 minutes or the next 25 hours,” said Ryan Wang, an associate professor and vice chair of research in civil and environmental engineering at Northeastern.

The hope is that RHYTHM will improve domains like transportation and traffic planning to make our lives easier, but in extreme cases, RHYTHM could even be deployed to respond to natural dis...

When large language models (LLMs) make decisions about networking and friendship, the models tend to act like people, across both synthetic simulations and real-world network contexts.

Marios Papachristou and Yuan Yuan developed a framework to study network formation behaviors of multiple LLM agents and compared these behaviors against human behaviors. The paper is published in the journal PNAS Nexus.

How LLMs form network connections
The authors conducted simulations using several large language models placed in a network, which were asked to choose which other nodes to connect with, given their number of connections, common neighbors, and shared attributes, like arbitrarily assigned “hobbies” or “location.”

The authors varied the network context, including simulations of fri...