I tested local AI on my M1 Mac, expecting magic - and got a reality check instead

At random, I chose glm-4.7-flash, from the Chinese AI startup Z.ai. Weighing in at 30 billion "parameters," or neural weights, GLM-4.7-flash would be a "small" large language model by today's ...
The Lancet

CARDBiomedBench: a benchmark for evaluating the performance of large language models in biomedical research

Although large language models (LLMs) have the potential to transform biomedical research, their ability to reason accurately across complex, data-rich domains remains unproven. To address this ...
Frontiers

Linguistic Representations in Large Language Models for High-Impact Discourse: Structure, Pragmatics, and Reliability

Learning robust linguistic representations from text has long been a central challenge in natural language processing and language sciences. NLP systems ...

Arcee's U.S.-made, open source Trinity Large and 10T-checkpoint offer rare look at raw model intelligence

San Francisco-based AI lab Arcee made waves last year for being one of the only U.S. companies to train large language models (LLMs) from scratch and release them under open or partially open source ...

Developers say AI coding tools work—and that’s precisely what worries them

Software developers have spent the past two years watching AI coding tools evolve from advanced autocomplete into something ...
Nature

How DeepMind's genome AI could help solve rare disease mysteries

Hackathons using AlphaGenome and other AI models are hunting down the genetic causes of devastating conditions that have ...
IEEE

Transforming Education With Large Language Models: Trends, Themes, and Untapped Potential

Abstract: Our research focuses on the transformative intersection of Large Language Models (LLMs) and education in the last six years (2019–2024), examining their potential to modernize educational ...
IEEE

A Reinforcement Learning-Based Retraining-Free Pruning for Encoder-Based Language Models

Abstract: Natural Language Processing (NLP) has achieved significant success in complex tasks across various domains, yet it also brings high computational costs and inference delays. Pruning, as a ...