The rise of large language models (LLMs) has sparked questions about their computational abilities compared to traditional models. While recent research has shown that LLMs can simulate a universal ...

Recent large language models (LLMs) have shown impressive performance across a diverse array of tasks. However, their use in high-stakes or computationally constrained environments has highlighted the ...

For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data quality and scale, and a lag in new, relevant information creation.

A research team from DeepMind and Chicago University presents a novel approach to Reinforcement Learning from Human Feedback. The proposed eva introduces a flexible, scalable framework that leverages ...

In a new paper FACTS About Building Retrieval Augmented Generation-based Chatbots, an NVIDIA research team introduces the FACTS framework, designed to create robust, secure, and enterprise-grade ...

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V, Gemini-1.5, and Claude-3.5 exemplify the impressive capabilities of ...

Large language models (LLMs) like GPTs, developed from extensive datasets, have shown remarkable abilities in understanding language, reasoning, and planning. Yet, for AI to reach its full potential, ...

Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing computational demands. Traditionally, significant computational ...

Cellular automata (CA) have become essential for exploring complex phenomena like emergence and self-organization across fields such as neuroscience, artificial life, and theoretical physics. Yet, the ...