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.

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 ...

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 ...

Building on MM1’s success, Apple’s new paper, MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning, introduces an improved model family aimed at enhancing capabilities in text-rich ...

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, ...

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 ...

In a new paper CAX: Cellular Automata Accelerated in JAX, a research team introduces Cellular Automata Accelerated in JAX, a powerful open-source library designed to enhance CA research, which enables ...

A Meta AI research team introduces Meta Large Language Model Compiler, a suite of robust, openly available, pre-trained models is specifically designed for code optimization tasks, aiming to provide a ...