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 ...

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, ...

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 ...

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 equipped AI agents with the ability to interact with users through natural, human-like conversations. As a result, these agents now face dual ...

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 ...

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 ...

To bring the vision of robot manipulators assisting with everyday activities in cluttered environments like living rooms, offices, and kitchens closer to reality, it’s essential to create robot ...

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