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 ...

Tools designed for rewriting, refactoring, and optimizing code should prioritize both speed and accuracy. Large language models (LLMs), however, often lack these critical attributes. Despite these ...

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 ...

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

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 ...

In a new paper Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning, a research team from the University of Oxford and Google DeepMind introduces methods to achieve ...

In a new paper Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning, a research team from the University of Oxford and Google DeepMind introduces methods to achieve ...

Although the connection between language modeling and data compression has been recognized for some time, current Large Language Models (LLMs) are not typically used for practical text compression due ...

Monocular Depth Estimation, which involves estimating depth from a single image, holds tremendous potential. It can add a third dimension to any image—regardless of when or how it was captured—without ...