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 ...

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 ...

Generative models aim to replicate realistic outcomes across various contexts, from text generation to visual effects. While much progress has been made in creating real-world simulators, the ...

The development and evaluation of Large Language Models (LLMs) have primarily focused on assessing individual abilities, overlooking the importance of how these capabilities intersect to handle ...

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 ...

Recent advancements in large language models (LLMs) have generated enthusiasm about their potential to accelerate scientific innovation. Many studies have proposed research agents that can ...

In the new paper Learning Robust Real-Time Cultural Transmission Without Human Data, a DeepMind research team proposes a procedure for training artificially intelligent agents capable of flexible, ...

“Global Vision, Ideas in Collision, Leading Cutting-Edge Innovations” – The 6th annual BAAI Conference successfully concluded on June 15. Over 200 AI scholars and industry leaders gathered to discuss ...

Reinforcement Learning from Human Feedback (RLHF) has become the go-to technique for refining large language models (LLMs), but it faces significant challenges in multi-task learning (MTL), ...