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 ...

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 ...

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 ...

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 ...

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 ...

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, ...

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 ...

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 Don’t Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs, a Meta research team proposes a novel chain-of-thought strategy to efficiently generate code ...