Towards zero-shot NeRFs: New HyperFields method generates 3D objects from text
Training AI to generate 3D objects from text descriptions
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Researchers find using MARL AI results in better urban planning outcomes
A new research paper proposes using Multi-Agent Reinforcement Learning (MARL) to vote on land use.
Researchers discover in-context learning creates task vectors in LLMs
The researchers show that ICL could be broken down into two distinct steps occurring within the neural network.
Combining Thermodynamics and Diffusion Models for Collision-Free Robot Motion Planning
A new AI-based method to help robots navigate 3D environments
3D-GPT: A new method for procedural Text-to-3D model generation
3D-GPT employs LLMs as a multi-agent system with three collaborative agents for procedural 3D generation
SELF-RAG: Improving the Factual Accuracy of Large Language Models through Self-Reflection
In recent years, artificial intelligence researchers have made astounding progress developing large language models that can generate remarkably human-like text. Models like GPT-3 and PaLM with billions or trillions of parameters display impressive versatility in language tasks. However, a major limitation persists - these models still frequently generate factual inaccuracies
Meta AI Presents Method for Real-Time Decoding of Images from Brain Activity
Understanding how the human brain represents and processes visual information remains one of the grand challenges of neuroscience. Recent advances in artificial intelligence (AI) and machine learning (ML) have unlocked new possibilities for modeling and decoding neural activity patterns underlying visual perception. In an exciting new study published in Nature,