LLM集団の合意形成における派閥化と多様性の重要性

AIエージェント同士の場合、「似た者」集団は仲間割れして永遠に合意できなくなるケースがあるとのこと。...

AIと宗教を「分散型意味生成システム」として捉える概念モデル

「宗教とAIは、実は同じ仕組みで動いている」と論じる哲学論文が出ています。 メルボルン工科大学の研究...

医療データ分析を自律実行するAIエージェントの性能評価と課題

今のAIエージェントに「病気を予測するAIを作って」と丸投げすると、専門家が手作業で組んだものと同等...

GPT-4 Technical Report

OpenAI

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks.

Diffusion Models Beat GANs on Image Synthesis

Prafulla Dhariwal, Alex Nichol

We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations.

LoRA: Low-Rank Adaptation of Large Language Models

Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen

We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.