Foundation models like GPT-3 and CLIP are reshaping AI by enabling general-purpose systems trained on massive, unlabelled data. This article explores their key concepts—emergence and homogenization—their capabilities across language, vision, and more, and the risks they pose, from bias to environmental impact. Based on the Stanford report, it highlights why foundation models are powerful, unpredictable, and demand responsible development.

Tech giants turn to nuclear energy to power AI, tackling rising energy demands and environmental impact with bold new strategies.

In this article we will go through a brief review of diffusion models, how they work, what their usages are and what are the existing tools.