Foundation models in brief: A historical, socio-technical focus

• URL: http://arxiv.org/abs/2212.08967v1
• Date: Sat, 17 Dec 2022 22:11:33 GMT
• Title: Foundation models in brief: A historical, socio-technical focus
• Authors: Johannes Schneider
• Abstract summary: Foundation models can be disruptive for future AI development by scaling up deep learning. Models achieve state-of-the-art performance on a variety of tasks in domains such as natural language processing and computer vision.
• Score: 2.5991265608180396
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models can be disruptive for future AI development by scaling up deep learning in terms of model size and training data's breadth and size. These models achieve state-of-the-art performance (often through further adaptation) on a variety of tasks in domains such as natural language processing and computer vision. Foundational models exhibit a novel {emergent behavior}: {In-context learning} enables users to provide a query and a few examples from which a model derives an answer without being trained on such queries. Additionally, {homogenization} of models might replace a myriad of task-specific models with fewer very large models controlled by few corporations leading to a shift in power and control over AI. This paper provides a short introduction to foundation models. It contributes by crafting a crisp distinction between foundation models and prior deep learning models, providing a history of machine learning leading to foundation models, elaborating more on socio-technical aspects, i.e., organizational issues and end-user interaction, and a discussion of future research.

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