Center for Research on Foundation Models (CRFM)

swlee0

Center for Research on Foundation Models (CRFM)

swlee0

Center for Research on Foundation Models (CRFM)

swlee0

Center for Research on Foundation Models (CRFM)
Stanford Institute for Human-Centered Artificial Intelligence (HAI)
Stanford University

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) trained on broad
data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks.
We call these models foundation models to underscore their critically central yet incomplete character.
This report provides a thorough account of the opportunities and risks of foundation models, ranging
from their capabilities (e.g., language, vision, robotic manipulation, reasoning, human interaction) and
technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation,
theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse,
economic and environmental impact, legal and ethical considerations). Though foundation models are
based on standard deep learning and transfer learning, their scale results in new emergent capabilities,
and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides
powerful leverage but demands caution, as the defects of the foundation model are inherited by all the
adapted models downstream. Despite the impending widespread deployment of foundation models,
we currently lack a clear understanding of how they work, when they fail, and what they are even
capable of due to their emergent properties. To tackle these questions, we believe much of the critical
research on foundation models will require deep interdisciplinary collaboration commensurate with
their fundamentally sociotechnical nature.

Billmtk

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