Document Note: The Stanford Center for Research on Foundation Models (CRFM) has recently released a report and hosted a workshop to foster dialogue on the responsible development of foundation models, which are models trained on broad data and can be adapted to a wide range of downstream tasks. CRFM emphasizes that foundation models present societal risks due to inequity, misuse, environmental impact, legal frameworks, ethics of scale, and economic consequences. They also discuss the choice of the name “foundation model” to emphasize the importance of critiques and that these models should not be assumed to be good by default. People are at the center of foundation models, as they create the data, develop the models, adapt them, and interact with the resulting applications.
We define foundation models as models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks (View Highlight)
we see foundation models as the subject of a growing paradigm shift, where many AI systems across domains will directly build upon or heavily integrate foundation models. (View Highlight)
Foundation models incentivize homogenization: the same few models are repeatedly reused as the basis for many applications. Such consolidation is a double-edged sword: centralization allows us to concentrate and amortize our efforts (e.g., to improve robustness, to reduce bias) on a small collection of models that can be repeatedly applied across applications to reap these benefits (akin to societal infrastructure), but centralization also pinpoints these models as singular points of failure that can radiate harms (e.g., security risks, inequities) to countless downstream applications. (View Highlight)
we emphasize that foundation models present clear and significant societal risks, both in their current implementation and their fundamental premise (View Highlight)
Foundation models can (and increasingly should) be grounded. (View Highlight)
the development of foundation models generally fails to center people and development itself is fairly closed off to a small collection of high-resourced actors. (View Highlight)
while foundation models are very much “bottom-up” in that structure emerges from the data, methods such as causal networks, probabilistic programs, and formal systems are “top-down”, imposing strong structure. (View Highlight)
By analogy to Daniel Kahneman’s System 1 and 2, foundation models may provide a (very good) implementation of fast, automatic, surface-level reasoning (System 1) that can be integrated with other approaches for slow, analytic, deliberative reasoning (System 2). (View Highlight)
model”. In choosing this term, we take “foundation” to designate the function of these models: a foundation is built first and it alone is fundamentally unfinished, requiring (possibly substantial) subsequent building to be useful. “Foundation” also conveys the gravity of building durable, robust, and reliable bedrock through deliberate and judicious action. (View Highlight)
