Practical AI "Foundation" models
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Mar 23, 2022 Discover the dual nature of powerful AI models, with potential benefits and risks. Dive into the landscape of foundation models as discussed in a pivotal Stanford report. Explore the definition, training, and ethical implications surrounding these models, including biases and accessibility. Understand the concept of 'grounding' in AI, essential for enhancing robust language models. Finally, learn about the emergence of specialized roles like foundation model security analysts, highlighting the importance of security in AI advancements.
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Foundation Models Defined
- Foundation models, trained on broad data at scale, adapt to various tasks.
- They leverage transfer learning and self-supervision, impacting many applications.
Accessibility and Homogeneity
- Few researchers can create foundation models due to resource limitations.
- Unexpected behaviors or biases in a model affect all downstream applications.
Risks of Widespread Use
- Foundation models' widespread use creates single points of failure.
- Bugs or biases in these models have a multiplicative downstream impact, similar to software dependencies.