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Response to NTIA’s Request for Comment on Dual Use Open Foundation Models | Stanford HAI
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policyResponse to Request

Response to NTIA’s Request for Comment on Dual Use Open Foundation Models

Date
March 27, 2024
Topics
Foundation Models
Regulation, Policy, Governance
Privacy, Safety, Security
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abstract

Stanford scholars respond to a federal RFC on dual use foundation models with widely available model weights, urging policymakers to consider their marginal risks.

Executive Summary

In this response to the National Telecommunications and Information Administration’s (NTIA) request for comment on dual use foundation AI models with widely available model weights, scholars from Stanford HAI, the Center for Research on Foundation Models (CRFM), the Regulation, Evaluation, and Governance Lab (RegLab), and other institutions urge policymakers to amplify the benefits of open foundation models while further assessing the extent of their marginal risks.

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Authors
  • Researchers from Stanford HAI
Related
  • What are Weights?

    Weights are the numerical parameters within a neural network that determine the strength of connections between artificial neurons and ultimately shape how the model processes information. During training, these weights are continuously adjusted through algorithms like backpropagation to minimize errors and improve the model's predictions. The learned weights represent the model's "knowledge"—a trained AI model is essentially a specific configuration of billions of these weight values that encode patterns discovered from training data.

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