cross-posted from: https://lemmy.world/post/25011462

SECTION 1. SHORT TITLE

This Act may be cited as the ‘‘Decoupling America’s Artificial Intelligence Capabilities from China Act of 2025’’.

SEC. 3. PROHIBITIONS ON IMPORT AND EXPORT OF ARTIFICIAL INTELLIGENCE OR GENERATIVE ARTIFICIAL INTELLIGENCE TECHNOLOGY OR INTELLECTUAL PROPERTY

(a) PROHIBITION ON IMPORTATION.—On and after the date that is 180 days after the date of the enactment of this Act, the importation into the United States of artificial intelligence or generative artificial intelligence technology or intellectual property developed or produced in the People’s Republic of China is prohibited.

Currently, China has the best open source models in text, video and music generation.

  • Crotaro@beehaw.org
    2·
    3 hours ago

    Does open sourcing require you to give out the training data? I thought it only means allowing access to the source code so that you could build it yourself and feed it your own training data.

    • jarfil@beehaw.org
      4·
      1 hour ago

      Open source requires giving whatever digital information is necessary to build a binary.

      In this case, the “binary” are the network weights, and “whatever is necessary” includes both training data, and training code.

      DeepSeek is sharing:

      • NO training data
      • NO training code
      • instead, PDFs with a description of the process
      • binary weights (a few snapshots)
      • fine-tune code
      • inference code
      • evaluation code
      • integration code

      In other words: a good amount of open source… with a huge binary blob in the middle.

      • teawrecks@sopuli.xyz
        1·
        30 minutes ago

        Is there any good LLM that fits this definition of open source, then? I thought the “training data” for good AI was always just: the entire internet, and they were all ethically dubious that way.

        What is the concern with only having weights? It’s not abritrary code exectution, so there’s no security risk or lack of computing control that are the usual goals of open source in the first place.

        To me the weights are less of a “blob” and more like an approximate solution to an NP-hard problem. Training is traversing the search space, and sharing a model is just saying “hey, this point looks useful, others should check it out”. But maybe that is a blob, since I don’t know how they got there.