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.

  • thingsiplay@beehaw.org
    18·
    15 hours ago

    None of the code and training data is available. Its just the usual Huggingface thing, where some weights and parameters are available, nothing else. People repeat DeepSeek (and many other) Ai LLM models being open source, but they aren’t.

    They even have a Github source code repository at https://github.com/deepseek-ai/DeepSeek-R1 , but its only an image and PDF file and links to download the model on Huggingface (plus optional weights and parameter files, to fine tune it). There is no source code, and no training data available. Also here is an interesting article talking about this issue: Liesenfeld, Andreas, and Mark Dingemanse. “Rethinking open source generative AI: open washing and the EU AI Act.” The 2024 ACM Conference on Fairness, Accountability, and Transparency. 2024

    • Gamers_mate@beehaw.org
      2·
      43 minutes ago

      Damn that sucks it should be open source. Let people fork and optimize it so it uses less electricity as possible.

      • thingsiplay@beehaw.org
        1·
        38 minutes ago

        Stop spreading lies. Read my reply and understand what it is saying before reply. Or link to the source.

        • Crotaro@beehaw.org
          3·
          7 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
            7·
            5 hours 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.

            • Crotaro@beehaw.org
              1·
              42 minutes ago

              Thanks for the explanation. I don’t understand enough about large language models to give a valuable judgement on this whole Deepseek happening from a technical standpoint. I think it’s excellent to have competition on the market and it feels that the US’ whole “But they’re spying on you and being a national security risk” is a hypocritical outcry when Facebook, OpenAI and the like still exist.

              What do you think about Deepseek? If I understood correctly, it’s being trained on the output of other LLMs, which makes it much more cheap but, to me it seems, also even less trustworthy because now all the actual human training data is missing and instead it’s a bunch of hallucinations, lies and (hopefully more often than not) correctly guessed answers to questions made by humans.

            • teawrecks@sopuli.xyz
              3·
              4 hours 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.

              • jarfil@beehaw.org
                2·
                3 hours ago

                There are several “good” LLMs trained on open datasets like FineWeb, LAION, DataComp, etc. They are still “ethically dubious”, but at least they can be downloaded, analyzed, filtered, and so on. Unfortunately businesses are keeping datasets and training code as a competitive advantage, even "Open"AI stopped publishing them when they saw an opportunity to make money.

                What is the concern with only having weights? It’s not abritrary code exectution

                Unless one plugs it into an agent… which is kind of the use we expect right now.

                Accessing the web, or even web searches, is already equivalent to arbitrary code execution: an LLM could decide to, for example, summarize and compress some context full of trade secrets, then proceed to “search” for it, sending it to wherever it has access to.

                Agents can also be allowed to run local commands… again a use we kind of want now (“hey Google, open my alarms” on a smartphone).

        • P03 Locke@lemmy.dbzer0.comEnglish
          2·
          4 hours ago

          Nobody releases training data. It’s too large and varied. The best I’ve seen was the LAION-2B set that Stable Diffusion used, and that’s still just a big collection of links. Even that isn’t going to fit on a GitHub repo.

          Besides, improving the model means using the model as a base and implementing new training data. Specialize, specialize, specialize.