Open weight is not the same thing as open source. "Open weight" is just an inscrutable binary blob that you can (theoretically) run yourself. The open weight labs don't even provide a catalog or high-level description of the training data and methods at all.
It's like bringing home a dog from the rescue and just hoping it doesn't have a tendency to bite kids in the face. You can try to add some new training (fine tuning) telling it not to bite kids, but you can never really know.
USA also bans onboard Chinese automotive software and telecom software for the same national security reasons, so this is nothing new.
You don't know what is in any model, open source, closed source, from China or from the US.
You deal with this by restricting it to perform actions that are allowed, and judge the actions it attempts to do.
Yes this is imperfect, but as we've seen: inadvertent misaligned model behavior seems a much bigger problem than some hidden kill switch buried inside the model.
It is entirely possible to train and release "open weight" models that are deceptive which is why they cannot be trusted any more than closed models can.
It is a great analogy actually. We indeed don't know what's in any model. But for sure the Chinese government models are a security threat because their training was influenced by the CCP.
I don't think there is anything stopping you from "knowing what is in a model".
For example, let's say we have a one-bit model. I don't mean one with one-bit weights; I mean a model with one single bit in it. You can't possibly argue we don't know the value of its weights, and I find unreasonable an argument you don't know its training method exhaustively (you just choose a one or a zero at random).
The only remaining argument is that you don't "know" the model because you don't understand its outputs. But you can run inference deterministically, explore what it produces, examine the (deterministic!) outputs, and then you know what it does. In the case of a one-bit model, probably nothing much.
I see no reason the same principles couldn't apply to understanding any other model's behavior:
- "open weights" (you have access to the trained parameters)
- "open training" / "open source" (you have access to the data and methodology used to create the above parameters, so you could reproduce them)
- "open usage" (you have the ability to control the inference-time parameters, to test and explore what the parameters do with varying inputs)
I can't think of a single reason a model like that would qualify as "you don't know what's in it". You know what it is, you know what it does, and you know how it was made. Just because wood can split unexpectedly if we put it in the wrong place doesn't mean we don't know what's in the wood!
That's still not source code according to Stallman's GPL definition.
Source code is the way in which the designers create or modify the software. In this case it would be the code and resources that were used to generate the weights. Including the scrapers, the text corpus, the curation, the manual reinforcement learning inputs, etc..
Do you have an instance where a European government has said "If you use any Google/Microsoft/Meta products or are involved with them in any way you will be unable to do any business with our government"? Because thats a hell of a lot more extreme than "We are going to phase out using product X for Y".
Open weight is not the same thing as open source. "Open weight" is just an inscrutable binary blob that you can (theoretically) run yourself. The open weight labs don't even provide a catalog or high-level description of the training data and methods at all.
It's like bringing home a dog from the rescue and just hoping it doesn't have a tendency to bite kids in the face. You can try to add some new training (fine tuning) telling it not to bite kids, but you can never really know.
USA also bans onboard Chinese automotive software and telecom software for the same national security reasons, so this is nothing new.
This is a bad analogy in so many ways.
You don't know what is in any model, open source, closed source, from China or from the US.
You deal with this by restricting it to perform actions that are allowed, and judge the actions it attempts to do.
Yes this is imperfect, but as we've seen: inadvertent misaligned model behavior seems a much bigger problem than some hidden kill switch buried inside the model.
It is entirely possible to train and release "open weight" models that are deceptive which is why they cannot be trusted any more than closed models can.
https://arxiv.org/abs/2401.05566
4 replies →
The model doesn't have to perform unusual actions. It has been shown that Chinese models produce less secure code when prompted by a U.S. government persona. https://www.boozallen.com/expertise/cybersecurity/whats-in-a...
3 replies →
It is a great analogy actually. We indeed don't know what's in any model. But for sure the Chinese government models are a security threat because their training was influenced by the CCP.
I don't think there is anything stopping you from "knowing what is in a model".
For example, let's say we have a one-bit model. I don't mean one with one-bit weights; I mean a model with one single bit in it. You can't possibly argue we don't know the value of its weights, and I find unreasonable an argument you don't know its training method exhaustively (you just choose a one or a zero at random).
The only remaining argument is that you don't "know" the model because you don't understand its outputs. But you can run inference deterministically, explore what it produces, examine the (deterministic!) outputs, and then you know what it does. In the case of a one-bit model, probably nothing much.
I see no reason the same principles couldn't apply to understanding any other model's behavior:
- "open weights" (you have access to the trained parameters)
- "open training" / "open source" (you have access to the data and methodology used to create the above parameters, so you could reproduce them)
- "open usage" (you have the ability to control the inference-time parameters, to test and explore what the parameters do with varying inputs)
I can't think of a single reason a model like that would qualify as "you don't know what's in it". You know what it is, you know what it does, and you know how it was made. Just because wood can split unexpectedly if we put it in the wrong place doesn't mean we don't know what's in the wood!
8 replies →
> Open weight is not the same thing as open source.
Posted today:
DeepSeek just open-sourced its inference engine
https://news.ycombinator.com/item?id=50163277
That's still not source code according to Stallman's GPL definition. Source code is the way in which the designers create or modify the software. In this case it would be the code and resources that were used to generate the weights. Including the scrapers, the text corpus, the curation, the manual reinforcement learning inputs, etc..
The model is not "open source". That is just some Python scripts and tooling to run the model. There are a million of those already.
Might? At least one of them has done nothing but lose money since it's inception.
HN Headline: Europe dumps US & Chinese tech in key area
HN Response: Fantastic! EU is amazing! Sovereignty over all else!
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HN Headline: US dumps Chinese tech in key area
HN Response: Booo evil USA! Evil Big Tech! Regulatory capture!!
Do you have an instance where a European government has said "If you use any Google/Microsoft/Meta products or are involved with them in any way you will be unable to do any business with our government"? Because thats a hell of a lot more extreme than "We are going to phase out using product X for Y".
I don't think he has. You must chose US made spyware over Chinese made spyware!
Needles to say, I would not really trust Chinese goverment either. Let alone from perspective of their biggest rival.
If it were just a phase out vs. a more aggressive ban do you think this funny HN hypocrisy would suddenly not exist?
Comments would be the same.