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We absolutely had a choice (D), in that no one was forced to call it "open source" at all, which was arguably done to unfaithfully communicate benefits that don

by sho_hn 2y ago

We absolutely had a choice (D), in that no one was forced to call it "open source" at all, which was arguably done to unfaithfully communicate benefits that don't exist. This is the part that riles people up, and that furthermore is causing collateral damage outside the AI bubble, and is nothing like Betamax vs. VHS. If you want to prioritize pragmatism, that every discussion of this includes a lengthy "so what open source do you mean, exactly?" subthread proves this was a poor choice. It causes uncertainly that also makes it harder for the folks releasing these models to make their case and be taken seriously for their approach. We should probably call them "free to run", if the "it's cheap" connotation of "freeware" needs to be avoided. Or maybe "open architecture" to appreciate the Python file that utilizes the weights more.

JumpCrisscross 2y ago
> We absolutely had a choice (D), in that no one was forced to call it "open source" at all Technically yes, practically no. You’re describing a prisoner’s dilemma. The term was available, there was (and remains) genuine ambiguity over what it meant in this context, and there are first-mover advantages in branding. (Exhibit A: how we label charges). > causing collateral damage outside the AI bubble, and is nothing like Betamax vs. VHS Standards wars have collateral damage. > We should probably call them "free to run", if the "it's cheap" connotation of "freeware" needs to be avoided. Or maybe "open architecture" Language is parsimonious. A neologism will never win when a semantic shift will do.
- sho_hn 2y ago
  > Language is parsimonious. A neologism will never win when a semantic shift will do. Agreed, but I think it's worth lamenting the danger in that. History is certainly full of transitory calamity and harm when semantic shifts detach labels from reality. I guess we're in any case in "damage is done" territory. The question is more about where to go next. It does appear that the term "open source" isn't working for what these folks are doing (you could even argue whether the "available" term they chose was a strong one to lean on in the first place), so we'll see what direction the next shift takes.
  - JumpCrisscross 2y ago
    > we're in any case in "damage is done" territory. The question is more about where to go next Sort of. We can learn from the example. Perfect is the enemy of the good.
nightski 2y ago
The source code is absolutely open which is the traditional meaning of open source. You are wanting to expand this to include data sets, which is fine, but that is the divergence.
- JumpCrisscross 2y ago
  > source code is absolutely open It’s ambiguously open.
- lyu07282 2y ago
  Nonono the code for (pre-)training wasn't released either and is non trivial to replicate. Releasing the weights without the dataset and training code is equivalent of releasing a binary executable and calling it open source. Freeware would be more accurate terminology.
  - seberino 2y ago
    I think I see what you mean. I suppose it is kinda like an opaque binary, nevertheless, you can use it freely since all is under the MIT license right?
    - lyu07282 2y ago
      Yes even for commercial purposes which is great, but the point of and reason why "open source" became popular is that you can modify the underlying source code of the binary which you can then recompile with your modifications included (as well as selling/publishing your modifications). You can't do that with deepseek or most other LLMs that claim to be open source. The point isn't that this makes it bad, the point is we shouldn't call it open source because we shouldn't loose focus on the goal of a truly open source (or free software) LLM on the same level than chatgpt/o1.
      - nightski 2y ago
        You can modify the weights which is exactly what they do when training initially. You do not even need to do it in exactly the same fashion. You could change things such as the optimizer and it would still work. So in my opinion it is nothing like an opaque binary. It's just data.

by sho_hn 2y ago

JumpCrisscross 2y ago

> We absolutely had a choice (D), in that no one was forced to call it "open source" at all Technically yes, practically no. You’re describing a prisoner’s dilemma. The term was available, there was (and remains) genuine ambiguity over what it meant in this context, and there are first-mover advantages in branding. (Exhibit A: how we label charges). > causing collateral damage outside the AI bubble, and is nothing like Betamax vs. VHS Standards wars have collateral damage. > We should probably call them "free to run", if the "it's cheap" connotation of "freeware" needs to be avoided. Or maybe "open architecture" Language is parsimonious. A neologism will never win when a semantic shift will do.

sho_hn 2y ago
> Language is parsimonious. A neologism will never win when a semantic shift will do. Agreed, but I think it's worth lamenting the danger in that. History is certainly full of transitory calamity and harm when semantic shifts detach labels from reality. I guess we're in any case in "damage is done" territory. The question is more about where to go next. It does appear that the term "open source" isn't working for what these folks are doing (you could even argue whether the "available" term they chose was a strong one to lean on in the first place), so we'll see what direction the next shift takes.
- JumpCrisscross 2y ago
  > we're in any case in "damage is done" territory. The question is more about where to go next Sort of. We can learn from the example. Perfect is the enemy of the good.

nightski 2y ago

The source code is absolutely open which is the traditional meaning of open source. You are wanting to expand this to include data sets, which is fine, but that is the divergence.

JumpCrisscross 2y ago
> source code is absolutely open It’s ambiguously open.
lyu07282 2y ago
Nonono the code for (pre-)training wasn't released either and is non trivial to replicate. Releasing the weights without the dataset and training code is equivalent of releasing a binary executable and calling it open source. Freeware would be more accurate terminology.
- seberino 2y ago
  I think I see what you mean. I suppose it is kinda like an opaque binary, nevertheless, you can use it freely since all is under the MIT license right?
  - lyu07282 2y ago
    Yes even for commercial purposes which is great, but the point of and reason why "open source" became popular is that you can modify the underlying source code of the binary which you can then recompile with your modifications included (as well as selling/publishing your modifications). You can't do that with deepseek or most other LLMs that claim to be open source. The point isn't that this makes it bad, the point is we shouldn't call it open source because we shouldn't loose focus on the goal of a truly open source (or free software) LLM on the same level than chatgpt/o1.
    - nightski 2y ago
      You can modify the weights which is exactly what they do when training initially. You do not even need to do it in exactly the same fashion. You could change things such as the optimizer and it would still work. So in my opinion it is nothing like an opaque binary. It's just data.