It definitely is open source even if they don’t disclose all details behind the training
A good definition of “truly open” is whether the exact same results can be reproduced by someone with no extra information from only what has been made available. If that is not possible, because the reproduction methodology is closed (a common reason, like in this case) then what has been made available is not truly open.
We can sit here and technically argue whether or not the subject matter violated some arbitrary “open source” definition but it still doesn’t change the fact that it’s not truly open in spirit
We already have a definition of open source. I don't see any reason to change it.
It seems unreasonable to require the training data just to be called open source, given it has similar copyright challenges as game assets.
Of course, this wouldn't make the model reproducible. But that's different from open source.
Imagine if Facebook open-sourced their front-end libraries like React but not the back-end.
Imagine if Twitter or Google didn’t publish its Algorithm for how they rank things to display to different people.
You don’t need to imagine. That’s exactly what’s happening! Would you call them open source because their front end is open source? Could you host your own back end on your choice of computers?
No. That’s why I even started https://qbix.com/platform
A better analogy would be some graphics card drivers which ship a massive proprietary GPU firmware blob, and a small(ish) kernel shim to talk with said blob.
Sometimes though the software alone can be near useless without additional assets that aren't necessarily covered by the code license.
Like Quake, having the engine without the assets is useless if what you wanted was to play Quake the game. Neural nets are another prime example, as you mention. Simulators that rely on measured material property databases for usable results also fall into this category, and so on.
So perhaps what we need is new open source licenses that includes the assets needed for the user to be able to reasonably use the program as a whole.
It's basically like giving people a binary program and calling it open source because the compiler and runtime used are open source.
All sorts of intangibles end up in open source projects. This isn’t a science experiment that needs replication. They’re not trying to prove how they came up with the image/code/model.
Look into Affero GPL. Images are inert static assets. Here we are talking about the back end engine. The fact that neural networks and model weights are non-von-neumann architecture doesn’t negate the fact that they are executable code and not just static assets!
What if someone gave you a binary and the source code, but not a compiler? Maybe not even a language spec?
Or what if they gave you a binary and the source code and a fully documented language spec, and both of 'em all the way down to the compiler? BUT it only runs on special proprietary silicon? Or maybe even the silicon is fully documented, but producing that silicon is effectively out of reach to all but F100 companies?
It's turtles all the way down...
Free(dom Respecting) Software wasn’t just about the source code.
https://www.gnu.org/philosophy/open-source-misses-the-point....
Make no mistake, I am super grateful to OSI for their efforts and most of my code out there uses one of their licenses. I just think they are limited by the circumstances. Some things I consider open are not conforming to their licenses and, like here, some things that conform might not be really open.
The parallel can be made with model weights being static assets delivered in their completed state.
(I favor the full process being released especially for scientific reproducibility, but this is an other point)
By this logic any freely downloadable executable software (a.k.a. freeware) is also open source, even though they don't disclose all details on how to build it.
If I hand you a beer for free that’s freeware. If I hand you the recipe and instructions to brew the beer that is open source.
We muddy the waters too much lately and call “free” to use things “open source”.
Yeah, but what those "open source" models are is like you handing me a bottle of beer, plus the instructions to make the glass bottle. You're open-sourcing something, just not the part that matters. It's not "open source beer", it's "beer in an open-source bottle". In the same fashion, those models aren't open source - they're closed models inside a tiny open-source inference script.
The model weights in eg TensorFlow are the source code.
It is not a von-Neumann architecture but a gigabyte of model weights is the executable part, no less than a gigabyte of imperative code.
Now, the training of the model is akin to the process of writing the code. In classical imperative languages that code may be such spaghetti code that each part would be intertwined with 40 others, so you can’t just modify something easily.
So the fact that you can’t modify the code is Freedom 2 or whatever. But at least you have Freedom 0 of hosting the model where You want and not getting charged for it an exorbitant amount or getting cut off, or having the model change out from under you via RLHF for political correctnesss or whatever.
OpenAI has not even met Freedom Zero of FSR or OSI’s definition. But others can.
The model weights aren't source code. They are the binary result of compiling that source code.
The source code is the combination of the training data and configuration of model architecture that runs against it.
The model architecture could be considered the compiler.
If you give me gcc and your C code I can compile the binary myself.
If you give me your training data and code that implements your model architecture, I can run those to compile the model weights myself.
A very slow, very expensive compiler - but it's still taking the source code (the training material and model architecture) and compiling that into a binary executable (the model).
Maybe it helps to think about this at a much smaller scale. There are plenty of interesting machine learning models which can be trained on a laptop in a few seconds (or a few minutes). That process feels very much like a compiler - takes less time to compile than a lot of large C++ projects.
Running on a GPU cluster for a month is the exact same process, just scaled up.
Huge projects like Microsoft Windows take hours to compile and that process often runs on expensive clusters, but it's still considered compilation.
https://time.com/6247678/openai-chatgpt-kenya-workers/
And billion-dollar companies made their money off it:
https://www.forbes.com/sites/kenrickcai/2023/04/11/how-alexa...
That’s the dirty secret of why ChatGPT 4 is better. But they’ll tell you it has to do with chaining ChatGPT 3’s together, more fine tuning etc. They go to these poor countries and recruit people to work on training the AI.
Not to mention all the uncompensated work of humans around the world who put their content up on the Web.