Don’t worry, until you’re chewing on your foot mid lecture, you’re good

> The older I get the more I fear I am turning into Richard Stallman

You and me both. I always preferred and promoted FLOSS where possible but still had a bit of a pragmatic approach, but now the older I get the more I just want to rip out everything not free (as in freedom) from my life, and/or just go become a goat farmer.

Stallman was right from the beginning, and big tech have proven over and over again that they are incapable of being good citizens.

I'm probably a few more years away from "I'd like to interject for a moment..."

The last time I heard a comment along those lines I was attending a session by an Open Source person and up on screen they had a picture of RMS dressed as Che Guevara.

All those silly ethics, they get in the way of the real work!

I'm not sure I fully understand - whilst I agree there's been useful legal work, we now have such a plethora of licenses I ended up having to back what I'd call basic common sense when someone was suggesting using a highly restrictive "community" license that had ridiculous intents such as saying you can't use it in this particular industry because that industry is "bad".

The reason Free/Libre Open Source Software wins - and always will do in the long run - is because the four freedoms are super-simple and they reflect how the natural world works.

Too bad. The OSI owns "open source".

Big tech has been abusing open source to cheaply capture most of the internet and e-commerce anyway, so perhaps it's time we walked away from the term altogether.

The OSI has abdicated the future of open machine learning. And that's fine. We don't need them.

"Free software" is still a thing and it means a very specific and narrow set of criteria. [1, 2]

There's also "Fair software" [3], which walks the line between CC BY-NC-SA and shareware, but also sticks it to big tech by preventing Redis/Elasticsearch capture by the hyperscalers. There's an open game engine [4] that has a pretty nice "Apache + NC" type license.

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Back on the main topic of "open machine learning": since the OSI fucked up, I came up with a ten point scale here [5] defining open AI models. It's just a draft, but if other people agree with the idea, I'll publish a website about it (so I'd appreciate your feedback!)

There are ten measures by which a model can/should be open:

1. The model code (pytorch, whatever)

2. The pre-training code

3. The fine-tuning code (which might be very different from the pre-training code)

4. The inference code

5. The raw training data (pre-training + fine-tuning)

6. The processed training data (which might vary across various stages of pre-training and fine-tuning: different sizes, features, batches, etc.)

7. The resultant weights blob(s)

8. The inference inputs and outputs (which also need a license; see also usage limits like O-RAIL)

9. The research paper(s) (hopefully the model is also described and characterized in the literature!)

10. The patents (or lack thereof)

A good open model will have nearly all of these made available. A fake "open" model might only give you two of ten.

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[1] https://www.fsf.org/

[2] https://en.wikipedia.org/wiki/Free_software

[3] https://fair.io/

[4] https://defold.com/license/

[5] https://news.ycombinator.com/item?id=44438329

Are you going to examine a few petabytes of data for each model you want to run, to check if a random paragraph from Main Kampf is in there? How?

We need better tools to examine the weights (what gets activated to which extent for which topics, for example). Getting full training corpus, while nice, cannot be our only choice.

Their justification for this was that, for many consequential models, releasing the training data just isn't possible.

Obviously the biggest example here is all of that training data which was scraped from the public web (or worse) and cannot be relicensed because the model producers do not have permission to relicense it.

There are other factors too though. A big one is things like health data - if you train a model that can e.g. visually detect cancer cells you want to be able to release that model without having to release the private health scans that it was trained on.

See their FAQ item: Why do you allow the exclusion of some training data? https://opensource.org/ai/faq#why-do-you-allow-the-exclusion...

> Poor move IMO. Training data should be required to be released to be considered an open source model.

The actual poor move is trying to fit the term "open source" onto AI models at all, rather than new terms with names that actually match how models are developed.

This notably marks a schism with the FSF; it's the first time and context in which "open-source" and "free software" have not been synonymous, coextensive terms.

I think it greatly diminishes the value of the concept and label of open-source. And it's honestly a bit tragic.

I am plenty able to keep up with experiments others are doing and their results, but as for me my time is best spent building things that have never existed before, that models have no prior art to implement.