Microsoft Phi-2 model changes licence to MIT
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What is interesting is that the AI “ethicists” all want to serve as a high priesthood controlling access to ML models in the name of safety. However, I think the biggest danger from AI is that these models will be used by those who control the models to control and censor what people are allowed to write.
These open source models in the hands of the public, are, IMO the best defense against the true danger of AI.
Kudos to Facebook and Microsoft and Mistral for pushing this.
A proprietary software program running on your hardware is more defacto open than an AGPL SAAS running on the cloud.
But you said "AGPL". AGPL SAAS running on someone else's computer that you can access requires that they provide you with the source code they're running. Barring shenanigans, that source code would enable you to run the same SAAS yourself if you desired to do so.
I'd say having the ability to run the program locally _and_ its source code is "more open" than just having the ability to run the program locally in binary form. With AGPL in your scenario you get all three of source access, local execution, and remote-SAAS exection. Proprietary local code you get one of those three.
Why not just ask it how to reach heaven with the lowest effort possible? Why don't good guys like you have your LLM pre-un-radicalize all those poor young men?
Becoming a martyr is the fastest way and with the lowest effort.
It was available even before this, all they changed is that law abiding citizens can put apps in the App Store and charge money for it.
(More importantly law abiding companies can build on and fine tune it in hopes of profit).
ISIS, etc. can easily abuse an open-source model and abusing a closed source model running in the cloud, e.g. ChatGPT 4 is a lot harder.
[0] https://www.counterextremism.com/blog/infamous-isis-bomb-mak...
Even with code/weights common to the public, a significant resource divide remains (e.g compute, infrastructure, R&D). I'm not arguing against more permissive licensing here, but I do not see it as a clear determinant for levelling the field either.
Lets say for example I have access to exactly the models Facebook is using to target my elderly relatives with right-wing radicalising propaganda. How does that help me?
This assumption that it helps somehow sounds like you've internalised some of the arguments people make about gun control and just assume those same points work in this case as well.
is it working?
Local models will be mandatory once the web gets filled with AI bots. You need your own AI bot to fight them off.
https://standards.ieee.org/ieee/7010/7718/
I wrote the implementation annex
For every reasonable and non-authoritarian suggestion I read for regulating AI, I feel like I wade through 10 Neuromancer-level takes. It's definitely a me-problem, I gotta stop scrolling through /new...
This was the effort of dozens of engineers, ethicists and systems people all done prior to the LLM revolution so it doesn’t have all the mystical junk that the newcomers seem to be latching onto.
Please also see the other comment from John Havens who led the effort with Laura Musikanski from IEEE
The best analog that comes to mind for me is Open Source software, and viral licenses that encourage a literal obligation to "give back" to the community. As helpful as that is, Open Source software still consumes power and can't ensure ecological symbiosis with it's users (even if it's ethically superior to proprietary alternatives). With that in mind, I'm curious how AI licensing differs, and how it's increased cost of training/execution will be subsidized by the value of the gross output.
The other more common question that comes to mind is enforcement. In your "agenda" as it were, would AI governance be obligatory or optional? Should we draw the line differently for research/nonprofit/commercial entities? In a traditional economy, the existence of Open Source software has enabled better value extraction for businesses which ultimately do not prioritize environmental concerns. Along the same line of thought as the first question, I'd be interested to hear how AI governance can avoid overreach while still addressing the same issue we had with traditional software creating excess value that mostly does not benefit the ecology or greater good.
This is something I'm very interested in generally, but I question if we have the framework to actually achieve meaningful human-AI symbiosis. Open Source succeeded in it's goal to subvert copyright and capitalism by carefully following the rules and managing it's expectations from the start. I worry that you're biting off more than you can chew asking for human-computer, human-AI or even AI-ecology symbiosis. I'd be glad to summon another boffin who can prove me wrong though :P
That is just not something that’s built into anything post Norbert wiener cybernetics shift in the late 1950s, 1960s which was just totally blown out of the software side of engineering.
I’ve been working on ASI for two decades and now as we’re going to achieve it, I’m switching to working on alternative socio-economic systems to capitalism so that ASI doesn’t control human systems.
And there's a growing community of people doing work under proper OSS licenses where interesting things are happening at an accelerating pace. So, alternate licenses lack effectiveness, isolate you from that community and complicates collaboration, and they increasingly represent a minority of the overall research happening. Which makes these licenses a bit pointless.
So, fixing this simplifies and normalizes things from a legal point of view which in turn simplifies commercialization, collaboration, and research. MS is being rational enough to recognize that there is value in that and is adjusting to this reality.
I understand why the geopolitical focus, but let's not forget there are plenty of ill intentioned actors everywhere.
This is a very uncharitable take. I would suggest familiarizing yourself with the actual arguments rather than summaries on social media. There’s considerably more thought than you’re crediting them with, and extensive discussion around the risk you’re worried about along with proposed solutions which – unlike your “best defense” – could actually work.
Thinking about it, both groups don't talk a lot about the risk from AI being used for censorship...
This is a pretty common topic in the academic community I follow, along with related things like how it’ll impact relationships with employers, governments, etc. I see that most as a counterpoint to the more sci-fi ideas as in “don’t worry about AIs annihilating humanity, worry about your boss saying an AI graded your work and said you are overpaid”.
Eliezer Yudkowsky, high priest of AGI risk
You'd be surprised how much you can advance in life just by avoiding talking or thinking about other people too much, much less grouping them. It's a fundamental part of our animal brains and it's gotten oh-so-much worse as we migrated to the internet. And it leads to epistemic closure.
n.b. I think the AGI risk stuff is bunko and the original AI ethics cheerleaders ruined their own field. You don't need to agree to understand
Who says that this is not an (or even the) actual hidden agenda behind these insane AI investments: building an infrastructure for large-scale censorship?
Given its performance and size, a commercial-friendly license is actually a big deal.
But it was on purpose not trained on the big “web crawled” datasets to not learn how to build bombs etc, or be naughty.
So it is the “smartest thinking” model in weight class or even comparable to higher param models, but it is not knowledgeable about the world and trivia as much.
This might change in the future but it is the current state.
* by want, I mean need. People self-peasantized heavily on "censorsed models" and don't really understand how these work, and the SNR is out of wack because there's a 100000x more waifu creators and culture warriors than knowledgable people sharing on this subject
If someone read a set of dictionaries, but then talked to actual people... you'd get about the same.
E.g. complete obliviousness to colloquialisms, etc.
Having less data embedded also means that the model is more generally usable outside the realm of chat assistants, where you only want the model to be aware about data you provide it. One example could be in games where you might have a medieval fantasy setting, it would be really weird if you could get a character to start talking to you about US politics. That probably still wouldn't work with Phi-2 without fine-tuning (as I imagine it does have some data of US politics embedded), but I hope it illustrates the point.
It wasn't trained on web crawled data to make it less obvious that microsoft steals property and personal data to monetise it.
The question is - if we train a model on synthetic data generated by GPT-4 which has copyright issues, what is the status of this model? Will MS have to delete it as well? And all models trained with GPT-4 data?
Is that the new directive from HQ? I see a lot of folks parroting this logic, ignoring that proceeds of crime are criminal themselves.
There are plenty of synthetic datasets generated from GPT-4 and other models^[1]. But MS created a large one, 150B tokens. Still 2 orders of magnitude smaller than the 13T used to train GPT-4.
But in the future this will be the main way to improve models - put them to work, and filter their good stuff. Then retrain. Very expensive, but that is the cost of evolution. It took humans a very long time to create the culture and technology that underlies LLMs, it will take a similar effort to push them forward.
Human generated text was the low hanging fruit, but now that it's picked, synthetic data is the only way forward. Models generating their own experience and feedback, doing exploration, combinatorial search, learning from their interactions with humans, from games, experiments and simulations.
But if we're talking about synthetic data - then the elephant in the room is the chat logs of OpenAI. They got 180M users, assume 10K tokens/user/month, that would be 1.8B tokens per month, mostly AI written but interspersed with human replies and tool generated output. This means they can collect in less than a year about as much synthetic data as the original training set.
What if they train GPT-5 solely on synthetic data? That would simplify the copyright issues a lot, and give a 5x boost in efficiency.
The structure and the training data are also interesting - sparse model using curated synthetic data to achieve much better accuracy than is achieved in models trained on random internet text.
You can try Phi-2 with wasm here, I mostly just get gibberish out of it though: https://huggingface.co/spaces/radames/Candle-phi1-phi2-wasm-...
Mixtral is the only model that properly matches 3.5 at the moment.
Both 3.5 and 4 have changed drastically over the past year with continued fine tuning, quantization, etc. so what some people consider their level is not exactly a fixed point either.
[0] The actual leaderboard I'm referencing, it has its biases but it's the most generally indicative thing available right now: https://chat.lmsys.org
Elo is a proper name, not an acronym!
Best 2.7b? sure.
I have always thought of this model as something you fine tune for a very specific task or dataset.
For others that don’t strongly disagree with the parent comment, can you point me to some examples?