I'm playing games at home. I'm running models at home (I linked in other similar answers to it) for benchmarking.
My point is that models are just like anything I bring into my home I try to only buy products that are manufactured properly. Someone else in this thread asked me about child labor for electronics and IMHO that was actually a good analogy. You here mention buying a microwave and that's another good example.
Yes, if we do want to establish feedback in the supply chain, we must know how everything we rely on is made. It's that simple.
There are already quite a few initiatives for that with e.g. coffee with Fair Trade Certification or ISO 14001, in electronics Fair Materials, etc.
The point being that there are already mechanisms for feedback in other fields and in ML there are already model cards with a co2_eq_emissions field, so why couldn't feedback also work in this field?
Right, reminds me of the hacker mindset or more recently the workshop I did on "Future wheel foresight" with Karin Hannes. One can try their best to predict how an invention might be used but in practice it goes beyond what its inventors want it to be, it is truly about how what "it" does through actual usage.
Fundamental cryptocurrency is fascinating. It is mathematically sound, just like cryptography in general (computational complexity, one way functions, etc) and it had the theoretical potential to change existing political and economical structures. Unfortunately (arguably) the very foundation it is based on, namely mining for greed, brought a different community who inexorably modified not the technology itself but its usages. What was initially a potential infrastructure for exchange of value became a way to speculate, buy and sell goods and services banned, ransomware, scam payments, etc).
AI also is fascinating as a research fields. It asks deep question with complex answers. Research for centuries about it lead to not just interesting philosophical questions, like what it's like to be think, to be human, and mathematics used in all walks of life, like in logistics for your parcel to get delivered this morning. Yet... gradually the field, or at least its commercialization, got captured by venture capitalists, entrepreneurs, regulators, who main interest was greed. This in turn changed what was until then open to something closed, something small to something required gigantic infrastructure capturing resources hitherto used for farming, polluting due to lack of proper permit for temporary electricity sources, etc. The pinnacle right now being regulation to ban regulation on AI in the US.
So... yes, technology itself can be fascinating, useful, even important and yet how we collectively, as a society, decide to use it remains what matters, the actual impact of an idea rather than its idealization.
Aren't we 18months until developers get replaced by AI... for like few years now?
Of course "AI" even loosely defined progressed a lot and it is genuinely impressive (even though the actual use case for most hype, i.e. LLM and GenAI, is mostly lazier search, more efficient spam&scam personalized text or impersonation) but exponential is not sustainable. It's a marketing term to keep on fueling the hype.
That's despite so much resources, namely R&D and data centers, being poured in... and yet there is not "GPT5" or anything that most people use on a daily basis for anything "productive" except unreliable summarization or STT (which both had plenty of tools for decades).
So... yeah, it's a slow take off, as expected. shrug
Yes indeed, yet my point is that we keep on training models TODAY so if keep on not caring, then we do postpone the same problem, cf https://lemmy.world/post/30563785/17400518
Basically yes, use trained model today if you want but if we don't set a trend then despite the undeniable ecological impact, there will be no corrective measure.
It's not enough to just say "Oh well, it used a ton of energy. We MUST use it now."
Anyway, my overall point was that training takes a ton of energy. I'm not asking your or OP or anyone else NOT to use such models. I'm solely pointing out that doing so without understand the process that lead to such models, including but not limited to energy for training, is naive at best.
Edit: it's also important to point out alternatives that are not models, namely there are already plenty of specialized tools that are MORE efficient AND accurate today. So even if the model took a ton of energy to train, in such case it's still not rational to use it. It's a sunk cost.
Indeed, the argument is mostly for future usage and future models. The overall point being that assuming training costs are negligible is either naive or showing that one does not care much for the environment.
From a business perspective, if I'm Microsoft or OpenAI, and I see a trend to prioritize models that minimize training costs, or even that users are avoiding costly to train model, I will adapt to it. On the other hand if I see nobody cares for that, or that even building more data center drives the value up, I will build bigger models regardless of usage or energy cost.
The point is that training is expensive and that pointing only to inference is like the Titanic going full speed ahead toward the iceberg saying how small it is. It is not small.
Right, my point is exactly that though, that OP by having just downloaded it might not realize the training costs. They might be low but on average they are quite high, at least relative to fine-tuning or inference. So my question was precisely to highlight that running locally while not knowing the training cost is naive, ecologically speaking. They did clarify though that they do not care so that's coherent for them. I'm insisting on that point because maybe others would think "Oh... I can run a model locally, then it's not <
<evil>
>" so I'm trying to clarify (and please let me know if I'm wrong) that it is good for privacy but the upfront training cost are not insignificant and might lead some people to prefer NOT relying on very costly to train models and prefer others, or a even a totally different solution.
Results? I have no idea what you are talking about. I thought we were discussing the training cost (my initial question) and that the truckload was an analogy to argue that the impact from that upfront costs is spread among users.
Great point, so are you saying there is a certain threshold above which training is energetically useful but under which it is not, e.g. if training of a large model is used by 1 person, it is not sustainable but if 1 million people use it (assuming it's done productively, not spam or scam) then it is fine?
I see. Well, I checked your post history because I thought "Heck, they sound smart, maybe I'm the problem." and my conclusion based on the floral language you often use with others is that you are clearly provoking on purpose.
Unfortunately I don't have the luxury of time to argue this way so I'll just block you, this way we won't have to interact in the future.
I'm playing games at home. I'm running models at home (I linked in other similar answers to it) for benchmarking.
My point is that models are just like anything I bring into my home I try to only buy products that are manufactured properly. Someone else in this thread asked me about child labor for electronics and IMHO that was actually a good analogy. You here mention buying a microwave and that's another good example.
Yes, if we do want to establish feedback in the supply chain, we must know how everything we rely on is made. It's that simple.
There are already quite a few initiatives for that with e.g. coffee with Fair Trade Certification or ISO 14001, in electronics Fair Materials, etc.
The point being that there are already mechanisms for feedback in other fields and in ML there are already model cards with a
co2_eq_emissionsfield, so why couldn't feedback also work in this field?