Dual color filaments exist, and they do not mix at all... It gives the objects a nice transition when rotated. But indicates that color mixing in the nozzle is probably pretty difficult?
That OpenAI was in the wrong when they ignored everyone copyright, does not make it right to ignore their ToU. If a one wants IP and rule of law (incl contracts) to be respected, one should not violate others rights when it is convenient.
On a more risk-strategy level there is the size of their legal team, general endowment, and supplier and political connections to consider.
Everyone is free to ignore their ToU, but I can understand why a company would avoid it...
A 10'000 hour entry fee does rule out a fair bunch of people though, in practice. While there are few artificial barriers to learning to code, there still are some natural ones, like time.
is time a barrier or a requirement? its a bit like saying time is a natural barrier to solving hashes. like yeah, its an inherent part of the process that cannot be skipped. You cannot learn without time, but thats just the laws of the universe not a barrier to learning.
It would require an investment, but those will pay dividends later, as it becomes easier to train LLMs on/for Norwegian. If we need to translate everything to English we might as well just drop using Norwegian altogether. Practically everyone speaks English fluently already...
> as it becomes easier to train LLMs on/for Norwegian
Why would it be easier in the future? The advances we see with LLMs today require a huge amount of data, and it's getting hard getting the amount of data just using any language, I'm having a hard time seeing how it'd get easier for Norwegians to build their own LLM, unless they seriously start to ramp up how much Norwegian content they're putting out.
> If we need to translate everything to English we might as well just drop using Norwegian altogether. Practically everyone speaks English fluently already...
Yeah I mean with that black and white perspective you can pretty much do anything and it won't matter for anything :) I think for the rest of us, what we speak daily and what we rely on professionally, can differ, and that's OK. But maybe this is just my broken Swedish mind being so used to using English professionally but then conversing in Spanish outside of work daily, YMMV.
These models will never compete with frontier models and do not need to - it is about hitting a good-enough, not being the best.
Behind the frontier, getting to a certain performance level, is getting easier over time - both sample and compute efficiency is going up.
Furthermore one can reuse investments in data (both agreements, infrastructure and datasets), compute (GPUs, servers) and know-how (training scripts, experienced engineers).
But are you seriously under the belief that all of that, plus all the other things you're forgetting about, is easier, cheaper and faster than transcriptions and translations?
I understand and agree building the LLMs yourself comes with more benefits, long-term ones especially, but still it's harder, more expensive and really time consuming work.
I do not know which is easier. I am not sure that is even well established in research for generative text tasks whether a translation-first or native-language-first is the most sample efficient?
But for a national lab I think it is money well spent to figure out the possibilities and limitations of a native-language LLMs for languages with order of 5M-10M speakers.
As a precondition I think we have to assume that the person in question 1) wants to learn and 2) is smart enough to absorb new info and apply it and 3) reflects enough to adjust their approach when hitting bottlenecks or making mistakes 4) has a drive to create. Without these, self driven learning is not viable - and that has very little to do with AI.
For such a person, I believe AI can be very empowering for learning. Like Google, wikipedia and stack overflow, Arxiv before it - AI tools give access to a lot of information. It allows to quickly dig deep into any topic you can imagine. And yes, the quality is variable - so one needs to find ways to filter and synthesize from imperfect info. But that was also the case before.
Furthermore AI tools can be used to find holes in arguments or a paper. And by coding one can use it to test out things in practice. These are also powerful (albeit imperfect) learning tools. But they will not apply themselves.
Who is talking about self driven learning? Every workplace teachers their juniors how to do their job, and how to become better at their jobs.
And as we are talking about junior developers it is safe to assume your conditions (1), (2), and (4) are all true, if any of them are false, then why did that person apply for and get a job as a junior developer? As for condition (3), all workplaces eventually hires a person who does not fulfill this, then they either fire that person, or they give them a talk and the developer grows out of it and changes their behavior to fulfill that condition.
Aside: you listed 4 conditions for learning. I am not sure these are actually conditions recognized as such by behavior science. In fact, I doubt they are and that these conditions are just your opinions (man).
Why are these sockets "ruled out"? Pipeline/layer parallelism doesn't need high bandwidth between nodes, and tensor parallelism has middling performance unless you have very fast networking and very slow compute. It all depends on what you're doing.
You are correct that bandwidth requirements depends a lot on the exact workload. And that in specific cases, it might be doable to have AM5 for multiple RTX6000Pro. The parent mentioned workloads that are general, and broader than inference-only. In that case I would consider spending a bit extra on the motherboard to ensure that PCIE bandwidth is not an issue.
There are likely _many_ paths to sustainable business models based on AI tech, that will come to fruition over the next decades. However whether they might not be as profitable as OpenAI and Anthropic are gambling on, is more uncertain.
Communication tech/tools enable more people to collaborate. It increases ability for labor that is far away from high value markets to contribute. Same goes for shipping tech wrt physical goods. On the global scale that is empowering the labor class.
Any productivity tool that individual laborers can purchase also (and that still needs the worker) is probably good for labor, overall.
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