I work in the field. Generally, jobs that include AI development generally require advanced degrees and the vast majority require a PhD with peer reviewed publications in major conferences. You will be fighting an uphill battle if you don’t have an advanced degree in mathematics or computer science. You also need to know calculus, linear algebra and statistics to understand how modern machine learning models work.

In short, while online courses can be perfectly effective, unless they’re through an accredited higher education institution, I don’t think it will help you compete with other applicants who have 8+ years of schooling and published papers.

That being said, Georgia Tech and the City University of New York both offer master’s degrees in data science via remote master’s programs where the courses happen after work hours and are meant to be completed while working full-time.

it’s a mobile application first, but desktop options are available. Good luck!

Signal is super user friendly. I got my family to switch over years ago and even my mom can manage it. Though, it’s probably a tough sell for a kite flying group more generally. I do think it’s probably a lower barrier than making a new social media account on some fediverse alternative, but hard to explain the advantages to people who don’t care about privacy from Zuck.

I use signal (messenger app) groups, but my hobbies are often tech related, so that community is already there.

I’ve always found the best people at foodnotbombs, which has local chapters in most cities. Start there.

link

a variety of independent news sources.

Wikipedia is notoriously susceptible to bias when it comes to history and politics and has a noted left center bias (according to researchers at Harvard, not my words).

https://en.m.wikipedia.org/wiki/Ideological_bias_on_Wikipedia

I’m not saying it’s a terrible sources but it definitely should not be the last stop and anything controversial (or the lack thereof) isn’t a meaningful indicator of whether or not something is actually true. Note the numerous examples of historical revisionism in the linked article.

This is a shrine to (among others) recognized war criminals

For the young ones:

https://en.m.wikipedia.org/wiki/Dixie_Chicks_comments_on_George_W._Bush

Wild that you’re getting down voted for wanting to comply with international humanitarian law.

Removed by mod

no, no. I promise you that it’s delicious. it’s like biting into a ball of miso.

definitely A. Eat a big spoonful sometime.

MSG because it’s delicious.

I intended B, but A is also true, no?

Yeah. I’m thinking more along the lines of research and open models than anything to do with OpenAI. Fair use, above all else, generally requires that the derivative work not threaten the economic viability of the original and that’s categorically untrue of ChatGPT/Copilot which are marketed and sold as products meant to replace human workers.

The clean room development analogy is definitely an analogy I can get behind, but raises further questions since LLMs are multi stage. Technically, only the tokenization stage will “see” the source code, which is a bit like a “clean room” from the perspective of subsequent stages. When does something stop being just a list of technical requirements and veer into infringement? I’m not sure that line is so clear.

I don’t think the generative copyright thing is so straightforward since the model requires a human agent to generate the input even if the output is deterministic. I know, for example, Microsoft’s Image Generator says that the images fall under creative Commons, which is distinct from public domain given that some rights are withheld. Maybe that won’t hold up in court forever, but Microsoft’s lawyers seem to think it’s a bit more nuanced than “this output can’t be copyrighted”. If it’s not subject to copyright, then what product are they selling? Maybe the court agrees that LLMs and monkeys are the same, but I’m skeptical that that will happen considering how much money these tech companies have poured into it and how much the United States seems to bend over backwards to accommodate tech monopolies and their human rights violations.

Again, I think it’s clear that commerical entities using their market position to eliminate the need for artists and writers is clearly against the spirit of copyright and intellectual property, but I also think there are genuinely interesting questions when it comes to models that are themselves open source or non-commercial.

For example, if I ask it to produce python code for addition, which GPL’d library is it drawing from?

I think it’s clear that the fair use doctrine no longer applies when OpenAI turns it into a commercial code assistant, but then it gets a bit trickier when used for research or education purposes, right?

I’m not trying to be obtuse-- I’m an AI researcher who is highly skeptical of AI. I just think the imperfect compression that neural networks use to “store” data is a bit less clear than copy/pasting code wholesale.

would you agree that somebody reading source code and then reimplenting it (assuming no reverse engineering or proprietary source code) would not violate the GPL?

If so, then the argument that these models infringe on right holders seems to hinge on the verbatim argument that their exact work was used without attribution/license requirements. This surely happens sometimes, but is not, in general, a thing these models are capable of since they’re using loss-y compression to “learn” the model parameters. As an additional point, it would be straightforward to then comply with DMCA requests using any number of published “forced forgetting” methods.

Then, that raises a further question.

If I as an academic researcher wanted to make a model that writes code using GPL’d training data, would I be in compliance if I listed the training data and licensed my resulting model under the GPL?

I work for a university and hate big tech as much as anyone on Lemmy. I am just not entirely sure GPL makes sense here. GPL 3 was written because GPL 2 had loopholes that Microsoft exploited and I suspect their lawyers are pretty informed on the topic.

I hate big tech too, but I’m not really sure how the GPL or MIT licenses (for example) would apply. LLMs don’t really memorize stuff like a database would and there are certain (academic/research) domains that would almost certainly fall under fair use. LLMs aren’t really capable of storing the entire training set, though I admit there are almost certainly edge cases where stuff is taken verbatim.

I’m not advocating for OpenAI by any means, but I’m genuinely skeptical that most copyleft licenses have any stake in this. There’s no static linking or source code distribution happening. Many basic algorithms don’t follow under copyright, and, in practice, stack overflow code is copy/pasted all the time without that being released under any special license.

If your code is on GitHub, it really doesn’t matter what license you provide in the repository – you’ve already agreed to allowing any user to “fork” it for any reason whatsoever.

People who use LLMs to write code (incorrectly) perceived their code to be more secure than code written by expert humans.

https://arxiv.org/abs/2211.03622

By no means the best option, but the tikz latex package works and pandoc can handle the conversion to your preferred format. I would limit this to very simple diagrams.