yeah. find the es_input.cfg file

Forum link

On Linux, that's usually the case. Finding the config file is the problem. I suspect that's why emulation Station isn't working. I don't know where that's installed, but I'd assume there's another configuration file for ES. It's probably in the home directory, ~. maybe ~/.emulation_station or or ~/.ES. I don't recall, but there will be a file structure similar to the RetroArch tree.

In either case, it would be very kind to post the full solution for the next person.

Reality gas a well known liberal bias

~Stephen Colbert, 2006 White House Correspondents Dinner

I've never had issues with the 8bitdo Controllers on rpi, Bluetooth or wired, but I found a thread where others solved the same problem. Looks like that particular controller isn't perfectly supported and you need to update xpad and a configuration file.

RetroPie forum.

Yes. I have confused Slovakia with Czechia and I apologize for that. I have corrected it, but this was 100% my mistake. I'm severely jet lagged and definitely know the difference under normal circumstances. Thanks for correcting my accidental misinformation.

Hiding the true cost of road maintenance and offloading it on everyone else is a dark pattern, homey.

Lol. OP uses public roads for $43 a year and is complaining that trees cost $5. Does OP actually think roads only cost $43 per vehicle per year?

Or maybe the US government massively subsidizes car travel using income taxes collected from everyone (even those who walk, use public transit, or bike).

Whereas, the ability to convert CO2 into oxygen via plant respiration is something everyone needs.

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.

Hey. I saw your post history and want to remind you that you're a worthwhile human being and that this rating shit is toxic.

Please find a community that isn't so superficial and take care of yourself. You are worth so much more than your appearance.

sėkmės

Which kind of European?

Meloni, Le Pen, Wilders, and Germany's AfD, the new Czech Slovak government are all using the same rhetoric.

The EU Parliament has been waging its own trade war in Europe and Europe's equivalent of a border wall is called Frontex and is there to stop people from claiming their legal right to asylum.

I'd suggest rephrasing the question to be less inflammatory and more self aware.

but to answer your question as it's the same for all listed examples...

racism and economic anxiety pepeturated by the ruling class to keep us fighting each other instead of realizing that a handful of billionaires own everything and contribute nothing.

Adding Austria as of 29 September 2024

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

COVID research made generic sequencing for viruses and bacteria incredibly cheap. You can run a PCR test for most things now for $10 (USD) or less. This opens a whole world of highly specific diagnostics and cheap, hyper-personalized treatments.

Also, MRNA vaccines are being tested for several other diseases and it seems very promising.

and my point was explaining that that work has likely been done because the paper I linked was 20 years old and they talk about the deep connection between "similarity" and "compresses well". I bet if you read the paper, you'd see exactly why I chose to share it-- particularly the equations that define NID and NCD.

The difference between "seeing how well similar images compress" and figuring out "which of these images are similar" is the quantized, classficiation step which is trivial compared to doing the distance comparison across all samples with all other samples. My point was that this distance measure (using compressors to measure similarity) has been published for at least 20 years and that you should probably google "normalized compression distance" before spending any time implementing stuff, since it's very much been done before.

I think there's probably a difference between an intro to computer science course and the PhD level papers that discuss the ability of machines to learn and decide, but my experience in this is limited to my PhD in the topic.

And, no, textbooks are often not peer reviewed in the same way and generally written by graduate students. They have mistakes in them all the time. Or grand statements taken out of context. Or are simplified explanations because introducing the nuances of PAC-learnability to somebody who doesn't understand a "for" loop is probably not very productive.

I came here to share some interesting material from my PhD research topic and you're calling me an asshole. It sounds like you did not have a wonderful day and I'm sorry for that.

Did you try learning about how computers learn things and make decisions? It's pretty neat