You can install Plex on your mobile device and toggle the “share media from this device” setting. Otherwise, a steam deck would have everything an RPI has plus a GPU and a touch screen. Since there are two radios (2 and 5Ghz) on the device, you should be able to set it up as a bridge device, but I’ve not tried this personally.

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and what is “what they asked for”

I’m definitely not confused. Perhaps we have irreconcilable philosophical differences, but I’m certainly not confused by percentages.

Personally, I would a 30% voter turnout as a damning indictment of the system, particularly when Switzerland was one of the last countries in Europe to legalize women’s right to vote and the right to gay marriage.

For most of the US’s history, most people were simply not allowed to participate in that system and twice this century the winner lost the popular vote. How is it do hard to believe that someone would feel legitimately disenfranchised and frustrated by that system?

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“both sides bad” has won almost every US election, according to this chart.

It’s literally the most popular position when you consider voter turnout and % of votes for each main party.

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

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Militant, nationalistic, white supremacist violent extremism has increased in the United States. In fact, the number of far-right attacks continues to outpace all other types of terrorism and domestic violent extremism. Since 1990, far-right extremists have committed far more ideologically motivated homicides than far-left or radical Islamist extremists, including 227 events that took more than 520 lives.[1]

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.

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.

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

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.