hedgehog

Your comment wasn’t in a meta discussion; it was on a post where they were venting about people complaining about them having a women’s only space. There was certainly no indication that the regular community rules didn’t apply, nor any invitation for men to comment.

Commenting that it’s hostile for them to have a women’s only space might be ironic, but couldn’t possibly be good faith, in that context. And if the same mod banned you from multiple communities, then either it was out of line and you could appeal it, or it was warranted due to the perceived likelihood of you causing problems in those other communities and the perceived low likelihood of you contributing anything of value to them.

Even now, you’re acting like the mod(s) banned you because of her / their emotions. You don’t see how that’s misogynistic?

It makes logical sense for bad actors to be preemptively banned. Emotions have nothing to do with it.

You got the idea!

We’re in c/showerthoughts. “What if my grandma was a bike?” would fit right in

It was already known before the whistleblower that:

1. Siri inputs (all STT at that time, really) were processed off device
2. Siri had false activations

The “sinister” thing that we learned was that Apple was reviewing those activations to see if they were false, with the stated intent (as confirmed by the whistleblower) of using them to reduce false activations.

There are also black box methods to verify that data isn’t being sent and that particular hardware (like the microphone) isn’t being used, and there are people who look for vulnerabilities as a hobby. If the microphones on the most/second most popular phone brand (iPhone, Samsung) were secretly recording all the time, evidence of that would be easy to find and would be a huge scoop - why haven’t we heard about it yet?

Snowden and Wikileaks dumped a huge amount of info about governments spying, but nothing in there involved always on microphones in our cell phones.

To be fair, an individual phone is a single compromise away from actually listening to you, so it still makes sense to avoid having sensitive conversations within earshot of a wirelessly connected microphone. But generally that’s not the concern most people should have.

Advertising tracking is much more sinister and complicated and harder to wrap your head around than “my phone is listening to me” and as a result makes for a much less glamorous story, but there are dozens, if not hundreds or thousands, of stories out there about how invasive advertising companies’ methods are, about how they know too much, etc.. Think about what LLMs do with text. The level of prediction that they can do. That’s what ML algorithms can do with your behavior.

If you’re misattributing what advertisers know about you to the phone listening and reporting back, then you’re not paying attention to what they’re actually doing.

So yes - be vigilant. Just be vigilant about the right thing.

proven by a whistleblower from apple

Assuming you have an iPhone. And even then, the whistleblower you’re referencing was part of a team who reviewed utterances by users with the “Hey Siri” wake word feature enabled. If you had Siri disabled entirely or had the wake word feature disabled, you weren’t impacted at all.

This may have been limited to impacting only users who also had some option like “Improve Siri and Dictation” enabled, but it’s not clear. Today, the Privacy Policy explicitly says that Apple can have employees review your interactions with Siri and Dictation (my understanding is the reason for the settlement is that they were not explicit that human review was occurring). I strongly recommend disabling that setting, particularly if you have a wake word enabled.

If you have wake words enabled on your phone or device, your phone has to listen to be able to react to them. At that point, of course the phone is listening. Whether it’s sending the info back somewhere is a different story, and there isn’t any evidence that I’m aware of that any major phone company does this.

Sure - Wikipedia says it better than I could hope to:

As English-linguist Larry Andrews describes it, descriptive grammar is the linguistic approach which studies what a language is like, as opposed to prescriptive, which declares what a language should be like.[11]: 25  In other words, descriptive grammarians focus analysis on how all kinds of people in all sorts of environments, usually in more casual, everyday settings, communicate, whereas prescriptive grammarians focus on the grammatical rules and structures predetermined by linguistic registers and figures of power. An example that Andrews uses in his book is fewer than vs less than.[11]: 26  A descriptive grammarian would state that both statements are equally valid, as long as the meaning behind the statement can be understood. A prescriptive grammarian would analyze the rules and conventions behind both statements to determine which statement is correct or otherwise preferable. Andrews also believes that, although most linguists would be descriptive grammarians, most public school teachers tend to be prescriptive.[11]: 26

You might be interested in reading up on the debate of “Prescriptive vs Descriptive” approaches in a linguistics context.

You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.

You don't have to finish the file to share it though, that's a major part of bittorrent. Each peer shares parts of the files that they've partially downloaded already. So Meta didn't need to finish and share the whole file to have technically shared some parts of copyrighted works. Unless they just had uploading completely disabled,

The argument was not that it didn’t matter if a user didn’t download the entirety of a work from Meta, but that it didn’t matter whether a user downloaded anything from Meta, regardless of whether Meta was a peer or seed at the time.

Theoretically, Meta could have disabled uploading but not blocked their client from signaling that they could upload. This would, according to that argument, still counts as reproducing the works, under the logic that signaling that it was available is the same as “making it available.”

but they still "reproduced" those works by vectorizing them into an LLM. If Gemini can reproduce a copyrighted work "from memory" then that still counts.

That’s irrelevant to the plaintiff’s argument. And beyond that, it would need to be proven on its own merits. This argument about torrenting wouldn’t be relevant if LLAMA were obviously a derivative creation that wasn’t subject to fair use protections.

It’s also irrelevant if Gemini can reproduce a work, as Meta did not create Gemini.

Does any Llama model reproduce the entirety of The Bedwetter by Sarah Silverman if you provide the first paragraph? Does it even get the first chapter? I highly doubt it.

By the same logic, almost any computer on the internet is guilty of copyright infringement. Proxy servers, VPNs, basically any compute that routed those packets temporarily had (or still has for caches, logs, etc) copies of that protected data.

There have been lawsuits against both ISPs and VPNs in recent years for being complicit in copyright infringement, but that’s a bit different. Generally speaking, there are laws, like the DMCA, that specifically limit the liability of network providers and network services, so long as they respect things like takedown notices.

Why should we know this?

Not watching that video for a number of reasons, namely that ten seconds in they hadn’t said anything of substance, their first claim was incorrect (Amazon does not prohibit use of gen ai in books, nor do they require its use be disclosed to the public, no matter how much you might wish it did), and there was nothing in the description of substance, which in instances like this generally means the video will largely be devoid of substance.

What books is the Math Sorcerer selling? Are they the ones on Amazon linked from their page? Are they selling all of those or just promoting most of them?

Why do we think they were generated with AI?

When you say “generated with AI,” what do you mean?

• Generated entirely with AI, without even editing? Then why do they have so many 5 star reviews?
• Generated with AI and then heavily edited?
• Written partly by hand with some pieces written by unedited GenAI?
• Written partly by hand with some pieces written by edited GenAI?
• AI was used for ideation?
• AI was used during editing? E.g., Grammarly?
• GenAI was used during editing?E.g., “ChatGPT, review this chapter and give me any feedback. If sections need rewritten go ahead and take a first pass.”
• AI might have been used, but we don’t know for sure, and the issue is that some passages just “read like AI?”

And what’s the result? Are the books misleading in some way? That’s the most legitimate actual concern I can think of (I’m sure the people screaming that AI isn’t fair use would disagree, but if that’s the concern, settle it in court).

Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)

If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.

For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:

• q4_K_M (the default): 43 GB
• fp16: 141 GB
• q8: 75 GB
• q6_K: 58 GB
• q5_k_m: 50 GB
• q4: 40 GB
• q3_K_M: 34 GB
• q2_K: 26 GB

This is why I run a lot of Q4_K_M 70B models on two 3090s.

Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.

TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.

Then why are you doing that, and why aren’t you at least hosting your own instance?