Different person here.

For me the big disqualifying factor is that LLMs don’t have any mutable state.

We humans have a part of our brain that can change our state from one to another as a reaction to input (through hormones, memories, etc). Some of those state changes are reversible, others aren’t. Some can be done consciously, some can be influenced consciously, some are entirely subconscious. This is also true for most animals we have observed. We can change their states through various means. In my opinion, this is a prerequisite in order to feel anything.

Once we use models with bits dedicated to such functionality, it’ll become a lot harder for me personally to argue against them having “feelings”, especially because in my worldview, continuity is not a prerequisite, and instead mostly an illusion.

His Hyprland setup looks cool if you’re into that sorta thing but it’s just not what users just switching to mint, fedora, whatever might be looking for.

I would not underestimate how much of a draw “it looks cool” can have on people who are not tech savy at all. If you think about what drives new phone purchases, their major version upgrades always include lots of things that are nothing but eye-candy and those are often heavily featured in their promotion material.

If the goal is to get casual users to convert to Linux, I would argue that aesthetics is a lot more important than ANY talk about technical details, privacy, etc. If those users cared about those things, they would’ve switched already.

Now my bigger worry is that those users will bounce off before they manage to get their setup to look as (subjectively) cool as his.

Sure! Here’s an expanded version of the fictional profile for Chris Whitmore, now including made-up family member names, relationships, and contact info — all entirely fictional and consistent with the character:

You forgot to remove that part of the LLM response…

I don’t think it’s more crime because more tension. It’s instead a self fulfilling prophecy. Who do you think detects and records crime if not the police? Therefore more police in a area increases the number of crime data points in that area.

One field it impacts is radio astronomy. We can already see Musk’s satellites mess with it (unintentionally) and it’s probably only going to get worse from here.

In my experience, it is good at simple to medium complexity regex. For the harder ones it starts being quite useless though, at best providing a decent starting point to begin debugging from.

I wanna add to what other users already answered that this problem is not created by federation, only exacerbated.

If I’m mod of a community and I ban your Lost_My_Mind@lemmy.world account, I cannot stop you from creating, e.g. Lost_My_M1nd@lemmy.world and coming back. Most servers have some barriers against spam account creation in place, but I’d wager you could easily create a handful of accounts on a server until they start to grip.

Even completely centralized platforms such as Twitter and Reddit are the same. You can easily ban/block evade a couple times per timeframe.

Whcih makes sense when explained, but it seems like few hear that kind of comparison.

And then you bring up defederation and/or how instances can die at any time and you lose them again…

At least that’s how it usually goes for me and trying to advertise Lemmy. Not really a fan of “microblogging” to begin with no matter the platform.

People who claim “guys” is gender neutral would most often only count men when asked the question “How many guys did you sleep with in your life?”

Until I find a single person who immediately thinks of people of any gender at that question, I will not fall for the internalized misogyny of “‘guys’ is gender neutral” meme. (Same with “dudes” and all the other ones I’ve seen over the years. I’ve even seen someone say “bro” is gender neutral.)

Sure. You have to solve it from inside out:

• not()…See comment below for this one, I was tricked is a base function that negates what’s inside (turning True to False and vice versa) giving it no parameter returns “True” (because no parameter counts as False)
• str(x) turns x into a string, in this case it turns the boolean True into the text string ‘True’
• min(x) returns the minimal element of an iterable. In this case the character ‘T’ because capital letters come before non-capital letters, otherwise it would return ‘e’ (I’m not entirely sure if it uses unicode, ascii or something else to compare characters, but usually capitals have a lower value than non-capitals and otherwise in alphabetical order ascending)
• ord(x) returns the unicode number of x, in this case turning ‘T’ into the integer 84
• range(x) creates an iterable from 0 to x (non-inclusive), in this case you can think of it as the list [0, 1, 2, …82, 83] (it’s technically an object of type range but details…)
• sum(x) sums up all elements of a list, summing all numbers between 0 and 84 (non-inclusive) is 3486
• chr(x) is the inverse of ord(x) and returns the character at position x, which, you guessed it, is ‘ඞ’ at position 3486.

The huge coincidental part is that ඞ lies at a position that can be reached by a cumulative sum of integers between 0 and a given integer. From there on it’s only a question of finding a way to feed that integer into chr(sum(range(x)))

That data is also publicly available (of course), so a model could be trained on it. I’d love to say I’d doubt Google/YouTube would ever do that, but at this point nothing would surprise me.

I trained the generative models all from scratch. Pretrained models are not that helpful when it’s important to accurately capture very domain specific features.

One of the classifiers I tried was based on zoobot with a custom head. Assuming the publications around zoobot are truthful, it was trained exclusively on similar data from a multitude of different sky surveys.

Does it? I worked on training a classifier and a generative model on freely available galaxy images taken by Hubble and labelled in a citizen science approach. Where’s the theft?

Reading this comment gave me the idea to get Gemini’s “opinion”. Ironically, it turns out Google might’ve actually done better if they did that.

I didn’t say that.

I expect it to be about as awful as Starfield. However, unlike Starfield (which didn’t sell horrendously by any source I can find, just not great) it has incredible brand recognition behind it. I have no doubts it will sell based on that alone as long as it looks like Skyrim 2 at first glance.

Edit: right after posting I figured out how to formulate what else I wanted to say but couldn’t find the correct words for: “Sadly profitability and quality don’t always correlate.”

5.5 years? No way they’ll shut down this quickly. The next Elder Scrolls alone will carry them into 2030. (As much as I would enjoy you being right though…)

Have you tried reading it? It’s written so poorly that I really hope no human was involved in this and it’s just AI generated garbage.

I find it wild that, to this day, Windows defaults to opening them in a browser. Windows has an image viewer right there.

Can that image viewer extract text so that a user could easily copy/paste it? I think if whatever pdf I was opening didn’t allow me to do that I would be really frustrated.

Remember the people who created malicious libraries that ChatGPT made up and suggested, in the hopes someone would blindly install them? You can do this a lot easier here. Check what websites this tends to hallucinate when typing “google” “youtube” “facebook” etc. and if any of them don’t exist yet, register that address and host a phishing version of the corresponding site there.

It’s not as accurate as you’d like it to be. Some issues are:

• It’s quite lossy.
• It’ll do better on images containing common objects vs rare or even novel objects.
• You won’t know how much the result deviates from the original if all you’re given is the prompt/conditioning vector and what model to use it on.
• You cannot easily “compress” new images, instead you would have to either finetune the model (at which point you’d also mess with everyone else’s decompression) or do an adversarial attack onto the model with another model to find the prompt/conditioning vector most likely to create something as close as possible to the original image you have.
• It’s rather slow.

Also it’s not all that novel. People have been doing this with (variational) autoencoders (another class of generative model). This also doesn’t have the flaw that you have no easy way to compress new images since an autoencoder is a trained encoder/decoder pair. It’s also quite a bit faster than diffusion models when it comes to decoding, but often with a greater decrease in quality.

Most widespread diffusion models even use an autoencoder adjacent architecture to “compress” the input. The actual diffusion model then works in that “compressed data space” called latent space. The generated images are then decompressed before shown to users. Last time I checked, iirc, that compression rate was at around 1/4 to 1/8, but it’s been a while, so don’t quote me on this number.

edit: fixed some ambiguous wordings.