I would recommend using a keyd layout for this

I use f2fs on ssd’s and ext4 on hdd’s

I don’t see the need for snapshots, I backup externally

It has been argued by many that cats have actually domesticated humans

This is actually extremely difficult to study

https://www.youtube.com/watch?v=idlq8zCrUkY

A fork wouldn’t help anything at all, the problem is that nobody is working on the patches not that the devs won’t accept them

How did you get swayfx on sericea?

that was one of my only blockers for switching.

Not at all, it was annoying for a few weeks when reddit imploded but since then I haven’t had any problems.

Weird considering they are on the side of the slaveowner…

It’s literally the worst distro, https://github.com/arindas/manjarno

Endeavoros is fundamentally better in every way, everything manjaro adds makes arch worse, and everything good they have comes from arch.

I brute forced my way through learning starcraft, it took literal years to get out of the bronze league, probably my most played game of all time. Can’t wait for immortal gates of pyre.

I use keyd for this.

For graphical installer - there are already plenty of these for arch that aren’t manjaro and don’t fuck up your system like manjaro does

As for package manager frontend… pamac is awful, just use octopi or any TUI.

There was no need to make a whole new distro for those two things, and those two things aren’t even well polished. They should’ve just made those tools for arch, and called it a day.

Manjaro 100%

Everything they add to arch makes arch worse. Everything good about manjaro comes from arch.

Did she hate him or something?

He said worst, not best.

There is no consensus on this, some say they do, some say they don’t, i’ve read both sides extensively and have determined that it is obvious they are currently less intelligent than dogs, duh, what a shifted goalpost, dogs are highly intelligent, that is obvious, because their scale is nowhere near a dog brain. It is an open question if scaling solves this, but I think the potential with scaling is obvious due to two simple facts:

1. Human brains work using the same unit parts, the structure is more complex, and number of connections is much higher, but none the less we are neural networks.
2. Scaling has already been demonstrated to improve things.

You shouldn’t pretend a consensus has been reached based on those few articles, that is simply not the case. They also all pretend intelligence is magic and that we’ve reached a dead end, neither is true, one could say you don’t think, you predict muscle movements… You seem to not realize in order to predict text accurately you must reason. I’m fully aware they are advanced text predictors, but you are the same.

You should study neuroscience, you’ll find the purpose of a brain is to predict. “True AI” is just an endlessly shifting goalpost. It won’t be one until it is the size of a human brain, expecting a much smaller brain to outperform ours is silly.

You are vastly more complex than an LLM. There are hundreds of neural transmitters. 20 billion neocortical neurons and 7 thousand connections per neuron. A naive complexity of 2.8e16 combinations. Each thought tweaking those ~7000 connections as it passes from neuron to neuron.

Aware and irrelevant, if anything that helps my point.

The brains training that model are not that model. They are what does the thinking, the model is nothing more than a black box of statistical analysis that spits out stats in a human digestible format.

The brains training that model are thinking, in the exact same way that the AI is. You can pretend that isn’t thinking all you want, but what it’s doing is quite obviously reasoning in tokens.

It’s Stochastic: entirely based on statistical probabilities with no reasoning behind any of its outputs. It’s a parrot in that it can only construct that which has been fed to it as an output. It’s not “thinking in Estonian”: Estonian had been programmed into it either directly via Estonian language material being introduced or indirectly by Estonian patterns matching other similarly trained languages.

So are you. As we scale up, they quite obviously get better at reasoning, just like you said, we’re a much more complex version of this, it’s still bad at it, because it’s tiny. In your mind, how does thinking work, if not in the exact same way LLM’s do that?

Gpt4 still hallucinates all the time. It still fails to reason.

Sometimes it fails to reason, sometimes it hallucinates… so do you. Have you never accidentally said something false? Have you never failed to reason? Consider the following: the thoughts you actually share are a much smaller, filtered version of the thoughts that go on in your head, you’ve censored a massive portion of your thoughts, and you don’t share the bad ones, you’re the same, you just have a filter. You’re a much more complex, significantly more performant, significantly better version of an LLM that converts your sensations into muscle movements.

It is not doing novel things: it’s doing exactly what it has always done in unexpected ways but that’s very different from intelligence and thinking. What’s tripping people up is they’ve never interacted with anything that had the width of knowledge readily available. The complexity of encoding the entire world makes a huge surface where anyone can interact with it and get reasonable responses. That doesn’t make it reasoning, just that it has a very detailed latent space which is very good at natural language queries.

You have not demonstrated the difference, you’ve demonstrated that they’re worse at it, and you even explained WHY, they’re much smaller. That is exactly how the neural network in the brain works, you just want to say that your brain is magical. Yes, it is worse at reasoning than humans, but as demonstrated in that link, it gets better and better with size, unless you believe that the instructions for stacking every single object exists online, you have to accept that while these models are tiny, they’re already beginning to reason in the exact same way you do.

I’m aware of all of those things.

An LLM doesn’t think. It takes input and runs it through statistical layers until it returns output. It doesn’t learn either: the input does not change the model. Models are tuned, tweaked and generally curated to get the best experience. All experiments with letting a model be exposed to the public and “learn” directly have gone horribly wrong.

That’s what thinking is, you’re a model that takes sensory input and converts it into muscle movement. You do realize you too are a neural network… neurons are literally what we’ve based this technology on.

The input not changing the model is irrelevant, and I never claimed that, I claimed that they aren’t stochastic parrots.

Alpha go isn’t an LLM: It’s a reinforcement learning model combined with a Monte Carlo search. It uses deep learning and is fundamentally a different method of machine learning.

Yes, it’s something EVEN more primitive than an LLM, is my point, and yet it still does novel things. This even more primitive than LLM AI is already NOT a stochastic parrot, why would language models be one?

LLMs are definitely dangerous: the danger is that people believe them too much. It’s that CEOs believe they can replace their writers, it’s that the general public can generate bullshit faster than ever. They have irradiated the online sphere just like the nukes of the 40s irradiated all steel.

…duh?

I don’t think you responded to my post in any meaningful way.

https://www.businessinsider.com/chatgpt-open-ai-balancing-task-convinced-microsoft-agi-closer-2023-5

This is not something you would see a stochastic parrot do, and I can point to many other articles displaying emergent properties not contained in their datasets. If it does things that are outside of its dataset, it’s not a stochastic parrot.

I read about this shit constantly, the notion that they’re stochastic parrots is reductionist nonsense.

You sound like you’re so used to people saying stupid shit about how it’s conscious that you expected me to believe that, and then argued with that belief. I don’t think they’re conscious, sentient, whatever, I think they’re not stochastic parrots and they have novel behaviors that aren’t necessarily in their datasets, or rather, can be inferred from parts of their datasets in order to make new things.

Why are you so ready to believe they’re stochastic parrots when they’ve done so many novel behaviors?

Even primitive versions of this technology (and gpt is already primitive), remember move 37 of alphago? It was unprecedented, we’re far too willing to believe that nothing is happening in the world and there’s nothing to worry about, but there’s plenty of warning signs, laughing these things off as stochastic parrots is genuinely harmful to society. It’s also just blatant misinformation.

Not in the long-run. This is absolutely a temporary issue due to the beta quality of lemmy.