I use f2fs on ssd’s and ext4 on hdd’s

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

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 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.

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.

Yes, but we would refer to our consciousness as an emergent property of our brain.

And we’re trying to build artificial brains.

We also understand the math underlying it. Humans designed and constructed it; we know exactly what it is capable of and what it does

This is false. Read about their emergent properties. We have no way of knowing when emergent properties appear, we just notice them.

Except calculators aren’t models capable of understanding language that appear to become more and more capable as they grow. It’s nothing like that.

It literally can’t worry about its own existence; it can’t worry about anything because it has no thoughts or feelings. Adding computational power will not miraculously change that.

Who cares? This has no real world practical usecase. Its thoughts are what it says, it doesn’t have a hidden layer of thoughts, which is quite frankly a feature to me. Whether it’s conscious or not has nothing to do with its level of functionality.

And (from what I’ve seen) they get things wrong with extreme regularity, increasingly so as thing diverge from the training data. I wouldn’t say they’re a “stochastic parrot” but they don’t seem to be much better when things need to be correct… and again, based on my (admittedly limited) understanding of their design, I don’t anticipate this technology (at least without some kind of augmented approach that can reason about the substance) overcoming that.

Keep in mind, you’re talking about a rudimentary, introductory version of this, my argument is that we don’t know what will happen when they’ve scaled up, we know for certain hallucinations become less frequent as the model size increases (see the statistics on gpt3 vs 4 on hallucinations), perhaps this only occurs because they haven’t met a critical size yet? We don’t know.

There’s so much we don’t know.

That’s missing the forest for the trees. Of course an AI isn’t going to go fishing. However, I should be able to assert some facts about fishing and it should be able to reason based on those assertions. e.g. a child can work off of facts presented about fishing, “fish are hard to catch in muddy water” -> “the water is muddy, does that impact my chances of a catching a bluegill?” -> “yes, it does, bluegill are fish, and fish don’t like muddy water”.

https://blog.research.google/2022/05/language-models-perform-reasoning-via.html

they do this already, albeit imperfectly, but again, this is like, a baby LLM.

and just to prove it:

https://chat.openai.com/share/54455afb-3eb8-4b7f-8fcc-e144a48b6798

You’re assuming i’m saying something that i’m not, and then arguing with that, instead of my actual claim.

I’m saying we don’t know for sure what they will be able to do when they’re scaled up. That’s the end of my assertion. I don’t have to prove that they will suddenly come alive, i’m not claiming they will, i’m just claiming we don’t know what will happen when they’re scaled, and they seem to have emergent properties as they scale up. Nobody has devised a way of predicting what emergent properties happen when, nobody has made any progress whatsoever on knowing what scaling up accomplishes.

Can they reason? Yes, but poorly right now, will that get better? Who knows.

The end of my claim is that we don’t know what’ll happen when they scale up, and that you can’t just write it off like you are.

If you want proof that they reason, see the research article I linked. If they can do that in their rudimentary form that we’ve created with very little time, we can’t write off the possibility that they will scale.

Whether or not they reason LIKE HUMANS is irrelevant if they can do the job.

And i’m not anthropomorphizing them without reason, there aren’t terms for this already, what would you call this behavior of answering questions significantly better when asked to fully explain reasoning? I would say it is taking the easiest option that still meets the qualifications of what it is requested to do, following the path of least resistance, I don’t have a better word for this than laziness.

https://www.downtoearth.org.in/news/science-technology/artificial-intelligence-gpt-4-shows-sparks-of-common-sense-human-like-reasoning-finds-microsoft-89429

Furthermore predictive power is just another way of achieving reasoning, better predictive power IS better reasoning, because you can’t predict well without reasoning.

I’m not guessing. When I say it’s a difference of kind, I really did mean that. There is no cognition here; and we know enough about cognition to say that LLMs are not performing anything like it.

We do not know that, I challenge you to find a source for that, in fact, i’ve seen sources showing the opposite, they seem to reason in tokens, for example, LLM’s perform significantly better at tasks when asked to give a step by step reasoned explanation, this indicates that they are doing a form of reasoning, and their reasoning is limited by what I have no better term for than laziness.

https://blog.research.google/2022/05/language-models-perform-reasoning-via.html

If I teach a real AI about fishing, it should be able to reason about fishing and it shouldn’t need to have read a supplementary knowledge of mankind to do it.

This is a faulty assumption.

In order for you to learn about fishing, you had to learn a shitload about the world. Babies don’t come out of the womb able to do such tasks, there is a shitload of prerequisite knowledge in order to fish, it’s unfair to expect an ai to do this without prerequisite knowledge.

Furthermore, LLM’s have been shown to do many things that aren’t in their training data, so the notion that it’s a stochastic parrot is also false.

You’re guessing, you don’t actually know that for sure, it seems intuitively correct, but we simply do not know enough about cognition to make that assumption.

Perhaps our ability to reason exclusively comes from our ability to predict, and by scaling up the ability to predict, we become more and more able to reason.

These are guesses, all we have now are guesses, you can say “it doesn’t reason” and “it’s just autocorrect” all you want, but if that were the case why did scaling it up eventually enable it to perform basic math? Why did scaling it up improve its ability to problemsolve significantly (gpt3 vs gpt4), there’s so many unknowns in this field, to just say “nah, can’t be, it works differently from us” doesn’t mean it can’t do the same things as us given enough scale.

We don’t know that for sure yet, we saw a lot of emergent intelligent properties appear as we scaled up, and we’re nowhere near done scaling LLM’s, I’m not saying it will be solved, just that we don’t know one way or the other yet.

I’d also like hall effect joysticks and a better d-pad