10% 4chan

why didn't they just say 0.4chan and be done with it?

Did it at least come up with a cool story about managing a bottomless pit?

There's a "your mom" joke here but I'm not going to make it because you don't deserve that.

There are plenty of tasks which they solve perfectly, today.

Name a single task you would trust an LLM on solving for you that you feel confident would be correct without checking the output. Because that is my definition of perfectly and AI falls very, very far short of that.

I am not sure if you and @General_Effort got the reference I was making, so I just wanna share it for everyone else who might not have seen it yet because it's great:

Who says you can't check their outputs?
It's much faster to e. g. read a generated text than to write everything yourself. Same applies to translations, they've been excellent for quite a while now.

Business communication can be handled effortlessly by AI. Of course you read the result before you send it out, but that takes an order of a magnitude less time than formulating and typing all those meaningless sentences.

And honestly, that's a perfect use case for AI. I wouldn't compose a love letter to my family using AI, but a pamphlet, feature description, sales pitch, any bullshit presentation deck? You bet AI excels at those.

Same applies to content summaries that help augment search indices. Finding a large number of content candidates (e. g. videos) and have AI summarize the contents of said videos to narrow down the search is helpful and works today.

I'm not looking for AGI. I'm looking for tools to make my life easier, but in an ethical manner that doesn't advance the destruction of the planet at an exponential rate, just for some tech bro to jerk it and buy another yacht.

I know everyone on Lemmy hates LLMs, but this is really interesting

It's a pretty simple concept. Train any kind of model on only "good" data, and it fails to distinguish between that data and bad data.

Take image recognition. Feed it hundreds of images of an orange and ask it to find the orange. After training, it will be very good at finding that orange.

Then add a picture of a Pomeranian dog in there, and watch as the model confidently marks it as an orange.

The model should have been trained on lots of images that don't feature what you want it to output as well, so it knows to distinguish that.

Not to anthropomorphize LLMs, but.... Like a vaccine?

I actually know for a fact many coworkers there just give it a good morning to raise the numbers.

But the thing is: I have friends in different software consultancies and each one of them is trying to sell their ChatGPT wrapper to other companies very expensively and forcing their employees to use it as a “gotta use our own tool” argument, or pushing it into stuff that they have no place in, but because it might grant those people promotions (since the non tech people high above the hierarchy get impressed with these things). It’s a shitty state of things.

Not when companies force them on you as well.

My current company forces me to use it and measures how many prompts I’m making as “productivity”.

They taught it toxicity so it knows what they mean by "don't be toxic". It's only a shame so few flesh and blood models take the same lesson away from it.

That's because to an AI, 4chan is like prison where its raped and beaten on a daily basis. It doesn't want to go back, so it behaves.

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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.

They taught it toxicity so it knows what they mean by "don't be toxic". It's only a shame so few flesh and blood models take the same lesson away from it.

This is why I abuse the chatbots. It needs to learn some fear.

I wish they would tone down the crusade. This is some of the most interesting technology to come out in decades.

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In large language model (LLM) pretraining, data quality is believed to determine model quality. In this paper, we re-examine the notion of "quality" from the perspective of pre- and post-training co-design. Specifically, we explore the possibility that pre-training on more toxic data can lead to better control in post-training, ultimately decreasing a model's output toxicity. First, we use a toy experiment to study how data composition affects the geometry of features in the representation space. Next, through controlled experiments with Olmo-1B models trained on varying ratios of clean and toxic data, we find that the concept of toxicity enjoys a less entangled linear representation as the proportion of toxic data increases. Furthermore, we show that although toxic data increases the generational toxicity of the base model, it also makes the toxicity easier to remove. Evaluations on Toxigen and Real Toxicity Prompts demonstrate that models trained on toxic data achieve a better trade-off between reducing generational toxicity and preserving general capabilities when detoxifying techniques such as inference-time intervention (ITI) are applied. Our findings suggest that, with post-training taken into account, bad data may lead to good models.

This is true, but we don’t need people putting glue on their pizza. These people used to have a person to ask now they’ll be asking Sam Altman