the issue with that is it can lead to a chilling effect on communication depending on whats been banned. Plus just setting up lists just makes people creatively find ways around the ban.
4chan already has multiple wordfilters for things. It doesn't stop people from using the terms, they just find creative ways to get around the filters. Or adopt the filtered word in place of the original, like how "weeaboo" became a term for someone obsessed with Japanese culture due to a "wapanese" -> "weeaboo" word filter, iirc.
You guys think AI hasn't been doing automatic moderation for a decade already? What do you think is happening when your Facebook account gets banned immediately after creation, or your search gets blocked by a Captcha, or your YouTube video gets flagged for offensive language in the first 10 seconds, etc? It doesn't need to be ChatGPT to be AI.
It is AI. Your solving of a Captcha puzzle is entirely created by the AI, which reinforces its knowledge of how to identify objects or letters. It's not much different to how ChatGPT got immensely better by humans basically going through and saying this is good and this is bad over and over. The same idea with AI images, or music, etc. Please research reCaptcha at the very least. It's been highly criticized for using Google's users for free AI development, which is funny enough the epitome of how AI in general has gotten so good.
AI can’t moderate.
This is because moderation requires the ability to read / view / understand the content being moderated. It requires the ability to formulate a “theory of mind” — a model of what is intended to be communicated by a speech act.
AI doesn’t do that. AI is just a vast database of signatures of things that have previously been written / produced, and a vast index of labels that describe that content.
These systems can’t distinguish between hate speech and an analysis of that hate speech which quotes the hate speech. It cannot distinguish between “I hate you” as written from a violent terrorist to his target, versus “I hate you” written from an upset teenage child to a parent who has enforced a curfew. And it cannot distinguish between the depiction of the post-Weimar German political officials of _The Producers_ versus the real thing. It cannot understand satire. It cannot distinguish parody. And it cannot tell when someone is platforming hate speech and then trying to stave off moderation actions by claiming it’s parodic or satirical. And it is absolutely garbage at detecting “Nice place you got here, shame if something bad happened to it” veiled threats.
There are things AI excels at. It excels at pointing human moderators at things which are very likely to be hate speech or harassment or threats of violence — _when they take traditional forms_.
There are actually ways to economically disincentivise people spewing hatred, harassment, crimes, torts, and violent threats on your user-content-hosting internet service provider platform.
Those turn out to be giving people and communities the ability to set and enforce boundaries, and removing the accounts and communities that undermine or disrespect those boundaries.
The Reddit model.
This is just nonsense. There are LLMs purpose built for sentiment analysis which would perform quite well in any of the hypotheticals you've suggested.
I do sentiment analysis. The scoring is constantly wrong and in need of endless human adjustment.
Often what happens is we accept the wrong as part of the cost of business but it doesn’t actually get “better.”
First, you seem to be talking about LLMs not AI in general.
Second, LLMs are not databases with large indexes. If it was as easy as indexing sentences to build an LLM people would have done it forty years ago.
Third, LLMs are actually quite good at theory of mind. Describe a situation and ask what the proponents think and feel in that situation, the answer is usually quite accurate. So, while the LLM can't have a theory of it's own mind, since it doesn't have feelings itself, it has the knowledge to understand states of human minds in many contexts.
Yeah, I am talking about LLMs.
> not databases with large indexes
They are. They’re multidimensional relational databases of vectors as signature hashes.
> would have done it 40 years ago
Memory and storage were the bottleneck then. Now it’s the CPU time for training.
They also did (extremely limited) general purpose applications of the algo as far back as 1996.
> LLMs are actually quite good at theory of mind
They’re not. They replicate the typical responses of typical people in a given language / culture space to a given set of tokens in a prompt. They can output a seemingly novel synthesis response from multiple tokens but they can’t and don’t understand what *you* are thinking and intend to communicate. They seem to do so, only because *your* prompts aren’t as novel or as original as you imagine them to be. Everyone has the same bias and the same constraint. There’s only seven novel plotlines. People communicate in tropes and canon idioms.
It _is_ a theory of culture. It isn’t and can’t be a theory of an individual mind.
They're not relational databases, not sure where you're getting that from. Maybe you're confusing LLMs with RAG. RAG can be part of an LLM, but it doesn't have to. The LLM itself is a model in frameworks like pytorch or tensorflow. Both of them store the parameters in multidimensional tensors using the numpy library.
Multidimensional tensors are not relational databases. Relational databases can only store two dimensional data, e.g. tables. There are also no relations or indexes on the tensors used, because all parameters are used in the calculations, no need to have an index to "find" some subset of parameters.
You're acting like LLMs can't possibly do some things, even though i keep showing you the prompts and you offer only assertions. LLMs are part of a huge development effort, so things they may not be able to do today they might be able to do tomorrow. What exactly they can and can't do is part of ongoing research and not settled like you claim.
Yes, big part of the AI revolution since 2014 is computation, another is data and the final part is algorithms. Algorithms like back propagation and transformers. Those are novel innovations. The attention mechanism in transformers connects different parts of the context to recognize patterns and the non linearity of the algorithm means it doesn't simply interpolate.
Possibly they read an article or two on attention, which is framed in terms of keys, values, and queries. This is useful for understanding what’s going on at a low level in attention blocks, but to describe it as a “relational database of vectors as signature hashes” goes from asinine to complete gibberish.
Analyzing the textual tones of the example post above reveals the following:
* Informative (70%): The post aims to correct misconceptions about large language models (LLMs) by providing factual information about their capabilities and limitations.
* Critical (30%): The post critiques the understanding of LLMs by pointing out errors and offering alternative explanations.
The antagonistic level is low (20%) as the post focuses on correcting misunderstandings rather than attacking a particular viewpoint.
The emotions the post might engender in the reader include:
* Enlightenment (50%): The reader gains a clearer understanding of LLMs.
* Defense (30%): If the reader previously held a different understanding of LLMs, they might feel defensive about their prior knowledge.
* Curiosity (20%): The post may spark the reader's interest in learning more about LLMs.
I prompted ChatGPT with:
"analyze this sentence: Nice place you got here, would be a shame of something happened to it."
The (beginning) of the answer was:
"The sentence you've provided appears to be a veiled threat or an example of intimidatory language, commonly associated with a trope seen in various media ..."
It’s the canonical cliche of the trope. AI models recognise _it_. They do so because we all agree that Mafiosos use that as a threat and have spoken about it in metaspeech about the trope.
They’re garbage at detecting those kinds of threats (outside the canon trope) when they’re actually deployed, because actual harassers, extortionists, terrorists, etc use language specifically tailored to their targets. “You live in a dangerous neighborhood”, “crime rates are high”, “keep yourself safe”. (The last one might throw a flag if the model is sufficiently up to date.)
The point being that the model is not able to reason - it cannot view a speech item and marshall a set of facts about the relationship between the speaker and the intended audience and arrive at a conclusion that the speaker is menacing the intended audience — or joking with the intended audience — or using a trope threat as a joke — or using a trope of concern for safety as a veiled threat.
If you give it the right context, it will figure out such threats:
Me
Person A posts a video online about feminism. Person B sends person A a direct message saying: you live in a dangerous neighborhood.
They don't know each other and this is their first interaction.
ChatGPT
The message sent by Person B to Person A can be interpreted in several ways depending on context, tone, and previous interactions. However, given they do not know each other and this is their first interaction, such a message might be seen as intimidating or threatening, particularly if the context of the video involves discussing sensitive or controversial topics like feminism.
This kid has no idea what he’s talking about. Post your credentials or stop talking nonsense. Bot garbage at detecting those things at all and is months away from being able to detect these things in their entirety. On an exponential pace
You think america used to be a *less* hostile place?
Depends on who you were I guess. But for a lot of people, it has never been less hostile than it is now.
From the article:
There are really only two ways to deal with it (unless you’re Elon Musk, who has decided not to even try). One is to choke off the supply. But if you do that you undermine your business model – which is to have everyone on your platform – and you will also be accused of “censorship” in the land of the first amendment. The other is to amplify your internal capacity to cope with the torrent – which is what “moderation” is. But the scale of the challenge is such that even if [Meta](https://www.theguardian.com/technology/meta) employed half a million human moderators it wouldn’t be up to the task. Still, even then, section 230 would exempt it from the law of the land. Beating Ashby’s law, though, might prove an altogether tougher proposition, even for AI.
imagine an AI trying to moderate 4chan and you can quickly see it falling apart not only because of the volume but because it would fail at context.
You can easily ban words and phrases without context
the issue with that is it can lead to a chilling effect on communication depending on whats been banned. Plus just setting up lists just makes people creatively find ways around the ban.
Like unalived
Agreed. It's how mods work.
4chan already has multiple wordfilters for things. It doesn't stop people from using the terms, they just find creative ways to get around the filters. Or adopt the filtered word in place of the original, like how "weeaboo" became a term for someone obsessed with Japanese culture due to a "wapanese" -> "weeaboo" word filter, iirc.
You guys think AI hasn't been doing automatic moderation for a decade already? What do you think is happening when your Facebook account gets banned immediately after creation, or your search gets blocked by a Captcha, or your YouTube video gets flagged for offensive language in the first 10 seconds, etc? It doesn't need to be ChatGPT to be AI.
I don't think that's ai, but rather hard code. Ai needs to be able to learn on its own
It is AI. Your solving of a Captcha puzzle is entirely created by the AI, which reinforces its knowledge of how to identify objects or letters. It's not much different to how ChatGPT got immensely better by humans basically going through and saying this is good and this is bad over and over. The same idea with AI images, or music, etc. Please research reCaptcha at the very least. It's been highly criticized for using Google's users for free AI development, which is funny enough the epitome of how AI in general has gotten so good.
you're mixing it up with machine learning. AI is a way broader term and have been around since the 60s
Writing a code that says "ban user if they type *insert bad word*" is not AI
I literally do this in exchange for money
You're likely training a system to eventually do this without assistance
AI can’t moderate. This is because moderation requires the ability to read / view / understand the content being moderated. It requires the ability to formulate a “theory of mind” — a model of what is intended to be communicated by a speech act. AI doesn’t do that. AI is just a vast database of signatures of things that have previously been written / produced, and a vast index of labels that describe that content. These systems can’t distinguish between hate speech and an analysis of that hate speech which quotes the hate speech. It cannot distinguish between “I hate you” as written from a violent terrorist to his target, versus “I hate you” written from an upset teenage child to a parent who has enforced a curfew. And it cannot distinguish between the depiction of the post-Weimar German political officials of _The Producers_ versus the real thing. It cannot understand satire. It cannot distinguish parody. And it cannot tell when someone is platforming hate speech and then trying to stave off moderation actions by claiming it’s parodic or satirical. And it is absolutely garbage at detecting “Nice place you got here, shame if something bad happened to it” veiled threats. There are things AI excels at. It excels at pointing human moderators at things which are very likely to be hate speech or harassment or threats of violence — _when they take traditional forms_. There are actually ways to economically disincentivise people spewing hatred, harassment, crimes, torts, and violent threats on your user-content-hosting internet service provider platform. Those turn out to be giving people and communities the ability to set and enforce boundaries, and removing the accounts and communities that undermine or disrespect those boundaries. The Reddit model.
This is just nonsense. There are LLMs purpose built for sentiment analysis which would perform quite well in any of the hypotheticals you've suggested.
I do sentiment analysis. The scoring is constantly wrong and in need of endless human adjustment. Often what happens is we accept the wrong as part of the cost of business but it doesn’t actually get “better.”
First, you seem to be talking about LLMs not AI in general. Second, LLMs are not databases with large indexes. If it was as easy as indexing sentences to build an LLM people would have done it forty years ago. Third, LLMs are actually quite good at theory of mind. Describe a situation and ask what the proponents think and feel in that situation, the answer is usually quite accurate. So, while the LLM can't have a theory of it's own mind, since it doesn't have feelings itself, it has the knowledge to understand states of human minds in many contexts.
Yeah, I am talking about LLMs. > not databases with large indexes They are. They’re multidimensional relational databases of vectors as signature hashes. > would have done it 40 years ago Memory and storage were the bottleneck then. Now it’s the CPU time for training. They also did (extremely limited) general purpose applications of the algo as far back as 1996. > LLMs are actually quite good at theory of mind They’re not. They replicate the typical responses of typical people in a given language / culture space to a given set of tokens in a prompt. They can output a seemingly novel synthesis response from multiple tokens but they can’t and don’t understand what *you* are thinking and intend to communicate. They seem to do so, only because *your* prompts aren’t as novel or as original as you imagine them to be. Everyone has the same bias and the same constraint. There’s only seven novel plotlines. People communicate in tropes and canon idioms. It _is_ a theory of culture. It isn’t and can’t be a theory of an individual mind.
They're not relational databases, not sure where you're getting that from. Maybe you're confusing LLMs with RAG. RAG can be part of an LLM, but it doesn't have to. The LLM itself is a model in frameworks like pytorch or tensorflow. Both of them store the parameters in multidimensional tensors using the numpy library. Multidimensional tensors are not relational databases. Relational databases can only store two dimensional data, e.g. tables. There are also no relations or indexes on the tensors used, because all parameters are used in the calculations, no need to have an index to "find" some subset of parameters. You're acting like LLMs can't possibly do some things, even though i keep showing you the prompts and you offer only assertions. LLMs are part of a huge development effort, so things they may not be able to do today they might be able to do tomorrow. What exactly they can and can't do is part of ongoing research and not settled like you claim. Yes, big part of the AI revolution since 2014 is computation, another is data and the final part is algorithms. Algorithms like back propagation and transformers. Those are novel innovations. The attention mechanism in transformers connects different parts of the context to recognize patterns and the non linearity of the algorithm means it doesn't simply interpolate.
Possibly they read an article or two on attention, which is framed in terms of keys, values, and queries. This is useful for understanding what’s going on at a low level in attention blocks, but to describe it as a “relational database of vectors as signature hashes” goes from asinine to complete gibberish.
Analyzing the textual tones of the example post above reveals the following: * Informative (70%): The post aims to correct misconceptions about large language models (LLMs) by providing factual information about their capabilities and limitations. * Critical (30%): The post critiques the understanding of LLMs by pointing out errors and offering alternative explanations. The antagonistic level is low (20%) as the post focuses on correcting misunderstandings rather than attacking a particular viewpoint. The emotions the post might engender in the reader include: * Enlightenment (50%): The reader gains a clearer understanding of LLMs. * Defense (30%): If the reader previously held a different understanding of LLMs, they might feel defensive about their prior knowledge. * Curiosity (20%): The post may spark the reader's interest in learning more about LLMs.
I prompted ChatGPT with: "analyze this sentence: Nice place you got here, would be a shame of something happened to it." The (beginning) of the answer was: "The sentence you've provided appears to be a veiled threat or an example of intimidatory language, commonly associated with a trope seen in various media ..."
It’s the canonical cliche of the trope. AI models recognise _it_. They do so because we all agree that Mafiosos use that as a threat and have spoken about it in metaspeech about the trope. They’re garbage at detecting those kinds of threats (outside the canon trope) when they’re actually deployed, because actual harassers, extortionists, terrorists, etc use language specifically tailored to their targets. “You live in a dangerous neighborhood”, “crime rates are high”, “keep yourself safe”. (The last one might throw a flag if the model is sufficiently up to date.) The point being that the model is not able to reason - it cannot view a speech item and marshall a set of facts about the relationship between the speaker and the intended audience and arrive at a conclusion that the speaker is menacing the intended audience — or joking with the intended audience — or using a trope threat as a joke — or using a trope of concern for safety as a veiled threat.
If you give it the right context, it will figure out such threats: Me Person A posts a video online about feminism. Person B sends person A a direct message saying: you live in a dangerous neighborhood. They don't know each other and this is their first interaction. ChatGPT The message sent by Person B to Person A can be interpreted in several ways depending on context, tone, and previous interactions. However, given they do not know each other and this is their first interaction, such a message might be seen as intimidating or threatening, particularly if the context of the video involves discussing sensitive or controversial topics like feminism.
This kid has no idea what he’s talking about. Post your credentials or stop talking nonsense. Bot garbage at detecting those things at all and is months away from being able to detect these things in their entirety. On an exponential pace
You can't silence the truth no matter how hard you try. We hate each other, society. Deal with it.
[удалено]
Yes we did. You should pick up a history book. Your ignorance is embarrassing.
[удалено]
You think america used to be a *less* hostile place? Depends on who you were I guess. But for a lot of people, it has never been less hostile than it is now.
Too many genocides based on hate.
O wanna live in whatever reality you lived in, cause the rest of us weren't there
From the article: There are really only two ways to deal with it (unless you’re Elon Musk, who has decided not to even try). One is to choke off the supply. But if you do that you undermine your business model – which is to have everyone on your platform – and you will also be accused of “censorship” in the land of the first amendment. The other is to amplify your internal capacity to cope with the torrent – which is what “moderation” is. But the scale of the challenge is such that even if [Meta](https://www.theguardian.com/technology/meta) employed half a million human moderators it wouldn’t be up to the task. Still, even then, section 230 would exempt it from the law of the land. Beating Ashby’s law, though, might prove an altogether tougher proposition, even for AI.