This is pretty amusing to see. Nothing really related to Linux / Steam Deck gaming, but more a state of the industry post that I thought you might also find fun. Redditors managed to trick an AI-powered news scraper.
Could a language model actually independently discern if a source is trustworthy? Seems that’s something difficult to determine when it comes to possible leaks. The kinds of AIs that we have today can’t really conceptualize a world outside the texts they process, they can only check based on other texts and user input.
It would need to be told to do so, of course. I can think of a couple of approaches. You could have it use a database to track the identities of information sources, so the AI would know whether it was coming from new or well-established sources. It could check to see if the news is appearing in other sources. A lot of this isn’t strictly large-language-model-based capability, but it would be using LLMs to interpret its inputs.
Analysis is social media through the lens of tracking source reliability would be damned useful without AI and if that could easily be done I think it would already be. I’ve thought about this for about five years, thinking we could track bots and disinformation based on the patterns of who promotes/upvotes it, but it’s beyond my meager means.
I think certain places (reddit?) Have been using algorithms to find and stamp out bots/vote manipulation for quite a while. I remember at least one major wave of bans for smurfed accounts participating in manipulation.
Human journalists already do this, though. All I’m suggesting is that these automated journalists should do likewise. That clearly wasn’t the case in this particular instance.
I mean, chatGPT with its knowledge cutoff and no internet connection figured it out. See my comment below, I asked it and posted its response.
The guys who run that news website just didn’t include any checks in their algorithm. It doesn’t seem like an LLM problem at this point. A properly set up AutoGPT with an ability to look stuff up online would have no problem sorting though and fact-checking posts to decide which ones to use for an article.
The kinds of AIs that we have today can’t really conceptualize a world outside the texts they process
The LLMs we have today process “tokens”, which can represent anything. That they happen to look “more intelligent” to humans when used as “text goes in, text comes out”, is a purely human bias, not a limitation of the AI.
Don’t be mistaken, LLMs can process, conceptualize, and output, anything that can be represented with a token, including the initial, intermediary, or final states of other AIs, for which even humans lack a token/word. That’s how multimodal AIs with plugins work right now.
Using text (with or without emojis) as an external input/output system, is just a way to interact with humans, other AIs designed to input/output text, and to feedback (reflect) on themselves.
Could a language model actually independently discern if a source is trustworthy? Seems that’s something difficult to determine when it comes to possible leaks. The kinds of AIs that we have today can’t really conceptualize a world outside the texts they process, they can only check based on other texts and user input.
It would need to be told to do so, of course. I can think of a couple of approaches. You could have it use a database to track the identities of information sources, so the AI would know whether it was coming from new or well-established sources. It could check to see if the news is appearing in other sources. A lot of this isn’t strictly large-language-model-based capability, but it would be using LLMs to interpret its inputs.
Analysis is social media through the lens of tracking source reliability would be damned useful without AI and if that could easily be done I think it would already be. I’ve thought about this for about five years, thinking we could track bots and disinformation based on the patterns of who promotes/upvotes it, but it’s beyond my meager means.
I think certain places (reddit?) Have been using algorithms to find and stamp out bots/vote manipulation for quite a while. I remember at least one major wave of bans for smurfed accounts participating in manipulation.
Human journalists already do this, though. All I’m suggesting is that these automated journalists should do likewise. That clearly wasn’t the case in this particular instance.
I mean, chatGPT with its knowledge cutoff and no internet connection figured it out. See my comment below, I asked it and posted its response.
The guys who run that news website just didn’t include any checks in their algorithm. It doesn’t seem like an LLM problem at this point. A properly set up AutoGPT with an ability to look stuff up online would have no problem sorting though and fact-checking posts to decide which ones to use for an article.
The LLMs we have today process “tokens”, which can represent anything. That they happen to look “more intelligent” to humans when used as “text goes in, text comes out”, is a purely human bias, not a limitation of the AI.
Don’t be mistaken, LLMs can process, conceptualize, and output, anything that can be represented with a token, including the initial, intermediary, or final states of other AIs, for which even humans lack a token/word. That’s how multimodal AIs with plugins work right now.
Using text (with or without emojis) as an external input/output system, is just a way to interact with humans, other AIs designed to input/output text, and to feedback (reflect) on themselves.