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How to differentiate between a AGI and an LLM that has read every book in existence? (discuss.tchncs.de)

submitted 1 month ago* (last edited 1 month ago) by brokenwing@discuss.tchncs.de to c/asklemmy@lemmy.ml

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Look, I don't believe that an AGI is possible or atleast within the next few decade. But I was thinking about, if one came to be, how can we differentiate it from a Large Language Model (LLM) that has read every book ever written by humans?

Such an LLM would have the "knowledge" of almost every human emotions, morals, and can even infer from the past if the situations are slightly changed. Also such LLM would be backed by pretty powerful infrastructure, so hallucinations might be eliminated and can handle different context at a single time.

One might say, it also has to have emotions to be considered an AGI and that's a valid one. But an LLM is capable of putting on a facade at-least in a conversation. So we might have to hard time reading if the emotions are genuine or just some texts churned out by some rules and algorithms.

In a pure TEXTUAL context, I feel it would be hard to tell them apart. What are your thoughts on this? BTW this is a shower-thought, so I might be wrong.

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[–] HiddenLayer555@lemmy.ml 6 points 1 month ago* (last edited 1 month ago)

An AGI wouldn't need to read every book because it can build on the knowledge it already has to draw new conclusions it wasn't "taught."

Also, an AGI would be able to keep a consistent narrative regardless of the amount of data or context it has, because it would be able to create an internal model of what is happening and selectively remember the most important things more so than things that are inconsequential (not to mention assess what's important and what can be forgotten to shed processing overhead), all things a human does instinctively when given more information than your brain can immediately handle. Meanwhile, an LLM is totally dependent on how much context it actually has bufferered, and giving it too much information will literally push all the old information out of its context, never to be recalled again. It has no ability to determine what's worth keeping and that's not, only what's more or less recent. I've personally noticed this especially with smaller locally run LLMs with very limited context windows. If I begin troubleshooting some Linux issue using it, I have to be careful with how much of a log I paste into the prompt, because if I paste too much, it will literally forget why I pasted the log in the first place. This is most obvious with Deepseek and other reasoning models because it will actually start trying to figure out why it was given that input when "thinking," but it's a problem with any context based model because that's its only active memory. I think the reason this happens so obviously when you paste too much in a single prompt and less so when having a conversation with smaller prompts is because it also has its previous outputs in its context, so while it might have forgotten the very first prompt and response, it repeats the information enough times in subsequent prompts to keep it in its more recent context (ever notice how verbose AI tends to be? That could potentially be a mitigation strategy). Meanwhile, when you give it a very large prompt as big or bigger than its context window, it completely overwrites the previous responses, leaving no hints to what was there before.