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.
There was actually a paper recently that tested this exactly.
They made up a new type of problem that had never before been published. They wrote a paper explaining the problem and how to solve it.
They fed this to an AI, not as training material but as part of the query, and then fed it the same problem but with different inputs and asked it to solve it.
It could not.
AGI would be able to learn from the queries given to it, not just its training base data.
It’s really easy to show this even with a known problem. Ask an LLM to play a game of chess, and then give it 1. h3 as a first move. They always screw up immediately, by making an illegal move. This happens because 1. h3 is hardly ever played, so it isn’t part of it’s model. In fact, it’ll usually play a move that ‘normally’ responds to h3, like Bh5 for example
Link to the paper? Seems interesting and google isn’t giving me much.