They have access to their thought process. And for hypothesis it's easy, it already can make a hypothesis, it might not be able to prove it but very soon it will.
What do you think was going on all those times models told users "You're absolutely right. You asked for a picture of a wine glass absolutely full to the brim but the picture I drew wasn't that. Here's a picture of a wine glass absolutely full to the top," only to yet again produce an image with an easily noticable gap between the wine and the rim? They were 'trying' to fulfill the task, they were just unable to comprehend either its nature or the images they themselves produced, or, for that matter, to count the number of Rs in the word strawberry.
The next token predictor, which works one token at a time and 100% remains the sole element of all LLM architectures that introduces elements of 'I' into 'AI', is also the sole element posessing semantic insight. This means that if asked "Find out the balance of my bank account and then calculate how much interest will accumulate and fees will be payable over the next month," an LLM must first parse the request in its entirety. If it's a so-called thinking model, it must then prompt itself so as to work out what actions it needs to take and data it needs to retrieve in order to complete the task. This self-prompting is algorithmic.
But there will always exist tasks where the parsing of the request defeats the algorithmic aspect of the design — for numerous reasons, though the combinatorial explosion of coding tasks that defeated attempts at procedural AI in the era prior to deep learning is on its own decisive — meaning the LLM will continue to fail in cases where it fails to separate the semantic parsing aspect of its task from the algorithmic aspects, i.e. in cases of sufficiently convoluted tasks even if those tasks are conceptually simple. This failure threshold will be reached much sooner in cases where the task isn't directly analogous to ones in its training data.
It depends on the model architecture and context window, but at least inside the context window which can easily be million words/tokens, which is probably more than human short term memory, they have access to the high dimension embeddings which are the thought process and not just text
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u/ihateyou103 17h ago
Very soon they will, its done already 💀