such as the interpretation of visual images, e.g. in target-acquisition in weapons. Their focus is on things like finding edges and shapes, detecting movement, etc. I wouldn't be surprised if they didn't also utilize some kind of averaging, but I'm not sure just how closely they resemble LLMs, so I didn't want to overgeneralize.
As for whether LLMs use probabilities per se, if they don't, how do they select words, word order, etc.? (Because it's not based on signification or meaning.) Granted the particular coding is also critical, probably including rules re- grammar and any guidelines as to sources or topics to weigh more heavily or avoid, and also the particular selection of data the AIs are trained on; but my understanding is that the coding actually calls for the kind of averaging I have in mind, since it can't possibly specify in advance all possible answers to all possible questions.
Here's something from MIT that I just found with a quick search:
The base models underlying ChatGPT and similar systems work in much the same way as a Markov model. But one big difference is that ChatGPT is far larger and more complex, with billions of parameters. And it has been trained on an enormous amount of data — in this case, much of the publicly available text on the internet.
In this huge corpus of text, words and sentences appear in sequences with certain dependencies. This recurrence helps the model understand how to cut text into statistical chunks that have some predictability. It learns the patterns of these blocks of text and uses this knowledge to propose what might come next.
(Emphasis supplied; more at
https://news.mit.edu/2023/explained-generative-ai-1109 .) So as I read this, the "parameters" may be the kind of guidelines I supposed would be included in what you're calling the code; but the process used to actually compose ChatGPT responses would be based on statistical probabilities.
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