How do you predict the next word in answering an abstract logic question without being capable of abstract reasoning, though?
In some sense it probably is possible, but this is a gaping flaw in your argument. A sufficiently advanced text prediction process has to encompass the process of abstract reasoning. The text prediction problem is necessarily a superset of the abstract reasoning problem. Ie, in the limit text prediction is fundamentally harder than abstract reasoning.
Sure, the exact form using the made up word gronk might not be in the training data, but the general form of that reasoning problem definitely exists, quite frequently in fact.
``` You will be given a name of an object (such as Car, Chair, Elephant) and a letter in the alphabet. Your goal is to first produce a 1-line description of how that object can be combined with the letter in an image (for example, for an elephant and the letter J, the trunk of the elephant can have a J shape, and for the letter A and a house, the house can have an A shape with the upper triangle of the A being the roof). Following the short description, please create SVG code to produce this (in the SVG use shapes like ellipses, triangles etc and polygons but try to defer from using quadratic curves). ```
``` Round 5: A car and the letter E. Description: The car has an E shape on its front bumper, with the horizontal lines of the E being lights and the vertical line being the license plate. ```
Image generated here: https://imgur.com/a/Ia4Q2h3
How does it "just" predict the letter E could be used in such a way to draw a car? How does it just text predict working SVG code that draws the car made out of basic shapes and the letter E?
I don't know how anyone could suggest there are no conceptual models embedded in there.