When you design and build a computer system, you first formulate a model of the problem you want it to solve, and then construct the computer program in its terms.
He then writes
computers have a special dependence on these models: you write an explicit description of the model down inside the computer, in the form of a set of rules or what are called representations - essentially linguistic formulae encoding, in the terms of the model, the facts and data thought to be relevant to the system's behavior. It is with respect to these representations that computer systems work. In fact, that's really what computers are (and how they differ from other machines): they run by manipulating representations, and representations are always formulated in terms of models. This can all be summarized in a slogan: no computation without representation.
And then he says
Models have to ignore things exactly because they view the world at a level of abstraction
He then writes in section 7
The systems that land airplanes are hybrids - combinations of computers and people - exactly because the unforeseeable happens, and because what happens is in part the result of human action, requiring human interpretation
As quoted above, computers depend on models, which are abstractions (i.e. they ignore a lot of details), which are written inside the computer. Therefore, the true world cannot really be encoded into an algorithm, but only an abstraction and thus simplification of the world can.
So, will AI always depend on models and thus approximations? Can it get rid of or overcome this limitation?