pages 70 to 86

Generalization, i.e. inductive reasoning, is undoubtedly one of the key capabilities of human intelligence or intelligent systems in general.
How does generalization work? First of all one must form concepts to begin with.
I like to think of a concept as a collection of ideas governing one entity or another.
Very deliberately I chose the word entity. Because concepts don't even have to be representations of real world occurrences. One must also consider all the kinds of mythological and theological thoughts that can arise.
However I enjoyed reading about Hofstadter's view on concepts very much, interpreting them as spheres with representations of most typical conceptual examples in the core and less typical examples forming the outer layers. Least typical examples which may already count as examples for other concepts form the sphere's blurry edges.
There is undoubtedly quite a lot of generalization going on in the everyday life of each and everyone of us. Just think about the abundance of incomplete sentences uttered in natural language.

Computer scientist: I'm in artificial intelligence because it's a mixture of psychology, philosophy, linguistics and computer science.
Architect: That's the reason I'm in architecture.
(page 76)

And I believe that this that is exactly the reason why artificial systems will never be able to grasp what a human being is trying to actually convey with his or her utterances before it actually has some intelligence of its own.
This that in question does not even refer to any specific linguistic constituent of the utterance of the computer scientist. It rather refers to a concept - the concept of enjoying one's job due to enjoying its underlying intellectual framework.

As far as I know so far nobody has come up with any solid idea on how to model this kind of ability to generalize in an artificial system.
But in order to be able to create something truly intelligent this is definitely one of the starting points we have to look at.