Read the first paragraph of the Wikipedia article on machine learning or the introduction of any of the literature on the subject. The “generalization” includes that model building capability. They go a bit into detail later. They specifically mention “to unseen data”. And “leaning” is also there. I don’t think the Wikipedia article is particularly good in explaining it, but at least the first sentences lay down what it’s about.
And what do you think language and words are for? To transport information. There is semantics… Words have meanings. They name things, abstract and concrete concepts. The word “hungry” isn’t just a funny accumulation of lines and arcs, which statistically get followed by other specific lines and arcs… There is more to it. (a meaning.)
And this is what makes language useful. And the generalization and prediction capabilities is what makes ML useful.
How do you learn as a human when not from words? I mean there are a few other posibilities. But an efficient way is to use language. You sit in school or uni and someone in the front of the room speaks a lot of words… You read books and they also contain words?! And language is super useful. A lion mother also teaches their cubs how to hunt, without words. But humans have language and it’s really a step up what we can pass down to following generations. We record knowledge in books, can talk about abstract concepts, feelings, ethics, theoretical concepts. We can write down how gravity and physics and nature works, just with words. That’s all possible with language.
I can look it up if there is a good article explaining how learning concepts works and why that’s the fundamental thing that makes machine learning a field in science… I mean ultimately I’m not a science teacher… And my literature is all in German and I returned them to the library a long time ago. Maybe I can find something.
Are you by any chance familiar with the concept of embeddings, or vector databases? I think that showcases that it’s not just letters and words in the models. These vectors / embeddings that the input gets converted to, match concepts. They point at the concept of “cat” or “presidential speech”. And you can query these databases. Point at “presidential speech” and find a representation of it in that area. Store the speech with that key and find it later on by querying it what obama said at his inauguration… That’s oversimplified but maybe that visualizes it a bit more that it’s not just letters of words in the models, but the actual meanings that get stored. Words get converted into an (multidimensional) vector space and it operates there. These word representations are called “embeddings” and transformer models which is the current architecture for large language models, use these word embeddings.