what is the hyphotesis in computer science research(especifically AI research)? I'm a computer science and software engineer that worked in industry for some years with deep applied knowledge in ML and AI, i commonly read research papers and this year started a graduate scientific research program , in this first weeks we are studying basic concepts like what is research, and common descriptions like :"research seeks to generate new knowledge" , "you observe a problem or a phenomenon , create hypothesis about that which then you seek to prove experimentally and collecting data" , my question is in computer science and AI, what are the problem and phenomenon? and what are the hypothesis in AI research? Commonly in papers researchers propose new algorithms or new models but what were the common "observed problem" and the hypothesis? it's not clear to me how CS and AI research fits in this classic research definitions.

If possible can anyone provide examples on specific AI research papers ,how they fit in the classic definitions, what are the problems under study,hypothesis and all scientific method parts in the paper?
AI is a very applied domain, so from a big picture point of view the problem is often defined by a real world application. For example, machine translation fills a need for low cost translation (so this particular need is the problem). In this general setting, an hypothesis is for instance: "the machine can produce good quality translation", and researchers try to prove it.

However it's more common for a paper to motivate the work at a more fine-grained level using the existing body of literature. For example many approaches have been studied for machine translation, with varying levels of performance and flaws. A researcher in this field might want to study a specific question, e.g. "how to improve translation of multiword expressions from language A to language B" and formulate an hypothesis such as: "recent approach X can improve translation of multiword expressions from language A to language B". Here the problem is formulated as the need to improve over the current state of the art, implicitly assuming that the underlying problem (machine translation in general) is sufficiently established.