Scientific knowledge:

This is not to be confused with the applications of "AI" programs to other sciences, discussed later. Its subfields can be classified into a variety of theoretical and practical streams, such as computational complexity theory which explores the fundamental properties of computational problems, computer graphics emphasise real-world applications. Although now other fields focus on the challenges in implementing computation Rather, it is worth pointing out that some "AI" researchers don't write intelligent programs and are certainly not interested in human intelligence or breathing life into programs. They are really interested in the many different scientific problems that occur in the study of "AI". One example is the question of algorithmic complexity - how bad will a particular algorithm get at solving a specific problem (in terms of the time taken to find the solution)with a perfect optimization  as the problem instances get bigger.

These kinds of studies certainly have an impact on the other long term relation, but the pursuit of knowledge itself is frequently overlooked as a reason for "AI" to exist as a scientific stream. We won't be covering all the matter such as algorithmic complexity in this course as per our necessity however.