Over the last two decades, the field of artificial intelligence has experienced a separation into two schools that hold opposite opinions on how uncertainty should be treated. This separation is the result of a debate that began at the end of the 1960's when AI first faced the problem of building machines required to make decisions and act in the real world. This debate witnessed the contraposition between the mainstream school, which relied on probability for handling uncertainty, and an alternative school, which criticised the adequacy of probability in AI applications and developed alternative formalisms. In this talk, I contend that the reason why the uncertainty field has split into two schools is because a crucial element on which the two schools diverge has not been properly addressed. Indeed, the debate has focused on the technical aspects of the criticisms raised against probability while neglecting an important element of contrast. This element is of an epistemological nature. Through a philosophical analysis of the epistemological element I argue that this element is metaphysical in Popper's sense. I conclude that a philosophical analysis is now compelling in order to both solve the problematic division that characterises the uncertainty field and to secure the foundations of the field itself.