The universal computing model of Turing, which was central to the birth of modern computer science, identified also the essential notion of a problem, as an input output function to be computed by a Turing machine. In distributed computing, \emph{tasks} are the equivalent of a function: each process gets only part of the input, and computes part of the output after communicating with other processes.

In distributed computing tasks have been studied from early on, in parallel, but independently of \emph{sequential objects}. While tasks explicitly state what might happen when a set of processes run concurrently, sequential objects only specify what happens when processes run sequentially. Indeed, many distributed problems considered in the literature, seem to have no natural specification neither as tasks nor as sequential objects. I will concentrate on our recent work on interval-linearizability, a notion we introduced to specify objects more general than the usual sequential objects. I will describe the bridges we establish between these two classical paradigms, and our discussions about what is a distributed problem, and what it means to solve it.