MemComputing is a new approach to computation that employs memory (time non-locality) to both process and store information on the same physical location. Memory is a remarkable feature since it can generate correlations between the machine units that do not decay exponentially, rather algebraically, both temporally and spatially. A memcomputing machine then navigates its phase space by following specific trajectories which showcase these long-range correlations, namely during dynamics the machine can change the values of just a few or as many variables in the problem specification as needed to reach the solution efficiently. Massimiliano will discuss the fundamental reasons behind this efficiency and show many applications of MemComputing to combinatorial optimization problems, machine learning, and quantum problems. He will finally discuss the path to its hardware realization. Work supported by DARPA, DOE, NSF, CMRR, and MemComputing, Inc.