The goal of this chapter is to outline the attention machine computational framework designed to make a significant advance towards creating systems with human-level intelligence (HLI). This work is based on the hypotheses that: 1. most characteristics of human-level intelligence are exhibited by some existing algorithm, but that no single algorithm exhibits all of the characteristics and that 2. creating a system that does exhibit HLI requires adaptive hybrids of these algorithms. Attention machines enable algorithms to be executed as sequences of attention fixations that are executed using the same set of common functions and thus can integrate algorithms from many different subfields of artificial intelligence. These hybrids enable the strengths of each algorithm to compensate for the weaknesses of others so that the total system exhibits more intelligence than had previously been possible.