Lower bound resource requirements for machine intelligence

Abstract

Recent advancements in technology and the field of artificial intelligence provide a platform for new applications in a wide range of areas, including healthcare, engineering, vision, and natural language processing, that would be considered unattainable one or two decades ago. With the expected compound annual growth rate of 50% during the years of 2017–2021, the field of global artificial intelligence is set to observe increases in computational complexities and amounts of sensor data processed. In spite of the advancements in the field, truly intelligent machine behavior operating in real time is yet an unachieved milestone. First, in order to quantify such behavior, a definition of machine intelligence would be required, which has not been agreed upon by the community at large. Second, delivering full machine intelligence, as defined in this work, is beyond the scope of today’s cutting-edge high-performance computing machines. One important aspect of machine intelligent systems is resource requirements and the limitations that today’s and future machines could impose on such systems. The goal of this research effort is to provide an estimate on the lower bound resource requirements for machine intelligence. A working definition of machine intelligence for purposes of this research is provided, along with definitions of an abstract architecture, workflow, and performance model. Combined together, these tools allow an estimate on resource requirements for problems of machine intelligence, and provide an estimate of such requirements in the future.