Problems in AI research and how the SP System may help to solve them

Abstract

This paper describes problems in AI research and how the SP System (described in sources referenced in the paper) may help to solve them. Most of the problems considered in the paper are described by leading researchers in AI in interviews with science writer Martin Ford, and reported by him in his book Architects of Intelligence. These problems, each with potential solutions via SP, are: the divide between symbolic and non-symbolic kinds of knowledge and processing, and how the SP System may bridge the divide; the tendency of deep neural networks (DNNs) to make large and unexpected errors in recognition, something that does not happen with the SP System; in most AI research, unsupervised learning is regarded as a challenge, but unsupervised learning is central in how SP learns; in other AI research, generalisation, with under-and over-generalisation is seen as a problem, but it is a

How the SP System May Promote Sustainability in Energy Consumption in IT Systems

“…A striking difference between the SPS and DNNs is that the former, like people, are capable of learning usable knowledge from a single exposure or experience (see Section 8 in [19]) but a DNN needs many repetitions to learn any one concept well enough for it to enter into any other computation.…”