Abstract
The SP System (SPS), referring to the SP Theory of Intelligence and its realisation as the SP Computer Model, has the potential to reduce demands for energy from IT, especially in AI applications and in the processing of big data, in addition to reductions in CO2 emissions when the energy comes from the burning of fossil fuels. The biological foundations of the SPS suggest that with further development, the SPS may approach the extraordinarily low (20 W)energy demands of the human brain. Some of these savings may arise in the SPS because, like people, the SPS may learn usable knowledge from a single exposure or experience. As a comparison, deep neural networks (DNNs) need many repetitions, with much consumption of energy, for the learning of one concept. Another potential saving with the SPS is that like people, it can incorporate old learning in new. This contrasts with DNNs where new learning wipes out old learning (‘catastrophic forgetting’). Other ways in which the mature SPS is likely to prove relatively parsimonious in its demands for energy arise from the central role of information compression (IC) in the organisation and workings of the system: by making data smaller, there is less to process; because the efficiency of searching for matches between patterns can be improved by exploiting probabilities that arise from the intimate connection between IC and probabilities; and because, with SPS-derived ’Model-Based Codings’ of data, there can be substantial reductions in the demand for energy in transmitting data from one place to another.
Highlights
This paper describes the potential of the SP System (SPS), referring to the SP Theory of Intelligence and its realisation in the SP Computer Model (SPCM), for reductions in demands for energy, in comparison with current AI and IT systems
The main reason for this strong focus on information compression (IC) is extensive evidence for the importance of IC in human learning, perception, and cognition [9]. Another reason which has emerged with the development and testing of the SPCM is that IC as it is incorporated in the SPCM provides for the modelling of diverse aspects of intelligence
For the avoidance of any confusion, this is not a feature of the SPS but it is a feature of the standard. This loss of stored information in deep neural networks (DNNs) is because learning is achieved by adjusting the weights of links between the layers of the DNN, and a set of weights which is right for one concept will not be right for any other concept
Summary
This paper describes the potential of the SP System (SPS), referring to the SP Theory of Intelligence and its realisation in the SP Computer Model (SPCM), for reductions in demands for energy, in comparison with current AI and IT systems. When the energy comes from the burning of fossil fuels, there is potential for corresponding reductions in emissions of CO2. The origin of the name ‘SP’ is described in Section 1.1.2, below. The SPS is introduced, with more detail in Appendix A. Other sections describe aspects of the system with potential for substantial reductions in the energy required for AI and other IT processing. There is some overlap of content with a previously published paper about big data [1], but the focus of this paper is on sustainability, not big data, and the audiences for the two papers are likely to be largely different
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