Tuesday, November 28, 2017

Knowledge Engineering: Human "Intelligence" Mirrors That of Eusocial Insects


Cambridge |  The World Wide Web has had a notable impact on a variety of epistemically-relevant activities, many of which lie at the heart of the discipline of knowledge engineering. Systems like Wikipedia, for example, have altered our views regarding the acquisition of knowledge, while citizen science systems such as Galaxy Zoo have arguably transformed our approach to knowledge discovery. Other Web-based systems have highlighted the ways in which the human social environment can be used to support the development of intelligent systems, either by contributing to the provision of epistemic resources or by helping to shape the profile of machine learning. In the present paper, such systems are referred to as ‘knowledge machines’. In addition to providing an overview of the knowledge machine concept, the present paper reviews a number of issues that are associated with the scientific and philosophical study of knowledge machines. These include the potential impact of knowledge machines on the theory and practice of knowledge engineering, the role of social participation in the realization of intelligent systems, and the role of standardized, semantically enriched data formats in supporting the ad hoc assembly of special-purpose knowledge systems and knowledge processing pipelines.

Knowledge machines are a specific form of social machine that is concerned with the sociotechnical
realization of a broad range of knowledge processes. These include processes that are thetraditional focus of the discipline of knowledge engineering, for example, knowledge acquisition, knowledge modeling and the development of knowledge-based systems.

In the present paper, I have sought to provide an initial overview of the knowledge machine concept, and I have highlighted some of the ways in which the knowledge machine concept can be applied to existing areas of research. In particular, the present paper has identified a number of examples of knowledge machines (see Section 3), discussed some of the mechanisms that underlie their operation (see Section 5), and highlighted the role of Web technologies in supporting the emergence of ever-larger knowledge processing organizations (see Section 8). The paper has also highlighted a number of opportunities for collaboration between a range of disciplines. These include the disciplines of knowledge engineering, WAIS, sociology, philosophy, cognitive science, data science, and machine learning.

Given that our success as a species is, at least to some extent, predicated on our ability to manufacture, represent, communicate and exploit knowledge (see Gaines 2013), there can be little doubt about the importance and relevance of knowledge machines as a focus area for future scientific and philosophical enquiry. In addition to their ability to harness the cognitive and epistemic capabilities of the human social environment, knowledge machines provide us with a potentially important opportunity to scaffold the development of new forms of machine intelligence. Just as much of our own human intelligence may be rooted in the fact that we are born into a superbly structured and deliberately engineered environment (see Sterelny 2003), so too the next generation of synthetic intelligent systems may benefit from a rich and structured informational environment that houses the sum total of human knowledge. In this sense, knowledge machines are important not just with respect to the potential transformation of our own (human) epistemic capabilities, they are also important with respect to the attempt to create the sort of environments that enable future forms of intelligent system to press maximal benefit from the knowledge that our species has managed to create and codify.