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September 1, 2022AI Tools in Healthcare
October 1, 2022Knowledge Representation
Knowledge representation is a familiar concept within AI. A representation plays five fundamentally different roles, each placing different expectations on...
While knowledge representation is a central and, in many ways, most familiar concept within Artificial Intelligence, the most fundamental questions remain. In response to these questions, we think a representation plays five fundamentally different roles, each placing different expectations on the properties a representation must have, which sometimes conflicts.(1)
While knowledge representation is a central and, in many ways, most familiar concept within Artificial Intelligence, the most fundamental questions remain. In response to these questions, we think a representation plays five fundamentally different roles, each placing different expectations on the properties a representation must have, which sometimes conflicts.(1)
Knowledge Representation Roles
In its most basic sense, knowledge representations are surrogates, a substitute for things themselves, that may be used to allow an entity to determine its future actions by thinking instead of acting, that is, by reasoning about the world rather than acting on it. The second aspect of it is that it entails some ontological commitments. It answers the question: “In what terms should I think about the world?” The third analysis is a fragmentary theory of intelligent reasoning with three components: the representation’s concept of intelligent reasoning, the inferences the representation sanctions, and the inferences the representation recommends. Furthermore, it is a tool that facilitates pragmatically efficient computation, i.e., the environment in which thinking happens. Finally, it is a tool for expressing human ideas, e.g., the language in which we discuss things. in 2010.
Consequences for Research and Practice
As a result of these considerations, knowledge representation can be usefully influenced in practice and help inform debates regarding the topic. Practically, it helps in making explicit the valuable insights and spirit of a representation, as well as showing the difference in design that results from appreciating rather than violating this spirit. Researchers can apply this view to a broader conception of representation, emphasizing that all roles should be considered when developing representation languages and that representation is embedded in an argumentative theory of intelligence, using a broader view of representation to guide the combination of representations. Additionally, it can be applied to the ability to dissect some of the arguments regarding formal equivalents. According to this belief, knowledge representation consists of capturing the complexity of reality.(2)
Knowledge Representation and Human White Matter
It is a fundamental challenge in neuroscience to understand how knowledge is represented in the human brain. There has been much debate on whether knowledge is distributed or localized in cortical areas. The bulk of work has been focused on knowledge representation in cortical areas. It is expected that multiple spatially localized and functionally specialized but interconnected brain regions will contribute to the representation of semantic knowledge in the human cortex (Fig 1A).(2) It has been suggested that several regions of the cortex respond preferentially to objects, such as animate or inanimate artifacts, and motor planning related to objects. It is crucial to understand the importance of white matter tissue organization and the connection pattern it supports if we are to understand brain function(3).
Fang and colleagues’ work extends our understanding of the human brain and cognition by offering new approaches. Their findings imply that if knowledge is extensively dispersed across the cortex, disturbances to a subset of connections can cause semantic tuning to be altered, even if the majority of knowledge representations scattered across the cortex remain intact.(4)
It is a fundamental challenge in neuroscience to understand how knowledge is represented in the human brain. There has been much debate on whether knowledge is distributed or localized in cortical areas. The bulk of work has been focused on knowledge representation in cortical areas. It is expected that multiple spatially localized and functionally specialized but interconnected brain regions will contribute to the representation of semantic knowledge in the human cortex (Fig 1A).(2) It has been suggested that several regions of the cortex respond preferentially to objects, such as animate or inanimate artifacts, and motor planning related to objects.
It is crucial to understand the importance of white matter tissue organization and the connection pattern it supports if we are to understand brain function(3). Fang and colleagues’ work extends our understanding of the human brain and cognition by offering new approaches. Their findings imply that if knowledge is extensively dispersed across the cortex, disturbances to a subset of connections can cause semantic tuning to be altered, even if the majority of knowledge representations scattered across the cortex remain intact.(4)