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Applying Metrics to Machine-Learning Tools: A Knowledge Engineering Approach

AI Magazine

The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. The benchmark centers on the knowledge engineering viewpoint, covering some of the characteristics the knowledge engineer wants to find in a machine-learning tool. The proposed model has been applied to a set of machine-learning tools, comparing expected and obtained results.


Knowledge Engineering

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Knowledge engineering is the process of creating rules that apply to data in order to imitate the way a human thinks and approaches problems. A task and its solution are broken down to their structure, and based on that information, AI determines how the solution was reached. Often, a library of problem-solving methods and knowledge to solve a particular set of problems is fed into a system as raw data. Then, the system can diagnose the problem and find the solution without further human input. The result can be used as a self-help troubleshooting software, or as a support module to a human agent.


Pinaki Laskar on LinkedIn: #machinelearning #ai #algorithms

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What is the difference between knowledge and learning in artificial and human intelligence? The primary difference between the Knowledge and Learning is that learning is a process whereas knowledge is the result, outcome or product or effect of the learning by experience, study, education or training data. Human and machine knowledge and its learning is led by scientific activities, as theoretical, experimental, computational or data analytic. There is one fundamental causal learning. But in the behavioristic psychology, learning is divided into the following groups: passive or active, non-associative, associative or active learning (the process by which a person or animal or machine learns an association between two stimuli or events). In ML/DL philosophy, learning is a change of behavior caused by interacting with the environment to form new patterns of responses to stimuli/inputs, internal or external, conscious or subconscious, voluntary or involuntary.