Cognitive Technology makes use of Language Processing and Machine Learning AI to Automate Tasks that require Human Judgment and Perception. Much has been written on the subject of machine intelligence in recent years, making it hard to tell the difference between future potential of Cognitive AI and its current, practical application. How does software 'learn to think' and what does it mean to make a machine mimic human decision making? Perhaps most importantly, is mimicking the results of human judgement and perception the same as thinking?
Cognitive AI does not intend to replace human expertise but rather, to augment the decision-making process with machine guided outcomes. The technology's goal is to enhance so-called Empirical Decision Making by improving information recall and extending the user's semantic memory, often through the use of tools such as Knowledge Graphs, Data Ontologies and Content Annotation. In cognitive science, this process of forming links between concepts, objects (data elements), images, sounds and sometimes, reflexive actions is called Associative Learning. It spans a broad set of disciplines, from psychology and behavioral science to cognitive technologies and machine learning. After all, if we can teach a dog to bark at strangers, can't we also teach a machine to alert operators and take corrective actions in response to critical system events? It would seem that the learning process and training tools are very similar.
Data Fabrics for AI + ML
Specialized Data Fabrics can enable AI perception and train machines to classify data and understand related concepts.
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