The interrelationship between knowledge and artificial intelligence: knowledge representation in AI

The interrelationship between knowledge and artificial intelligence: knowledge representation in AI

Unlike the human brain, an artificially intelligent entity is designed with the function in mind. The function, an AI is expected to serve, determines the knowledge it needs for swift execution. The knowledge here is data, all kinds of data picked up by all kinds of sensors and of course humans. This knowledge is then fed to AI entities for the purpose of training and optimization. However, the process of representing knowledge is not at all automated. Most of the time a human mind decides upon the selection by which an AI entity is expected to train. Automation is the need of the hour. developing specific and adept AI-enabled systems is of the essence. The knowledge we need for this purpose is already being generated by us in plenty. Thus the stage for a heavily automated future is ready for the demands of humanity in 2022. This article will concentrate on knowledge representation in AI in order to enlighten the reader regarding the types of knowledge and the approach in which an AI is perceiving them.

Why does an AI need knowledge?

Intelligence is a higher cognitive skill, consisting of a few major sub-skills. All of which need knowledge for a smooth flourishment. For instance, in order to understand a human tongue in a particular language, an AI needs a lot of verbal information consisting of different accents of the same language. This audio data is generated by human beings intentionally or by performing tasks that are poised to generate similar knowledge. This specific kind of knowledge is then fed to an AI so that it can be accessed for training and performance. Naturally, more knowledge and accuracy can be expected from an AI.

Importance of representation formats

In order to help an AI take up all the knowledge, it needs to call upon. This representation needs to be structured. Storing or installing do not require any structuring. But in order to be of use to ML or deep learning components, it needs to be presented in a form that they understand and can work with. Thus the sorting of data and structuring in accordance with protocol is a vital part of knowledge representation in AI.

Different genres of knowledge

  • Facts
  • Objects
  • Performance
  • Events
  • Meta knowledge
  • Knowledge base

Different types of representation

Declarative knowledge

This kind of knowledge concerns the representation of facts, events, objects, or concepts in a declarative manner.

Structural knowledge

Structural knowledge concerns structuring in terms of interrelationships within a data set. Structural knowledge is the knowledge of relationships between concepts and objects or facts. For example, a sentence can be comprehensively taught to an AI by designating different components of the sentence and co-relate the same with its meaning or conveying concept.

Procedural knowledge

Procedural knowledge is concerned with the protocols of getting things done. In order to prepare an AI to perform in accordance with a set of rules, those rules must be imprinted on the AI as procedural knowledge. Procedural knowledge can consist of instructions, protocols, strategies etc.

Meta knowledge

Meta knowledge is the collection of knowledge regarding knowledge. For instance, research on a subject matter can consist of multiple components. All of these areas might possess significant research in their respective domains and generate independent sets of knowledge. Meta knowledge is the collection of all that knowledge or rather the excerpt of the research done in different domains.

Heuristic knowledge

Heuristic knowledge is related to good experiences, good decisions, and good assumptions. Clearly, a specialized knowledge that can be conveyed by only the most experiences and skills. This set of knowledge includes vital information that is known over time or by experiences at work.

Conclusion

An artificially intelligent entity is trained from the bottom up. The function is identified in detail ( all aspects ) and an AI is trained following that task. The task itself determines the software and hardware components an AI can get access to. Naturally, the knowledge an AI entity needs to perform certain tasks is curated based on these requirements. Thus, knowledge representation in AI is a set of protocols developed by the demands and requirements of the times we live in.

Muzzbit

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