Knowledge, for the machines, is not just storage units and file details. Knowledge is information and instructions put into context and bolstered by instances. Human beings acquire knowledge through experiences. And the machines they have built are programmed to develop knowledge similarly. Artificially intelligent entities gain knowledge by training with large data volumes and recognizing patterns that require specific responses. And when the training is over the acquired knowledge is implemented for determining an analysis or problem-solving approach.
Machines do not think, they reason, assess the knowledge they have acquired through training, and come to a conclusion. And reasoning and analysis depend on acquired knowledge. We must break down and classify knowledge into fundamental components to understand how knowledge is represented by an artificial entity and how they use it. By simply asking the right questions; “what to represent?” and “what are the types of knowledge in AI?”. This article will ensure these questions are answered before a reader encounters the final full stop.
What are represented?
Knowledge can be differentiated into the following groups based on what the AI entity is expected to learn.
AI entities are taught to treat every tangible existence in the world as objects. In addition to that, the same is programmed to identify the kind of object that entity is. E.g., a mandolin has strings, that can be taught to an AI in association with images of strings, a mandolin, and a mandolin with those strings on it. So that the next time the AI entity encounters a mandolin it can identify which are the strings and why they are on the mandolin.
Events are phenomena, occurrences, and incidents. For example, the sunrise in an event. An AI can be trained to identify sunrise in the east and differentiate the same from sunset in the west, by arming the same with a compass. In that case, the AI will be trained to associate the direction denoted in the compass and the state of the sun for making a deduction about sunrise and sunsets.
Truths, regular occurrences, and laws of nature are facts. We can understand the same by the same example of sunrise. An AI can be taught that the sun rises in the east for a fact. And the same can be bolstered by compass and navigation data.
Performance knowledge is knowledge related to performance. Everything from why to do something to how to do something can be considered performance knowledge. Like an Ai can be taught to multiply by adding the same number the number of times it is being multiplied with.
Meta knowledge is the simple knowledge that concentrates on different kinds and types of knowledge.
5 Types of knowledge in AI
1. Structural knowledge
Structural knowledge is about the relationship between objects and concepts. Structural knowledge is useful for fundamental problem-solving. The same can be used for classifying an object by comparing the same with other objects in terms of characteristics and relationships between concepts and structural aspects.
2. Declarative knowledge
Declarative knowledge covers all kinds of objects, events, and facts that can be known. It is descriptive knowledge that can be presented as a declaration. We can consider the example of multiplication in this context. And in that case, the multiplication AI knows, multiplication is nothing but repeated additions. And the same is an example of declarative knowledge.
3. Heuristic knowledge
Heuristic knowledge is exceptional knowledge that can only be offered by a certain scenario. And gained by an expert present at the right place at the right time. Heuristic knowledge in the case of Ai can be gained by training with real-world example datasets. So the AI can learn and perform the right activity when they encounter a particular data set. Just like the rules of thumb!
4. Meta knowledge
Meta knowledge is knowledge about different genres of knowledge.
5. Procedural knowledge
Procedural knowledge is perhaps the most complicated among all types of knowledge in AI. procedural knowledge is rigid and is a cascade of “if” and “then” responses. Procedural knowledge is like a protocol that must be followed for solving a particular problem. And the problem itself must be recognized by procedural knowledge as well. For different kinds of tasks, different kinds of procedural knowledge can come in handy. And the same includes agendas, rules, strategies and protocols, and paradigms.