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Artificial Intelligence Subfields – Definition, Intelligence Elements, Types Of Machine Learning, And More


Artificial Intelligence Subfields – The computer network system has improved the human lifestyle by providing gadgets and devices that reduce human physical and mental efforts to perform various tasks. Artificial intelligence is the next step in making it more effective by applying logical, analytical, and productive technologies in this effort. This tutorial will elucidate what artificial intelligence is and its definition and modules with the help of alter examples. We will also explore the change between human and machine intelligence.

Intelligence Elements

Reasoning: It is the procedure that helps us provide the basic standards and guidelines to make a judgment, prediction, and decision in any problem.

The reasoning can be of two types. One is the generalized reasoning bases on the incidences and general statements observed. The conclusion can be false occasionally in this case. The other is logical reasoning, base on specific facts, figures and opinions, and specific occurrences mention and observe. Therefore, the conclusion is correct and rational in this case.

Types Of Machine Learning

Machine learning doesn’t always work the same way, and there are variants. Microsoft dubs the first type as “supervised learning,” in which the machine is train with labeled data; that is, we show images with cats so that it can identify and tag a photo with cats in the future. In this way, the machines have a kind of learning guide.

The second type is “unsupervised learning.” In this case, the machine does not take labeled data to train on but instead looks for similarities; groups of images are analyz from which similar examples are taken that can be group.

With “reinforcement learning,” computers learn by trial and error until they reach the point where they decide on the best way to carry out tasks. Microsoft uses this technique in video games to see how “software agents improve their work.”

Deep Learning

Deep learning is another type of machine learning that is much more complex than the previous ones. This system is inspired by the functioning of the human brain’s neural networks to process information.

Machines employing deep learning achieve a very detailed level of understanding, similar to intelligent reasoning. Thus, if the engine with deep learning sees an image of a cat, it will first detect its shape, then the edges, contours, and so on, until it completely identifies the image. Deep learning has been the main driver of AI as we know it today. Some examples of this type of learning are image or voice recognition.

The Four Types Of Artificial Intelligence

Arend Hintze, an AI researcher, has established his classification based on the level of complexity of AI. The first type is reactive machines, the most basic AI system that cannot have memories or use past experiences to make current decisions.

Deep Blue, the IBM chess supercomputer that beat international player Garry Kasparov, is the perfect example of a reactive machine. These machines perceive the world directly and act on what they see, only suitable for the specific tasks assigned to them.

Limited memory machines are the second type for Hintze. These manage to use past experiences to make decisions, as some autonomous cars already do. For example, these vehicles consider traffic lights, curves in the road, speed and direction of other vehicles, etc.

The third type is the theory of notice machines. Hintze establishes in this type an essential division between the current devices and those of the future. These not only form representations about the world but also about other agents or entities.

In psychology, it is called “theory of mind” and is explained as the ability to be aware of the differences between the personal point of view and that of others. This type of AI does not yet exist; however, Hintze believes it will be essential to work together.

Self-aware machines are the latest modality. Machines with their conscience have not yet been created and look like something out of a movie. In a way, it is an extension of the complexity of machines with the theory of mind. We would be talking about robots practically like humans, a situation for which decades of research are still left if they even exist.

Artificial Intelligence Terminology

Artificial Intelligence Terminology (2)


AI has become an umbrella term for applications that perform complex tasks that previously requires  human input, such as communicating with customers online or playing chess. Time is often using interchangeably with its subfields, which include machine learning and deep learning. However, there are specific differences. For example, machine learning focuses on creating systems that learn or improve their performance bases on the data they consume. It is significant to note that while all machine learning is AI, not all AI is machine learning.

Many companies are investing in data science teams to get the total value of AI. An interdisciplinary field that uses scientific and other methods to extract value from data, data science certification combines skills from areas such as statistics and computer science with business knowledge to analyze data collects from multiple sources.

Artificial Intelligence And Developers

Developers use artificial intelligence to achieve tasks that would otherwise be done manually: customer communications, pattern identification, and problem resolution. To get starts with AI, developers need to have a background in math and be comfortable working with algorithms.

It is best to start small when using artificial intelligence to develop applications. By designing a relatively simple project, such as a Match 3 game, you will learn the fundamentals of artificial intelligence. Knowledge by doing is a great way to increase any skill, and AI is no different in this regard. After completing one or more small projects, there are no limits to artificial intelligence.


Machine Learning, Cognitive Systems, Robotics, and Intelligent Machines are often use about AI, or sometimes even synonymously. AI is a multipart field of interest with many forms. It isn’t easy to generalize and establish which devices are and which are not intelligent since the field of AI is very broad. The main idea is that the system acts similarly to the human brain. If fulfilled, it could be classify as Artificial Intelligence according to the existing meanings.

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