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Types of Artificial Intelligence

There are three main types of artificial intelligence: rule-based, decision tree, and neural networks.

1⃣Narrow AI is a type of AI that helps you perform a dedicated task with intelligence.

2⃣General AI is a type of AI intelligence that can perform any intellectual task efficiently like a human.

3⃣Rule-based AI is based on a set of pre-determined rules that are applied to an input data set. The system then produces a corresponding output.

4⃣Decision tree AI is similar to rule-based AI in that it uses sets of pre-determined rules to make decisions. However, the decision tree also allows for branching and looping to consider different options.

5⃣Super AI is a type of AI that allows computers to understand human language and respond in a natural way.

6⃣Robot intelligence is a type of AI that allows robots to have complex cognitive abilities, including reasoning, planning, and learning.

Subfields of Artificial Intelligence

Here, are some important subfields of Artificial Intelligence:

Machine Learning: Machine learning is the art of studying algorithms that learn from examples and experiences. Machine learning is based on the idea that some patterns in the data were identified and used for future predictions. The difference from hardcoding rules is that the machine learns to find such rules.

Deep Learning: Deep learning is a sub-field of machine learning. Deep learning does not mean the machine learns more in-depth knowledge; it uses different layers to learn from the data. The depth of the model is represented by the number of layers in the model. For instance, the Google LeNet model for image recognition counts 22 layers.

Natural Language Processing: A neural network is a group of connected I/O units where each connection has a weight associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon the human nervous system. You can use this model to conduct image understanding, human learning, computer speech, etc.

Expert Systems: An expert system is an interactive and reliable computer-based decision-making system that uses facts and heuristics to solve complex decision-making problems. It is also considered at the highest level of human intelligence. The main goal of an expert system is to solve the most complex issues in a specific domain.

Fuzzy Logic: Fuzzy Logic is defined as a many-valued logic form that may have truth values of variables in any real number between 0 and 1. It is the handle concept of partial truth. In real life, we may encounter a situation where we can’t decide whether the statement is true or false.

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Machine learning will be the first subfield.

It will be very long and simple to understand, so it will be posted in sections over the next few days to ensure that everyone understands it.

What is Machine Learning?

Machine Learning is a system of computer algorithms that can learn from example through self-improvement without being explicitly coded by a programmer. Machine learning is a part of artificial Intelligence which combines data with statistical tools to predict an output which can be used to make actionable insights.

The breakthrough comes with the idea that a machine can singularly learn from the data (i.e., example) to produce accurate results. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input and uses an algorithm to formulate answers.

A typical machine learning tasks are to provide a recommendation. For those who have a Netflix account, all recommendations of movies or series are based on the user’s historical data. Tech companies are using unsupervised learning to improve the user experience with personalizing recommendation.

Machine learning is also used for a variety of tasks like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.