Artificial Intelligence

StevenFebruary 14, 2023

1 251 2 minutes read

Artificial Intelligence and Machine learning are very hot buzzwords, both crop
frequently when the topics are Big Data and analytics and also have the broader waves of
technological change that are sweeping through the world. Artificial Intelligence and machine
learning are not the same.
Artificial Intelligence will be applied when a machine mimics cognitive functions
where the humans associate with other human minds such as learning and problem-solving. It is
the study of how to create intelligent agents. It is about how to program a computer to behave
and also perform a task as an intelligent agent.
Machine learning will be a class of algorithms that automates the analytical model that is
built and gives computers that have the capability to learn without being explicitly programmed.
This is a science of creating algorithms and programs able to learn their own on the basis of
heterogeneous data sources such as systems, things, and humans.

There are 4 approaches to Artificial Intelligence

Acting humanly:

When a computer will act perfectly like a human being it will be
difficult to differentiate between the two by using the technologies like natural language
processing, automated reasoning, machine learning, and also automated reasoning.

Thinking humanly:

The computer thinks just as human and performs tasks will be performed with human intelligence like a driving car. Thinking rationally: The different study of how human thinks will use some standards which are helping to create a guideline for human behavior. The person who is considered rational and the computer thinks rationally as the recorded behavior and solves problems.

Acting Rationally:

It is all about a study of how humans act in uncertainty. As per the rational thought, the actions will depend on conditions, environmental factors and existing data to maximize the expected value of its performance.

Main characteristics of AI

Feature engineering: Feature extraction is a method of identifying a proper nominal set of attributes from the given dataset of information. The performance depends on choosing the correct set of features instead of the wrong ones. There are

Efficient features like:

We can classify the dataset, as the main heuristic approach to reduce the entropy of the system that is being modeled. It is called the algorithmic approach and when a system of data will be classified that has been reduced to a point where it cannot be divided, feature selection will be recycled and applied to another dataset. There are different kinds of selection algorithms are used to select a subset of the features as the importance model. This subset will be selected so that it has zero co-relation among the features by achieving independence of the feature set.

Artificial neural networks- It is called a neural network that is based on the collection of connected nodes which are artificial neurons like human brain cells. Each connection will transmit a signal at the connection. There are two types of networks, one is a feed-forward neural network, also called acyclic in which the signal travels only from one direction to another. Deep Learning- In this modern world which is stuffed with a lot of data and with help of deep learning the digital world that is transforming into a beautiful place. It is nothing but a machine learning technique that automates computers to think just like humans.

The architecture of the technique involves multiple hidden layers between the input and output layers as compared to artificial neural networks. It will perform automatic features after the extraction along the classification learning. Natural language processing- It is a subfield of linguistics, artificial intelligence, and computer science. It highlights computers understand human language in the form of text or spoken words and understand it just like human beings.