Artificial Intelligence Tutorial

Introduction to Artificial Intelligence Intelligent Agents

Search Algorithms

Problem-solving Uninformed Search Informed Search Heuristic Functions Local Search Algorithms and Optimization Problems Hill Climbing search Differences in Artificial Intelligence Adversarial Search in Artificial Intelligence Minimax Strategy Alpha-beta Pruning Constraint Satisfaction Problems in Artificial Intelligence Cryptarithmetic Problem in Artificial Intelligence

Knowledge, Reasoning and Planning

Knowledge based agents in AI Knowledge Representation in AI The Wumpus world Propositional Logic Inference Rules in Propositional Logic Theory of First Order Logic Inference in First Order Logic Resolution method in AI Forward Chaining Backward Chaining Classical Planning

Uncertain Knowledge and Reasoning

Quantifying Uncertainty Probabilistic Reasoning Hidden Markov Models Dynamic Bayesian Networks Utility Functions in Artificial Intelligence


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Difference between AI and Neural Network


Although previously considered science fiction or futuristic technology, artificial intelligence (AI) is now a reality. What was once only discussed in academic settings has come to light. Almost every element of contemporary life already incorporates artificial Intelligence (AI), including the bank, the hospital, the workplace, the classroom, and your smartphone. These are the brains behind the sensors in autonomous cars, the faces of virtual assistants like Alexa and Siri, the meteorologists and robotic surgeons, and so much more. AI is now present almost everywhere in the modern world. In recent years, artificial intelligence has grown to be an essential component of many IT systems.

Later, a highly developed AI technique known as machine learning emerged, enabling computers to learn from the data they are given without any prior knowledge. That's not where it ends. The programming strategies were built upon a collection of computational techniques referred to as deep learning.

Although the phrase "deep learning" has gained popularity recently, the field of biologically motivated neural networks invented the underlying technology back in the 1960s. In the recent wave of Artificial Intelligence and deep learning, neural networks have made a strong comeback, albeit with new names, definitions, and applications to new fields of study.

Artificial Intelligence (AI)

The ability of a machine to simulate human cognitive processes is known as artificial intelligence (AI), sometimes known as machine intelligence. The objective is to create machines with intelligence that is at least as high as that of humans. Despite being centuries old, the concept has gained traction with the advent of digital computers. The goal of the computer science subfield of artificial intelligence (AI) is to create computer programs that can perform tasks either as well as or better than humans. The focus is not on whether these programs think like humans do. There are numerous shapes, sizes, and algorithms for AI. Artificial Intelligence is pervasive in today's world, found in everything from factories and schools to banks and hospitals, your television and toothbrush, smartphone chips and automobiles, Siri and Alexa, and more.

Neural Networks

Inspired by the biological neural networks found in animal brains, an artificial neural network, or network of nodes, is referred to as a "neural network". It served as a sort of basis for a large portion of contemporary artificial Intelligence. Studies actually indicate that the current uses and implications of artificial Intelligence are nothing more than a progression of the special qualities that neural networks possess, such as machine learning and deep learning. A solid paradigm with roots in computer science, physics, information science, psychology, and engineering are represented by neural networks. Neural networks are systems of linked nodes that function similarly to animal neurons. These days, neural networks are used for many different purposes, such as customer research, risk management, sales forecasting, data validation, and problem-solving.

Difference between AI and Neural Network


A branch of computer science called artificial intelligence, or AI, aims to create intelligent machines that mimic human intelligence rather than the innate intelligence displayed by people. Machines with artificial intelligence (AI) can imitate human cognitive abilities. Conversely, a network of artificial neurons or nodes that is loosely modelled after the biological neural networks that make up an animal's brain is referred to as a neural network. A network of interconnected nodes, neural networks function somewhat like animal neurons.


Strong and weak AI are the two main types on which AI is based.  Strong AI upholds the idea that machines are truly capable of developing human consciousness on par with humans, while weak AI refers to the application of sophisticated algorithms to complete specific problem-solving tasks within a constrained set of functions. A solid paradigm with roots in computer science, physics, information science, psychology, and engineering are represented by neural networks. Neural networks are designed to identify patterns in data so that you can use that information to inform decisions.


Currently AI is pervasive in contemporary culture. AI is applied in many different fields, such as weather forecasting, clinical diagnosis, self-driving cars, Siri, and Alexa. Artificial Intelligence is utilised in a wide range of industries, such as utilities, healthcare, education, and entertainment, to handle complicated tasks. Neural networks create algorithms that can recognize complex patterns by modelling the brain. Artificial neural networks have many applications, including text classification and categorization, named entity recognition (NER), fraud detection, pattern recognition, paraphrase detection, natural language processing, and more.

Here, we learned about the distinctions between artificial intelligence (AI) and neural networks.