Artificial Intelligence: Introduction

Artificial Intelligence

Artificial Intelligence: Introduction

This article will provide an overview of Introduction to Artificial Intelligence. Artificial intelligence is the ability to create smart machines or self-learning software programs that mimic the human mind’s thinking, problem-solving and planning. Artificial intelligence approaches have the potential to outperform human actions when it comes to knowledge discovery. This field has seen rapid development over the past 20 years.

AI’s Main Components and Characteristics

Below are the main components and frameworks that contribute to the implementation of intelligent systems such as the following:

Feature Engineering

Using the dataset, feature extraction is the process of extracting a minimum set of information features. A meaningful set of features can improve the performance of machine-learning processes.

The efficient feature extract process ensures that

Reduced degree of disorder, also known as entropy, while classifying datasets using selected features. This optimal combination of features maximizes information gain.

Zero correlation between features is achieved, which results in independence and minimal feature-set. This is possible using techniques such as Gram-Schmidt orthogonalization, Principal Component Analysis (PCA), and others.

Artificial Neural Networks

A Neural Network is a set of interconnections that are weighted between computing nodes at successive layers. In the learning phase, the optimal weights for connections are determined by adapting them to the common weight sharing strategy and the feedback from the implemented backward propagation algorithm. Each node calculates the weighted total of all values that have been propagated to it. Activation functions regulate the criteria that determine which values are to be fed-forward to the next layer. After a series epochs, the criteria for feed-forward and backward propagation stages, weights and other parameters of the network converge to find optimal values, resulting in the most suitable model.

These are the most popular Artificial Neural Networks:

Convolutional Neural Networks, or CNN, combine the input received with the learned spatial patterns/filters to identify features at a convolution layer. These signals are fed to the next layers, which are fully connected to perform recognition tasks.

This approach is widely used in image recognition applications because of its robustness to translational and convolutional variations.

Recurrent Neural Networks use Long Term Short Memory to smartly estimate unknown values from past data.

Deep Learning

Deep learning architecture is more complex than artificial neural networks because it has many layers between the input layers and the output layers. This architectural change allows deep learning frameworks to automatically extract feature and classify data. These models use supervised learning to train using well-labeled data. The learning time for the model can be dramatically reduced despite its complexity and many hidden layers.

AI Applications

We are now discussing how AI has revolutionized many fields including retail, finance, space research, healthcare, automobiles, and other areas.

Details for a few applications can be found below:

Editing ethical genes

Personalized medical care is possible by understanding the genetic blueprint of each patient. Genome sequencing is the process of identifying the order in which nucleotides are ordered. Genome sequencing would reveal susceptible mutations that could be used to provide a treatment plan for the patient.

Intelligent Disaster Response System

Modern rescue systems use AI-powered drones and robots to rapidly gather information about the extent of damage, whereabouts of victims trapped, topographical details, and the location of people during crises. Intelligent systems aid rescue workers in locating the closest and most secure assemble points, while also evacuating victims from disaster-hit regions. AI-equipped disaster management modules stimulate simulations of disasters to help identify vulnerable areas and plan preventive actions. They also allow for seamless resource allocation monitoring.

Recommendation Systems

Based on item profiles and inferences about user behaviour, the best recommendation systems can predict or identify users’ preferences for items. In the utility matrix, user-item pair is a representation of the willingness of users to accept different items.

There are two ways to discover users’ responses.

Based on user ratings and feedback, content-based recommendations can help users find similar products.

Collaborative filtering is about identifying similar users and recommending items to them.

Mathematically, there is a sparse utility matrix. The recommendation algorithm aims at deducing the unknown/missed entries using clustering algorithms, matrix factorization methods such as singular value decomposition (SVD) and matrix factorization methods such as matrix factorization, matrix factorization, etc.

Artificial Intelligence has many advantages

The following are the benefits of artificial intelligence:

  • Minimum Human Intervention: AI powered systems provide the best solutions for environments that are more likely to pose a risk to human life. These scenarios include space exploration, defense operations such as bomb defusing, and workplaces that are characterized by extreme heat, mineral mining, and others.
  • More accurate and faster: Well-trained AI-enabled apps dramatically reduce the risk of human errors. These AI versions were faster at solving computationally complex tasks, particularly in scientific research. They also performed better at tedious tasks. The technology can automate most routine, repetitive and trivial tasks to increase operational efficiency.


The challenges discussed below:

Massive Data Corpus is required

Intelligent systems must learn a model that is optimized using large amounts of data. This helps them to train and validate their models before they can be deployed in the real world. For AI-enabled systems, the main limitations are inability to manage large amounts of data and lack of the necessary tools to do so. It is imperative to develop sophisticated modeling techniques that can accurately estimate model parameters using very limited data samples.

Multimodal Interactions

By leveraging the ability of multiple data modes to be processed simultaneously, perception-based recognition systems that incorporate computer-vision methods can improve their efficiency and precision. This allows the recognition paradigm to best emulate human intelligence, which works with multiple senses such as touch, vision, hearing, and others.

Beyond Human Control

AI technology’s extraordinary ability to comprehend and learn large libraries of information faster than humans means that there are very few instances in which an AI framework could be considered a threat. Unregulated AI systems’ behavior could lead to irreparable disaster.

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