Learn what artificial intelligence (AI), elements of intelligence, and sub-disciplines of AI are, such as machine learning, deep learning, NLP, and more.
Definition and Subfields of AI
What Is Artificial Intelligence?
Computer networking systems have improved human lifestyles by providing different types of devices and devices that reduce human physical and mental effort to perform various tasks. Artificial intelligence is the next step in this process to make it more effective by applying logical, analytical and productive skills to this task.
This tutorial explains what artificial intelligence is
and its definitions and components through various examples. We will also
explore the difference between human and machine intelligence.
What is Artificial Intelligence (AI)?
There are many different technical definitions that
can be used to describe artificial intelligence, but they are all very complex
and confusing. For better understanding, let me elaborate the definition in
simple words.
Humans are considered the most intelligent species on
the planet because they can solve any problem and analyze big data with skills
such as analytical thinking, logical reasoning, statistical knowledge,
mathematical or computational intelligence.
With all these combinations of technologies in mind,
artificial intelligence has been developed for machines and robots that give
them the ability to solve complex problems similar to what humans can do.
Artificial intelligence is applied in all fields
including medicine, automotive, everyday life applications, electronics,
telecommunications and computer networking systems.
So, technically related to computer networks, AI can
be defined as computer devices and networking systems that can accurately
understand raw data, gather useful information from that data, and then use
these results to arrive at a final solution. Assign problems with a flexible
approach and easily adaptable solutions.
Element of Artificial intelligence
1) Reasoning: A procedure that can
provide basic criteria and guidance for making judgments, predictions and
decisions in any matter.
Inferences can be of two types, one is generalized
inferences that are usually based on observed occurrences and statements. In
this case, the conclusion can sometimes be wrong. The other is logical
reasoning, which is based on facts, figures, and specific statements and
specific, stated and observed events. Therefore, the conclusion in this case is
correct and logical.
2) Learning: The act of acquiring
knowledge and skills from a variety of sources, including books, real life
events, experiences, and taught by some experts. Learning improves his
knowledge in areas he does not know.
The ability to learn shows that not only humans, but
some animals and artificial intelligence systems have this skill.
Learning is of different types:
Learning to speak is based on a process in which a
teacher gives a lecture and then auditory students hear it, memorize it, and
use it to gain knowledge.
Linear learning is based on memorizing a series of
events that a person has experienced and learned.
Observational learning refers to learning by observing
the behavior and facial expressions of living things, such as other people or
animals. For example, young children learn to speak by imitating their parents.
Perceptual learning is based on learning to memorize
by identifying and classifying visual objects.
Relational learning is based on an effort to learn
from past thoughts and mistakes and learn them on the fly.
Spatial learning means learning from visual materials
such as images, videos, colors, maps, movies, etc., which will help people
create images they like whenever they need it for future reference.
3) Troubleshooting: This is the process of
determining the cause of the problem and figuring out how to fix it. This is
done by analyzing a problem, making a decision, and then finding two or more
solutions to arrive at a final and most appropriate solution to the problem.
The final motto here is to find the best solution
among the available solutions to achieve the best troubleshooting results in
the least amount of time.
4) Perception: The phenomenon of
obtaining, inferring, selecting, and systematizing useful data from raw input.
For humans, perception is derived from the experience
of the environment, the sense organs, and the conditions of the situation.
However, logically in relation to artificial intelligence perception, in
relation to data, it is obtained by artificial sensor mechanisms.
5) Linguistic intelligence: The phenomenon of the
ability to develop, grasp, read, and write speech in other languages. It is a
fundamental component of the way two or more individuals communicate and is
necessary for analysis and logical understanding.
The difference between human and machine Artificial intelligence
The following explains the difference.
1) We have described above the
components of human intelligence on the grounds that humans perform different
types of complex tasks and solve different kinds of unique problems in
different situations.
2) Humans, like humans, develop
machines that are intelligent, and provide nearly as good results for complex
problems as humans.
3) Humans segment data into visual and
auditory patterns, past contexts, and situational events, whereas artificial
intelligence machines recognize and process problems based on predefined rules
and backlog data.
4) Humans memorize data from the past,
learn it, and store it in the brain to remember it, but machines search
algorithms to find data from the past.
5) Linguistic intelligence also allows
humans to recognize distorted images and shapes, missing speech, data, and
image patterns. However, machines do not have this intelligence and use
computer learning methodologies and deep learning processes that include
various algorithms to achieve the desired results.
6) Humans always follow their
instincts, visions, experiences, situations, surrounding information, visual
and raw data, and what some teacher or elder has been taught to analyze and
solve any problem and produce effective and meaningful results. any problem.
On the other hand, artificial intelligence machines at
all levels deploy different algorithms, predefined steps, backlog data, and
machine learning to achieve useful results.
7) The process followed by machines is
complex and requires many procedures, but it provides the best results when you
need to analyze large sources of complex data and precisely perform unique
tasks in different disciplines in the same amount of time. accurate and within
the given time
The error rate for these machines is much lower than
for humans.
Subfields of Artificial Intelligence
Subfields of Artificial Intelligence
1) Machine Learning (ML)
Machine learning is a feature of artificial
intelligence that gives computers the ability to automatically collect data and
learn from the experience of problems or cases that have arisen, rather than
being specifically programmed to perform a given task or task.
Machine learning emphasizes the growth of algorithms
that can scrutinize data and make predictions. Its main use is in the medical
industry where it is used for disease diagnosis, medical scan interpretation,
etc.
It is a subcategory of pattern recognition machine
learning. It can be described as the automatic recognition of blueprints from
raw data using computer algorithms.
A pattern can be a continuous data series used to
predict a set of events and trends, specific characteristics of image features
to identify objects, repetitive combinations of words and sentences for
language support, or it can be specific data. A collection of behaviors of
people in any network that can indicate social activity and many more.
(i) Data Acquisition and Detection: This includes the
acquisition of raw data such as physical variables and the measurement of
frequency, bandwidth, resolution, etc.; There are two types of data: training
data and training data.
Training data is data that the system classifies by
applying clusters for which no labeling of the data set is provided. The
training data has a well-labeled dataset for use directly with the classifier.
(ii) Pre-processing of input data : This includes
filtering out unwanted data such as noise from the input source and is done
through signal processing. This step also performs filtering of existing
patterns in the input data for further reference.
(iii) Feature extraction : In order to find the
matching pattern required by the characteristics, various algorithms are
performed like the pattern matching algorithm.
(iv) Classification :Classes are assigned to patterns
based on the outputs of the algorithms performed and the various models trained
to obtain matching patterns.
(v) Post-processing : Here you will see the final
output and you can be sure that the results achieved are most likely what you
need.
Model for pattern recognition:
As shown in the figure above, feature extractors
derive features from input raw data such as audio, image, video, sound, etc.
Now the classifier takes x as input, and class 1,
class 2… Assign different categories to input values such as Class C.
Depending on the data class, further recognition and analysis of patterns is
performed.
Example of triangular shape recognition with this
model:
Pattern recognition is used in identification and
authentication processors such as voice-based recognition and facial
authentication, defense systems for target recognition and navigation guidance,
and in the automotive industry.
2) Deep Learning (DP)
It is a process in which a machine learns by
processing and analyzing input data in several ways until it finds one desired
output. Also called self-learning of machines.
Machines run a variety of arbitrary programs and
algorithms to map input raw sequences of input data to outputs. By deploying
various algorithms such as neuroevolution and other approaches such as gradient
descent in neural topologies, the output y finally assumes that x and y are
correlated in the unknown input function f(x). .
Interestingly, the role of the neural network is to
find the correct f function.
Deep learning witnesses a database of all possible
human traits and behaviors and performs supervised learning. This process
includes:
Detection of different types of human emotions and
signs.
Identifies people and animals by images, such as
specific symbols, marks, or features.
Recognize and memorize another speaker's voice.
Convert video and voice to text data.
Identification of right or wrong gestures,
classification of spam items and cases of fraud (e.g. alleging fraud).
All other characteristics, including those mentioned
above, are used to prepare artificial neural networks through deep learning.
Predictive analytics: After collecting and training
vast datasets, we cluster similar datasets by accessing a set of available
models, such as comparing a set of similar types of speech, images, or
documents.
Now that we have done classification and clustering of
the dataset, we will approach the prediction of future events based on the
rationale of the current event case by establishing a correlation between the
two data sets. Remember that predictive decisions and approaches are
time-limited.
The only thing to keep in mind when making predictions
is that the output must be meaningful and logical to some degree.
Through iterative takes and self-analysis, a solution
to the machine's problem can be obtained. An example of deep learning is speech
recognition in cell phones, which allows smartphones to understand different
kinds of accents in a speaker and translate them into meaningful speech.
3) Neural Networks
Neural networks are the brains of artificial
intelligence. They are computer systems that replicate the neural connections
of the human brain. The artificial corresponding neurons in the brain are known
as perceptrons.
Stacks of various perceptrons are combined to create
artificial neural networks of machines. Before providing the desired output,
the neural network processes various training examples to gain knowledge.
This process of analyzing data using a variety of
learning models provides solutions to many related queries that were previously
unanswered.
Deep learning, as it relates to neural networks, can
unfold multiple layers of hidden data, including the output layers of complex
problems, and is an assistant in subfields such as speech recognition, natural
language processing, and computer vision.
Types of neural networks
Early types of neural networks consisted of one input
and one output, and only consisted of at most one hidden layer or a single
layer of perceptrons.
Deep neural networks consist of two or more hidden
layers between the input and output layers. Therefore, deep learning processes
are required to unfold hidden layers of data units.
In deep learning of neural networks, each layer is
adept at a unique set of properties based on the output features of the
previous layer. The more we get into the neural network, the more nodes gain
the ability to recognize more complex properties as they predict and recombine
the outputs of all previous layers to produce a clearer final output.
This whole process is called the functional layer.
Also referred to as a hierarchy of complex, intangible data sets. It enhances
the capabilities of deep neural networks where very large, wide-dimensional
data units with billions of constraints will be subjected to linear and
non-linear functions.
The main problem machine intelligence is struggling to
solve is processing and managing the unlabeled and unstructured data of the
world spread across all sectors and countries. Neural networks now have the
ability to handle the latency and complex features of these subsets of data.
Deep learning involving artificial neural networks has
classified and characterized raw, unnamed data in the form of pictures, text,
audio, etc., into an organized relational database with appropriate labeling.
For example, deep learning takes thousands of raw
images as input and classifies them based on basic features and characters,
such as all animals such as dogs on one side, inanimate objects such as
furniture in one corner, and all pictures of a family. The third side completes
the whole photo, also known as a smart photo album.
As another example, consider the case of text data
with thousands of emails as input. Here, deep learning categorizes emails into
different categories based on their content: primary email, social email,
promotional email, and spam email.
Feed-forward neural networks: The goal of using neural
networks is to achieve the final result with minimal error and high level of
accuracy.
This procedure involves several steps, each level
involving prediction, error management, and weight update, which slowly moves
to the desired function, increasing the coefficients slightly.
At the starting point of a neural network, we don't
know which weights and subsets of data transform the input into the best
prediction. So, consider any kind of data and subset of weights as a model,
making predictions sequentially to get the best results, and learning from
mistakes each time.
For example, we can refer to neural networks as young
children know nothing about the world around them when they are born and have
no intelligence, but learn from their life experiences and mistakes to become
better humans and intellectuals as they age.
The architecture of the feed-forward network is
represented by the mathematical formula below.
Input * weight = prediction
then,
ground truth - prediction = error
then,
error * weight contribution to error = adjustment
It can be explained here, the input data set is mapped
with coefficients to get multiple predictions for the network.
Now the predictions are compared to the actual facts
taken from the real-time scenario, and the facts end the experience to find the
error rate. Adjustments are made to handle errors and correlate the
contribution of the weights.
These three functions are the three core components of
a neural network that score inputs, evaluate losses, and deploy upgrades to the
model.
So it is a feedback loop that compensates for the
coefficients that support a good prediction and discards the coefficients that
lead to errors.
Handwriting recognition, face and digital signature
recognition, and missing pattern identification are some of the real-time
examples of neural networks.
4) Cognitive Computing
The purpose of this component of artificial
intelligence is to initiate and accelerate interactions to complete complex
tasks and solve problems between humans and machines.
While performing a variety of tasks alongside humans,
machines learn and understand human behavior, emotions in a variety of unique
conditions, and reproduce human thought processes in computer models.
By practicing this, the machine acquires the ability
to understand human language and image reflexes. So, cognitive thinking, along
with artificial intelligence, could create products that could behave like
humans, and could even have data processing capabilities.
Cognitive computing can make accurate decisions for
complex problems. Therefore, it applies to areas where solutions need to be
improved at optimal cost, and is obtained by analyzing natural language and
evidence-based learning.
Google Assistant , for example, is a very large
example of cognitive computing.
5) Natural L anguage Processing
This capability of artificial intelligence allows
computers to interpret, identify, search for, and process human language and
voice.
The concept that introduced this component is to
facilitate the interaction between machine and human language and allow the
computer to provide logical responses to human voices or queries.
Natural language processing refers to active and
passive modes of using algorithms that focus on both the oral and written
sections of human language.
Natural language generation (NLG) processes and
decodes the sentences and words that humans use to speak (oral communication),
while natural language understanding (NLU) emphasizes written vocabulary to
translate the language of text or pixels. machine.
Graphical User Interfaces (GUI)-based applications on
computers are the best example of natural language processing.
Various types of translators that translate one
language into another are examples of natural language processing systems.
Google features in voice assistants and voice search engines are examples of
this.
6) Computer Vision
Computer vision is a very important part of artificial
intelligence as it allows computers to automatically recognize, analyze and
interpret visual data by capturing and intercepting real-world images and
visuals.
It integrates deep learning and pattern recognition
technologies to extract the content of images from given data, including image
or video files such as
PDF documents, Word documents, PPT documents, XL files,
graphs and pictures, etc.
I have a complex image of a bunch of things, and I
assume that just looking at the image and memorizing it is not easily possible
for everyone. Computer vision incorporates a series of transformations on an
image so that bit and byte details can be extracted, such as the sharp edges of
an object, the unusual design or color used, and so on.
This is done using various algorithms by applying
mathematical expressions and statistics. Robots use computer vision technology
to see the world and act in real-time situations.
The application of this component is very widely used
in the medical industry to analyze the health condition of patients using MRI
scans, X-rays, etc. It is also used in the automotive industry dealing with
computer-controlled vehicles and drones.
Conclusion
In this tutorial, I first diagrammed the various
elements of intelligence and their importance in applying it in real-world
situations to achieve the desired results.
We then explored in detail the various subfields of
artificial intelligence and their importance in machine intelligence and the
real world through mathematical representations, real-time applications, and
various examples.
We also learned in detail about the concepts of
machine learning, pattern recognition and neural networks in artificial
intelligence, which play a very important role in all applications of
artificial intelligence.
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