https://bugs.documentfoundation.org/show_bug.cgi?id=152269
Bug ID: 152269
Summary: What is AI
Product: Document Liberation Project
Version: unspecified
Hardware: All
OS: All
Status: UNCONFIRMED
Severity: normal
Priority: medium
Component: libcdr
Assignee: libreoffice-bugs@lists.freedesktop.org
Reporter: koushik12...@gmail.com
The mimicking of human thought processes by machines, particularly computer
systems, is known as artificial intelligence. Expert systems, natural language
processing, speech recognition, and computer vision are examples of AI
applications.
How Does AI Work?
In general, AI systems operate by consuming huge volumes of labeled training
data, evaluating the information for patterns and structures, and then using
these patterns to forecast future states. By examining millions of examples, a
chatbot that provided case studies of text chats can figure out how to make
lifelike dialogues with people, while an image recognition program can learn to
recognize and describe items in photographs.
Learning, Reasoning, and Self-correction are the three cognitive functions that
AI programming focuses on:
Learning Process: This component of AI programming is concerned with gathering
data and developing rules for converting the data into usable information. The
rules, known as algorithms, teach computing equipment how to execute a certain
task in a step-by-step manner.
Reasoning Process: This part of AI programming emphasizes selecting the best
algorithm to achieve a given result.
Self-correction Process: This element of AI programming is intended to
constantly fine-tune algorithms to produce the most reliable data feasible.
Why AI is a Need of the Hour?
AI is essential because this will provide organizations with previously unknown
insights into their business and, in some situations, can do tasks better than
humans. AI systems generally accomplish operations quickly and with minimal
errors, especially when it comes to repeated, detail-oriented activities like
evaluating vast quantities of legal papers to verify key fields are filled in
correctly.
This has contributed to an increase in efficiency and opened up totally new
business options for certain larger firms. Before the current wave of AI, it
would have been difficult to conceive of employing computer software to connect
riders to cabs, but Uber has grown to become one of the world's largest firms
by doing precisely that. It employs powerful machine learning algorithms to
estimate when people are likely to require trips in specific places, allowing
drivers to be on the road ahead of time. Today's biggest and most successful
businesses have utilized AI to better their operations and acquire a
competitive advantage.
Popular Algorithms Used in Artificial Intelligence
Classification Algorithms
Naïve Bayes
Decision Tree
Random Forest
Support Vector Machines
K Nearest Neighbors
Regression Algorithms
Linear Regression
Lasso Regression
Logistic Regression
Multivariate Regression
Multiple Regression
Clustering Algorithms
K-Means Clustering
Fuzzy C-means
Expectation-Maximization (EM)
Hierarchical Clustering
Classification Algorithm
Supervised learning includes classification algorithms. These algorithms are
used to categorize the subjected variable and then forecast the class for a
given input. Classification algorithms, for example, can be used to determine
whether an email is a spam or not. Artificial intelligence identifies a new
category of observations in classification algorithms based on previous data,
which we may also refer to as training data. The application learns from the
previously provided dataset. Let's look at some of the most popular
classification algorithms.
Naïve Bayes
Unlike other classification algorithms, the Naive Bayes algorithm is based on
the Bayes theorem and uses a probabilistic approach. For each class, the
algorithm has a set of prior probabilities. When data is supplied into the
process, the probabilities are updated to generate what is known as posterior
probability. This is useful when you need to forecast whether or not an input
corresponds to a specific set of classes.
Decision Tree
The decision tree algorithm seems more like a flowchart-like process, with
nodes representing tests on input attributes and branches representing test
results. It is a very simple type of probabilistic tree that allows you to make
decisions about a process. This tool is based on a tree-like concept and its
probable outcomes.
Random Forest
Random forest functions similarly to a grove of trees. The source data set is
partitioned and fed into various decision trees. The average of all decision
tree outputs is taken into account. When compared to the Decision tree
technique, Random Forests provide a more accurate classifier. Random forests
are employed in a variety of industries, including healthcare, manufacturing,
banking, and retail. One of the practical uses of random forest is determining
whether or not an email is a spam.
Support Vector Machines
SVM is a data classification algorithm that uses a hyperplane to ensure that
the gap between the support vectors and the support vectors is as little as
possible. It is a supervised learning technique that can be applied to
classification or regression issues. Face detection, image classification,
handwriting detection, text and hypertext categorization, and other
applications of SVM are examples.
K-Nearest Neighbor
To forecast the class of a new sample data point, the KNN algorithm employs a
large number of data points that have been classified. It is referred to as a
"lazy learning algorithm" since it is relatively short in comparison to other
algorithms. KNN has applications in finance and medicine, such as bank customer
profiles and credit ratings, among others. KNN has several advantages,
including its ease of implementation and comprehension, as well as its
simplicity and intuitiveness.
Regression Algorithms
Regression algorithms are a common type of supervised machine learning method.
Based on the input data points supplied into the learning system, regression
algorithms can predict the output values. Regression algorithms' principal
applications include predicting stock market prices, predicting the weather,
and so on. The regression algorithms also aid in predicting the output values
based on the data input attributes. There are several types of regression,
including linear regression, polynomial regression, and so on. The most
commonly used algorithms in this section include
Linear Regression
It is utilized to assess genuine qualities by taking into account consistent
variables. It is the most basic of all regression techniques, however, it can
only be used in circumstances where there is a linear relationship or a
linearly separable problem. The method predicts new values by drawing a
straight line between data points known as the best-fit line or regression
line. One common application of linear regression is in medical practice, where
practitioners recognize the association between sugar intake and high blood
sugar levels.
Lasso Regression
The Lasso regression technique finds the subset of predictors that minimizes
prediction error for a given response variable. This is accomplished by
constraining data points and allowing some of them to shrink to zero value. The
lasso regression method is used to identify a group of predictors that aid in
minimizing prediction error. Lasso constrains the model parameters, causing the
regression coefficients to shrink to zero.
Logistic Regression
Logistic regression is mostly employed in binary classification. You can use
this strategy to assess a group of variables and predict a category outcome.
Its main uses include forecasting customer lifetime value, housing values, and
so on. Banking is one of the many real-world applications of logistic
regression. A credit card company can determine whether or not the transaction
amount and credit score will result in a fraudulent transaction.
Multivariate Regression
When there are multiple predictor variables, this approach must be utilized.
This algorithm is widely used in retail sector product recommendation engines,
where customers' chosen products are determined by a variety of characteristics
such as brand, quality, price, review, and so on. The multivariate regression
method aids in the discovery of relationships between numerous variables. Also,
discover the relationship between independent and dependent variables.
Multiple Regression Algorithm
Several Regression Algorithm employs a hybrid of linear and non-linear
regression techniques, with multiple independent variables as inputs. The most
common uses are social science study, insurance claim authenticity, behavioral
analysis, and so on.
Clustering Algorithms
Clustering is the method of categorizing and organizing data items into groups
that are similar among group members. This is an example of unsupervised
learning. The basic goal is to group comparable products. For instance, it can
group all fraudulent transactions based on specific attributes of the
transaction.
K-Means Clustering
It is the most basic unsupervised learning algorithm. The method groups
comparable data points into clusters. Clustering is accomplished by first
determining the centroid of the collection of data points and then computing
the distance of each data point from the centroid of the cluster. The examined
data point is subsequently given to the closest cluster based on its distance.
The letter 'K' in K-means indicates the number of clusters into which the data
points are divided.
Fuzzy C-Means
The FCM algorithm is based on probability. Each data point is thought to have a
chance of belonging to another cluster. Because data points do not have
absolute membership in a single cluster, the technique is referred to be fuzzy.
Fuzzy C- Means is a clustering approach in which the data set is divided into N
clusters, with each data point belonging to one of the groups in some way.
Expectation-Maximization
It is based on the Gaussian distribution, which we learned about in statistics.
To answer the challenge, data is represented as a Gaussian distribution model.
Following the assignment of a probability, a point sample is calculated using
expectation and maximization formulae. The Expectation-Maximization (EM)
approach is employed when it is necessary to discover the local maximum
likelihood parameters of a statistical model. It is also utilized for solving
equations that cannot be solved directly.
Hierarchical Clustering
After learning the data points and generating similarity observations, these
algorithms arrange clusters in hierarchical order. It can be of two kinds.
For a top-down method, use divisive clustering.
Bottom-up clustering via agglomerative clustering
Strong AI vs Weak AI
AI is classified as either weak or strong.
Weak AI, also known as narrow AI, is an artificial intelligence system that is
created and taught to do a specific task. Weak AI is used by industrial robots
and virtual personal assistants such as Apple's Siri.
Strong AI, often known as artificial general intelligence (AGI), refers to
programming that can mimic the cognitive capacities of the human brain. When
faced with an unexpected issue, a powerful AI system can employ fuzzy logic to
apply information from one domain to another and find a solution on its own. A
strong AI program should, in theory, be able to pass both the Turing Test and
the Chinese room test.
Types of Artificial Intelligence
AI is divided into four categories, beginning with task-specific intelligent
systems that are widely used today and progressing to sentiment systems that do
not yet exist. The following are the categories:
Type 1: Reactive Machines - These AI systems have no memory and are only used
for specialized tasks. Deep Blue, the IBM chess software that defeated Garry
Kasparov in the 1990s, is one example. Deep Blue can identify pieces on the
chessboard and make predictions, but it cannot use past experiences to
influence future ones since it lacks memory.
Type 2: Limited Memory – Because these AI systems have memories, they can use
prior experiences to make better decisions in the future. This is how some of
the decision-making functions in self-driving automobiles are created.
Type 3: Theory of Mind - The theory of mind is a psychological concept. When
applied to AI, this indicates that the machine has the social intelligence to
comprehend emotions. This sort of AI will be able to predict human behavior and
infer human intents, which is an essential talent for AI systems to become
integral members of human teams.
Type 4: Self-Awareness - AI systems in this category have a feeling of self,
which provides them with awareness. Machines with self-awareness are aware of
their current state. This form of artificial intelligence does not yet emerge.
Applications of AI
In today's culture, artificial intelligence is applied in a variety of ways. It
is becoming increasingly significant in today's society because of its ability
to tackle complex problems in a range of fields such as healthcare,
entertainment, banking, and education. As a result of artificial intelligence,
our daily lives are getting more comfortable and efficient.
Healthcare
To save human lives, many corporations and healthcare institutions are moving
to artificial intelligence (AI). There are countless examples of how artificial
intelligence has aided patients all across the world. Let's look at some of the
applications of AI in healthcare areas.
- Administration
- Assisted Diagnosis
- Robotic Surgery
- Health Monitoring
E-Commerce
AI is providing a competitive advantage to the e-commerce industry, and it is
growing increasingly popular in the market. AI can help shoppers identify
related products in their selected size, color, or brand. Let's look at some AI
applications in e-commerce.
Personalized Shopping
AI-Powered Assistance
Fraud Detection and Prevention
Robotics
The field of robotics was progressing even before AI has become a reality.
Currently, artificial intelligence is supporting robotics in producing more
efficient robots. AI-enabled robots have found applications in a wide range of
verticals and sectors, especially in manufacturing and packaging. AI, or
artificial intelligence, offers computer vision to robots, allowing them to
navigate, sense, and react correctly.
Manufacturing
Transport
Surgery
Space Exploration
Finance
In finance, artificial intelligence is transforming the way we interact with
money. AI is supporting the financial industry in streamlining and optimizing
procedures ranging from credit determinations to quantitative trading and
financial risk management. Artificial intelligence in finance provides features
such as risk assessment, fraud detection and management, financial advisory
services, and automated trading.
Personal Finance
Consumer Finance
Corporate Finance
Facial Recognition
Facial Recognition is a sort of technology that maps and saves a person's
facial characteristics as a face print. To authenticate identification, the
software uses deep learning techniques to match a current image to a stored
facial print. Image processing and machine learning are the foundations of this
technology. The next time you log in without typing a password, your phone will
unlock itself using only your imagination. This is because when you take a
selfie and register it for facial recognition, your phone learns a face
recognition algorithm.
Marketing
Applications of artificial intelligence (AI) are frequently employed in online
retail marketing. Artificial intelligence (AI) marketing generates automated
decisions based on information gathering, analysis, and additional observations
of audience or economic patterns that may affect marketing efforts. In
marketing initiatives when speed is essential, AI is routinely used.
Social Media
Social networking companies utilize artificial intelligence to sift through
vast volumes of data to identify patterns, hashtags, and trends. The
understanding of user behavior is aided by this research. Let's examine several
well-known social media platforms' or applications' use of artificial
intelligence:
Instagram: When choosing which posts to display in your Explore tab on
Instagram, AI takes into consideration your preferences and the accounts you
follow.
Facebook(meta): DeepText is a method that is used in conjunction with
artificial intelligence. Using this technology, Facebook can analyze
conversations more accurately. It can be used to translate posts across
languages automatically.
Twitter: Twitter employs AI for the elimination of hateful content, propaganda,
and fraud identification. Depending on the kinds of tweets users interact with,
Twitter also utilizes AI to make suggestions to users.
Conclusion
In conclusion, artificial intelligence plays a big role in people's lives. The
development of artificial intelligence at the beginning of the twenty-first
century greatly increased the use of technology in a range of disciplines.
Sharpen your abilities with in-demand AI technologies with Softlogic Systems'
AI Training in Chennai and IBM Certification.
URL : https://www.softlogicsys.in/artificial-intelligence-training-in-chennai/
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