What is artificial intelligence Definition, top 10 types and examples.pdf

What is artificial intelligence?
Definition, top 10 types and
examples

What is artificial intelligence?
Although many definitions of artificial intelligence (AI) have emerged over the
past few decades, John McCarthy provided the following definition in this 2004
paper (link is located outside ibm.com): MASU. Especially intelligent computer
programs. It deals with the same task of using computers to understand
human intelligence, but AI does not need to be limited to biologically
observable methods.
Definition of artificial intelligence
Artificial intelligence is the imitation of human intelligence processes by
machines, especially computer systems. Typical applications of AI include
expert systems, natural language processing, speech recognition, and
machine vision.

How does artificial intelligence (AI)
work?
As the hype around AI grows, vendors are making efforts to promote how AI is
used in their products and services. Often, what they call AI is just a
component of technologies like machine learning. AI requires specialized
hardware and software infrastructure to write and train machine learning
algorithms. Although no single programming language is synonymous with AI,
Python, R, Java, C++, and Julia have features that are popular among AI
developers.
Generally, AI systems work by ingesting large amounts of labeled training
data, analyzing correlations and patterns in the data, and using these patterns
to predict future situations. This way, given examples of text, chatbots can
learn to generate authentic-like conversations with people. Image recognition
tools can also learn to recognize and describe objects in images by
considering millions of examples. New and rapidly advancing generic AI
technology allows you to create realistic text, images, music, and other media.
Artificial intelligence programming focuses on
cognitive skills such as:
• Learn: This aspect of AI programming focuses on taking data and creating
rules to turn it into actionable information. Rules, called algorithms, provide
step-by-step instructions for computing devices to accomplish a particular
task.
• Logic. This aspect of AI programming focuses on selecting the appropriate
algorithm to achieve the desired result.

• Self-correction: This aspect of AI programming is designed to continuously
improve the algorithms and provide the most accurate results possible.
• Creativity. This aspect of AI uses neural networks, rule-based systems,
statistical methods, and other AI techniques to generate new images, new
text, new music, and new ideas.
Differences between AI, machine learning and
deep learning
AI, machine learning, and deep learning are common terms in enterprise IT,
especially when companies use them interchangeably in marketing materials.
But there are differences too. The term AI was coined in the 1950s and refers
to the emulation of human intelligence by machines. A constantly changing set
of capabilities is incorporated as new technologies are developed.
Technologies falling under the umbrella of AI include machine learning and
deep learning.
Machine learning allows software applications to more accurately predict
outcomes without having to be explicitly programmed. Machine learning
algorithms use historical data as input to predict new output values. This
approach has become very effective as the number of large datasets on which
it can be trained increases. Deep learning, a subset of machine learning, is
based on an understanding of how the brain is structured. The use of artificial
neural network structures through deep learning powers recent advances in
AI, such as self-driving cars and chat GPTs.
Why is artificial intelligence so
important?

AI is important because it has the potential to change the way we live, work,
and play. It is used effectively by businesses to automate tasks performed by
humans, such as customer service tasks, lead generation, fraud detection,
and quality control. In many areas, AI can work much better than humans.
Especially when it comes to repetitive, detail-oriented tasks, such as analyzing
large amounts of legal documents to ensure that relevant fields are filled out
properly, AI tools are often more than enough to get the job done. are faster
and with fewer errors. Because AI can process large data sets, it can also give
businesses insights into their operations that they were unaware of. The
rapidly expanding audience for generic AI tools will become important in areas
ranging from education and marketing to product design.
In fact, advances in AI technology have not only contributed to an explosion in
efficiency but have also opened the door to entirely new business
opportunities for some large companies. Before the current wave of AI, it was
hard to imagine using computer software to connect passengers with taxis.
But Uber became a Fortune 500 company by doing so.
AI is at the heart of today’s largest and most successful companies, including
Alphabet, Apple, Microsoft, and Meta, which use AI technology to improve
their operations and outperform their competitors. At Alphabet Inc.’s Google,
for example, AI is at the heart of Google Brain, which invented the company’s
search engine, Waymo self-driving cars, and electrical transformers.
What are the advantages and
disadvantages of artificial
intelligence?
Artificial neural networks and deep learning AI technologies are developing
rapidly. The main reason for this is that AI can process large amounts of data
much faster and make more accurate predictions than humans.

While human researchers are overwhelmed by the vast amounts of data
created every day, AI applications using machine learning can take that data
and instantly transform it into actionable information. At the time of this writing,
the main drawback of AI is the high cost of processing the large amounts of
data required for AI programming. As AI technology is incorporated into more
products and services, organizations must also pay attention to the potential
of AI to create biased and discriminatory systems, intentionally or
unintentionally.
Advantages of artificial intelligence
Some of the benefits of artificial intelligence include:
• Good at detailed work. AI has proven to be as good or better than doctors
at diagnosing some cancers, such as breast cancer and melanoma.
• Spend less time on data-intensive tasks. AI is widely used in
data-intensive industries such as banking, securities, pharmaceuticals, and
insurance to reduce the time taken to analyze large data sets. For example,
financial services routinely use AI to process loan applications and detect
fraud.
• Labor savings and increased productivity. An example here is the use of
warehouse automation. This has increased during the pandemic and is
expected to increase with the integration of AI and machine learning.
• Provides consistent results. The best AI translation tools provide a high
level of consistency and give small businesses the ability to reach customers
in their native language.
• Increase customer satisfaction through personalization. AI can
personalize content, messages, advertisements, recommendations, and
websites for individual customers.

AI-powered virtual agents are always available. AI programs do not need to
sleep or take breaks, and they provide 24/7 service.
Disadvantages of artificial intelligence
The disadvantages of artificial intelligence
include:
• expensive.
requires deep technical expertise.
• There is a limited supply of qualified workers to build AI tools.
• Reflect training data bias at scale.
• Lack of ability to generalize from one task to another.
• End human employment and increase unemployment.
Strong AI vs. weak AI
AI can be classified as weak or strong.

• Weak AI (also known as narrow AI) is designed and trained to perform
specific tasks. Industrial robots and virtual personal assistants, like Apple’s
Siri, use weak AI.
• Strong AI, also known as artificial general intelligence (AGI), refers to
programming that can replicate the cognitive abilities of the human brain.
Given an unfamiliar task, powerful AI systems can use fuzzy logic to apply
knowledge from one domain to another and autonomously find a solution. In
theory, a powerful AI program should be able to pass both the Turing Test and
the Chinese Room Argument.
What are the four types of artificial
intelligence?
Erlend Hintz, assistant professor of integrative biology and computer science
and engineering at Michigan State University, said AI can be classified into
four types. AI begins with task-specific intelligent systems widely used today
and evolves into perceptual systems. Still exists. The categories are as
follows:.
• Type 1: Reactive Machine. These AI systems have no memory and are
task-specific. An example is Deep Blue, IBM’s chess program that defeated
Garry Kasparov in the 1990s. Deep Blue can identify and predict pieces on a
chessboard, but because he has no memory, he cannot transfer past
experiences to future experiences.
• Type 2: Limited memory. These AI systems have memories, so they can use
past experience to make future decisions. Some of the decision-making
capabilities of self-driving cars are designed this way.
• Type 3: Theory of Mind. The theory of mind is a psychological term. When
applied to AI, it gives systems the social intelligence to understand emotions.

This type of AI would be able to infer human intentions and predict behavior.
This is a skill that AI systems need to become essential members of human
teams.
• Type 4: self-aware. In this category, AI systems are self-aware, which gives
them consciousness. A self-aware machine understands its current state. This
type of AI does not exist yet.
What are examples of artificial
intelligence technology, and how is it
used today?
AI is embedded in many different types of
technology. Here are seven examples.
Automation. Automation tools can be combined with AI technology to measure
the number and type of tasks performed. One example is robotic process
automation (RPA), a type of software that automates repetitive, rule-based
data processing tasks traditionally performed by humans. Combining RPA with
machine learning and new AI tools can automate large parts of a company’s
operations, allowing RPA’s strategic bots to draw intelligence from AI and
respond to process changes.
machine learning. It is the science of running a computer without
programming. Deep learning is a subset of machine learning and, in very
simple terms, can be thought of as automated predictive analysis. There are
three types of machine learning algorithms.
• supervised learning. Data sets are labeled so that patterns can be detected
and used to label new data sets.

• Unsupervised learning. The dataset is unlabeled and sorted according to
similarity or difference.
• Reinforcement learning. Although the dataset is not labeled, it provides
feedback to the AI system after performing one or more actions.
Machine vision. This technology gives machines the ability to see. Machine
vision uses cameras, analog-to-digital conversion, and digital signal
processing to capture and analyze visual information. Although often
compared to human vision, machine vision is not tied to biology and can, for
example, be programmed to see through walls. It is used in a wide variety of
applications, from signature recognition to medical image analysis. Computer
vision focuses on machine-based image processing and is often confused
with machine vision.
Natural Language Processing (NLP). It is the processing of human
language by a computer program. The oldest and best-known example of NLP
is spam detection, which examines the subject and body of an email to
determine whether it is spam. Current approaches to NLP are based on
machine learning. NLP tasks include text translation, sentiment analysis, and
speech recognition.
Robotics. This engineering field focuses on the design and manufacturing of
robots. Robots are often used to perform tasks that are difficult for humans to
perform or that are difficult to perform consistently. For example, robots are
used on car manufacturing assembly lines and by NASA to move large
objects in space. Researchers are also using machine learning to create
robots that can interact in social environments.
Self-driving car. Self-driving cars use a combination of computer vision,
image recognition, and deep learning to create automated skills to steer the
vehicle while staying in a specific lane and avoiding unexpected obstacles
such as pedestrians. Meat.

Creation of text, images, and audio. Generative AI techniques that create
different types of media from text prompts are being widely used across
enterprises to create unlimited types of content, from photorealistic art to email
responses and screenplays. Was implemented.
What are the applications of artificial
intelligence?
Artificial intelligence is making inroads into
various markets. Here are 11 examples.
AI in health care The biggest stakes are to improve patient outcomes and
reduce costs. Companies are using machine learning to make better and
faster medical diagnoses than humans. One of the most famous health care
technologies is IBM Watson. Understand natural language and be able to
answer natural language questions. The system mines patient data and other
available data sources to generate hypotheses and present them using a
confidence scoring scheme. Other AI applications include using online virtual
medical assistants and chatbots to help patients and health care customers
find medical information, schedule appointments, understand billing
processes, and complete other administrative processes. These include: A
variety of AI technologies are also being used to predict, fight, and understand
pandemics such as the coronavirus disease (COVID-19).
AI in business Machine learning algorithms are integrated into analytics and
customer relationship management (CRM) platforms to gain insights on how
to better serve customers. Chatbots are integrated into websites to provide
immediate service to customers. The rapid advancement of generic AI
technologies like Chat GPT is expected to have far-reaching effects, including
job cuts, changes in product design, and disrupting business models.

AI in education AI automates grading, freeing up teachers to spend more
time on other tasks. Assess your students, adapt to their needs, and help
them learn at their own pace. AI tutors can provide extra support to students
and keep them on track. This technology could also change where and how
students learn and perhaps even replace some teachers. As demonstrated by
Chat GPIT, Google Bard, and other large-scale language models, generative
AI can help teachers create curriculum and other content and engage
students in new ways. The advent of these tools also requires teachers to
rethink student assignments and tests and modify plagiarism policies.
AI in finance. AI in personal finance applications like Intuit Mint and TurboTax
is disrupting financial institutions. Such applications collect personal data and
provide financial advice. Other programs, such as IBM Watson, have been
applied to the home-buying process. Today, artificial intelligence software
drives most of the trading on Wall Street.
AI in law The legal discovery process, or reviewing documents, is often a
difficult task for humans. Using AI to automate labor-intensive processes in the
legal industry can save time and improve customer service. Law firms use
machine learning to describe data and predict outcomes, use computer vision
to classify and extract information from documents, and use NLP to interpret
requests for information. Are.
AI in entertainment and media Entertainment businesses use AI
technology for targeted advertising, content recommendations and delivery,
fraud detection, screenwriting, and film production. Automated journalism
helps newsrooms streamline media workflows while reducing time, costs, and
complexity. Newsrooms use AI to automate routine tasks like data entry and
proofreading. Research topics and help with titles. Questions remain about
how journalism can reliably use Chat GPT and other generative AI to generate
content.
AI in software coding and IT processes. New generative AI tools can
generate application code based on natural language signals, but these tools
are still in their infancy and are unlikely to replace software engineers in the

near future. AI is also being used to automate many IT processes, including
data entry, fraud detection, customer service, predictiveness, and security.
Security. Security vendors rank AI and machine learning high on the list of
buzzwords they use to market their products, so buyers should approach them
with caution. Nevertheless, AI techniques have been successfully applied to
various aspects of cybersecurity, such as detecting anomalies, solving false
positive problems, and analyzing behavioral threats. Organizations use
machine learning in security information and event management (SIEM)
software and related areas to detect anomalies and identify suspicious activity
that signals a threat. By analyzing data and using logic to identify similarities
to known malicious code, AI can counter new attacks much faster than human
workers or previous iterations of the technology.
AI in banking. Banks have successfully deployed chatbots to inform
customers about services and offers and process transactions without human
intervention. AI virtual assistants are used to improve and reduce the cost of
banking regulatory compliance. Banking organizations are using AI to improve
lending decisions, set loan limits, and identify investment opportunities.
AI in transportation sector Manufacturing has been at the forefront of
incorporating robots into the workflow. In addition to its fundamental role in the
operation of autonomous vehicles, AI technology is also being used in the
transportation sector to manage traffic, predict flight delays, and improve the
safety and efficiency of maritime transportation. In the supply chain, AI is
replacing traditional methods of forecasting demand and anticipating
disruption. This trend was accelerated by COVID-19, when many companies
became distressed by the impact of the global pandemic on the supply and
demand of goods.
How does artificial intelligence (AI)
work?

But decades before this definition, the conversation about artificial intelligence
began with Alan Turing’s seminal book Computing Machinery and Intelligence,
published in 1950 (link is located outside ibm.com). It was shown. In this
paper, Turing often says: Known as the “Father of Computer Science, he asks
the question, “Can machines think?” From there, he proposed a test now
famously known as the “Turing Test.”. In this test, a human interrogator
attempts to distinguish between computer and human text responses.
Although this test has received much scrutiny since its release, it is still an
important part of the history of AI as well as an ongoing concept within
philosophy, as it leverages ideas about linguistics. .
Stuart Russell and Peter Nerving then published Artificial Intelligence: A
Modern Approach (link off ibm.com), which became one of the leading
textbooks in AI research. In it, they highlight four possible goals or definitions
of AI that differentiate computer systems based on rationality, thinking, and
action.
Human approach:
● Systems that think like humans
● Systems that act like humans
Ideal approach:
● Systems that think rationally
● Systems that act rationally
Alan Turing’s definition would fall into the category of “systems that
behave like humans.”.
In its simplest form, artificial intelligence is a field that combines computer
science with robust datasets to enable problem-solving. It also includes the
subfields of machine learning and deep learning, which are often mentioned

alongside artificial intelligence. These fields include AI algorithms that aim to
create expert systems that make predictions and classifications based on
input data.
Over the past few years, artificial intelligence has gone through several cycles
of hype, but even for skeptics, OpenAI’s release of ChatGPT seems to be a
turning point. The last time generative AI became this big, the breakthroughs
were in computer vision, and now we’re seeing breakthroughs in natural
language processing. And it’s not just language. Generative models can also
learn grammars for software code, molecules, natural images, and many other
types of data.
The applications of this technology are growing every day, and we are just
beginning to explore its potential. But as the hype around the use of AI in
business grows, the conversation around ethics becomes increasingly
important. Learn more about IBM’s position in the AI ethics conversation.
Types of artificial intelligence:
Weak AI vs. strong AI
Weak AI (also known as narrow AI or narrow artificial intelligence (ANI)) is AI
that is trained and focused on performing a specific task. Most of the AI
around us today is powered by weak AI. This type of AI is far from weak, so
“narrow” might be a more accurate description. This enables extremely
powerful applications such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and
self-driving cars.
Strong AI includes artificial general intelligence (AGI) and artificial
superintelligence (ASI). Artificial general intelligence (AGI), or general AI, is a
theoretical form of AI in which machines have intelligence comparable to that
of humans. It will have a self-aware consciousness with the ability to solve
problems, learn, and plan for the future. Artificial superintelligence (ASI), also

known as superintelligence, will exceed the intelligence and capabilities of the
human brain. Although powerful AI is still entirely theoretical and currently has
no practical examples of its use, this does not mean that AI researchers are
not considering developing it. On the other hand, the best example of an ASI
may be in science fiction works such as HAL, the extraterrestrial and evil
computer assistant from 2001: A Space Odyssey.
Comparison of Deep Learning and
Machine Learning
The terms deep learning and machine learning are used interchangeably, so it
is worth paying attention to the nuances between the two. As mentioned
earlier, deep learning and machine learning are both subfields of artificial
intelligence, and deep learning is actually a subfield of machine learning.
Deep learning actually involves neural networks. “Depth” in deep learning
refers to a neural network that has three or more layers with inputs and
outputs and can be considered a deep learning algorithm. This is usually
represented using the following diagram:.
The difference between deep learning and machine learning lies in the
learning method of each algorithm. Deep learning automates much of the
feature extraction part of the process, eliminating some of the necessary
manual intervention and allowing the use of larger datasets. As Lex Friedman
said above in his MIT lecture, deep learning can be thought of as “scalable
machine learning.”. Classical, or “shallow,” machine learning relies heavily on
human intervention to learn. Human experts determine a hierarchy of features
to understand the differences between data inputs. Training usually requires
more structured data.
“Deep” machine learning can take advantage of labeled datasets to inform
algorithms, also known as supervised learning, but does not necessarily
require labeled datasets. It can encapsulate unstructured data in its raw form

(text, images, etc.) and automatically determine a hierarchy of features that
distinguish different categories of data from each other. Unlike machine
learning, processing data requires no human intervention, which allows you to
extend machine learning in more interesting ways.
The rise of generative models
Generative AI refers to deep learning models that can take raw data (for
example, the entire Wikipedia or a collection of Rembrandts) and “learn” to
generate statistically probable outputs in response to prompts. Broadly
speaking, generative models entail simplification.
Represent training data and extract quotes from it to create similar new tasks.
However, it is not the same as the original data.
Generative models have been used in statistics for many years to analyze
numerical data. However, the rise of deep learning has made it possible to
extend deep learning to images, audio, and other complex data types. The

first class of models to accomplish this crossover feat was the Variable
Autoencoder (VAE), introduced in 2013. VAE was the first deep learning
model widely used to generate realistic images and sounds.
“VAE opens the door to deeper generative modeling by making it easier to
create models.
Akash Srivastava, a generic AI expert at the MIT-IBM Watson AI Lab.
“A lot of what we think of as generic AI today started here.”
Early examples from models like GPT-3, BERT, and DALL-E 2 show what is
possible. The future will have models trained on a wide range of unlabeled
datasets that can be used for a variety of tasks with minimal fine-tuning.
Systems that perform specific tasks in a single domain are being replaced by
pervasive AI that learns more generally and works across domains and
problems. Fundamental models trained on large, unlabeled datasets and
fine-tuned for different applications are driving this change.
When it comes to generic AI, the underlying models are predicted to change
dramatically.
Accelerate AI adoption in your enterprise. Reducing labeling requirements
would lead to significant improvements
The accuracy and efficiency of AI-powered automation enabled by AI will
enable far more companies to deploy AI in a wider range of mission-critical
situations. For IBM, the hope is that the power of the foundational model will
eventually be available to all businesses in a frictionless hybrid cloud
environment.
Artificial intelligence applications

Many real-world applications of AI systems currently exist. Below are some of
the most common use cases.
• Speech recognition: Also known as automatic speech recognition (ASR),
computer speech recognition, or speech-to-text, is the ability to process
human speech in written form using natural language processing (NLP). .
Many mobile devices have voice recognition built into their systems to perform
voice searches. Siri—or improve accessibility when it comes to texting.
• Customer Service: Online virtual agents are replacing human agents in the
customer journey. These include customer engagement on websites and
social media platforms by answering frequently asked questions (FAQs) on
topics such as shipping and providing personalized advice, cross-selling
products, and size suggestions to users. Change the way you think Examples
include messaging bots on e-commerce sites that use virtual agents,
messaging apps like Slack and Facebook Messenger, and tasks typically
performed by virtual or voice assistants.
• Computer vision: This AI technology allows computers and systems to derive
meaningful information from digital images, videos, and other visual inputs
and take actions based on those inputs. This ability to provide
recommendations differentiates it from image recognition tasks. Computer
vision powered by convolutional neural networks is used in photo tagging in
social media, radiology imaging in medicine, self-driving cars in the
automotive industry, and much more.
• Recommendation Engine: AI algorithms use historical consumer behavior
data to help discover data trends that can be used to develop more effective
cross-selling strategies. It is used to recommend relevant add-ons to
customers during the checkout process at an online retailer.
• Automated stock trading: AI-powered high-frequency trading platforms
designed to optimize stock portfolios execute thousands or even millions of
trades per day without human intervention.

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many courses are in AI?
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History of artificial intelligence: Key
dates and names
The idea of the “thinking machine” dates back to ancient Greece. However,
since the advent of electronic computing (and related to some of the topics
discussed in this article), there have been some significant events and
milestones in the development of artificial intelligence, including:
• 1950: Alan Turing published Computing Machinery and Intelligence. Turing,
famous for breaking the Nazi Enigma code during World War II, proposed in
his paper to answer the question, “Can machines think?” It presents the Turing
test to determine whether computers can demonstrate intelligence similar to
humans (or results similar to intelligence). The significance of the Turing test
has been debated ever since.

• 1967: Frank Rosenblatt created the Mark 1 Perceptron, the first computer
based on neural networks that “learned” through trial and error. Just a year
later, Marvin Minsky and Seymour Papert published a book called “The
Perceptron.”. This book is a groundbreaking work on neural networks and, at
least for the time being, sets the tone for future neural network research
projects.
• 1980s: Neural networks that train themselves using backpropagation
algorithms begin to be widely used in AI applications.
• 1997: IBM’s Deep Blue defeated reigning world chess champion Garry
Kasparov in a chess match (and rematch).
• 2011: IBM Watson defeated defending champions Ken Jennings and Brad
Rutter in Jeopardy.
• 2015: Baidu’s Minwa supercomputer uses a special type of deep neural
network called a convolutional neural network to recognize and classify
images with greater accuracy than the average human.
• 2016: DeepMind’s AlphaGo program, powered by deep neural networks,
defeated world Go champion Lee Sodol in five games. This win is significant
given the large number of moves to be made as the game progresses (over
14.5 trillion in just 4 moves!). After this, Google reportedly acquired DeepMind
for US$400 million.
• 2023: With the rise of large-scale language models like Chat GPT, or LLM;
Significant changes in AI performance and its potential to drive enterprise
value.

These new generative AI practices allow you to pre-train deep learning
models.
Large amounts of unlabeled raw data.
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