Verisavo- Introduction to Artificial Intelligence and Machine Learning

www.verisavo.com Introduction to Artificial Intelligence 2
Artificial Intelligence AI
AI stands for Artificial Intelligence, which refers to the
simulation of human intelligence in machines that are
designed to think and act like humans. AI technologies
include machine learning, natural language processing,
robotics, and more, which enable computers to perform
tasks that would typically require human intelligence, such
as recognizing images, understanding spoken language,
and making decisions.
AI is used in a wide range of applications and industries.
Some of the common areas where AI is applied include:
Healthcare, Finance, Retail, Manufacturing,
Transportation, Customer Service, Security. These are just
a few examples of the many industries where AI is making
a significant impact. AI has the potential to revolutionize
the way we live, work, and interact with each other.
ASI
ANI
AGI

3
01
02
03
04
08
09
10
11
05 13
06 14
07 15
12
16 20
17 19
18
Unsupervised
learning
Reinforcement
learning
Supervised
learning
Dimensionally
reduction
Clustering Regression
Classification
Machine
Learning
Feature Elicitation
Structure Discovery
Meaningful compression
Big data Visualisation
Recommended Systems
Targeted Marketing
Customer Segmentation
01.
02.
03.
04.
05.
06.
07.
Fraud Detection
Image Classification
Customer Retention
Diagnostics
Forecasting
Predictions
Process Optimization
New Insights
Real-Time Decisions
Gane AI
Learning Tasks
Skill Acquisition
Robot Navigation
08.
09.
10.
11.
12.
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www.verisavo.com Introduction to Artificial Intelligence

4
Today About 2040 Soon after AGI
ASI Artificial
Superintelligence
AGI General or
Strong AI
ANI Narrow or
Weak AI
Execute specific focused tasks,
without ability to self-expand
functionality.
Outperform humans in specific
repetitive functions, such as
driving, medical diagnosis and
financial advice.
Jobs Enhanced
Perform broad tasks, reason,
and improve capabilities
comparable to humans.
Compete with humans across all
endeavors, such as earning
university degrees and convincing
humans that it is human.
Job at Risk
Demonstrate intelligence
beyond human capabilities.
Superior to humans, helping to
achieve societal goals or
threatening the human race.
Humanity at Risk
Implications
Feature description
AI Stages
3 Type of Artificial Intelligence levels

5
Natural Language Processing (NLP):
The ability of a machine to understand,
interpret, and generate human
language.
Machine Learning: The ability of a
machine to learn from data and
improve its performance over time
without being explicitly programmed.
Computer Vision: The ability of a
machine to understand and interpret
visual information, such as images and
videos.
Robotics: The use of robots and other
physical devices to automate tasks and
perform physical actions.
Key
components of
AI
8
Natural
Language
Processing
(NLP)
Machine
Learning
Computer
Vision
Robotics
Knowledge
Representation
Planning and
Decision
Making
Reasoning
Natural
Interaction
Knowledge Representation: The way
in which information is stored and
represented in a machine, such as in
the form of a database or a
knowledge graph.
Planning and Decision Making: The
ability of a machine to make
decisions and plan actions based on
its goals and knowledge.
Reasoning: The ability of a machine
to draw inferences, make predictions,
and solve problems based on its
knowledge and understanding of the
world.
Natural Interaction: The ability of a
machine to interact with humans in a
natural and intuitive way, such as
through speech, touch, or gesture.
The 8 key components of AI can vary depending on the context and specific
application, but some of the most commonly cited components include:
8 Key components of AI

6
3 Type of Artificial Intelligence levels
This is a hypothetical future level of AI
that exceeds human intelligence in all
domains, including creativity, intuition,
and problem-solving ability. The concept
of artificial superintelligence is still
largely speculative and is a topic of
much debate among experts in the field
of AI.
ASI
Artificial
Superintelligence
This type of AI is designed to exhibit
human-like intelligence, including the
ability to reason, understand
language, and make decisions.
Strong AI has the potential to
perform any intellectual task that a
human can perform.
AGI
General or
Strong AI
This type of AI is designed to
perform a specific task or set of
tasks, such as playing a game or
recognizing speech. Narrow AI
operates within a limited scope and
does not have the ability to reason or
understand the world in a general
sense.
ANI
Narrow or
Weak AI
Stage B Stage C
Stage A

7
Application of
Machine
Learning
Email spam
Application of Machine
Learning
Machine learning is a rapidly growing field in computer
science with numerous applications across different
industries. The main idea behind machine learning is that
computers can learn from data and improve their
performance over time without explicit programming.
In healthcare, machine learning is used for disease
diagnosis, patient outcome prediction, and supporting
decision-making by healthcare professionals. At the same
time, in marketing, machine learning is used for creating
personalized recommendations such as recommendation
engines for online shopping, music, and video streaming
services.
Overall, machine learning is a versatile and powerful tool
that has the potential to revolutionize many industries and
areas of our lives.

8
Artificial Intelligence (AI):
AI refers to the simulation of human intelligence in machines that are
designed to think and act like humans. AI can encompass a wide range of
technologies, including machine learning, natural language processing,
computer vision, and robotics.
Machine Learning (ML):
Machine learning is a type of AI that enables computers to learn from data
without being explicitly programmed. Machine learning algorithms use
statistical models to analyze data and make predictions or decisions.
Deep Learning (DL):
Deep learning is a subfield of machine learning that uses artificial neural
networks with multiple layers to process and analyze large amounts of
complex data. Deep learning algorithms are particularly well-suited to tasks
such as image and speech recognition, where they can automatically identify
patterns and features in large amounts of data.
Artificial Intelligence
Machine Learning
Deep Learning
Artificial Intelligence vs
Machine Learning vs Deep Learning
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are
related but distinct concepts in the field of computer science and artificial
intelligence.

9
Neuromorphic computing
Cognitive cyber security
Robotic personal assistants
Autonomous surgical robotics
Next Gen cloud robotics
Thought controlled gaming
Real time universal translation
Virtual companions
Autonomous systems
Machine learning
Deep learning
Neural networks
Pattern recognition
Natural language processing
Chatbots
Real time emotion analytics
Artificial
Intelligence AI
Technology
Landscape

10
Artificial Intelligence (AI) is a broad field that encompasses several subfields, each with its own
specific focus and techniques. Some of the most common AI subfields include:
6 Artificial Intelligence Subfields
A subfield of AI that focuses on the use
of artificial neural networks, a type of
machine learning algorithm, to process
and analyze large amounts of complex
data, such as in the case of image and
speech recognition.
A subfield of AI that focuses on
enabling computers to learn
from data and improve their
performance over time without
explicit programming.
A subfield of AI that focuses
on enabling computers to
understand, interpret, and
generate human language.
on enabling computers to
understand and interpret
visual information, such as
images and videos.
on the design and
development of robots and
other physical devices that
can perform tasks and
actions in the real world.
A subfield of AI that focuses on the
representation and manipulation of knowledge
and information in a machine, and the ability of
the machine to reason and make decisions
based on that knowledge.
Machine
Learning
Computer Vision
Knowledge
Representation and
Reasoning
Neural Networks
and Deep
Learning
Robotics
Natural
Language
Processing
(NLP)

11
These systems try to
emulate human thought
quite literally using artificial
neural network models.
They try to emulate human
behaviour in a rational way,
obtaining their own conclusions
to given environmental
conditions. The differential point
in these systems is trying to
apply rationality to their
decisions.
These systems focus on acting
as humans; They are more
linked to classical robotics and
are less flexible.
These systems try to apply human
logic when it comes to perceiving,
reasoning and acting. They are not
focused on emulating the neuronal
behaviour of the brain but are trained
to act in a human way in a given
environment. An example of this is
expert agents.
Russell and Norvig co-wrote the popular textbook "Artificial Intelligence: A Modern Approach," which is widely used in AI courses and
is considered a seminal work in the field. In their book, they proposed a categorization of AI systems into four categories based on
their level of intelligence and capability:
6 Artificial Intelligence Subfields
Systems that
think like humans
Systems that
act rationally
Systems that
act like humans
Systems that
think rationally

12
Artificial Intelligence (AI) is a branch of computer science that aims to create intelligent machines that can perform tasks that normally require human
intelligence, such as visual perception, speech recognition, decision-making, and language translation.
The core of an AI system typically involves a model, which is a representation of the problem the AI system is trying to solve, and an algorithm, which is a
set of instructions for how the model should be trained and used to make predictions.
How AI Artificial intelligence work
Once an AI system is trained, it can be used to make predictions or decisions in real-world situations. For example, a deep learning algorithm trained on
images of animals might be able to identify the breed of a dog in a new image. AI systems can be designed to operate in a variety of environments, from
simple rule-based systems to complex deep learning models. The choice of approach depends on the specific problem being addressed, as well as the
resources available for training and deploying the AI system.
Cognitive
Computing
Cognitive computational algorithms try to
mimic the human brain by analyzing text,
speech, objects, and images in the way a
human would, and try to give the desired
result.
Computer
Vision
Computer vision algorithms try to
understand an image by decomposing it
and studying different parts of objects. Machine
Learning (ML)
Machine learning teaches a machine how
to make decisions based on past
experiences.
Neural
Networks
A neural network is a series of algorithms
that try to recognize relationships in a data
set through a process that mimics how the
human brain works.
Deep
Learning (DL)
Deep Leaming is a function of Al that
mimics the functioning of the human brain
in data processing and creating models for
their use in decision making.
Natural
Language
Natural language processing (NLP).
Natural language processing is a science
of reading, understanding, interpreting a
language by a machine

Machines and programmes with artificial
intelligence Artificial intelligence can perform
many tasks with greater accuracy, speed and
efficiency than humans. Other possible
applications include support systems for
diagnosing and fighting depression and other
mental disorders using pets with artificial
intelligence.
13
Artificial Intelligence is a science similar to
disciplines such as biology or mathematics. It
is the study and work of creating intelligent
programmes and machines to improve our
ability to solve problems in today's
environment.
Machine Learning
Machine learning is a subset of Al, or more
precisely, a method of implementing artificial
intelligence. The goal of of machine learning
is to improve the way artificial intelligence is
used in applications. ML is a method of
training algorithms so that they can learn and
grow on their own.
Applications powered by machine learning can
review a large volume of data. They can
quickly and accurately identity patterns and
trends that might not be visible to human
beings. It allows for instantaneous adaptation,
without human intervention.
Deep Learning
Deep learning is also known as deep neural
learning, which is a subset of ML. It uses
neural networks to learn different factors that
go into the structure and is similar to the
human neural system. Just as the human brain
analyses and classifies different things, deep
learning in machines helps us understand and
classify things in the same way that the human
brain does.
One of the main benefits of deep learning is the
ability to teach how to perform engineering feats
without instructions or even prompting.
According to experts, deep learning can act as
a reliable engine for problem solving and action.
Definition
Benefits
AI is the broader concept of machines being able to perform tasks that would normally require human intelligence, while ML and DL are subfields of AI that
focus on the development of algorithms and models that enable computers to learn from data.
Artificial Intelligence vs Machine Learning vs Deep Learning

14
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers
to learn from data, without being explicitly programmed. In ML, a model is trained on a large dataset, and the algorithm uses this training data to make
predictions or decisions about new, unseen data. The goal of ML is to build models that can generalize well to new data, rather than simply memorizing the
training data.
Machine Learning Types
Machine Learning Types
Supervised Learning
Housing price
prediction
Medical Imaging
Unsupervised Learning
Customer
segmentation
Market basket
analysis
Semi-supervised Learning
Text classification
Lane-finding on
GPS data
Reinforcement Learning
Optimised
marketing
Driverless cars

15
Usages of
Artificial
Intelligence
AI
Banking & Personal Finance
Transport
Retail Spaces
Education
Communication
Gaming
Media
Hospitality
Entertainment
Events
Insurance
Smart Homes
Real Estate
Healthcare
Online Shopping
Sports
Workplace
Reducing travel time
Autonomous vehicles
Virtual mirrors
Analysis and optimisation
Plagiarism checkers
Adaptive learning
Spam filters
Real time translation
Spam Improved visual quality
AI coach
Automated journalism
Data analysis
AI concierge
Personalised communications
Music, film and TV suggestions
Search optimisation
Facial recognition to scan attendees
Sales chatbots
Risk identification
Client support
Personal assistants
Home security
Targeted advertising
Market analysis
Autonomous surgical robots
Personalised treatment
Search recommendation
3D models
Wearable tech to analyse performance
Computer vision referee
Robotics in manufacturing
Automated safety check in factories

16
Machine
Learning
Unsupervised
Learning
Supervised
Learning
Reinforcement
Learning
Dimensio
nality
reduction
Regression
Classificat
ion
Clustering
K-means
Mean shift
K-medoids
Principal component
analysis PCA
Feature selection
Linear discriminant
analysis LDA
Decision tree
Leaner regression
Logistic regression
Navie bayes
SVM
K-nearest neighbor
Machine Learning MA
Machine Learning (ML) is a branch of Artificial
Intelligence (AI) that focuses on enabling computers
to learn from data and make predictions or
decisions. In ML, a model is trained on a dataset
and uses this training data to make predictions
about new, unseen data. There are various types of
ML, including supervised learning, unsupervised
learning, reinforcement learning, and semi-
supervised learning.
ML has become increasingly popular due to the
abundance of data and improvements in computing
power. It has the potential to impact various
industries, from healthcare to finance to
transportation, by allowing computers to make
decisions that would normally require human
intelligence. The success of ML depends on the
quality of the training data and the choice of
algorithm.

17
Artificial Intelligence AI: Pros and Cons
Enhanced Decision-Making: AI can provide valuable insights and
support for decision-making in various industries, such as finance
and healthcare.
VS
1
2
3
4
5
Job Loss: AI systems can automate many tasks that were
previously performed by humans, potentially leading to job losses
and unemployment.
Bias and Discrimination: AI systems can perpetuate and amplify
existing biases and discrimination, especially if they are trained
on biased data.
Lack of Empathy: AI systems lack the ability to understand
emotions and empathy, which can limit their effectiveness in
certain domains, such as healthcare and customer service.
Privacy Concerns: AI systems often require access to large
amounts of personal data, which can raise privacy concerns and
increase the risk of data breaches.
Unintended Consequences: AI systems can have unintended
consequences, such as the spread of misinformation and fake
news, that can negatively impact society.
Increased Efficiency: AI systems can automate many tasks and
processes, reducing the time and effort required to complete them
and increasing efficiency.
Improved Accuracy: AI systems can analyze large amounts of
data more quickly and accurately than humans, reducing the risk
of errors and increasing the reliability of decision-making.
Personalization: AI can be used to provide personalized experiences,
such as personalized recommendations and advertisements, based
on an individual's preferences.
Increased Safety: AI can be used to enhance safety in various
industries, such as transportation and healthcare, by automating
tasks and processes that are dangerous for humans.
Pros Cons

18
Machine Learning ML Process
1. Classification 2. Regression 3. Deep Learning 1. Clustering
2. Dimensionality
Reduction
3.
Anomaly
Detection
Logistic Regression
Support Vector
Machine
Artificial Neural
Network
Decision Trees
Linear Regression
Polynomial
Regression
Convolutional Neural
Networks
Recursive Neural
Networks
K-Means
K-Medoids
Fuzzy C-Means
Hidden Markov
Model
Principal
Component
Analysis
1. Self Training
2. Low Density
Separation Models
1. Monte Carlo
Methods
2. Dinamic
Programming
Supervised
Learning
Unsupervised
Learning
Semi-supervised
Learning
Reinforcement
Learning

19
Data Science is used
for data sourcing,
cleaning, processing,
and visualizing for
analytical purposes.
Data Science
Artificial Intelligent
Machine Learning
Al combines iterative
processing and
intelligent algorithms to
imitate the human brain's
functions.
Machine Learning is a
part of Al where
mathematical models are
used to empower a
machine to learn with or
without being
programmed regularly.
Data science deals with
both structured and
unstructured data for
analytics.
Al uses decision trees
and logic theories to find
the best possible
solution to the given
problem.
Machine learning uses
statistical models and
neural networks to train
the machine.
As a subset of Al,
Machine Learning also
use the same libraries,
along with tools such as
Amazon Lex2, IBM
Watson, and Azure ML
Studio.
ML is a subset of
Artificial Intelligence.
Online
recommendations, facial
recognition, and NLP are
a few examples of ML
Applications of Al include
chatbots, voice
assistants, and weather
prediction
Al includes predictive
modelling to predict
events based on
previous and current
data.
Some of the popular
libraries to run AI
algorithms include
Keras, Scikit-Learn, and
TensorFlow.
It is mainly used for
fraud detection,
healthcare, BI analysis,
etc.
Data Science includes
data operations based
on user requirements.
Some of the popular
tools in Data Science are
Tableau, SAS2, Apache,
MATLAB, Spark, and
more.
Definition Type of Data Tools Technique Application
Data Science vs Artificial Intelligence vs Machine Learning

20
Reinforcement
Learning
Unsupervised
Learning
Supervised
Learning
Machine
Learning
Deep
Learning
AI
AI
Schematic relationship
between AI, Machine Learning
and Deep Learning
AI, Machine Learning, and Deep Learning are interrelated
fields within artificial intelligence. AI refers to the overall
goal of creating intelligent machines that can perform tasks
that would normally require human intelligence. Machine
Learning (ML) is a subfield of AI that focuses on using
algorithms to enable computers to learn from data. Deep
Learning (DL) is a type of ML that uses artificial neural
networks with multiple layers to model complex patterns
and relationships in data.
In summary, AI encompasses the goal of creating intelligent
machines, ML provides the approach for achieving this goal
through learning from data, and DL is a specific type of ML
that uses deep neural networks. The relationship between
these fields is hierarchical, with AI at the top and DL as a
specialized subfield within ML.

AI Artificial Intelligence
21
Data Science
Focus: Data Science is focused on extracting
insights and knowledge from data.
Approaches: Data Science uses a combination
of statistical and computational methods to
analyze and interpret data.
Applications: Data Science is applied in a wide
range of fields, including finance, healthcare,
and retail, to support decision-making and gain
insights from data.
Outcomes: The outcome of Data Science is
the extraction of insights and knowledge from
data. Data Science can support and improve AI
systems.
Focus: AI is focused on creating intelligent
machines that can perform tasks that would
normally require human intelligence.
Approaches: AI relies on machine learning
algorithms and models to enable computers to
learn from data.
Applications: AI is applied in a variety of fields,
including robotics, speech recognition, and
natural language processing.
Outcomes: AI result is the creation of intelligent
machines that can perform tasks that would
normally require human intelligence. AI can
automate and enhance many data science
tasks.
VS
Difference between Data Science and

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