AI and Machine Learning – Know the core aspects of the deadly duo.pdf

Why AI is the most dangerous thing you can imagine?
Last Friday, I was returning home after catching the 9 pm show of the latest Terminator movie. As I was driving home, I was plagued by the same questions that every sci-fi movie about evil AI has raised in my mind.

1. AI and Machine Learning –
Know the core aspects of the
deadly duo

2. AI and Machine Learning
Last Friday, I was returning home after catching the 9 pm show of the latest
Terminator movie. As I was driving home, I was plagued by the same
questions that every sci-fi movie about evil AI has raised in my mind.
Questions like Could machines really take over? Can an AI be successful at
world domination? I had heard in the news two years or so ago, about
facebook’s AI chatbots going out of control and needing to be shut down.
The comments by scientists and technology enthusiasts after that were not in
favor of AI research. The more I thought, the more questions I had. Can AI be
truly evil? Can AI have emotions? What exactly is an AI?
Unlike all the previous times, this time I tried to find answers to these
questions. Today, I will share with you what I learned about AI and its key to
advancement in the 21st century.
What is AI?
AI is the discipline of the computer sciences that focuses on the creation of
intelligent computer programs. The aim of creating such programs is to
emulate human behavior in situations where humans either can’t go or can’t
stay for prolonged periods of time.
It is also very useful in cases where automation is profitable by reducing
human effort and eliminating human error. Some of the common features that
AI programs are designed to have are:
●​Problem-solving
●​Natural language processing
●​Planning
●​Learning
The field of AI research saw significant growth in the first decade of the 21st
century due to successful implementations of machine learning techniques
and improvement in available hardware. Today, AI has found applications in
many different fields and also in households.
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3. Types of AI

AI can be classified in many ways. However, there are two most popular ways
of classification based on their capabilities and functionality.
1. Type-1 AI (Classification based on ability)
There are three types of AI in the Type-1 classification:
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1.1. Narrow AI
Narrow AI is also called weak AI. This type of AI can only work in predefined
situations. They can only perform certain pre-programmed tasks.

4. 1.2. General AI
General AI can perform any task with efficiency equal to that of a human.
1.3. Super AI
Super AI is a hypothetical concept in AI research. It is an AI that can perform
any task better than humans with greater efficiency without any human error.
2. Type-2 AI (Classification based on
functionality)
There are four types of AI in Type-2 classification:
2.1. Reactive Machines
Reactive machines are the most basic type of artificial intelligence. This type of
AI looks at the world around it and responds based on its observation. They do
not form memory nor do they learn from past experiences.
IBM’s chess-playing supercomputer, Deep Blue is a good example of a
reactive machine.
2.2. Limited Memory
Limited memory AI is similar to reactive machines except they have a small
memory that they can use to make observations over a period of time to judge
the situation and give a response based on that.
An example of limited memory AI would be self-driving cars. Self-driving cars
need at least short term memory to react properly to road signs and to observe
the speeds and paths of other vehicles around them.

5. 2.3. Theory of Mind
This type of AI does not exist yet. It perceives the world around them and
other agents inside it as well. They understand how other objects and entities
will react to their actions and act accordingly.
2.4. Self-Awareness
Self-aware AI is an AI with an idea of self. This type of AI has consciousness
and sentiments as well. Currently, this type of AI is purely hypothetical.
“AI and its offshoot, machine learning, will be a foundational tool for
creating social good as well as business success.” ~Mark Hurd
Examples of Artificial Intelligence

AI is all around us today. They are apart of any netizen’s everyday life. Some
common examples of AI are:

6. 1. Virtual Assistants
Virtual assistants like Alexa, Siri, and Google Assistant are the most commonly
used AI. More than 90% of smartphone users use the virtual assistants
provided in their devices.
2. Suggestions and Recommendations at
E-commerce Websites
The suggestion and recommendations given by the e-commerce website are
done by AI that checks a customer’s order history and searches to determine
the best products to recommend.
3. Cogito
Cogito is a speech recognition software that tries to identify the emotions
behind said words by noticing the tone, volume, and stress on the words. It
has been very useful for customer service calls and on-call assistance.
4. Spam Filters
Spam filters in email and messaging services and apps check incoming
messages and emails for certain identifiers. They also learn based on your
decisions to move a message to or from spam.
5. Loan and Credit Card Processing
The credit score given to a customer is calculated by an AI, based on certain
pre-defined characteristics. The banks then approve or reject loan and credit
card applications based on this.
By now, we know that Artificial Intelligence is the simulation of human
behaviour by a machine/computer. In the 21st century, AI has reached almost
every house, like Alexa, Siri, product recommendations to name a few.
According to a study by Creative Strategies, only 2% of iPhone owners have
never used Siri, and only 4% of Android owners have never leveraged the
power of OK Google. When it comes to usage, 51% use voice assistants in the
car, 6% in public, and 1.3% at work.

7. While studying AI, there is one term that you are going to come across a lot,
and it is Machine Learning (ML). Now, even if you are not acquainted with the
latest updates in the tech world, I am pretty sure you must have heard about
AI and ML creating a storm there.
But what exactly is Machine Learning? And how is it related to AI? Read this
AI and Machine Learning tutorial to find out! Before anything else, let us
start with understanding Machine Learning.
What is Machine Learning?
University of Washington defines Machine Learning as “Machine learning
algorithms can figure out how to perform important tasks by generalizing
from examples.”
ML expert Tom M Mitchell states that “Machine learning is the study of
computer algorithms that allow computer programs to automatically
improve through experience.”
In simple words, machine learning involves algorithms that allow computers
to learn automatically from previous interactions with users, without being
distinctly programmed with the help of neural networks.
It gives computers the skill to learn from previous data without an expert
having to program it. With the help of machine learning, a system takes
decisions based on previous patterns.

8. Now you must be wondering what a neural network is. A neural network is a
series of algorithms that are somewhat like a biological neural network
(revolving around animal brains). These algorithms are such that they
facilitate the recognition of relationships in a set of data.
Know more about the neural network in detail.
Very much technical? Let’s go through this everyday example which will
make you understand ML in a better manner.
My friend’s birthday is coming up. Knowing that he is an Ironman fan, I
decided to gift him something related to that. I started looking for products on
Amazon and Flipkart, and soon I found a combo of a diary and a poster- both
featuring Ironman. I ordered the combo and started casually surfing the
internet.
While reading an article, I found that all the ads being displayed on the page
were recommendations of Marvel merchandise – assorted especially for me.
At the same time, I decided to open the exact same website on my mother’s
phone to check if she’s getting similar ads. Upon opening the link on her
phone, I could see ads of kurtas from Myntra.
This brought me to the conclusion that on the basis of the interactions of me
with various websites, I was receiving product recommendations.
This means that e-commerce websites like Amazon and Flipkart are using AI
to gather information about my preferences, to provide me a tailor-made
experience.
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9. Components of Machine Learning
ML experts develop thousands of ML algorithms every year. Below mentioned
are the three vital components that every algorithm has:
1. Representation
It includes the selection of a model that represents data. Decision trees,
instances, set of rules, etc are some examples of this component.
A decision tree is a tree-like model comprising of various decisions and their
consequences. Instance-based learning occurs when the machine compares
new problems with previously occurred instances.
2. Evaluation
Evaluation is that component which provides the machine to evaluate and
optimize hypotheses (candidate programs). It is also known as objective,
utility, or scoring function. Some examples of evaluation are accuracy, squared
error, posterior probability, etc.
Accuracy basically measures or evaluates classification models. Posterior
probability is that probability which arises upon taking into account updated
information.

10. 3. Optimization
Optimization is the way in which hypotheses are generated. Examples of
optimization include combinatorial optimization, convex optimization, and
constrained optimization. Combinatorial optimization uses combinatorial
techniques to solve discrete combination problems.
“A baby learns to crawl, walk and then run. We are in the crawling stage
when it comes to applying machine learning.” ~Dave Waters
Types of Machine Learning
1. Supervised Learning

In supervised learning, we have labeled dataset which means that we already
know the input and their correspondings output. We train the model using an
algorithm that maps the input to their outputs.
Here, we try to minimize the error and then we can use new data to predict
their outcomes. Supervised learning tasks include classification problems and
regression problems.
2. Unsupervised Learning
The unsupervised learning technique is used when we don’t have labeled data.
So the machine only knows input data and it has to act on the information
without any guidance or output data.

11. Therefore the machine is restricted to find hidden patterns and similarities
within input data. Unsupervised learning is used for clustering and association
problems.

Any doubts in AI and machine learning article till now? If yes mention in the
comment section.
3. Semi-Supervised Learning
Labeled data are expensive and hard to find when the problem you are
working on is not so common. In semi-supervised learning, we use some
amount of labeled data with unlabelled data. The models accuracy of an
unlabelled data can be increased by using some of the labeled data.

12. 4. Reinforcement Learning
In Reinforcement learning, the machine uses previous data to evolve and
learn. It uses rewards and punishments to train the algorithm by taking
positive rewards for good decisions and negative rewards for bad decisions.
This learning doesn’t require a dataset to train. It’s a self-sustained system
that learns to improve itself from the real-world environment.

Jump into a detailed explanation of Types of Machine Learning Algorithms.
Example of Machine Learning application in
Artificial Intelligence
There is no denying that Cortana, a virtual assistant developed by Microsoft
for Windows 10, is the result of progress in AI. It functions exactly like Siri or
the Google Assistant.
Cortana helps you find information on everything when requested using voice-
even questions like What will the weather be like tomorrow? Or it even does
any calculations for you. It also performs particular functions or commands
other applications to perform an activity (setting an alarm, placing calls).
AI is an integral part of this assistant, as it gathers data on the basis of user
interaction and then provides customized results. Cool, isn’t it?
But there’s more to Cortana than just answering questions. Microsoft claims
that with each interaction, Cortana constantly keeps learning about its users,
and tries to anticipate the requirements of the users.

13. Which means that it constantly uses machine learning to intelligently operate
to cater to the user’s needs.
Now in AI and machine learning article let’s discuss what is the difference
between them.
Difference between AI and Machine
Learning
Artificial Intelligence Machine Learning
AI is a broad concept involving other
concepts, such as machine learning,
neural networks, NLP (Natural Language
Processing)
ML is a subset of AI in that it is a
technique used to implement AI
Intelligence is the acquisition of
knowledge and the ability to apply it
Learning is using past instances to make
future decisions
It focuses more on automating a task or a
system, like cars
It focuses on gaining and applying
knowledge from the external environment,
like Cortana
AI enables the machines to think and
perform routine jobs that humans do,
such as assembly line operations in a
factory
ML provides solutions on the basis of a
constantly evolving neural network
AI aims at increasing the probability of
success, instead of accuracy
ML is more aimed at being accurate,
instead of being successful
An AI-enabled system is programmed in a
way that it simulates human behavior
ML tends to create self-learning
algorithms
It mimics human intelligence to solve
complex problems
ML learns from previously fetched data to
maximize performance
AI and ML sound so different, but in reality, it can cause confusion. Machine
Learning is an application of AI which implies that with sufficient progress,
machines can learn and enhance with each user interaction.

14. Artificial Intelligence is a more extensive concept involving the ability of
machines to carry out a variety of tasks.
Now!! explore in detail AI vs ML vs Deep learning vs Data science
Summary
I believe it is clear now that Artificial Intelligence is like an umbrella that
houses Machine Learning, along with other concepts. AI is more like a study
aimed at training computers to simplify and automate tasks, whereas ML
induces a machine to learn on its own.
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