How Artificial intelligence and machine learning are different?

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How AI and ML are different?

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1. What is Machine Learning?
2. What is Artificial Intelligence (AI)?
3. Why do tech companies tend to use AI and ML
interchangeably?
Topics

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AI, Machine Learning and Deep Learning

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What is Machine Learning ?

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• A scientific field is best defined by the
central question it studies. The field of
Machine Learning seeks to answer the
question:
“How can we build computer systems
that automatically improve with
experience, and what are the
fundamental laws that govern all
learning processes?

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• Machine learning (ML) is a branch of artificial intelligence, and as coined and defined
by Computer Scientist and machine learning pioneer.
• ML, it’s one of the ways we expect to achieve AI. Machine learning relies on working
with small to large data-sets, by examining and comparing the data to find common
patterns and explore nuances.
• Type of Machine Learning
– Supervised Learning
– Unsupervised Learning
– Reinforcement learning

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Supervised And Unsupervised Learning

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• Supervised Learning: Where supervised learning algorithms try to model relationships and
dependencies between the target prediction output and the input features, such that we
can predict the output values for new data based on those relationships, which it has
learned from previous data-sets.
• For Example: Suppose you are given a basket filled with different kinds of fruits. Now the
first step is to train the machine with all different fruits one by one like this
– If the shape of an object is rounded and depression at the top having color Red then it will be labeled as –
Apple
– If the shape of an object is a long curving cylinder having color Green-Yellow then it will be labeled as –
Banana
Supervised Learning

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• Supervised learning classified into two categories of algorithms:
– Classification: A classification problem is when the output variable is a
category, such as “Red” or “blue” or “disease” and “no disease”.
– Regression: A regression problem is when the output variable is a real
value, such as “dollars” or “weight”
Supervised Learning

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• Unsupervised Learning: Another type of machine learning are the family of machine
learning algorithms, which are mainly used in pattern detection and descriptive
modeling. These algorithms do not have output categories or labels on the data (the
model is trained with unlabeled data)
• For example:
– Suppose it is given an image having both dogs and cats which have not seen ever.
– Thus the machine has no idea about the features of dogs and cats so we can’t
categorize it in dogs and cats. But it can categorize them according to their
similarities, patterns, and differences i.e., we can easily categorize the above picture
into two parts. First first may contain all pics having dogs in it and the second part
may contain all pics having cats in it.
Unsupervised Learning

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• Unsupervised learning classified into two categories of algorithms:
– Clustering: A clustering problem is where you want to discover the inherent groupings in
the data, such as grouping customers by purchasing behavior.
– Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy X also tend to buy Y,
the prime example is an amazon recommendation system.
Unsupervised Learning

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• Reinforcement learning: The third popular type of machine learning aims at using
observations gathered from the interaction with its environment to take actions that would
maximize the reward or minimize the risk. In this case, the reinforcement learning algorithm
(called the agent) continuously learns from its environment using iteration. A great example
of reinforcement learning are computers reaching superhuman state and beating humans on
computer games.
Reinforcement Learning

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• For Example: The problem is as follows: We have an agent and a reward, with many
hurdles in between. The agent is supposed to find the best possible path to reach the
reward. The following problem explains the problem more easily.
Reinforcement Learning

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What is Artificial Intelligence (AI)?

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• Artificial intelligence, on the other hand, is exceptionally wide in scope
• “Artificial intelligence is the science and engineering of making computers behave in ways
that, until recently, we thought required human intelligence.” by former-Dean of the School
of Computer Science at Carnegie Mellon University Andrew Moore
• AI as we know it today is symbolized with Human-AI interaction gadgets by Google Home,
Siri and Alexa, by the machine learning-powered video prediction systems that power
Netflix, Amazon and YouTube

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• AI can be categorized as either weak or
strong. Weak AI, also known as narrow
AI, is an AI system that is designed and
trained for a particular task. Virtual
personal assistants, such as Apple's Siri,
are a form of weak AI. Strong AI, also
known as artificial general intelligence,
is an AI system with generalized human
cognitive abilities. When presented with
an unfamiliar task, a strong AI system
can find a solution without human
intervention.

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Deep Blue, the AI that defeated the world’s chess champion in 1997, used a method called
tree search algorithms to evaluate millions of moves at every turn.
Why do tech companies tend to use AI and ML interchangeably?

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Thank you!

How Artificial intelligence and machine learning are different?

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