AI Evolution:The
Convergence of
AI Evolution:
Machine Learning,
The Convergence of
Deep Learning
Machineand
Learning,
Deep Learning and
Data Science
Data Science
What Is Artificial Intelligence?
• AI refers to the development of programs that
behave intelligently and mimic human intelligence
through a set of algorithms. The field focuses on
three skills: learning, reasoning, and self-correction
to obtain maximum efficiency. AI can refer to either
machine learning-based programs or even
explicitly programmed computer programs.
Types of artificial intelligence: weak AI vs.
strong AI
 Weak AI also known as narrow AI or artificial narrow intelligence (ANI)—is AI trained
and focused to perform specific tasks. Weak AI drives most of the AI that
surrounds us today. "Narrow" might be a more apt descriptor for this type of AI as it
is anything but weak: it enables some very robust applications, such as Apple's
Siri, Amazon's Alexa, IBM watsonx™, and self-driving vehicles.
 Strong AI is made up of artificial general intelligence (AGI) and artificial super
intelligence (ASI). AGI, or general AI, is a theoretical form of AI where a machine
would have an intelligence equal to humans; it would be self-aware with a
consciousness that would have the ability to solve problems, learn, and plan for
the future. ASI—also known as superintelligence—would surpass the intelligence
and ability of the human brain. While strong AI is still entirely theoretical with no
practical examples in use today, that doesn't mean AI researchers aren't also
exploring its development
Machine
Learning
What is Machine Learning?
Machine Learning, often abbreviated as ML, is a subset of artificial intelligence (AI) that
focuses on the development of computer algorithms that improve automatically through
experience and by the use of data. In simpler terms, machine learning enables computers
to learn from data and make decisions or predictions without being explicitly
programmed to do so.
Examples:
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Machine learning algorithms and machine vision are a critical component of self-driving
cars, helping them navigate the roads safely.
In healthcare, machine learning is used to diagnose and suggest treatment plans.
Other common ML use cases include fraud detection, spam filtering, malware threat
detection, predictive maintenance and business process automation.
How Does Machine Learning Work?
Deep Learning
What is Deep Learning?
Deep learning, is a subfield of machine learning dealing with algorithms based
essentially on multi-layered artificial neural networks (ANN) that are inspired by the
structure of the human brain.
Examples:
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Virtual assistants(Alexa,siri)
Translation
Chatbot
Facial recognition
Self-driving cars
Personalised shopping and entertainment(Netflix movie recommendation )
What is a Neuron
in Biology ?
One important observation was that a neuron by itself is
useless. Instead, you require networks of neurons to
generate any meaningful functionality.
This is because neurons function by receiving and sending
signals. More specifically, the neuron’s dendrites receive
signals and pass along those signals through the axon.
The dendrites of one neuron are connected to the axon of
another neuron. These connections are called synapses,
which is a concept that has been generalized to the field of
deep learning.
What is a Neuron
in Deep Learning ?
Neurons in deep learning models are nodes through which
data and computations flow.
Neurons work like this:
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They receive one or more input signals. These input
signals can come from either the raw data set or from
neurons positioned at a previous layer of the neural net.
They perform some calculations.
They send some output signals to neurons deeper in the
neural net through a synapse
Data Science
What is data
science ?
Data science combines maths and
statistics, specialized programming,
advanced analytics, artificial intelligence
(AI) and machine learning with specific
subject matter expertise to uncover
actionable insights hidden in an
organization’s data. These insights can
be used to guide decision making and
strategic planning.
AI vs Machine Learning vs Deep Learning vs Data Science
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