Artificial Intelligence (AI), Deep Learning& Neural Networks. What are the Key Differences?
Many
of us would have come across the words Artificial Intelligence (AI), Deep
Learning and Neural Networks in our day to day life in various circumstances.
But how many of us actually know what they mean and how they are related. Let’s
find out through this blog in simple terms.
Artificial
Intelligence (AI) is a broad branch of Computer Science, which has made its way
to almost every sector ranging from Healthcare to Robotics. Artificial
Intelligence offers a machine or computer system to mimic human intelligent functions
and activities, learn from experiences as well as adapt to new situations.We
can easily understand Artificial Intelligence concepts based on how human brain
works. In fact, Artificial Intelligence concepts are inspired by human brain
functions.
AI &
Human Brain
The Human
brain does a variety of cognitive functions.
Humans communicate through speech and language. Similarly, a system
that understands and processes the language data is termed Voice Recognition
which in turn is based on statistical information, hence called Statistical
Learning laying the foundation of Machine Learning.
Machine
Learning is the process by which a mechanical system is being programmed to
respond in a particular way based on certain statistical information or data,
the same way how children are being taught. The machine system tries to
identify similar patterns based on the statistical data being fed, called Pattern
Recognition. These patterns will be used to form predictions and improve from
experience in similar events without being explicitly programmed.
Humans
can see and process based on what they see, this is exactly how Computer Vision
works. Like humans use eyes for viewing, Computer Systems detect objects or
images via camera or sensors. The Computer Vision falls under a symbolic way of
processing information which is called Image Processing.
The Human brain consists of networks of neurons to process complex information to derive a conclusion or prediction. The concept of Neural Networks tries to replicate the functions of neurons to provide cognitive capacities in machines. These networks are used for processing and learning complex things, called Deep Learning.
The above conceptual example shows that Artificial Intelligence, Machine Learning and Deep Learning concepts are highly entwined with one another acting as a core of confusion. This is the reason you come across people using these terms interchangeably. However, despite the conceptual
similarities, these technologies are unique in their own way.
Machine Learning vs Deep Learning:
Deep
learning is the subset of Machine Learning which in turn is a subset of
Artificial Intelligence. Machine Learning is used for achieving AI trained
through algorithms and data. Whereas Deep Learning is the type of Machine
Learning inspired by the structure of the brain and how it works. This
structure is called Artificial Neural Networks. Let’s understand deep learning
better and how it is different from machine learning with a simple example.
Let’s say we want a machine to categorize two different objects. In the case of
machine learning, we need to program the system with the attributes of objects
based on which they can be differentiated. Whereas, in the case of deep learning,
the attributes of the objects are picked up the machine itself via neural
networks without any human intervention and automatically categorize between
two objects. However, this kind of cognitive ability comes from a much
higher volume of data.
Neural
Networks:
Artificial
Neural Networks tend to mimic the functions of neurons in the human brain. Neural
Networks form the basis of Deep Learning. Neural Networks automatically
acquires the data and train themselves to recognize the patterns in the data
set to predict the output or in a new set of similar data or events. Neural
Networks are made up of layers of neurons acting as the core processing units
of the system. Each layer consists of numerous neuron units. The first layer is
called the input layer which receives the signal or data. The output layer predicts
or draws a conclusion. In between the two layers, there is a set of layers called
hidden layers which perform most of the computational analysis required by the
system. The Neural Networks take input from an image or object in the form of
pixels and each pixel is received by each neuron of the first layer. And
neurons of one layer are connected to neurons of the next layer through channels
in criss-cross formation to transmit information.
Each
of these channels is assigned certain numerical values, known as weight. The
inputs are multiplied by the corresponding weights and the resulting sum is
sent as inputs to the hidden layers. Within the hidden layers, the neurons are
assigned with certain numerical values called bias value. These values are added
to the corresponding input sum. The newly formed values are then passed through a
threshold function called the activation function.
The
result of the activation function determines the activation of a particular
neuron in the bias layer. The activated neurons transmit the data to the next
layer. In this manner, the information is propagated through the network untill
the data reaches the output layer. In the out put layer, neurons with the
highest values determine the results. The neural networks provide the
statistical basis or parameters of a particular entity. Whereas, deep learning
offers cognitive abilities to a system for making a prediction or conclusion
based on the parameters received from neural networks.
Applications
in Biometrics:
Deep
Learning and Neural Networks have prime applications in biological pattern
recognition such as facial recognition, voice recognition, iris recognition,
palm recognition, etc. Deep Learning-based biometric systems offers the most
reliable, accurate and the fastest method of verification, since the systems
mimic human-like cognitive abilities. Hence it is virtually impossible to use
fake attacks against the system.
Furthermore,
Deep Learning has applications in Parking as well as in Video Surveillance
Systems. Deep Learning based Computer Vision systems automatically recognize
patterns in vehicle number plates and provide access to vehicles without any human
intervention. With respect to AI-driven Video Surveillance systems, deep
learning technology provides various cognitive abilities to the surveillance
systems in identifying matching faces, moving objects, potential threats, etc.
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