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.
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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