Nature has proved to be the biggest motivation for humans to create elegant and simple solutions. One of the biggest challenge of past 5 decades is to make an intelligent machine. Since human beings are the most intelligent creatures on the earth, our brain becomes a natural anchor point for computer engineers and computer scientists as a potential solution to this problem. Human brain is made up of billions of neurons. Although it’s not completely clear how these neurons work. Based upon the knowledge we already have about human brain it’s clear that network of these neurons or natural neural network plays a central role in vision, decision making, listening, computation and all other human actions.
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Capability to mimic the human intelligence by machines is called Artificial Intelligence (AI). Popular approaches towards achieving Artificial Intelligence are if-then formal reasoning, Bayesian inference, probabilistic reasoning and Artificial Neural Networks. Human brain inspired Artificial Neural Networks turned out to be the most effective problem-solving model for wide set of problems of AI.
Key abilities of Artificial Neural Networks:
- Feature Extraction
- Categorization
- Association
- Optimization
- Generalization.
Feature extraction: Feature extraction is used in pattern matching and image recognition. It is also called as dimensionality reduction.
Categorization: It is the process in which the ideas and objects are recognized, understood and interpreted. What a category does is create a relationship between the objects and subjects of knowledge.
Association: The association rule is used for various applications like uncovering patterns, correlations, in a set of data in order to refine it and make it readily usable.
Optimization: Optimization is used in many contexts. We can use analytical optimization for designing algorithms or writing proofs. The most important and toughest instance of optimization is with regard to neural network training.
Generalization: Generalization is the process of deploying the model that is completely trained onto new sets of data that is previously not encountered but gathered from the same distribution that is used to create the model.
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What is a Neuron?
Neuron is a central component of natural neural network. Neuron takes the input gathered by human senses, process this information and sends executable reactions to muscles. Neuron has three fundamental components viz. dendrites, axon and cell body or soma. A dendrite acts as an input point for neuron and the axon is output structure of neuron. A neuron can have two states viz. either it fires or it doesn’t fire. When neuron fires, it exchanges information among each other in the form of electrical signals. Two neurons are connected with each other through axon and form a network. This network is called Natural Neural Network (NNN).
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What is Artificial Neural Network (ANN)?
Artificial neural networks are computational systems vaguely inspired by design of natural neural networks (NNN). These systems are also called connectionist systems. Fundamental computational units are called nodes. These nodes represents neurons in natural neural networks. These nodes takes an input, perform computation on it and provides an output. Output of one node can be an input for another node. Nodes are connected with each other through edges. Each edge has a weight represented by a real number. Weight is used to guide the direction of execution process. Usually these nodes stay in layers. First layer of artificial neural network is called input layer. Input layer is responsible to take input. Input layer is directly connected with hidden layers. These hidden layers perform computations on input. If a neural network has more than one hidden layers, then it’s called Deep Neural Network. Every node carries a weight. It is a real number. If this value increases it is called strong neural connection. It act as a guiding factor in learning process for the Artificial Neural Network.
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ANN based systems acquire knowledge by doing things, it then uses this knowledge to other set of data. Usually we feed some data to train a neural network, this is called training data. Neural network acquires knowledge from this data and applies this knowledge on real world data. For example to train a neural network to identify image of jaguar we first have to feed many images of jaguar manually. This step creates a knowledge of jaguar into the network. Mind that we have not coded the instruction to identify the jaguar. If we have to manually code the instructions to identify jaguars then we have to define its characteristics like tail, skin colour, dot patterns etc. It will be a daunting task to manually code these instructions. By training the neural network we just helped the model to identify the image of jaguar independently and not code anything with respect to it. It is a lot easier than coding the instructions, certainly. ANN takes less time to solve the problems in the real-world scenario.
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Real world applications of artificial neural networks:
Most modern intelligent application uses ANN model as a main approach to solve complicated problems. While you are using direction feature of Google Maps, search for an image with Google Images, give voice instructions to Amazon Alexa, all these products exploit artificial neural network model. Artificial neural networks have broad applications in robotics, computer vision, pattern recognition, natural language processing, self-driving cars and countless other applications. Google Maps uses neural network capabilities to find out best route between two places. Various constraints like road condition, traffic, past experiences, weather etc. are considered as conditions for the computation of best path. Google uses neural network capability in Google Places to improve quality of listings. Various image search and speech recognition applications like Alexa, Bing uses artificial neural network model to differentiate between two images and accent of user. Most pattern recognition algorithms out there are based on ANN.
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