Artificial Intelligence Problems
Artificial intelligence can be described as the capability of machines to replicate the processing capacity of humans and to make decisions without any human effort or assistance. This is done by gathering and combining a large amount of data and then predicting the outcome according to the useful patterns extracted from a large dataset. Outcomes can be predicted from a number of algorithms such as Linear Regression, Logistic Regression, and Naive Bayes, etc. In the case of Neural Networks, a machine trains itself by learning from the available data just like a human brain learns from experiences. A machine learns automatically and compares from datasets, larger the amount of data greater the accuracy of machine learning. Artificial intelligence requires almost every above-explained terminology. Artificial intelligence is not limited to computers only but it draws motivation from psychology, neural networks, data analytics, cognition, image processing, and many more. Artificial intelligence is also applicable to unmanned vehicles, aviation, medical treatments, marketing strategies, power electronics, and transport. Advancement in the domain of Big Data has even spread out the applications of Artificial Intelligence.
Just like our brain, neural networks are made up of cells, two basic cells are perceptron and sigmoid. A perceptron makes the decision based on multiple inputs, if the sum of those inputs exceeds a threshold, only then a perceptron outputs one. A neural network can have a little amount of inaccuracy, to eliminate this inaccuracy we need to perform some changes. These changes cannot be done on Perceptron, for this purpose, Sigmoid is used. A little change in the input of Sigmoid produces a small change in output, thus maintaining accuracy. Training a neural network is an iterative procedure because the network is always improving, but this iterative nature introduces complexity in the network. Training a network is a complex job if done manually, therefore, we need programs and software to perform this highly repetitive task, the programs that can handle a huge amount of data.
MATLAB contains all the functionalities required to train a machine, hence making it the ideal choice for neural networks. In little or no time, MATLAB can perform numerous iterations. Datasets needed for artificial intelligence contain diverse data types and MATLAB reads and writes almost of the data types. MATLAB has multiple control flow functions that can be convenient for Artificial Intelligence. There is a separate user interface in MATLAB dedicated to neural networks. MATLAB can easily be coupled or interfaced with other data processing software, thus making it convenient to perform data analytics. MATLAB contains built-in functions for data interpolation which is used for predictive analysis. In addition to training the network, it is imperative to continuously update dataset to improve accuracy and, in MATLAB, existing datasets can easily be modified. Regression models of MATLAB make it easy to recognize patterns and interpolate the data. A neural network works in the form of layers of multiple parallel networks and MATLAB has a feature of parallel looping, hence it is convenient and feasible to develop the network on MATLAB. The software provides all the necessary operations and functions for neural networks in a single package. MATLAB can perform numerous iterations required for AI in a few seconds. The software has diverse built-in functions which decrease the complexity and processing time of code. The same functions are applicable for a huge amount of data without changes.