Neural networks are integral to the development of artificial intelligence and machine learning. Neural networks in machines closely resemble how humans process information. In humans, there are millions of neurons that send electrical impulses and communicate at synapses.
Neural networks are modeled from human neurons. In computer science, neural networks interpret data by categorizing data. Neural networks are composed of nodes. Nodes are instances where processing occurs, similar to the role of synapses in humans. A node uses sensory input and data that assigns a weight to each node in order to interpret a given input. All of the nodes are then summed and put into the systems activation function to determine if and to what extent a piece of sensory output should influence the final output (similar to how humans either sense a stimuli or do not sense the stimuli) to determine if and to what extent a piece of sensory output should influence the final output.
Neural networks are able to perform more advanced functions when the number of node layers increases. Normally, these neural networks are written in a feature hierarchy. A feature hierarchy increases the level of complexity of abstraction as the data goes deeper into the neural network.
Neural networks can also be applied to big data. This application can particularly be useful as scientists grapple with how to develop systems to interpret the mass amounts of data now available to us. For example, neural networks could be applied to forecasting stock prices, predicting disease outbreaks, and identifying criminals through face detection. Although neural networks have very useful applications, there are some broader implications that we must consider. For example, what if facial recognition misidentifies certain races as criminals at a higher rate; this would lead to a greater amount of discrimination.