Neural prediction uses an artificial neural network to process and contextualize data. Based on available data, training, and experience over time, the network can make predictions about future data. These projections can be used in a variety of ways, from making decisions about stock market purchases to developing scientific research. Computer scientists in the field of neural networks focus on the development of more accurate, extensive, and useful systems for use in neural prediction and related activities.
The human brain offers a number of advantages when it comes to processing information. Artificial neural networks aim to capitalize on some of these while adding substantial data-crunching ability. For instance, they make use of non-linear thinking, much like humans do.
Instead of working through a series of decision trees, the artificial neural network can think outside the box. It can also learn over time, both through initial training and experience after deployment. Search engines, for example, use neural networks to improve the accuracy and quality of search results.
One use of neural prediction is in the processing of large volumes of data to develop predictions for future datasets. The network can learn from its mistakes to refine its predictions. This can be used in activities like meteorology, economic forecasting, and retail product placement. A neural network might collect data on buying habits and make suggestions about how to place products in a store for maximum sales. Neural prediction data can take advantage of patterns of consumer behavior that may not be immediately evident without careful analysis.
This can also be useful in data mining. Neural network prediction allows for rapid data processing and extraction of useful material. Information gleaned from data mining can be used for activities ranging from refining customer service to filtering large amounts of intelligence data in search of important information. It can also predict data within a pattern, taking note of abnormalities that may be indicative of changes or a problem. For example, anomalies in travel patterns through an airport might be indicative of a security threat.
Developing advanced artificial neural networks can require complex programming skills. Programmers and technicians also may need substantial processing power to run their networks successfully. They may work in teams to develop efficient and effective networks capable of complex neural prediction. In experimental research, each failure becomes a learning opportunity. In the wild, failures can be much more costly, because people may have made decisions on the basis of the projection.
