Neural networks can solve a broad class of pattern recognition problems, and as such can be used to predict unknown outcomes, classify unseen data, or provide diagnosis.
Despite their esoteric name, neural networks are everywhere.
They are installed in microwave ovens to inform them when food is cooked, they diagnose disease, and tell us when our credit cards are being used fraudulently. Neural networks are frequently embedded in tiny microelectronic devices to make a clever decision about the devices environment. Do you remember the expert systems of the 80's?, well neural networks are the expert systems of the new millenium. Their novelty is that they do not rely on experts. Instead they rely on the fundamental nature of natural systems to behave in a manner consistent with mathematical models that we generally use to descibe them.
We have been developing mathematical models and neural networks for over seventeen years in a broad range of applications. It is possible that we might be able to help your business to exploit this technology to commercial advantage. For example, what if you could have some novel means of predicting where your business would earn its largest revenues? Alternatively what if you could slash your costs, or improve the quality of your output? Neural networks can address these issues.
We produce, search, manage and delete vast quantities of data every day. This has been an inevitable outcome of virtually costless data generation courtesy of computers, but now this precious resource - data - is being scrutinised to harvest its true deeper value.
The vast stores of data that litter the servers of banks, insurance companies, and social media servers for example, can be interrogated for novel and invaluable information about us, using techniques in artificial intelligence, (A.I,) and more mundane information technology. But what precisicely might we learn?
Given access to the right data about you, and the tools of A.I, a skilled investigator can predict what you will buy and when, idenfity fraudulent payments, indicate your prepensity for thrift or otherwise, calculate your likelyhood of insolvency, your general level of risk, your voting patterns, your gender, and sexuality, and your tolerance of diversity. As an example it is believed that the recent presendential election in the U.S. was swayed by big data analysis of Facebook data that succeeded in wooing the electorate to vote for Trump. Is even democracy not safe?