What is Neuromining?

What is neuromining? Most people with an interest in one, or more aspects of the triumvirate; Artificial Intelligence, machine learning, and human behavior, will probably be familiar with the term Neuromining, as it, plainly put, is the term for when all three things are applied at the same time in an attempt to predict online user behavior.

It is becoming increasingly difficult to collect meaningful online user data through traditional channels, such as cookies and other consent-based tracking software. This means that in order to get an idea of people’s behavior online, very large behavioral models must be made, and they require data – and a lot of it.

What is neuromining?
With the cookieless future fast becoming a reality, new ways of predicting user behavior online are needed, which have led many towards the concept of neuromining to do just that.

This is essentially what neuromining is. It is the process of extrapolating anonymized user data with the help of machine learning and artificial intelligence to build predictive models of future human behavior online. This is all in order to predict future behavior with the biggest possible certainty without potentially violating privacy laws across the web, or running up enormous costs to collect detailed behavioral data.

If done well, proponents say, machine learning and artificial intelligence can create synthetic data that is just as valuable and that will create just as many insights as organic data will. However, neuromining comes with an added twist. The algorithm can potentially be a self-sustaining loop of data, that can then be fed back into the algorithm to create more extrapolative data. This creation of more data can in some ways lead to more stability, but it also risks running away with itself and exaggerating minor differences into central components of the conclusions – without those conclusions necessarily being right. 

What is neuromining? Neuromining works by extrapolating data from a relatively small set of real-world data and approximating large-scale user behavior through machine learning.
Neuromining works by extrapolating data from a relatively small set of real-world data and approximating large-scale user behavior through machine learning.

The idea behind neuromining, which is building predictive behavioral models from small “real-life” sample sizes, is fairly common in statistics. It is often used to verify methods, where algorithms can be used to make, for example, best-guess predictions on missing data. Data and models generated from this sort of bootstrapping should be clearly marked as using synthetic data as it is synthetic data and not taken from a real-life data collection effort. 

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