AI systems typically struggle with two issues related to learning – the need for large quanta of clean data and the resultant time taken to train and retrain the system. With Sainapse, we have addressed one of these issues at source i.e., the need for large quanta of data. Every enterprise is a data-generating system and every process within the enterprise is constantly generating new data. This data is truly multidimensional; normal representation techniques fail at that scale.

So how does Sainapse learn from the “least”? The first part is in knowing what part of the data to focus on. Most of the data generated by the enterprise is noise, the specific issue that we are training Sainapse on. The second part is reducing the number of dimensions in the data, without compromising on the system’s ability to predict the classification or recommend resolutions for an incoming issue.

The art in machine learning is the derivation of the minimum sufficient statistic that defines the distribution of the function without having to store each individual data point with all its dimensions. Feeding the system more data points once it has derived this model is not adding to the accuracy of the system. More is not necessarily better.

The “secret sauce” in Sainapse is our patented multi-field distance function including indexing algorithm. This function is able to autonomously identify which subset of the data is most relevant to learn from as well as uses appropriate parameters to do this most efficiently. Ergo, the ability to learn effectively from the least amount of relevant data.