The patent that has just been awarded to Knowledge Leaps is for our continuous learning technology. Whether it is survey data, purchase data or website traffic / usage data., the technology we have developed will automatically search these complex data spaces. The data spaces covers the price-demand space for packaged goods, or the attitudinal space of market research surveys and other data where there could be complex interactions. In each case, as more data is gathered – more people shopping, more people completing a survey, more people using an app or website – the application updates its predictions and builds a better understanding of the space.
In the use-case for the price-demand for packaged goods, the updated predictions then alter the recommendations about price changes that are made. This feedback loop allows the application to update its beliefs about how shoppers are reacting to prices and make improved recommendations based on this knowledge.
In the survey data use-case, the technology will create an alert when the data set becomes self-predicting. At this point capturing further data is unnecessary to understand the data set and carries an additional expense.
The majority of statistical tools enable analysts to identify the relationships in data. In the hands of a human, this is a brute-force approach and is prone to human biases and time-constraints. The Knowledge Leaps technology allows for more systematic and parallelized approach – avoiding human bias and reducing human effort.
TechCrunch recently published an article which describes what I am building with the Knowledge Leaps platform (check out website here).
Knowledge Leaps, is a soup-to-nuts data management and analytics platform. With a focus on data engineering, the platform is aimed at helping people prepare data in readiness for predictive modeling.
The first step to incorporating AI in to an analytics process is to build an application that automates grunt work. The effort is in cleaning data, mapping it and converting it to the right structure for further manipulation. It’s time-consuming but can be systematized. The Knowledge Leaps application does this, right now. It seamlessly converts any data structure into user-level data using a simple interface, perfect for those who aren’t data scientists.
Any data can then be used in classification models using an unbiased algorithm combined with k-fold cross validation for rigorous,objective testing. This is just the tip of the iceberg of its current, and future, functionality.
Onward, to the future of analytics.
When we excitedly tell people that the new version of Knowledge Leaps incorporates k-fold validation, their eyes glaze over. When we tell people about the benefits of this feature, we usually get the opposite response.
In simple terms, k-fold validation is like having a team of 10 pHDs working on your data, independently and simultaneously. The application doesn’t produce just one prediction, it makes 10 which are all independent of one another. This approach outputs more general models, these are closer to a rule of thumb and are consequently useful in more contexts. Another step toward human-centered analytics without the human bias.