Quidgest systems have been always synonym of flexibility and agility for organizations and are the result of the mass use of machines un software production.
To guarantee productivity and the development of people, we have the need of letting machines do whatever they are able to do.
To fulfill its automations process, Genio turns to patterns implemented along our 30 our experience, this process requires to focus on normalization targeting to develop efficient management systems, or, in other words, Lean.
We look at Machine Learning in this terms, as present and future.
After focusing on collecting Big Data now the focus is learning with them. This is a concept far from being new… what have changing in this years? We have more data and more computing power, and this evolution make us being able to talk about artificial intelligence in the present and not in the future.
Machine Learning can be defined as a data analysis technique which teaches computers something inherent to humans and animals: learn from experience. In the same way we learn to speak a language, it is also possible to “teach” a computer to be a language expert.
Typically, there are two big areas in Machine Learning: supervised and unsupervised learning. In the first group, we know the expected inputs and outputs (for instance, the Spanish word and the Portuguese word); in the second one, we know the stablished inputs and we expect to know how they relate to each other.
The supervised learning gets better results in terms of forecasting, as we can establish a base of expected results and evaluate the performance of the algorithm with validation groups.
In the short term, we are going to see Machine learning applied to systems and projects in Health, Banking and Human resources, among others, using consultants’ expertise to develop mechanisms that allow not only to store data and perform processes, but also to create added value and optimize operations.
Let’s look at the example of One Stop Shop or Business Processes Management solutions developed by Quidgest. Or even the user interaction patterns available in any of our solutions. They correspond to well defined tasks and flows, created from an empiric definition. Will these workflows be the ideal paths? Machine Learning can help to redesign this processes based on evidence.
Quidgest R&D area is focusing in a field of Machine Learning, right now: RPA (Robotic Process Automation).
Another area of action of ML is fighting against fraud with crossed services or transactions, making logical incompatibilities evident, that would support decision-making. In Human Resources, ML is bringing smarter evaluation systems, able to measure the direct impact of each employee to the financial results of an entity, and, in Health, we are able to identify risk factors and simulate how certain behaviors can influence population’s health in short, mid and long term thanks to machines learning from the past to predict the future.