Machine Learning using RPA
It is difficult for RPA alone to be smart. RPA provides a quick way of automation however it has its own limitations. RPA works well in situations where processes and decision-making is clearly defined. In any RPA-based automation, it is important to split the larger process into two categories.
- Silent Automation
- Automation with Human Intervention
Many times, it is not practical to automate a big process fully. Most process requires a human to take the decision and continue to the next stage. In such cases where vast amount of knowledge cannot be effectively defined into algorithms, RPA will not be able to automate that process and will need human intervention. That is when Machine Learning (ML) comes into play to solve the “knowledge” problem.
Machine Learning (ML) is an emerging technology and has matured to a certain degree where it can be applied to solve real-life problems. ML essentially works on the principle of encapsulating a large amount of data (or knowledge) into some form of mathematical model. The model can be utilized to apply this knowledge for solving complex problems through Automation.
Machine learning works as follows: Employees work in a particular department or do certain tasks for many years and apply that knowledge to decide what to do and what not to do. ML is applied to a multitude of problems where there is access to large volumes of historical data which can be used to predict or make decisions in certain areas. Unlike developing an algorithm; ML is based on building a repository or knowledge base. It is like introducing a SMART AGENT into the RPA process. The Digital Worker becomes ‘smarter and more efficient” by gathering additional information so that more accurate decisions are made by the digital worker.
Many RPA platforms like Blue Prism, UiPath and Automation Anywhere have developed robust ecosystems and are implementing their own powerful machine learning algorithms. With the combination of RPA and ML, BOTS are now capable of capturing previously unidentifiable data like signatures, images in under 100 milliseconds, automating complex applications and optimizing image recognition as well.
ML uses Artificial Intelligence (AI) that enables systems to learn from data without being explicitly programmed. ML is based on the premise that technology can process data and learn from the data to help make better decisions based on new information without constant supervision of programmers. For example, Netflix or Spotify use ML to store what movies or music a subscriber is watching or listening to in a repository and will be able to create a pattern from that data, improving accuracy as it learns and offers music/movies specifically tailor made to your liking. Spotify’s machine learning algorithms uses your data (i.e. the songs you listen to) to create a weekly two-hour-long playlist of music it thinks you’ll enjoy.
Netflix, YouTube, Amazon, and many other services we consume, use ML in this way, using your viewing/ browsing history to recommend content or products they think you’ll like to view or buy. Hence companies like these seem to know you as well or even better than you know yourself. That’s because the more you use these or any other services, the more they learn about your viewing pattern and based on the pattern their recommendations end up being more and more accurate. This is machine learning in action.