4 things to know about ML - Understanding key automation technologies

October 19, 2020

Welcome to the second installment of ‘Understanding key automation technologies.’  This series aims to provide bitesize introductions to the complicated technological methods used in the automation world.  
For anyone who may have missed the first article on Robotic Process Automation, you can find it here.

This article will focus on Machine Learning (ML) and four key things to know, let’s get straight to it.

1. Definition:

  • Machine Learning is an Artificial Intelligence (AI) function which uses algorithms to enable computer programs to improve and learn over time, automatically.


2. Machines learning from experience

  • Algorithms are used to help the machines (computers, in most cases) emulate human behaviors by ‘learning’ from the information they are presented with.
  • A very effective use of ML is identifying trends from data and, overtime, predicting future patterns.
  • Once the machine begins learning, it will not need to follow predetermined formulas as it will begin to predict outcomes.  


3. Machine Learning has a huge range of uses

  • ML can be used in pharmaceuticals and medicine for diagnosis, research, drug manufacturing and radiotherapy; to support business process, e-commerce, image recognition, voice recognition – the list goes on.  
  • In the business world, ML is a great asset in the Financial industry’s ‘middle office’ processes. The ‘middle office’ tends to manage risk, business transactions and IT.
  • In this context, the types of ML we look to utilize at Roots Automation, can support the following process as they typically have a great deal of data to analyze: Exception Handling, Quality Assurance, Data Analysis, Reporting, Regulatory Compliance.  


4. Advantages of ML

  • Identifying trends
  • We touched on this briefly before, one of the most prominent advantages of ML is the ability to analyze vast quantities of data and pick up commonalities that may be missed by humans.
  • One common example of ML is through e-commerce, if we know person A likes product X, we think they will also like product Z.  
  • The algorithm has identified something person A likes and can continue to suggest further options to them.  
  • Continuous learning
  • With experience, the ML capabilities become more efficient and more accurate. To continue with the e-commerce example, if we know that person A in fact did not like product Z but likes product Y, this gives the machine further insight into person A and can make more informed future recommendations.  


This is the tip of the iceberg in Machine Learning and how it can be used in a variety of ways.

Here at Roots Automation, we have an out-of-the-box ML capability that allows our bots to learn the nuances of any process that it participates in. Could ML be an option to support your business processes?

To continue the conversation, book a free demo with our team or see how we can train the perfect Digital Coworker for your business, get in touch today: info@rootsautomation.com

Next up, 4 things to know about Cognitive Computing.

Machine Learning, Automation, ML, RPA

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