🍆 🔳 🌟 RPA + Machine Learning = Intelligent Automation 😽 🅿️ 👊🏿

Salute, Khabrovites! In March, OTUS launches a new course , Software Robot Developer (RPA), based on UiPath and PIX . In anticipation of the start of the course, we have translated for you useful material about what will happen if you integrate RPA into machine learning.

Robotic process automation (RPA) has made a buzz in many industries. As the business is focused on digital innovation, automating repetitive tasks in order to increase efficiency and reduce errors caused by the human factor is a priority.

Robots will not get tired, will not get bored and will clearly complete tasks in order to help their human colleagues to increase productivity and free up time for performing higher-level tasks.

In addition to a simple RPA, intelligent automation can be achieved by integrating machine learning and artificial intelligence into robotic process automation to automate repetitive tasks with an additional layer of human-like perception and forecasting.

RPA

According to the basic idea, RPA is not intended to imitate human intelligence. As a rule, she simply imitates primitive human activity. In other words, the RPA does not imitate human behavior; it imitates human actions. Behavior implies a reasonable choice from a range of possible options, while an action is simply a movement or a process. RPA processes are most often driven by predefined business rules that can be strictly defined, so RPAs have limited ability to work with ambiguous or complex environments.

You can also read about how to combine AI and RPA to create intelligent automation .

On the other hand, artificial intelligence is a simulation of a machinehuman intelligence , which implies the presence of a wider range of possible output and results. AI is both an intelligent decision-making mechanism and an imitation of human behavior. At the same time, machine learning is a necessary step towards the creation of artificial intelligence. It contributes to the emergence of deductive analytics and predictive decisions, which are getting closer and closer to the results that could be expected from a person.

The IEEE Standards Association published its IEEE Guide to Intelligent Automation Rules and Concepts in June 2017. In it, robotic process automation is defined as “a pre-configured instance of software that uses business logic and a predefined choreography of actions to fully autonomously perform a combination of processes, actions, transactions and tasks in one or more unrelated software systems to provide a result or service with the possibility of control person in exceptional cases. ”

In other words, an RPA is simply a system that can repeatedly and without failures perform a specific set of tasks, because it can be programmed to perform this type of work. However, the RPA cannot use the learning function to modify itself or adapt its skills to a different set of conditions, and this is where machine learning and artificial intelligence contribute most intensively to the creation of more intelligent systems.

Process control or data management?

Intelligent automation is a term that can be applied to the more complex area of the continuum of workflow automation, consisting of robotic workstation automation, robotic process automation, machine learning, and artificial intelligence. Depending on the type of business, companies often use one or more types of automation to increase efficiency or effectiveness. As you move from process-driven automation to more flexible data-driven automation, additional costs arise in the form of training data sets, technical development, infrastructure, and specialized knowledge. But the potential benefits in terms of new ideas and financial development can increase significantly.

Companies should now consider integrating machine learning and artificial intelligence with traditional RPs to achieve intelligent automation in order to stay competitive and work efficiently.

Intelligent Automation

Intelligent automation relies on data integrity

As part of intelligent automation, training data is a central component on which everything else depends. In industries such as autonomous driving and healthcare, where decisions made by AI / ML can have serious consequences, the accuracy of training data that informs about these types of decisions is critical. Since the accuracy of modern models of artificial intelligence and machine learning that use neural networks and deep learning approaches 100%, these mechanisms work more autonomously than ever and can make decisions without human intervention. Small deviations or inaccuracies in the training data can have fatal and unforeseen consequences. Thus, data integrity and accuracy is becoming an increasingly important aspect.as people begin to rely more on decisions made by smart machines for complex tasks.

Accurate machine learning models require accurate training data

The integrity of the data includes the presence of representative source data, the exact markup of this data before the training phase, testing and deployment of the machine learning model. The iterative workflow of data preparation, feature engineering, modeling and validation is the standard work plan for data processing.

Any Data Science professional will tell you that having well-labeled training data is probably the most important ingredient in making a model. Examples of “dirty” data can be missing, biased data, outliers, or simply data sets that are not representative of the data that will be worked on in production. Character engineering is also an important step in machine learning, i.e. selection of data characteristics that are likely to be the most important to ensure the accuracy of the forecasting of this model. In a neural network, where parameters are superimposed on one another, the correct definition of key features in each iteration is crucial for the successful construction of the model. Poor training data can lead to incorrect selection or weighting of signs,which in turn will lead to the formation of models that cannot be used for a wider set of data from production.

For example, for a model that detects individual organs on an MRI, you need to select representative training images from a specific MRI device, and then precisely identify specific areas of interest for each organ, which will lead to an improvement in recognition results, instead of simply using photographs of these organs from public sources. Another example is a vendor billing system that uses optical character recognition (OCR) to programmatically extract relevant information from invoices. Key fields in each invoice, such as “Address”, “Name” and “Summary”, must be clearly separated from the body of the various types of invoices so that the model can work accurately and efficiently. If these elements are not marked out completely or incorrectly, then the accuracy of the resulting model will suffer.

The problem with objectivity

Modern models of artificial intelligence and machine learning differ from human intelligence in that they are completely dependent on the source data and usually do not have an automatic recursive mechanism for obtaining and processing new data for course correction, that is, continuous retraining. This means that poorly balanced data obtained during training can eventually lead to unforeseen bias and unexpected (and sometimes offensive) results. When a significant amount of bias appears in the system, it becomes difficult to rely on the decisions made by this system.

Good data annotation leads to high-quality intelligent RPA

Accurate training data is at the heart of most successful Data Science projects. With accurate data annotation, machine learning models and artificial intelligence models can make more accurate decisions, and in combination with fundamental RPA processes, companies can achieve truly intelligent automation.

That's all. If you have read the article to the end, we invite you to a free lesson in which you will learn how to write a robot in UiPath, which reads data from csv and xlsx and automates the sending of results by e-mail.

RPA + Machine Learning = Intelligent Automation