Robotic process automation (RPA) is an exciting innovation in automating data, and knowledge-centric service works. A set of technologies underpins these metaphorical software robots(bots). To grasp the strength of RPA in taking over humans’ role in service and its likely progression, we need to pay attention to underlying technologies. Hence, this article provides a brief overview of a few of 14 robotic process automation technologies. These technologies are general-purpose; they are not necessarily developed for the sole purpose of RPA. However, each of them has the underlying capability of mimicking humans’ cognitive and sensory abilities. Instead of explaining basic principles, this review focuses on the strength of these technologies from the perspective of their contributions to RPA.
The concern of job loss due to automation began on the factory floor. Initially, service jobs were immune to automation. But the advancement of computers, connectivity, and ease of software development has changed the scenario. Initially, it took away number crunching, data organization, memorization, and document preparation work from humans. But increasingly, a higher-level role of humans like gathering data from filled forms, predicting the sales volume, screening resumes, processing invoices, and verifying signatures are being taken over by RPA. Furthermore, in addition to automating micro-tasks, innovators are leveraging robotic process automation technologies to mimic human roles in executing a complete business process, like assessing the credit rating of an individual or opening a bank account. Here is a brief overview of them from the perspective of RPA.
Optical character recognition:
Optical character recognition or optical character reader (OCR) is a technology for recognizing text in printed or handwritten documents. The basic technique is to produce the image of text and recognize characters. The origin of this robotic process automation technology is in the invention of Emanuel Goldberg. In 1914, using mechanical scanning, he developed a machine that read characters and converted them into standard telegraph code. Over the years, there had been significant progress in image formation, processing, and character recognition. Contemporary OCR can recognize handwritten characters of multiple languages.
Its automation applications include (i) data entry from cheque, passport, invoice, bank statement, receipts, etc. (ii) traffic sign recognition, (iii) extracting business card information into a contact list, (iv) converting handwriting in real-time, (v) reading number plates of automobiles, and (vi) making scanned documents searchable. Due to its ability to mimic human intelligence in stated applications, it’s a mighty member of robotic process automation technologies.
Voice recognition and speech synthesis (VR & SS) technologies:
This technology started in 1952 by recognizing formants (broad spectral maximum) in the power spectrum of each utterance. The objective of three Bell Labs scientists was for single-speaker digit recognition. However, this is not an isolated technology. It comprises many other members of robotic process automation technologies such as neural networks and deep feedforward and recurrent neural networks (deep learning). Despite having ancient history, modern speech synthesis began with the development of Noriko Umeda’sthe first general English text-to-speech system in 1968. Over the last more than 50 years, this technology has substantially improved, leading to supporting practical innovations like Apple’s Siri or Amazon’s Alexa.
The fusion of speech recognition and synthesis form a very powerful technology in the portfolio of robotic process automation technologies. Pertaining to RPA, some of the applications of voice or speech recognition technologies are interactive voice response, real-time speech writing (court reporting), automatic emotion recognition, and automatic subtitling with speech recognition.
Machine translation (MT):
This is a compelling RPA technology. The advancement of MT has the potential to make RPA overcome a fundamental limitation of human-centric service delivery; this is about language dependence. This technology has a root in the work of Al-Kindi, a ninth-century Arabic cryptographer. However, the use of digital computed in MT started in 1946. From neural to dictionary-based, there have been multiple approaches of MT.
Machine learning (ML):
ML is a powerful technology that supports algorithm development that can improve performance through experience earned data. This is a subset of artificial intelligence. Machine learning algorithms build a model based on sample data or training sets. Additional training from experience earned data keep improving the ML performance. ML is used in other RPA technologies such as speech recognition and computer vision. ML algorithms benefit from diverse subfields such as computational statistics, data mining, neural networks, mathematical optimization, exploratory data analysis, and unsupervised learning. In RPA, ML is vital to learn from observing how humans work in delivering services. Subsequently, this learning leads to mimicking human acts in critical RPA activities like recognizing and tracking customers’ affinity towards certain products, content, or services.
Artificial intelligence (AI):
Mimicking human-like intelligence or gaining artificial intelligence is a long-term vision of RPA. This is about human-like capability in perceiving the environment and taking actions that maximize its chance of achieving its goals. Some of the RPA related AI goals are (i) reasoning, problem-solving, (ii) knowledge representation, (iii) planning, (iv) learning, (v) natural language processing, and so on. Some of the technologies explained so far contribute to AI, and they are used in RPA.
Neural network (NN) and deep learning:
A neural network is a connectionist learning technique. It records learning from training by changing the weights of different neurons. Deep learning is mainly based on neural networks, having a considerable number of intermediary layers and a number of neurons. Learning can take place by supervised, semi-supervised, or unsupervised techniques. Deep-learning usages different architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks. It has been applied to in RPA related other technologies such as computer vision, speech recognition, natural language processing, and machine translation.
Image processing and computer vision (CV):
Image processing and computer vision technology underpin many other robotics process automation technologies. For example, this technology is at the core of OCR. Besides, extracting information from images and recognizing photographs or faces are indispensable activities for RPA to take over many business processes. Among many other developments, the invention of electronic image sensors and the continued evolution in producing high-quality images with even smartphones have substantially contributed to RPA. Furthermore, recognition technologies like neural networks and machine learning also enable computer vision to play an essential role in RPA.
For RPA, finding data patterns is essential to show human-like intelligence in executing business processes. To serve this purpose, data analytics, as it is a subject of systematic computational analysis of data or statistics for the discovery, interpretation, and communication of meaningful patterns in data, is a high-value RPA technology. It applies the science of statistics and operational research. It’s a valuable tool for RPA to analyze, predict, and improve business performance.
Other robotic process automation technologies:
Some other valuable technologies for RPA are (i) Pattern recognition, (ii) Deepfake, (iii) Smartphone, (iv) Interactive voice response (IVR), (v) Expert Systems, and Chatterbots, and (vi). Natural language processing (NLP). For example, Deepfake technology could support humans like artificial characters to offer customer care services. Similarly, chatterbots are helpful to engage in micro dialogue with service seekers for understanding the nature of service and progressively delivering it.
This article provides a brief snapshot of robotic process automation technologies. Each of these technologies has been progressing. As a result, RPA innovators have been finding more valuable means to improve the performance of RPA in taking over increasingly more complex business processes–transforming the future of work.
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