RPA, AI, ML: What’s the Difference?
A software robot mimicking human actions is RPA. A machine emulating human intelligence is AI. Two ends of an IA (Intelligent Automation) journey transcending from doing to thinking with rising complexities.
The Human Origin of Mechanised Intelligence
At first, humans were hunting in the jungle to fulfill their hunger — thanks to their realisation of the ability to see (eyes), listen (ears), and sense (mindfully aware of the surrounding environment). As humans were able to view, decide, and act, the smartest mechanisation species ever developed by Mother Nature started developing intelligence and learning capabilities. Continuous improvisation and iteration came handy for humans. Their further interaction with the external environment shaped their evolutionary ability to sequentially analyse for making smarter decisions with their learning brain.
AI, ML, & RPA: The Journey of Intelligent Learning
Just as humans act in sync with their core senses (intelligent mind) and body (procedural apparatus), modern robotics also have identical (albeit technological) automation capabilities. The advent of Robotic Process Automation (RPA) or metaphorical software robotics (bots — not robot software) has resulted in the development of business (secretarial) process automation technology. Powered by Artificial Intelligence (AI or machine intelligence), RPA essentially is a digital workforce of robots that mimic a variety of user-initiated human behaviour to perform a business process. However, it should strictly be noted that RPA isn’t AI and that the two are not substitutable but powerfully complementary. In the case of RPA, AI acts as an intelligent sensor, whereas RPA itself is the executing procedural apparatus. There are crucial differences that exist between RPA and AI. In order to elevate the robotic potential to the next level, Machine Learning (ML) capabilities are also harnessed.
Learning Key AI, ML, & RPA Variations via Definitions
To clearly set our learning in perspective, let’s individually define RPA, AI, and ML to also understand their vital differences in the process:
Robotic Process Automation is an emerging technology application practice that simplifies the execution of enterprise operations while minimising costs. Administered by logic and inputs, RPA automates business processes. RPA tools allow companies to robotically configure computer software and interpret applications. This allows companies to initiate transactional processing, data manipulation, response triggering, and communicating with digital systems. RPA systems develop a list of actions by observing the user perform a specific task in the graphical user interface (GUI) to then execute (in automation) by task repetition. RPA solutions generally use algorithms and are rule-based, requiring digitised and structured inputs for task automation.
Artificial Intelligence represents the demonstrably intelligent ability of machines to emulate (via machine learning methods) the natural intelligence (sensing) ability (or mentality) of humans. AI is capable of image recognition and problem-solving. A computer science definition of AI defines it as: “the study of ‘intelligent agents’ — any device that identifies its environment and takes actions that augment its probability of succeeding in achieving its goals is AI.” AI mimics cognitive functions associated with the minds of humans — e.g., learning. The Tesler Theorem states that “AI is whatever hasn’t been done yet.”
AI isn’t a routine task technology, unlike RPA. Research by Haugeland, 1985, defines the study of AI as, “the exciting new effort to make computers think…machines with minds, in the full and literal sense.” Its elaborative definition given by Andreas Kaplan and Michael Haenlein explains AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” AI (as a mechanical computer) receives unambiguous instructions from a set of algorithms (a finite sequence of clearly defined, computer-implementable instructions that solve problems or perform a computation).
Machine Learning’s acclaimed definition is given by Tom Mitchell as follows: “a computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E.” In other words, ML is the data analysis science that gets the computer algorithms (supervised, unsupervised, semi-supervised, and reinforcement) to act via automated analytical model building. These algorithms automatically get better by learning from experience — without the need for programming. In simpler words, in ML, a computer system is taught to accurately predict with data. Computer programs access and utilise complex data to detect (recognise) underlying patterns and learn (make decisions). In this, machines are trained to learn — acting as tools transforming information into knowledge. In essence, the goal of ML is to comprehend data structure for fitting in it the theoretical distributions that are considerably understood. An iterative approach is used by ML to learn from data — making automation easily possible.
The IEEE SA (IEEE Standard 2755) defines RPA as:
a “preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”
The IEEE SA (IEEE Standard 2755) defines AI as:
AI is “the combination of cognitive automation, machine learning (ML), reasoning, hypothesis generation and analysis, natural language processing and intentional algorithm mutation producing insights and analytics at or above human capability.”
The above stated clearly presents the fine lines of differences between AI, ML, & RPA. It is clear that while RPA is process-driven, AI (deductive analytics) and ML (prescriptive analytics and decision engines) are data-driven. While RPA ‘does,’ AI and ML respectively ‘think’ and ‘learn.’ While RPA requires to be unequivocally programmed, AI intelligently decodes — whereas ML acts as a distinctive scientific discipline (division) of AI poised to facilitate intelligent learning from experience. AI is essentially a technology that supports ML, RPA, and Natural Learning Processing (NLP). AI cumulates, manages, and comprehends unstructured data.
AI, ML, & RPA: The Core of Differences
Let’s quickly summarise the above-discussed core differences in AI, ML, and RPA for an easy reference:
· A notable difference among RPA, AI, & ML is that of ‘scope.’ The implementation timeline of RPA as a desktop solution is quick. It can instantly operate independently of the supporting underlying processes. AI solutions generally require higher integration to facilitate cognitive or human decisions.
· RPA imitates human actions. AI simulates human intelligence via machines.
· While RPA can deliver significant ‘value,’ AI can generate tremendous value.
· While RPA requires constant training, ML offers (adaptive) solutions based on learning.
· An RPA workflow can inculcate AI as its constituent — e.g., image recognition or text analysis.
· RPA is the rules-based automation that mimics human actions repetitive in nature.
· RPA assists in beating the current system shortcomings. AI converts these shortcomings into appropriate output.
· RPA facilitates chat-based human interaction that depends upon keywords.
· AI involves predictive analytics, speech, video, image, and emotional recognition. It also involves autonomous computing, deep learning, and machine learning.
· RPA is deterministic. AI is probabilistic but also has deterministic defends available.
· AI augments automation. RPA augments people with process automation.
· AI has the application of ML and RPA as its distinct processes.
· AI is the science of making the machines act.
· AI evolves with experience and hones execution. Learning, reasoning, and correction.
· ML (application of AI for trend recognition) is concerned with decision making via learning by pattern recognition in massive data sets assisted by algorithms.
· ML can review and interpret data to then act on that set of information.
The Way Forward Together for AI, ML, & RPA
RPA has made promising strides lately towards blurring its differences with AI. Upon assimilating AI with RPA, the automation process gets faster, and the automation continuum gets possible. As per Dave Costenaro, (AI R&D Head, Jane.ai), given the advancements now happening in cloud-based APIs and common data formats, various service types can now communicate with each other in automated workflows, reports The Enterprisers Project. This RPA technology development is poised to make it intelligent in the future — communication facilitates (growing) learning capabilities (vision and learning tasks). Upon its convergence with AI, businesses can augment useful automation growth in complex processes with predictive modeling capacities, states Kashif Mahbub (VP, Product Marketing, Automation Anywhere).
An illustration to summate the discussion is as follows: Consider an AI chatbot interacting with a customer as against with a call-center agent. Simultaneously, an ML-powered (adaptable) suggestion engine searches optimal matches capable of fulfilling customer needs (whilst autonomously bettering its own performance). At the same time, a straightforward RPA resource tool is busy making updates (executing actions depending on its configuration) in the customer profile for a reference in the future. What begins at the RPA level for organisations then transcends through AI to attain the final goal of ML-led automation. It suffices to note that AI and RPA are parallel technologies having varied goals and interfaces.
