Artificial Intelligence, Machine Learning, and Robotic Process Automation, also known as AI, ML, and RPA, respectively, are three technologies that are frequently discussed in the realm of digital transformation. They are also technologies with unique applications and capabilities.
AI, ML, and RPA fall under the umbrella of intelligent automation and the use of technology to improve business processes and differ in their approaches, goals, and applications.
This article looks at Artificial Intelligence, Machine Learning, and Robotic Process Automation individually.
Differences between Artificial Intelligence, Machine Learning, and Robotic Process Automation
As mentioned above, AI, ML, and RPA, while somewhat related, have differences that set them apart for different purposes and goals.
Artificial Intelligence (AI)
AI refers to the creation of intelligent machines. These machines mimic human behaviour and perform tasks that would typically require human intelligence. Tasks such as learning, perception, problem-solving, and decision-making.
AI can be classified into two main types: Narrow or Weak AI and General or Strong AI.
Narrow AI is designed to perform specific tasks, such as speech recognition or image recognition. Its learning algorithm is meant for one task only, and this knowledge won’t be used for any other task. A great example of narrow AI is the chatbot.
General AI, on the other hand, can perform any task that requires AI. It is a representation of human cognitive processes in software. This means that when faced with unfamiliar tasks, it searched for a solution.
There is a third type of AI, called “Super AI,” which is sometimes considered a hypothetical state of AI. It relates to a software-based system with cognitive abilities beyond those of humans across a wide range of categories and fields.
From the above classification, we see that AI systems exist in three layers: reactive machines, limited memory machines, and self-aware machines.
The impact of AI on future technologies is still emerging. AI systems can be designed to operate autonomously or in collaboration with humans. They can use many different methods, such as rule-based systems, deep learning, robotics, expert systems, neural networks, and natural language processing.
Machine Learning (ML)
ML is a part of AI that lets machines learn from data and get better over time without being told how to do so. Its algorithms use statistical techniques to find patterns in data and create models that can make predictions or decisions.
ML can be categorized into three types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised learning is when a machine learns from labelled data. In supervised learning, the algorithm is provided with labelled training data. It learns to predict the correct output based on the input data.
Unsupervised learning is when a machine learns from unlabeled data. In unsupervised learning, the algorithm is given data that hasn’t been labelled. It has to find patterns and relationships in the data.
Reinforcement learning is when a machine learns by trial and error through feedback from its environment. In reinforcement learning, an agent learns what to do in a certain environment to get the best result or the most reward.
The goal of ML is to enable machines to learn from data and improve their performance over time.
Robotic Process Automation (RPA)
RPA is a type of software automation that automates repetitive, rule-based tasks using AI and ML. RPA software can be set up to do things like click buttons and type on keyboards like a human would. It can also interact with digital systems and software applications, such as websites, databases, and enterprise resource planning (ERP) systems. It fills out forms or copies and pastes data between applications.
RPA performs tasks such as data entry, data extraction, invoice processing, customer service, and report generation. Its goal is to improve the efficiency and accuracy of business processes by automating repetitive tasks.
RPA can boost productivity and accuracy by reducing errors and allowing humans to focus on more complex tasks. However, it is not considered true AI, as it cannot learn from data or make decisions beyond the rules it has been programmed to follow.
Conclusion
While Artificial Intelligence, Machine Learning, and Robotic Process Automation are all related to the field of automation, they have distinct differences in terms of their capabilities and applications.
AI is a broad field that focuses on making machines that can do tasks that usually require human intelligence. ML is a subset of AI that focuses on making algorithms that can learn from data and make predictions or decisions. RPA is a type of software that automates repetitive, rule-based tasks. When compared to AI and ML, which focus more on thinking and learning, respectively, RPA can be thought of as performing tasks.
Each of these technologies has its own unique strengths and weaknesses, and they can be used together to create intelligent automation solutions that can transform businesses and industries.
