There are a lot of acronyms in the Artificial Intelligence (AI) world to keep straight. Robotics Process Automation (RPA), Intelligent Document Processing (IDP), Cognitive Automation, and Machine Learning (ML) are just a few important ones.
ML is practically important because it provides businesses with insights into consumer behavior and company operating patterns, as well as assisting in the creation of new rules. So, what is it?
What Is Machine Learning (ML)?
Machine learning is a type of Artificial Intelligence (AI) program to make predictions and decisions. ML algorithms can be trained to find patterns and features in huge amounts of data and make decisions and predictions based on new data.
There are several approaches to getting machine learning right, using basic decision trees, clusters, and layers of artificial neural networks (the latter giving way to deep learning ), depending on the task you want to accomplish and the type and amount of data at your disposal.
The training process involves the ML model optimizing its functionality so that it can make accurate predictions about the data. See an example of how ML predicts loan approval below:
The formation of more complex ML models, such as neural networks, differs in several ways, but they are similar in that they use a gradient-descent approach, combining a value-weighted variable with the input data to generate an output value, and then optimizing the output value to produce a model as close as possible to the desired one.
Read how to use machine learning for the underwriting process in insurance.
Types of Learning: Supervised vs. Unsupervised
Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are two basic approaches: supervised learning and unsupervised learning. The type of algorithm you choose to use depends on what type of data you want to predict.
Supervised learning is a type of ML that requires less training data than other machine learning methods. It facilitates training, and the resulting model can be compared with the actual labels results. Supervised learning can be separated into two types of problems when data mining: classification and regression.
Unsupervised learning is used by machine learning algorithms to analyze clusters of unmarked data sets. ML algorithms detect hidden patterns in data without human intervention, so they are unattended. Unsupervised learning models are used for three main tasks: clustering, association, and dimensionality reduction.
Difference Between RPA and ML
Machine learning is a component of AI, so it makes no sense to use the two terms interchangeably. The difference between RPA and ML is that RPA lacks the machine learning’s built-in intelligence, while intelligence is in RPA rather than AI.
The main difference between RPA and machine learning is the presence of a certain level of intelligence and learning ability. Moreover, artificial intelligence differs from ML because it demonstrates human thinking and can deal with complexity.
Note that machine learning uses structured and semi-structured historical data to learn and make prediction models. ML does not fall within the scope of artificial intelligence, because it works with predefined fields of knowledge.
How to Start Investing in Machine Learning
When investing in machine learning, consider the cost of training your learning model. Even if it’s not as expensive as the AI route, you still need to check your budget.
In addition, it is essential to have clean data at your disposal to train your model with data points with precise labels. Training your model for unclean data can negatively affect its effectiveness.
RPA, ML, and AI offer exciting opportunities for the future of work. Making the right decisions for your organization requires understanding your process needs and your company’s position in terms of technological innovation and adoption.
By: Elsa Ajarwati