Perhaps what gives artificial intelligence that characteristic of ‘intelligence’ is its ability to learn; to be taught through large datasets. AI applications can either self-learn or are fed large datasets in supervised learning. Naturally, there are debates about which learning model is better with people comparing self-learning to the effectiveness of learning a new language by immersing in a new culture. But is it really that simple?

This post explores the methods of self-learning in AI and discusses the most suitable ones for your business goals.

Supervised learning

Supervised learning is a machine learning approach where the algorithms determine a predictive model using known data points tagged with outcomes. It remains the most popular form of machine learning.

Essentially, if we have an input X and output Y, the algorithm will learn to map the function Y=f(X), till a new set of values of X can be fed to generate accurate Y values. Initially, the algorithm will have to be given sufficient databases containing X and Y values, adjusting for the error till a reasonable level of accuracy has been reached.

Supervised learning is generally used to solve two types of problems:

1. Classification: In these problems, the input data has to be placed into categories. The algorithm should learn to recognize specific attributes in each item within a dataset, and develop a model for defining them into acceptable categories. For instance, a classification problem may involve recognizing specific disease symptoms in a bunch of people and matching them to possible diseases.

2. Regression: In these problems, the relationship between variables has to be identified. Finding the correlation between variables will help to predict what the output variable will be. You can have a set of data points having real or continuous value and perform a regression to predict the next output. For instance, it could be used to track price movements and predict the next move.

Unsupervised Learning

In unsupervised learning, you only have the input dataset but no output variables for training the algorithm. The data is not labeled and the algorithm needs to discover entities, attributes, and patterns itself, with no ‘teacher’.

Unsupervised learning is generally used to solve two types of problems:

Clustering: This is an approach where unlabeled data gets grouped based on some shared attributes. The algorithm has to discover the patterns for groupings or ‘clusters of data’.

Associations: This approach involves finding out the rules that govern relations between variables in your dataset. For instance: finding associations between people’s purchase behaviors, such as people who buy one item are likely to like another.

Reinforced Learning

Another area in machine learning, reinforced learning aims to maximize the production of the desired result. It is used to identify the best behavior or path in given situations. There is no right or wrong answer. The goal is to get to the reward efficiently. The machine is taught to make a series of sequential decisions. The algorithm has to learn this on its own.

What type of self-learning works best?

As you may have realized, the choice of methods of self-learning in AI depends on the problem at hand.

In situations where both methods can be used, supervised learning wins. While unsupervised learning sounds like low effort, it may take a lot longer to find the solutions you are searching for. It has an edge in solving classification, and regression problems, which are more common to businesses looking for insights from financial and operational data, etc.

You have an idea of what is happening with supervised learning and therefore, it’s easier to prompt corrections and reward better accuracy. Unsupervised learning is more opaque.

Also, once the problem has been solved, the data can be discarded after just keeping the model as a decision boundary. This is why analytics software use supervised learning.

Businesses everywhere are adopting omnichannel analytics software to track customer behavior and glean insights. With customers present in multiple channels, there is a need to synchronize the data from disparate sources. Tools like aura365™ can easily categorize customers and illuminate interaction insights.

Artificial intelligence is the most efficient solution to help unify data from voice and non-voice channels. You can build a comprehensive CX blueprint and offer seamless journeys to users.