How Does Machine Learning Work in AI Chatbots?

How Does Machine Learning Work in AI Chatbots?

We humans need to learn new things to expand our level of intelligence. The same happens with AI chatbots through Machine Learning (ML).

Come and find out what ML is, its different algorithms, and how it enables a machine such as a chatbot to learn.

AI Chatbots: What Are They, and What Are They Good For?

The term “chatbot” comes from the word “chatterbot” (chatter + robot), created in the 1990s by Micheal Mauldin.

As the name implies, it’s a conversational robot.

Today, these systems can communicate through written or voice messages.

However, talking robots are often referred to as voice bots, as their primary input is voice commands.

These communication systems are widely used to assist people or companies that receive large volumes of contact and need to automate those interactions.

By being able to automate certain processes, they alleviate the influx of contacts. In this way, they can optimize their handling.

The Types of Chatbots

Chatbots are often associated with Artificial Intelligence (AI). This happens because AI gives them the ability to handle requests without the need for human intervention.

However, some chatbots don’t have AI and, as such, are more basic.

You could say that they’re chatbots based on rules like “if x, then y”.

Non-AI Chatbots cannot understand spontaneous questions and only work based on keywords and decision trees (buttons).

Click here to learn about the different types of chatbots and which one best fits your needs.

How Do AI Chatbots Understand Us?

Chatbots and AI are distinct elements, although they’re related.

As we already mentioned, chatbots need Artificial Intelligence to be able to communicate fluidly. But AI doesn’t only act on chatbots.

AI is a term also applied to any machines that perform tasks typically performed by humans.

In the case of chatbots, there are used technologies related to communication.

Just as we need to learn to read and write and intuitively learn to speak, through the inputs we receive from the people around us, so chatbots need to learn, albeit in a slightly different way than we do.

As the name implies, NLP or Human Language Processing is the technology that enables the understanding and analysis of the large volumes of linguistic data that bots receive.

However, to fully work, chatbots need something more. It’s crucial that the machine can learn automatically from this data. That’s where ML comes in.

What is Machine Learning (ML)?

ML is the other essential technology for a well-functioning chatbot.

The term “Machine Learning” was coined in 1959 by Samuel Arthur, an American computer scientist who pioneered Artificial Intelligence and computer games.

According to IBM, Machine Learning gives systems the ability to learn from experience and improve their decision-making ability and predictive accuracy.

In other words, through the interactions that bots have with users, they can extract information and predict acceptable outcomes (responses). Therefore, they increase their efficiency.

How Does ML Really Work in an AI Chatbots?

Well, just like Natural Language Processing, ML is based on algorithms.

It’s these algorithms introduced into the system that receives and analyzes the data and produces the predictions.

The more data they receive, the more optimized their performance is. So, as time goes by, the bot’s “intelligence” increases.

The Different Types of Machine Learning Algorithms

Without going into too much detail, there’re four types of algorithms: supervised, semi-supervised, unsupervised, and reinforcement learning.

1. Supervised Learning

In supervised learning, the machine learns through examples.

The algorithm is made up of a series of examples of inputs and outputs, and from these, the system has to find a method to arrive at those same inputs and outputs when faced with new data.

The machine identifies patterns in the data, learns, and makes predictions. The operator corrects these predictions, and the process continues until the system achieves a high level of performance.

2. Semi-supervised Learning

This second type of algorithm is similar to the previous one. However, it uses both labeled and unlabeled data.

Labeled data corresponds to a set of training examples with labeled information. These examples consist of pairs with one input and one output.

In this type of learning, the algorithm receives pairs of labeled data and, with the information, it takes from them, learns to label the unlabeled data.

3. Unsupervised Learning

Unlike the previous types, in unsupervised learning, there is no operator.

The algorithm learns to identify patterns and relate information by studying data.

In this type of learning, the algorithm has to deal with large volumes of data and develop a structure for it.

This structuring can be accomplished by organizing groups with similar information (clustering) or reducing the size, i.e., the smallest number of variables considered finding the exact information.

As with the previous types of algorithms, the larger the volume of data handled, the greater the certainty and efficiency of the system.

4. Reinforcement Learning

Finally, reinforcement learning focuses on regulated processes. These processes provide sets of actions, criteria, and final values.

With the rules defined, the machine tries to find the best result by exploring and monitoring different possibilities.

The Visor.ai Chatbot ML Algorithm

Visor.ai chatbots are all ruled by the type of supervised learning algorithm.

This means that, based on the input and output examples provided to the algorithm, the machine analyzes, identifies patterns, and predicts the results.

Even so, these same results need to be confirmed.

This confirmation and correction (if necessary) can be done through the AI Trainer in the Visor.ai platform.

The AI Trainer is the tool that allows you to confirm and correct interactions that the bot had with users.

In other words, it’s possible to analyze whether the chatbot is giving the right answers to its customers and what was its level of certainty.

In cases where the chatbot didn’t know how to answer or gave the wrong answer, you can teach it. For this, you don’t need any technical knowledge, as the Visor.ai platform is low-code.

After the introduction of these corrections, the system trains the new data set and gets better performance.

Learn more at: AI Trainer: How to Train a Successful Chatbot.

What Are the Benefits of Having a Machine Learning Chatbot?

As we have seen before, we consider that a chatbot has AI when it has technologies that enable it to communicate effectively with a human being.

Companies see many benefits in implementing interaction automation solutions such as chatbots or email bots because:

  • Provide 24/7 service
  • Decrease response time
  • Allow the client self-care
  • Increase your team’s productivity
  • Increase the level of user satisfaction
  • Provide a personalized service
  • Expand your customer base
  • Increase lead generation
  • Reduces cost

In sum, with Visor.ai’s chat and email solutions, you can automate up to about 80 % of the daily interactions your company has.

So, don’t waste any more time, invest in smart solutions.


Design: Marta Ramos; Text: Filipa Perdigão

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