7 Common AI Techniques Used in Chatbots

Chatbots are essentially computer programs that mimic real human conversation through audio or text. This convenient technology is becoming an increasingly valuable asset for companies. These customer service chatbots are used to communicate information to users, help solve problems, and even conduct surveys. With the help of modern chatbots, businesses can offer a deeper lever of interaction than ever before.

Since their invention, chatbots have revolutionized the way businesses interact with their clients. As such, it is critically important to understand the AI techniques used in chatbots. These techniques help to distinguish chatbots from other technologies and make them distinctively unique.

Here are the seven common AI techniques used in chatbots:

1. Natural Language Processing (NLP)

Natural language processing is one of the most fundamental AI techniques used in chatbots. In fact, this is the foundation of understanding how artificial intelligence is being used in chatbots. Essentially a subfield of linguistics, computer science, information engineering, and artificial intelligence, natural language processing is concerned with the interactions between computers and human languages.

In particular, it refers to how to program computers to process and analyze large amounts of natural language data. When applied to chatbots, it allows them to speak and understand people as they talk, as a language choice. The most important thing to understand about this is that it gives chatbots the ability to operate on an “intention-based” system, where they are able to discern and address a user’s intentions.

2. Named Entity Recognition (NER)

Another important AI technique used in chatbots is named entity recognition. A subtask of information extraction, NER seeks to locate and classify named entities in text into predefined categories such as the name of a person, location, organization, contact detail, expressions of time, quantity, monetary value, percentage, etc.

This technique can be done in a number of ways. When building any conversational bot or dialog system, one of following approaches will be used: generative based, retrieval based, or heuristic based. All of these approaches rely on NER in some way or another and use it to fulfill their communication tasks.

3. Machine Learning in Chatbots

Of course, the use of AI in chatbots is more or less depending on the developers’ viability and the requirements for the particular field. Machine learning, which refers to the ability of a system (in this case, the chatbot) to learn from the inputs it experiences, is what allows the chatbot to continue improving over time.

One of the ways they achieve this is through natural language processing, discussed above, but that is only part of it. To achieve true general artificial intelligence, a chatbot or dialogue system needs to be able to do three central things: offer an informative answer, maintain the context of the dialogue, and be indistinguishable from the human.

In terms of that final requirement, there is still work to be done. As it stands, even the most sophisticated chatbots are unlikely to be mistaken as a human over an extended period of time. Nevertheless, it seems that chatbots have advanced enough that most humans are still willing to talk with bots as long as they are helpful, funny, or interesting.

4. Augmentation capacities

In addition to their capacity for conversation, AI chatbots offer other real advantages. For example, AI and chatbots can be used to monitor and draw insights from every conversation and learn from them how to perform better in the next one.

Called augmentation, it means that the machine doesn’t have to conduct the entire conversation. These days, chatbots can “step in” for routine tasks such as answering straightforward questions from an organization’s knowledge base or taking payment details.

5. Personality AI in Chatbots

As this “intention-based” functioning becomes more sophisticated, it allows programmers to create chatbots that have a different kind of personality or even a sense of humour. This is a hugely valuable asset for companies looking to convey certain brand values in all their interactions with customers. AI chatbots give users a more personalized, and therefore more memorable, experience by customizing responses and content based on user questions and interests.

6. Conversational AI in Chatbots

Are you familiar with Siri, Google Assistant or Amazon Alexa? These handy assistants many of us now rely on for daily tasks are a good way to illustrate how conversational AI is playing an increasingly important role when it comes to chatbots. The inclusion of AI boosts customer engagement, and as most people have now experienced first-hand, voice experiences and digital assistants such as Google Assistant, Siri, and Amazon Alexa make the dialog more compelling.

7. Problem Solving in Chatbots

In other situations, the speed of real-time analytics that today’s chatbots are capable of mean that they can raise an alert when they detect a problem on the horizon. Thanks to sentimental analytics, for example, a chatbot can now sense when a customer is becoming unhappy and can prompt a human operator to take over the chat or call.

Chatbots are also increasingly present in collaborative working environments such as Slack, where they can monitor conversations between teams and provide relevant facts or statistics at pertinent points. In the future, chatbots will probably be able to take things even further and propose strategy and tactics for overcoming business problems.