Chatbots represent one of the first and most tangible results of the practical application of artificial intelligence. As relatively young intelligent systems, chatbots are constantly evolving to improve their performance. Studies in the field of AI have led experts to develop different types of chatbots, each with specific tasks and characteristics.
Generally speaking, chatbots are distinguished into two categories:
The fundamental distinction between these two categories of chatbots concerns the language they can process and the responses they are able to provide. Chatbots, by definition, converse with humans and provide relevant responses.
However, there are significant differences between the two groups. Old generation chatbots, also known as rule-based chatbots, process human language using a predefined set of data. On the other hand, next-generation chatbots leverage Natural Language Processing and Machine Learning to understand human language and offer articulate and complex responses.
Other, even more specific types of chatbots belong to the two categories:
For task-oriented chatbots or old generation chatbots, and
for the next-generation conversational chatbot category.
Let us see in detail what the characteristics of each type of bot are.
These chatbots are designed to have extensive and specific knowledge about certain topics or industries, enabling them to respond contextually to any question and continue the conversation effectively. They are multi-turn conversational agents capable of maintaining an enriched conversation beyond simply answering a question. For example, a support chatbot could be used by a company to guide a customer through the process of resetting an account password or to answer common service or product-related questions. The benefits to companies are many and obvious, such as the ability to offer a 24/7 available service to Clients and to optimize company resources by moving staff to other strategic areas.
Unlike support chatbots, skill chatbots are noncontextual, that is, they do not have specific skills but aim to provide quick responses to commands. They are often used to automate transactional processes and simplify user-application interactions. They are not necessarily service-oriented, but rather to perform specific tasks efficiently. For this reason they are referred to as single-task. An example of skill chatbots are virtual agents for spell-checking, which are very useful when a new language is being studied. Skill chatbots can be exploited at the enterprise level in human resources and training.
This is the next generation of chatbots, virtual assistants are multi-turn virtual agents that are expected to be able to both manage a conversation and offer a quick and accurate solution to a problem. They are powered by big data and use machine learning to enhance their conversational ability.
These chatbots are able to understand natural language in a more advanced way, which means they can handle more complex conversations and adapt to users' needs.
They are often used to improve customer experience and support. This type of bot must have in-depth knowledge in different areas and be able to interact and understand with natural language. Examples of conversational chatbots such as virtual assistants are Siri, Alexa, or Google Assistant. Virtual assistants differ from Support Chatbots and Skill Chatbots because they possess widespread contextual knowledge, unlike Support Chatbots which are highly specific.
In addition, the virtual assistant is designed to have immediate reaction time, exactly as required of a skill chatbot. However, if queried repeatedly, it must also be able to delve into the topic specifically or execute related commands. Let's take an example: if you ask the virtual assistant what the temperature in the house is, the chatbot, in addition to being able to provide the answer, must also be able to understand and execute the next command, which could be to turn on the air conditioning and turn it off when it reaches a certain temperature. Task-oriented chatbots are not designed to perform this type of task.
With the latest developments in research and new innovations such as those of ChatGPT or GPT-4, virtual assistants can be equipped with generative artificial intelligence models. This uses advanced artificial intelligence models, such as deep neural networks, to generate more complex and "creative" responses. They can handle more natural conversations and are able to answer more complex or unexpected questions. These chatbots are particularly useful for applications that require a deep understanding of context.
In conclusion, chatbots can become valuable allies in our business and daily lives, contributing significantly to the automation of tasks that would otherwise require human intervention. Each type of chatbot offers specific functionality, and in a business strategy of using chatbots to handle specific tasks, it is essential to turn to solutions that adopt the most advanced language models.
With indigo.ai's cutting-edge approach and its Conversational AI technologies, companies can leverage the full potential of chatbots to improve the customer experience, optimize resources, and gain deeper insight into conversations with their users.