The question of whether or not a chatbot should have artificial intelligence (AI) is a recurring one. And in my opinion, there is no one-size-fits-all answer.
For most use cases, I recommend utilizing the advances in artificial intelligence (AI).
In the following article, I will give you an introduction to what it means to develop a chatbot with or without AI and what the individual advantages and disadvantages are.
To reiterate: What does a chatbot actually do in the visible area?
A chatbot works in a similar way to a human: when the user starts a chat with the bot, it begins a natural conversation. This could look like this, for example: “When is your store open in the morning?” The chatbot then responds, based on the information it has available: “Our store opens tomorrow at 9 a.m. and closes at 5 p.m.”. The user is happy and ends the conversation or asks further questions.
But what happens in the background of the chatbot, in the part that we can’t see?
And now you have to decide whether you want a bot with artificial intelligence or a bot based on structured questions and answers.
In general, chatbots that consist of structured questions are less complex. They may even be sufficient for a very simple use case.
However, as soon as you want your bot to answer more questions and the user also wants to use the free text input, I recommend choosing an AI-based bot.
What does a simple, structured bot look like?
Such a bot has a small knowledge base with limited capabilities. This knowledge database must have been created in advance by us humans. This means that we define questions that the user could ask and write the appropriate answers.
The bot can then only provide the correct output for specific instructions. In other words, the questions asked must be programmed in advance by us humans. Let’s take the weather bot as an example. He can easily answer the question “Will it rain tomorrow?”. However, if the chatbot is not programmed for this, the question “Will I need an umbrella tomorrow?” can lead to confusion. The chatbot would probably respond with “I’m sorry, I didn’t understand the question”. The bot can only know as much as we have told it beforehand.
In this case, I usually recommend designing the chatbot with buttons or a predefined menu so that the user can only ask things that the bot already knows.
These types of chatbots are often implemented on messenger platforms, such as Facebook, when users don’t necessarily need to interact with the bot much. For example, to generate newsletter registrations.
And what does an AI-controlled bot look like?
This type of chatbot understands human language and therefore does not require such specific user input. If we take the example of rain from above, an AI-based bot would have immediately linked the umbrella to rain and rain to weather and would have told the user whether it was going to rain or not. In other words, a deviation from the standard question does not necessarily confuse them.
Chatbots based on machine learning get smarter with every interaction. The effort behind these chatbots is greater than with structured bots, but the advantage is that they can be used for more complex topics.
NLP (Natural Language Processing) is behind the chatbot’s better understanding of natural language. NLP is a branch of machine learning and artificial intelligence.
Natural Language Processing helps the bots to analyze the user’s request and search for semantics.
NLP is made up of three components. Natural refers to something natural, i.e. actually the opposite of artificially created helpers such as chatbots. Language refers to the language. Processing refers to how this natural language is processed by artificial intelligence. We can therefore see NLP as a manual for understanding and communicating.
Through a combination of NLP and machine learning, chatbots become better assistants and can do far more than structured bots. Communication goes far beyond a simple dialog tree of the structured bot.
The intelligent chatbots can recognize natural human speech and respond accordingly. This also enables them, for example, to recognize ambiguities or the emotional component of a topic that the person is talking about.
NLP algorithms are based on machine learning algorithms and the more data is analyzed, the more precisely the bot can react.
The first step with an AI bot is to convert our human input into an understandable context for the chatbot. This requires a kind of interpreter who translates human language into bot language. This is also called the decoder. The decoder ensures that the speech or text can be analyzed. Once the bot has understood what the user wants from it, it can give an answer based on what it has learned, i.e. based on previous conversations.
You will soon realize that the possibilities of such bots are endless. But start with a small use case first, then you can expand it as far as you like(my article on chatbot expectations is also helpful here)
