Difference between a bot, a chatbot, a NLP chatbot and all the rest?

Chatbots are an effective tool for helping businesses streamline their customer and employee interactions. The best chatbots communicate with users in a natural way that mimics the feel of human conversations. If a chatbot can do that successfully, it's probably an artificial intelligence chatbot instead of a simple rule-based bot.

But what is an artificial intelligence chatbot? Essentially, it's a chatbot that uses conversational AI to power its interactions with users. Because artificial intelligence chatbots are available at all hours of the day and can interact with multiple customers at once, they're a great way to improve customer service and boost brand loyalty.

What Is an NLP Chatbot?

An NLP chatbot is a more precise way of describing an artificial intelligence chatbot, but it can help us understand why chatbots powered by AI are important and how they work. Essentially, NLP is the specific type of artificial intelligence used in chatbots.

NLP stands for Natural Language Processing. It's the technology that allows chatbots to communicate with people in their own language. In other words, it's what makes a chatbot feel human. NLP achieves this by helping chatbots interpret human language the way a person would, grasping important nuances like a sentence's context.

In a more technical sense, NLP transforms text into structured data that the computer can understand. To do that, it must process large amounts of linguistic data. Keeping track of and interpreting that data allows chatbots to understand and respond to a customer's queries in a fluid, comprehensive way, just like a person would.

How is an NLP chatbot different from a bot?

As we've just seen, NLP chatbots use artificial intelligence to mimic human conversation. Standard bots don't use AI, which means their interactions usually feel less natural and human.

Most standard bots are what we call "rule-based" bots. They're designed to strictly follow conversational rules set up by their creator. If a user inputs a specific command, a rule-based bot will churn out a preformed response. However, outside of those rules, a standard bot can have trouble providing useful information to the user. What's missing is the flexibility that's such an important part of human conversations.

So what sets NLP chatbots apart? Here are a few of the characteristics of NLP chatbots that give them the edge over more traditional bots:

It can understand natural language. A Natural Language Processing (NLP) chatbot can understand and interpret natural language. But what exactly does that mean? NLP allows chatbots to interact with user input that includes spelling and grammatical mistakes, for one thing. It can even determine whether an input is an intention or a question, which can go a long way towards meeting the user's needs accurately and timely. Other aspects of natural language include emotional content and emphasis — things that you'd naturally pick up on if you were talking face to face with another person.

It feels more like a conversation than a questionnaire. One of the biggest challenges faced by chatbots is that a chatbot user can input anything literally. If the user interacts with a rules-based bot, any input that isn't expected can lead to a conversational dead end. Because of that, conversations with standard bots can often feel like questionnaires, which can be dispiriting. After all, at that point, you could just scroll through an FAQ to find what you're looking for. An NLP chatbot is different precisely because it can adapt to conversational cues, creating an environment that feels more like a natural conversation.

It continuously improves. The only way for a rule-based bot to improve is to add more rules. An NLP chatbot will improve using the data provided by the end-users. It makes it better at understanding different ways of formulating the questions or intent, but it also allows you to expand the capabilities by identifying what the chatbot couldn't answer.

The benefits offered by NLP chatbots won't just lead to better results for your customers. They'll make them feel more comfortable and valued as well.

Why you need an NLP Chatbot or AI Chatbot

As we pointed out earlier, simple bots can only take you so far. A simple bot can handle simple commands, but conversations are complex and fluid things, as we all know. If a user isn't entirely sure what their problem is or what they're looking for, a simple but likely won't be up to the task.

A natural language processing chatbot can serve your clients the same way an agent would. Natural Language Processing chatbots provide a better experience for your users, leading to higher customer satisfaction levels. And while that's often a good enough goal in its own right, once you've decided to create an NLP chatbot for your business, there are plenty of other benefits it can offer.

NLP chatbots can often serve as effective stand-ins for more expensive apps, for instance, saving your business time and money in terms of development costs. And in addition to customer support, NPL chatbots can be deployed for conversational marketing, recognizing a customer's intent and providing a seamless and immediate transaction. They can even be integrated with analytics platforms to simplify your business's data collection and aggregation.

But you don't have to take our word for it. Tech giants like Amazon and Google have been investing heavily in at-home assistants like Alexa and Google Home for several years. Though you might not realize it, these assistants rely on conversational AI to interact with their owners, offering their users conversations that feel dynamic and, most importantly, human.

Companies cannot afford to not use chatbots.

How do artificial intelligence chatbots work?

Specific skills

Different types of chatbots have diverse use cases. Chatbots are generally personalized according to an organization's needs and preferences, so you're the one that really decides what skills to give your chatbot. Businesses that leverage this technology must ask themselves the following questions to define their chatbot's key competencies:

What is the chatbot's purpose?
What user problems does it seek to solve? (this will ultimately help you improve customer satisfaction)
What are its functions?
What do you not want or need the chatbot to solve? (this will help you avoid user frustration)

Cognitive abilities

An AI chatbot uses its artificial intelligence skills to understand whether the text that the user enters corresponds to one of the chatbot's competencies. There are a series of factors that enable NLP chatbots to understand:

Semantic variations: Chatbots analyze the relationship between words to draw meaning from them. How many different ways can users ask the same question?
Keywords: What keywords does the phrase contain? Businesses must do an exhaustive keyword analysis to determine which keywords should be incorporated so that the chatbot can identify what type of question it is being asked and if it has the resources to solve the query.
Languages: What predetermined list of words do we have for a given skill? Questions can be formulated in different ways and in different languages (and language variations). The chatbot doesn't just have cognitive abilities applied to written text. It can also understand numbers, text from an image, information in a video, identify the gender and age of a person, understand the emotional level of a message, and extract keywords from a text.

Conversational capacity

To start a conversation with a user, businesses need to develop the most efficient way to guide users. They have to make sure their chatbot understands the context of the conversation to provide the appropriate answer. To achieve this, organizations need to define:

How many steps are needed to guide the user towards the answer
What contexts to keep alive to drive the next interaction
What opportunities for dialogue we detect for our marketing activities.

Channel

This is a key part of designing a chatbot. Businesses need to define the channel where the bot will interact with users. A user who talks through an application such as Facebook is not in the same situation as a desktop user who interacts through a bot on a website. There are several different channels, so it's essential to identify how your channel's users behave.

Training and machine learning

Machine learning is a subfield of Artificial Intelligence (AI), which aims to develop methodologies and techniques that allow machines to learn. Learning is carried out through algorithms and heuristics that analyze data by equating it with human experience. This makes it possible to develop programs that are capable of identifying patterns in data.

Thanks to machine learning, artificial intelligent chatbots can predict future behaviors, and those predictions are of high value. One of the most important elements of machine learning is automation; that is, the machine improves its predictions over time and without its programmers' intervention.

Even though intelligent chatbots rely on machine learning, businesses also need to train their chatbots. They need constant attention to provide the best response. Thanks to demos and user tests, organizations can find out what needs to be improved. Here are some key questions businesses should ask themselves to improve their virtual assistants:

What questions are giving us a correct answer?
What new questions do users ask?
What new integrations should we focus on?

What's the difference between NLP, NLG, NLU, and NLI?

Four main acronyms are used in the world of artificial intelligence, and that will help you further understand chatbots:

Natural Language Processing (NLP): is a field within artificial intelligence and applied linguistics that studies interactions through the use of natural language between humans and machines. More specifically, it focuses on processing human communications, dividing them into parts, and identifying the most relevant message elements to understand, interpret, and manipulate human language.
Natural Language Understanding (NLU): is a branch of natural language processing that relies on a machine learning classification algorithm, the statistical analysis of the order and frequency of words, and a wealth of training data, to understand the intent behind a user's message. NLU focuses on making sure the machine understands the meaning behind a text.
Natural Language Generation (NLG): is also a branch of natural language processing. It refers to AI processes that transform structured data into natural languages, such as text or speech, so that humans can easily understand it. It's the chatbot technology, which is responsible for generating a response to a user's query.
Natural Language Interaction (NLI): is another branch of NLP. As the name implies, it refers to the interaction and communication between humans and machines. NLI is a set of processes that can translate a programming language to human language and vice versa.