What are the Pros and Cons of using AIML to build chatbots?

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AIML to build chatbots

What is a Chatbot?

A chatbot is a computer software that can have a conversation with a human. Any user may, for example, ask the bot a question or make a statement, and the bot would respond or take appropriate action. A chatbot works similarly to an instant messenger.

A chatbot is a piece of software that mimics human communication. It facilitates human-machine communication, which can take the form of text messages or voice commands. A chatbot is programmed to function without the intervention of a human operator. As if it were a real person, the AI chatbot responds to questions in natural language. It uses a combination of pre-programmed scripts and machine learning algorithms to respond.

When a chatbot is asked a question, it will respond using the knowledge database it presently has access to. If an idea is introduced that it is not programmed to grasp, it will be passed to a human operator. In either instance, it will learn from that interaction and future interactions. As a result, the chatbot’s scope and importance will steadily grow.

Operation of Chatbot

Bots exist for a specific purpose. A business would most likely want chatbot services to help you purchase, whereas a telecommunications company would want to construct a bot to answer customer service questions.

Chatbots are divided into two types: those that follow a set of rules and those that use artificial intelligence.

A rule-based bot can only understand a set of options that have been programmed into it. Predefined rules guide the bot’s conversation. Rule-based chatbots are simpler to create since they rely on a basic true-false algorithm to comprehend user questions and respond appropriately.

An artificial brain, often known as artificial intelligence, is installed in this bot. Machine-learning methods were used to train it and interpret open-ended questions. It not only comprehends orders but also comprehends the language. The bot continues to improve as it learns from its interactions with users. The AI chatbot recognizes the language, context, and intent and responds appropriately. Check out the Artificial Intelligence courses in order to explore more about the AI applications in chatbot.

Natural Language Understanding (NLU)

By breaking down the query, NLU aids the chatbot in comprehending it. It is divided into three distinct concepts:

An entity collects keywords from a user’s query that the chatbot uses to figure out what the user wants. In your chatbot, it’s a notion. For example, the term ‘bill’ is used as an entity in the question ‘What is my overdue bill?’

Intents aid in determining what action the chatbot should do in response to the user’s input. For example, “I’d want to order a t-shirt” and “Do you have a t-shirt?” have different intentions. “Show me some t-shirts” and “I want to order one” are the same thing. These users’ texts trigger a single command that gives them t-shirt options.

Because an NLU algorithm does not have access to the user’s conversation history, it is difficult to determine the discourse context. It means that if it receives a response to a question it has just posed, it will forget the inquiry. The status of the chat conversation should be saved to distinguish the stages during the conversation. It can detect phrases such as “Ordering Pizza” or parameters such as “Restaurant: ‘Dominos.'” You can associate intents with context, and you don’t need to know what the prior question was.

AIML

AIML stands forArtificial Intelligence Markup Language. It’s an XML dialect for constructing software agents that speak in several languages.

The AIML language contains a set of rules that define the chatbot’s conversational capabilities. It’s utilized in conjunction with a linguistic communication Understanding (NLU) processor, which uses AIML rules to examine and respond to text inquiries submitted by the chatbot. The more AIML rules we add, the smarter the chatbot becomes.

AIML-based chatbots are classified as rule-based chatbots; however, they can have some self-learning capabilities. AIML is the language used to create a chatbot’s brain.

Pros and Cons of using AIML 

To model the interactions, one can utilize Artificial Intelligence Markup Language (AIML), which is coding from scratch, or use some platforms backed by industry giants like Microsoft or Google to get that phase done and code from there.

AIML is a markup language that allows you to create categories for specific patterns. The user’s responses can then be processed using the defined markup.

You can also employ platforms such as LUIS, Watson, or Api.ai, which will allow you to model the user’s inputs. Despite their extensive functionality, these platforms offer excellent documentation, resulting in a low learning curve.

In any case, the responses they generate must be handled in the code.

Pros of using AIML for chatbot

  • It is simple to use as this is a common markup language.
  • It’s simple to train and publish, and it takes less time.
  • AI chatbots make customer support simpler and more efficient. They can sift through massive data to locate and deliver the correct answer.
  • While constantly interacting with customers or leads, a chatbot may quickly gather and evaluate data. When an old consumer returns to the website, chatbots automatically recall the previous interaction. 
  • AI chatbots can instantly comprehend these consumers’ likes and interests and engage them until they reach their final destination, conversion goals.
  • Traditionally, chatbots could only respond to simple questions. Even if a consumer asks the same inquiry numerous times, he will always receive the same answer. AI chatbots, on the other hand, give consumers the impression that they are conversing with humans, thanks to advances in technology. If a consumer asks the same question numerous times, the system will respond differently to give users the best experience possible until they get the answer.
  • In real-time, chatbots converse with your website visitors and social media followers. This is in sharp contrast to your brand’s other material, which is typically watched passively. People will stay on your website longer if they are engaged, which will help you drive sales and improve your SEO.
  • Chatbots can be a useful tool for gathering information about your target audience. They can communicate with your target audience and collect data such as names, email addresses, and other details. By linking the chatbot with your CRM, you’ll have quick access to these facts. Additionally, you can use chatbots to question clients’ preferences and tailor your services to meet their needs better.

Cons of using AIML for chatbots

  • Expertise in NLP and programming and AIML training is required.
  • Access to information is restricted.
  • Because it’s harder to update a pattern once it’s been designed, it’s less scalable.
  • Some programming knowledge is required; certification courses in artificial intelligence would be useful.
  • The preconfigured domains can be overly generic and limiting at times.
  • During the discussion, avoid providing significant assistance to those now managing error situations.
  • The user input is completely reliant on a third-party API, and if that fails, the entire chatbot will fail.
  • The data must be securely delivered from the chatbot to your CRM. It must also be stored safely, and only relevant data from your audience should be collected. You must keep your audience data safe when you acquire it.
  • Because chatbots are codes, they have difficulty detecting the user’s emotions. As a result, individuals may be unable to tell whether the user with whom they are conversing is joyful, irritated, or depressed. This could make the chatbot appear emotionally insensitive, which could hurt your brand’s reputation.
  • To avoid a situation like this, consider adopting chatbots that allow customer service employees to take control of the conversation.

Machine learning has infiltrated the fabric of our daily lives – even if we aren’t aware of it. Machine learning algorithms have been powering the world around us, including product recommendations at Walmart, fraud detection at several top-tier financial institutions, surge pricing at Uber, and content utilized on users’ feeds by LinkedIn, Facebook, Instagram, and Twitter, to name a few examples. Learn Artificial and Machine learning Courses Online offered by Great Learning.

Having said that, it goes without saying that the future has already arrived – and machine learning plays a key part in how we imagine it now. 

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