Intelligent Created Machine Learning Chatbot

Intelligent Created Machine Learning Chatbot

Natural Language Processing is a prerequisite for our project. To process a large amount of data in natural language, AI will need NLP or natural language processing. NLP allows computers and algorithms to understand human interactions through various languages.

Chatbots are nothing but applications businesses or other organizations use to have an automated conversation between humans and AI. These conversations can be through text or speech. Chatbots must understand and mimic human conversation when interacting with people. Chatbots have developed from the first created chatbot, Eliza, to Amazon’s Alexa.

What is Machine Learning

At the beginning of this article, you came across machine learning several times, and you might be wondering what machine learning is and why it is so deeply rooted in AI chatbots.

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Machine learning is a subset of data analysis that uses artificial intelligence to create analytical models. It is a field of artificial intelligence based on the idea that computers learn from data, identify patterns, and make smart decisions with little or no human intervention. Machine learning allows computers to improve the accuracy of their decisions and predictions by learning from their mistakes. In other words, AI bots extract information and predict acceptable outcomes based on their interactions with consumers.

Machine learning chatbots are capable of much more than simple chatbots. These smart bots interpret concepts in a sentence, identify elements within an image, and extract entities and sentiments in the given text through advanced machine learning skills, including image analysis, NLP, and text analytics.

Why Does Your Business Need a Chatbot?

In an Oracle survey, 80% of companies said they use intelligent created machine learning chatbots, and 48% already use automation technology. Grand View Research shows the global chatbot market will reach $1.23 billion in a few years, with an annual growth rate of 24%.

To understand how best to use a bot in a company structure, you will need what tasks to be automated and augmented with artificial intelligence solutions. The first step is to identify an opportunity or problem to decide on the purpose and usefulness of the chatbot. The corresponding AI solution generally falls into two categories for each type of activity: Data Complexity or Operational Complexity. These two categories are divided into analytics models: Efficiency, Expert, Efficiency, and Innovation.

Scaling Operations

Chatbots are good to scale operations because they have no human limits. Chatbots communicate with customers anywhere and anytime. Chatbots simultaneously serve a large customer base in performance with enough computing power.

Task Automation

Chatbots are effective at automating specific tasks. Once programmed to perform a job, they complete it. Some customer questions are asked more than once and have the same answers. Automating the answers with the help of the chatbot to those questions would be valuable.

User Engagement

The main task is not to attract users to the website or application but to keep them on the site or application. Chatbot greetings prevent users from leaving your site by engaging them. Short chat invitations allow you to interact with users actively.

Integration with Social Networks

Chatbots are integrated with social media platforms like Facebook, Telegram, and WeChat wherever you are chatting. They also are combined with websites and mobile apps. Chatbot integration helps users get quick answers to their questions and 24/7 support, leading to more profits.

Data Generation

Chatbots communicate with users and store data that can be analyzed and used to improve the customer experience.

Ability to Speak Multiple Languages

In addition to having meaningful conversations, chatbots understand user requests in languages ​​other than English. Thanks to Natural Language Processing (NLP) and Neural Machine Translation (NMT), chatbots provide instant responses in the user’s language.

Connect with Younger Clients

Statistics show that millennials prefer to connect with brands through social media and chat rather than by phone. They are tech-savvy and have a lot of purchasing power. It’s good to cater to their needs and have a reliable chatbot.

Chatbots Types

There are many types of chatbots available and are categorized as follows:

  • Text chatbot: The bot answers the user’s questions through a text interface in a text chatbot.
  • Voice chatbot: The bot answers the user’s queries through a human voice interface in a voice or voice chatbot.

Types of chatbots are also categorized depending on the complexity:

  • Traditional chatbot. Traditional chatbots are driven by the system and automation, primarily through scripts with minimal functionality and the ability to maintain only the system context.
  • Current chatbot. Current chatbots are driven by two-way communication between the system and people. They maintain both system context and task context.
  • Future chatbot. Future chatbots communicate at multiple levels with system-level automation. They maintain the system, task, and people contexts. There is the possibility of introducing master bots and eventually a bot OS.

Chatbots are a relatively recent concept. Despite the vast number of NLP programs and tools, we only have two different categories of chatbots based on the NLP technology they use.

Intelligent Created Machine Learning Chatbot

Best Chatbot Apps

Businesses often use Chatbot applications to solve various tasks and streamline their workflow. Most commonly used:

  • reception virtual assistant;
  • help Desk Virtual Assistant;
  • tutor or teacher;
  • driving assistant;
  • email, complaints, or content distributor;
  • home assistant (Google Home);
  • operations assistant (Jarvis from the Iron Maiden movie);
  • entertainment assistant (Amazon Alexa);
  • phone assistant (Apple Siri).

These applications also help reduce the cost of doing business.

What is a Chatbot Platform?

A chatbot platform is a service through which developers, data scientists, and machine learning engineers create and maintain chatbots. They offer machine learning features like NLP. They allow you to integrate your chatbot with social media platforms such as Facebook Messenger.

Dialogflow Platform

Dialogflow, powered by Google Cloud, simplifies creating and designing NLP chatbots that accept voice and text data. Easily integrates with social media platforms.

Chatbot development takes place through the Dialogflow console and is easy to use. But you need to understand the key terminology used in Dialogflow — agents, intents, entities, etc. prior to starting development.

An Intent is a task defined by a developer. The developer uses it to identify possible user0 questions and correct answers from the chatbot.

The Entity is a property in Dialogflow used to respond to requests or user requests. They are defined inside the console so that when the user speaks or enters a query, Dialogflow looks up the object, and the ‘s value object is used in the question. Usually, this is a keyword that the user can call it.

Dialogflow has predefined system entities that you can use when building intents. If that’s not enough, define the objects according to your purposes.

Fulfillment provides a more dynamic response when you use more Dialogflow integration options. Executions are enabled for intents, and when enabled, Dialogflow then responds to that intent by calling the service you define. If a human wants to book a flight for Friday based on Fulfillment, the chatbot will look through the flight database and return the available flight time for Friday to the human.

The context can be customized for intent by setting input and output contexts identified by string names.

Dialogflow abstracts away the complexities of building an NLP application. In addition, it provides a console through which developers visually create, design, and train AI chatbots. The console has an emulator where you test and train the agent.

Dialogflow has two different virtual agent services, each with its agent type, user interface, API, client libraries, and documentation:

Amazon Lex

Amazon Lex is designed to create chatbot interfaces using voice and text in any application. Amazon Lex uses Automatic Speech Recognition for speech-to-text and Natural Language Understanding (NLU) for text recognition. There are advanced deep learning features to create applications with a beautiful user interface and realistic conversational interaction.

It uses the deep learning technology behind Amazon Alexa to quickly and easily build complex natural language conversational bots.

Lex has its own set of terms: intents, slots, executions, etc.

Intent: the action or conversation that the user will perform while communicating with the chatbot.

Amazon Lex users are TransUnion, GE, Citbot, John Creek, etc.

Azure Bot

It is an integrated environment for developing bots. It uses the Bot Framework Composer, an open-source visual editing canvas to design dialog flows using templates and tools to customize dialogs for specific use cases.

Composer is used on desktop and web components. It integrates NLU services like LUIS and QnA Maker and allows bots to respond using adaptive language generation. It provides access to adaptive dialogs and language generation.

Responsive Dialog: This flexible dialog model allows developers to update the save process based on context dynamically. This dramatically simplifies aborting, canceling, and executing scheduling semantics. Learn more about this in the Responsive Dialog documentation.

Language Understanding (LU) is a core component of Composer. It allows developers and dialog designers to train language understanding directly in dialog editing. Because the dialogs are edited in Composer, developers have the possibility to continually add natural language capabilities to their bots.

The Composer uses the adaptive dialogs in Language Generation (LG) to simplify interrupt handling and give bots character.

The visual design surface in Composer reduces the need for boilerplate code and makes bot development more accessible. You don’t need to switch between interfaces to support the LU model – it can be edited in the application. You need to set up your environment.

Composer provides everything you need to create a complex dialog interface:

  • Visual editing of conversation flows without the need to write code,
  • Tools to create and manage language understanding (NLU) and QnA components,
  • Powerful language and template generation system,
  • A ready-to-use bot runtime executable.

Businesses that use Azure Bot Service include Daimler, United Parcel Service Inc, Daikin Industries, Telefonica, etc.

Chatbot Testing

The most crucial step is to test the chatbot for its intended purpose. Although it is not so important to pass the Turing test the first time, it should still meet this goal.

The generated conversations will help identify gaps or dead ends in the communication flow.

With each new question asked, the bot learns to create new modules and relationships to cover 80% of the questions in the domain or given scenario. Through the use of AI features in the framework, the bot will get better every time.

It was the starting point for anyone who wanted to use deep learning and Python to create standalone text and voice applications and automation. The complete success or failure of such a model depends on the corpus used to build them. But sometimes putting all the scripts in one corpus can be a little tricky and time-consuming. Therefore, if available royalty-free, we explore options for getting a ready-made corpus and have all possible learning and interaction scenarios. Also, the corpus here was text data; you can also explore the voice corpus option.


The future of chatbots is very bright. With so much progress in the AI ​​sector, chatbots are the future. In the next 5 years chatbot will be not just a customer support agent but an advanced assistant for both businesses and consumers.

We humans don’t like doing repetitive, monotonous tasks. In the future, companies will hire AI chatbots to perform repetitive tasks that don’t require creativity.

You can try different tasks to improve performance and features as further improvements.

  • Use more training data: you can add more data to the training dataset. An extensive data set with many intents can lead to a cool solution for the chatbot.
  • Apply different NLP techniques: add more NLP solutions to your chatbot solution like NER (Named Object Recognition) to add more power to your chatbot. By having the NER model and your chatbot, you easily detect any entity that has appeared in the user’s chat messages and use it for further conversations. You also add a sentiment analysis model to identify the different shades of the sentiment behind user posts, giving your chatbot some extra color.
  • Try different neural network architectures with different hyperparameters.
  • Add emoticons: use emoticons when creating your models.

An AI chatbot takes on repetitive, monotonous jobs, so companies will use their human resources to tackle creative tasks. We can expect more amazing things to come to us in the future.

In addition, people do not like to accumulate content in their minds. With the Internet, they can use that part. Thus, tasks requiring information storage can be handed over to AI chatbots.