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Chatbots Getting Smarter (AI & ML)


Table of Content


In today's customer-centric environment, one of the critical concerns of organizations is keeping customers satisfied. Providing client service 24 hours a day, seven days a week, increases expenditures by ten. Companies are looking into various possibilities, including AI, specifically chatbots, to ensure a seamless two-way dialogue and a consistent experience for all customers. Because the millennial generation prefers texting to voice communication, chatbot usage has skyrocketed recently.

What Is a Chatbot?

At its most basic level, a chatbot is a computer program that simulates and processes human interaction (written or spoken), allowing people to connect with digital gadgets in the same way they would with a real person. Chatbots can range from simple one-line program that react to a simple query to sophisticated digital assistants that learn and adapt as they gather and process data to improve personalization. You've almost certainly interacted with a chatbot, whether you recognize it or not. For example, you're on your computer studying a product when a window shows on your screen asking if you need help.

Why were chatbots invented in the first place?

Society is becoming more "mobile-first" as a result of digitization. As messaging apps become increasingly common, chatbots are becoming more important in this mobility-driven revolution. Intelligent conversational chatbots are increasingly being employed as user interfaces for mobile apps, and they are revolutionizing how businesses and customers communicate. Chatbots allow businesses to engage with clients on a more personal level without having to hire human personnel. For example, many of the questions or issues that clients have are typical and easily answered. For this reason, companies produce FAQs and troubleshooting guides. Chatbots provide a more personalized experience than a textual FAQ or guide, and they can even triage enquiries, such as passing a customer issue to a live person if the chatbot is unable to resolve it.

Types of Chatbots

1. Task-Oriented chatbots

Task-oriented (declarative) chatbots are single-purpose program that perform a single function. They use rules, natural language processing, and very little machine learning to offer automated but conversational responses to user inquiries. The interactions of these chatbots are highly thorough and structured, making them ideal for support and service functions—think extensive interactive FAQs. Task-oriented chatbots can handle common enquiries like inquiries about business hours or basic transactions with few variables. Their powers are limited, despite the fact that they use natural language processing (NLP) to allow end-users to converse with them. Right now, these are the most popular chatbots.

2. Virtual Assistants

Virtual assistants, also known as digital assistants, are more complex, interactive, and personalized than task-oriented chatbots since they are data-driven and predictive (conversational) chatbots. These chatbots are aware of their surroundings and learn on the fly using natural language understanding (NLU), Natural Language Processing (NLP), and Machine Learning (ML). Personalization is based on user profiles and historical behaviour, and they use analytics and predictive intelligence to do so. Digital assistants can learn a user's preferences over time, provide recommendations, and even anticipate needs. In addition to monitoring data and intent, they can start conversations. Consumer-oriented, data-driven, predictive chatbots include Apple's Siri and Amazon's Alexa.

How do chatbots work?

Chatbots process data to respond to various requests using Artificial Intelligence, Automated Rules, Natural-Language Processing (NLP), and Machine Learning (ML).

As they come in different types, their functionality varies correspondingly. Virtual assistants, question-answer bots, and domain-specific bots are all examples of chatbots. Question-and-answer chatbots are simpler and require fewer abilities. They are primarily knowledge-based, and their skills are restricted to answering a limited number of queries. Chatbots that use AI and machine learning to their maximum extent, on the other hand, can resemble human communication and improve user experience. The majority of chatbots are built to function with one or more of the following features:

1. Pattern Matcher

The majority of chatbots in this category are rule-based, scripted, and structured. Such chatbots rely on a knowledge base that contains documents, each of which contains a unique pattern> and template>. The pattern> can be a phrase like "What's your name?" or it can be a number. Using a pattern like "My name is *," where "*" stands for a regular expression. These pattern> template> pairs are usually manually introduced. When the bot receives an input that matches the pattern, it responds with the message that was saved in the template.

This means that the queries a user must ask are pre-programmed into such QA chatbots. They separate a piece of information (word or sentence) or generic tags from various categories to classify content and create appropriate responses for the end-user.

2. Suitable Algorithms

Unlike rule-based models, acceptable algorithm-based chatbots do not simply match a pattern against a status or response. They select a pattern matching method and compare the input sentence to the data corpus's replies. Algorithms are crucial in this case since they assist chatbots in evaluating enormous datasets. Pattern matchers' workload is reduced as a result. A class of words is assigned to each input, and each word is counted for the number of times it appears. It is then counted for its common type, and each class is assigned an overall rank using algorithms. The algorithms assign a score to the class with the highest rank, which is very certainly related to the input sentence.

In brief, the chatbot selects the proper response from a prepared list of premade responses based on the message and context of the discussion. These chatbots deliver more predictable results than rule-based bots, even though the highest score merely provides relativity and does not ensure a perfect match.

What is an Artificial Intelligence (AI) chatbot?

Artificial Intelligence excels at automating tedious and repetitive tasks. When AI is used in a chatbot for these activities, the chatbot usually performs admirably. However, suppose a chatbot is asked to do anything beyond its capabilities or complicates its mission. In that case, the chatbot may struggle, which can severely affect businesses and customers. Complex service difficulties with a huge number of variables, for example, are queries and issues that chatbots may not be able to answer or address.

Unlike traditional chatbots, which are primarily rule-based, AI chatbots understand and respond to humans using natural language understanding, natural language processing, and natural language production. Machine learning, a feature of AI that makes bots smarter over time and with use, is used by AI chatbots. While the rise of chatbots may appear to be new, the first chatbot was developed in the 1960s by Joseph Weizenbaum, an MIT scientist. ELIZA, a computer software, might act as a psychotherapist. ELIZA might even pass the Turing Test, which is designed to assess a machine's intelligence. For the time being, this was a fantastic accomplishment.

While ELIZA was amazing at the time, technology has advanced significantly since then. The skills have significantly improved in the subsequent years. A conversational AI chatbot can now effortlessly engage with humans on a personal and even empathetic level, similar to conversing with a helpful assistant. Chatbots and speech bots can provide answers to questions and assist people with their problems. AI chatbots are a natural match for customer service because of the following elements:

Artificial Neural Networks (ANN)

An artificial neural network (ANN) is a computing system made up of layers of simple but densely interconnected pieces or nodes known as 'neurons.' These further process data by responding to external inputs with dynamic state responses.

Seq2seq artificial neural networks determine the personality of generative-based chatbots. Artificial neural network-based models construct responses on the fly, whereas acceptable algorithm-based models require a database of possible responses to pick from. The neural network of generative models is a deep learning model designed to process a series of sequences rather than prefabricated replies.

As the chatbot progresses through each layer of the AI neural network, the pattern detection used to derive a desirable response becomes more powerful and accurate. Input layers, hidden layers, and output layers are the three interconnected layers of the neural network that allow the generative model to analyze and learn data.

The input layer, which consists of one neuron for each component present in the input data, introduces patterns into the neural network. The information is sent downstream by the neuron to other neurons connected to it. It is then transferred to the hidden layer, which does all of the processing via a network of links. The hidden layer leads to the output layer, which has one neuron for each conceivable desired output at the end of the process.

It changes its hidden state by conceiving a sequence of context tokens (Input layer) one at a time. It generates a final hidden state (Hidden layer) after processing the entire context sequence, which incorporates the sense of context and is utilized to provide the answer (output layer). As a result, it takes two inputs: state and context. Such chatbots consider the state by looking at the chat history or previous transactions. Chatbots assess the context by analyzing external data points and sorting them into relevant levels.

Amazon's Alexa, Apple's Siri, Google's Assistant, and Microsoft's Cortana are examples of generative chatbots that are trained using a large number of past interactions and then generate responses for the user.

While these models will always respond, they may sound arbitrary and make little sense at times. They run the risk of providing inconsistent responses and are more likely to use poor language and syntax in their responses. With the ability to engage in small talk with users, generative models can be amusing. Chatbots, on the other hand, are designed to keep the customer's purpose in mind, assist users in resolving support issues, and supply them with relevant information.

Natural Language Processing (NLP)

NLP, a branch of AI and machine learning, is at the heart of a hybrid chatbot's framework, allowing it to interpret natural language. In the context of the speech, an AI-powered chatbot decodes and analyses human-understandable language. It recognizes the nuances of human interaction and recognizes that user commands or searches do not need to be as precise.

Chatbots that have been imbued with natural language processing (NLP) replicate human-like communication and decode user intent to provide intelligent responses. Unlike generative models, which make it difficult for chatbots to have open-ended discussions because of the predetermined flow, AI chatbots may engage users on a wide range of topics.

By depending on the following elements, NLP allows chatbots to understand multiple user intents and minimize failures.

  • Decoding intent - NLP decodes user intent by breaking down user inputs and understanding the meaning of words, their location, conjugation, plurality, and many other variables that can be present in a human conversation.

  • Recognizing utterance - NLP-enabled chatbots are capable of recognizing the instances of sentences that a user may use to refer to an intent.

  • Entity Recognition - Chatbots can recognize entities from a field of data or words connected to time, location, description, a synonym for a word, a person, a number, or anything else that describes an object.

  • NLP aids chatbots in deciphering context by assessing inputs such as time, place, conversation history, tone, sentence structure, sentiment, and so on. For example, the user response "Great!" might easily lead to the chatbot being misled. I'll have to wait another hour for my dinner to arrive." As a result, in order to successfully analyze user attitudes, the chatbot must process positive, negative, and neutral comments.

There are two different tasks at the core of a chatbot:

• User request analysis

• Returning the response

Analysis of User Requests

A chatbot's primary task is to answer questions. It examines the user's request to determine the user's intent and extract pertinent entities.

The first condition and most important step in the core of a chatbot is the capacity to understand the user's intent and extract data and relevant entities from the user's request: You won't be able to deliver the correct answer if you don't understand the user's request correctly.

Returning the Response

The chatbot must then deliver the most appropriate response to the user's request once the user's purpose has been established. The following is a possible response:

-a prepared and general text

-a text taken from a knowledge base that contains many replies

-a contextualized piece of information based on the data provided by the user

-data kept in business systems

-a disambiguating inquiry that aids the chatbot in accurately understanding the user's request

-the result of an action taken by the chatbot by communicating with one or more backend applications

Advanced digital assistants can also bring together multiple single-purpose chatbots under one umbrella, collect various data from each, and integrate it to complete a task while keeping context—so the chatbot doesn't become "confused."

Application of Chatbots

Chatbots boost customer satisfaction by streamlining interactions between people and services. Simultaneously, by lowering the typical cost of customer service, they give businesses new ways to improve customer engagement and operational efficiency.

In order to be successful, a chatbot solution must be able to do both of these duties correctly. In this case, human intervention is critical: Regardless of the approach or platform, human participation is crucial in configuring, training, and optimizing the chatbot system.

They are mainly employed in the following sectors:

  • Retail and E-commerce

  • Travel and Hospitality

  • Banking, Finance, and Fintech

  • Healthcare

  • Media and Entertainment

  • Education

A firm can expand, personalize, and be proactive while using chatbots, which is a key differentiation. When a business relies exclusively on human power, for example, it can only service a certain number of people at any given time. Human-powered firms are compelled to focus on standardized models in order to be cost-effective, and their proactive and personalized outreach capabilities are limited.

Patent Data Analysis

The rapid growth in chatbot development has led to an increase in the filing of patents. The graph shows steady growth, which rose exponentially from 2016 till 2019. Then suddenly the numbers dipped. This can be due to the versions of chatbots that prove to be inefficient and frustrating to use, besides being confined to answering simple queries that follow a set pattern. Thus, there is still some reluctance and worries on the side of consumers and employees because of disruption of user experience and decreasing the need for human resources, respectively. As a result, the number of applications fell drastically in the tenth year after replacing human interactions with chatbots.

Top 10 Players

Google has always been at the forefront of pioneering AI-powered technology. Google AI researches to advance the field's state-of-the-art and applies AI to products and new areas and developing tools to make AI accessible to everyone. This is why it tops the chart and proves to be the topmost player with 584 patents in the Chatbot (AI sector). Next in line is IBM, with 405 patents to its credit. IBM's suite of business-ready technologies, applications, and solutions aims to lower the costs and barriers to AI adoption while improving outcomes and ensuring responsible AI use. Thus, it is becoming quite a name in the AI sector. With announcing a deal with OpenAI to use the GPT-3 deep-learning model for natural-language processing (NLP) at the end of 2020, Microsoft Licensing ranks third with 324 patents. Pure Storage, Samsung Electronics, Oracle International rank fourth, fifth and sixth, respectively. These companies are trying to match up with the competitors with extensive R&D and investment to boost the AI tech they are working on. Accenture Global Solutions, Fujitsu, Capital One Services and Fujifilm Business Innovation seem to be just beginning to impact the sector and still have a long way to go.

Future of Chatbots

It is expected that chatbots will evolve from simple user-based queries to more powerful predictive analytics-based real-time dialogues.

  • By 2025, the market for Conversational AI is estimated to reach USD$1.3 billion, with a CAGR of 24%. (Cognizant)

  • By 2023, chatbot eCommerce transactions are expected to be worth $112 billion. (Source: Juniper Research)

  • More than half of businesses will spend more per year on bots and chatbot development than they will on traditional mobile app development. (Gartner)

  • By 2024, bots are expected to answer 75-90 percent of searches. (CNBC)

In the future, more and more companies will develop apps. Bots can gather information and evaluate it in order to conduct essential actions.

Bots are used to automate personal duties and daily activities such as exercise, parenting, children, e-learning, and so on. Chatbots are becoming more common in a variety of commercial operations and consumer applications. Going on, automation will strengthen its roots even more and overcome all of the chatbot obstacles that businesses encounter. The architecture and design of chatbots will progress to the point where interactive AI will become the norm for customer care. Advanced chatbots and machine learning technology are being developed by major technology businesses.


The digital connections between brands and customers are becoming increasingly complex. Chatbot architectures highlight the complexities that go into making conversational interfaces smart enough to handle these sophisticated digital interactions. Many businesses have realized that utilizing chatbots on social media might help them communicate with customers more effectively. As a result, the number of chatbots continues to rise, with over 300,000 currently active on Facebook.

Facebook has provided a slew of data demonstrating the value of bots in the workplace:

  • Every month, 2 billion messages are sent between individuals and businesses.

  • Customers would rather message than phone customer service, according to 56% of respondents.

  • People are more likely to shop with firms that they can message 53% of the time.

As the number of people using the internet grows, so will the number of people using chatbots, understanding what powers these chatbots will be important for businesses to properly realize their potential in the coming years.