Learn Everything About Machine Learning Chatbot(s)

  • Neelam Tyagi
  • Feb 20, 2020
  • Machine Learning
  • NLP
Learn Everything About Machine Learning Chatbot(s) title banner

“Chatbots are the latest in trends, that one machine interpretation of human language, You must have heard”

 

The most common thing in today’s world is the introduction of different inventions in the technical field on a daily basis, most of the technology is roaming around data only. It is a data-rich field that can’t leave racecourse without addressing automation in its realm. One such automation software is CHATBOTs.

 

Let’s start with Cortana, Alexa, and Siri, from doing some basic tasks like setting an alarm, make a call, read or send a message, to solve complex business problems, chatbots play a crucial role. They are an inseparable part of extensive technology accounted for us.  

 

 

First…….What is a Chatbot?

 

A chatbot is a computer program that conducts communication through audio or text methods, such programs are designed to adequately imitate how a human could behave as a communicating partner. 

 

Chatbot uses advanced Natural Language Processing techniques, but simple systems just scan for keywords within the input and pick a response with the most analogous keywords or most similar word-patterns from databases. 

 

Chatbots are becoming the machine interpretation of the virtual assistant, such as Google Assistant is the chatbot, Facebook uses Chatbot Messenger as chatbot platform. 

 

The non-assistant chatbots are used for entertainment like Jokebots or to collect particular data and Socialbots to promote products, services, candidates or issues.

 

 

Examples and Types of Chatbots

 

Chatbots are a domain of automation that synthesis beautiful live chat with software, it is a conversational tool used for automatic communication, and came up with two variations;

 

  • The first one is the standard rule-based chatbot that finishes conceived activities depending upon the keywords.

 

  • The second one is AI-powered chatbot that adopts machine learning to communicate more naturally.  

 

Recently, rule-based chatbots are famous e-commerce tools for regular customer service requests as they are quite easy to build and can get basic tasks done. Even though AI-based chatbot has advanced features and continues to improve, that is why we see much rise of more complicated ML chatbots.

 

Examples of topmost chatbots with the latest features;

 

1. Manychat: It is available on Facebook Messenger for e-commerce and support that helps small businesses to grow by making their marketing simple, like obtaining leads or introducing campaigns. 

 

It has multiple features such as to build a relationship with users by interactive and simple content, to book appointments, to sell products, to obtain contact details, transform leads into easy and personalized experiences. It gets connected to many tools like Google Sheets, MailChimp, Zapier, CovertKit.

 

2. Flow XO: It is a self-regulation software to create chatbots, it helps in retaining and communicating with customers over social media platforms and various sites. 

 

It reflects the features, like, greet new users virtually to e-commerce websites, collects user information by asking simple questions and verifying the given responses, reviews handy discussion with a human over chats, accepts payments for a specific service or product in which user is interested.

 

Showing examples of trending chatbots that are Manychat, Flow XO, and Amplify.

Examples of topmost chatbots- Manychat, Flow XO, and Amplify.

 

3. Amplify: It is a new generation AI tool and allows personalized and determine messaging-based user experience over a large and hugely diverse conversation interface. 

 

It has features like providing virtual assistants, secure and simple to make your own personal virtual assistant, giving communication experience on Facebook and Instagram for marketing.  

 

 

Machine Learning and Artificial Intelligence Chatbots

 

Chatbots are learning to understand the human language more fairly than before and getting intelligent when it comes to identifying tone, mood, and pattern in meaning. These advancements in ML chatbots are establishing more business communication opportunities. 

 

Illustrating ongoing conversation between ML chatbots and users.

Let’s talk with Machine Learning Chatbots

 

ML refers to the ability of the system to learn from the input it experiences, one possible way to achieve this by using Natural Language Processing that makes easy interaction between computers and human language. In order to get a general AI chatbot, it demands to do the main things that are it should display an informative response, control the context of the conversation and be identical from the human.

 

“Just a simple command and your work is done” 

 

People are using it for their daily routine work without actually searching or performing anything. Instead of this,  it is also difficult to take the word out from chatbots about public or logical content. Here, we firmly need machine learning, Artificial Intelligence, and deep learning.  

 

In simple words, a neural conversational chatbot can be designed with the help of machine learning, artificial intelligence needs to have interaction with the device in a natural language that could be easily interpreted and understood with the system. 

 

The main objective behind chatbots creation, or known as dialogue systems, is to obtain an informative answer, sustaining a structure of communication and be incoherent from the human. 

 

Although, the dialogue system majorly depends upon neural networks with many advanced methods.

 

 

Chatbots Can Aid

 

A chatbot can enhance the versatile application in which most of the domain covered improved quality of service and customer engagement, many organization repeatedly deal with many a high volume of frequently asked questions (FAQs), in examining and approving forms, communicating account information, executing simple and complex transactions (read our blog on planning structures for financial services), making services appointment, etc. 

 

Chatbot automates these particular tasks in which it doesn’t expect human intervention as this automation reduces the requirement of some human jobs. Chatbot has an additional feature of the potential for automated overact or invasion at a significant time of customer interaction.

 

Find below the industry examples that showcase how chatbots enhanced customer satisfaction,

 

1. In healthcare: Chatbot helps patients in managing their treatment in any hospital premises (read IoT application in human body), looking up waiting time in its urgent care center, receiving real-time updates on procedures, parking slots, etc. This can make patients and their guardians less stressed and allows better patients care

 

2. In higher education: Chatbots can be used to provide a consolidated initial point for exploring different knowledge bases. To give answers to FAQs such as how to obtain transcripts, application, and enrollment status, to introduce students’ assistance like financial aid, short-term courses, counseling, web pages connected to various departments. 

 

Chatbot acts as “one-stop shopping” to provide answers not only for current students but for faculty, alumni, prospects and general people. This can lengthen and enhance student outreach and increase enrollment.

 

3. In financial services: Chatbots are widely implemented in banking sectors, it aids bank customers who access account balance, alerts, credit card scores, personalized recommendations for products, etc. 

 

Chatbots offer more speed and comfort than a traditional bank website or help from the call center. The mixture of utilization and accuracy in an easy to use casual interface is making the link strong between financial bodies and their users.

 

 

Chatbot Faces Criticisms 

 

To build a smart chatbot faces a lot of aspects that need to be considered, such as context awareness building, personality development, sensitive results, internal data translation, positive or negative feedback, and appropriate caliber, impact examination, etc. Some of them are discussing below;

 

1. Context assimilation into the smart system of a chatbot is confronting and complicated, it is very significant to define and address the way how interactions are executed by the chatbots. To obtain sensitive or emotional responses, physical and linguistic text or situations must be considered.

 

For this, Installing communication into vector format requires a lot of attempts in computer programming, along with this, timestamp and location data of users are essential that need to be integrated with chatbots.    

 

2. Response consistency involves responding to the same answer to identical semantic queries but put in different ways, such as “what is your name?”, “what can I call you?” etc. Again, multiple efforts in programming to achieve such consistency is challenging.

 

3. Instructions for reading intention are essential in providing admissible responses to separate queries for which chatbots are instructed. The variations required to manage multiple situation-based questions is difficult to be programmed into chatbots. 

 

In recent times, many companies are using chatbots for marketing activities for user reservations and better business conversations which require appropriate reading instructions. 

 

4. Performance evaluation of the chatbots has become more in demand as most of the responses are dynamic and to the moment depending on the real-time data and information mining. 

 

The human statement of chatbots’ performance varies from person to person and is challenging to determine. It is easy to indicate for domain-specific chatbots that only performs input-output mapping.

 

 

Conclusion

 

A chatbot is a robotic chat that mimics human conferences through sound commands, text chats, or both”, a virtual conversation in which one body is having words online with robots. Chatbots connect audio technology, AI, and machine learning by collecting sensor data, i.e. using ML algorithms (neural networks algorithms) to discover actionable inferences, reacting based on inferences, and then resourcefully learn from the following input to engage customers regularly.

 

For sure, you have found chatbots interesting topics that excite you to seek a virtual chat with your chatbot. For more blogs on Analytics, Do read Analytics Steps

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Neelam Tyagi

A versatile and creative technical writer in Analytics Steps. She has cross-functional experience in qualitative and quantitative analysis. She is focused and enthusiastic to achieve targets on time.

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