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Chatbots: Aligning Enterprises

Written by Abhinav Pandey

If you picture the journey of chatbots from the 1960s to now, you can see that what was once a fantasy of being able to communicate with a nonliving virtual being is now part of our everyday lives. —Rashid Khan (Build Better Chatbots)

A Chatbot is an Artificial Intelligence system that can simulate a conversation with the user, either through messaging apps, blogs, mobile applications or by phone, in a natural language. A chatbot is often described as one of the most advanced and enticing personifications of human-machine interaction. Even so, from a computing standpoint, the chatbot tends to reflect the logical progression of the Natural Language Processing (NLP) Question-Response Program. Formulation of answers to questions in natural language is one of the most perfect examples of Natural Language Processing ascribed to end-use applications by different companies.



Natural language processing, or NLP, is a component of artificial intelligence that allows chatbots to understand human language. NLP operates via machine learning, which is very closely related to the use of statistical analysis to make inferences. Using optimization algorithms, computers can be programmed to accurately predict a potential outcome based on an input number of events or other details.

Several NLP algorithms can even be used to deal with grammatical feedback or slang errors. It encourages computers to surpass basic language recognition and brings it an inch closer to the concept of being considered human


NLP is a strong and lucrative branch of artificial intelligence; by 2025, it is expected to exceed USD 4.5 million.2 Since machine learning relies on the number of users with information on the Internet and other databases, and this is expected to continue, it is obvious that NLP, machine learning and artificial intelligence as a whole will only become stronger and more effective.


NLP refers to computers that comprehend both the written word and the spoken expression. The algorithms involved require machines to link grammatical labels such as nouns or verbs to various parts of speech and thus infer interpretation from the overall content.

Chatbot applications simplify people-service interactions, improving user experience. At the same time, they offer companies new opportunities to enhance customer satisfaction and operational efficiency by minimizing average customer service costs.


In order to succeed, both tasks should be carried out effectively by means of a chatbot solution. The key function of human assistance is to ensure the configuration, training, and optimization of the chatbot system, regardless of the type of approach and platform.


Power of chatbots


With popular devices like Alexa and Echo from Amazon and personal assistants like Siri and Bixby, chatbots are alive and well in 2020. Given a persistent demand for human touch, twice as many customers replied in 2019 as in 2018, actively interacting with chatbots as they felt that the technology was "really helpful." Customers indicated that they liked the quick response that chatbots could provide, and in Europe, the acceptance of chatbots increases dramatically.

Chatbots may provide more than just good customer service. We may facilitate the recruiting of teams by helping with the evaluation of applicants and conducting background checks. The designs


for chatbots are, very frankly, infinite, based on what the company needs.


But go back to the question of rendering chatbots more human Is that possible? Okay, the technology that allows chatbots to understand human language is getting better. While it is inclined to take some time for chatbots to replace real human beings in jobs and other functions, technology can still be used for lower-level tasks and free staff for work that is still being done better by a real person.


How a chatbot works


How a Chatbot Works: As you can see in this graphic a chatbot returns a response based on a user's request. This cycle might look simple; things are quite complicated in reality.


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

1) user request analysis

2) returning the response


User request analysis: This is the first task a chatbot undertakes. It analyzes the request of the user to define the intent of the user and to extract the entities concerned.

The ability to identify the objective of the user and to extract data and relevant entities found in the request of the user is the first requirement and the most important step at the heart of a chatbot: if you are unable to interpret the request of the user correctly, you will not be able to provide the correct answer.


Returning the response: Once a user's motive has been established, the chatbot has to provide the user's request with the most adequate response. The Answer Could be:

A generic and predefined response

• A text obtained from a knowledge base containing various responses

• A contextual piece of data based on information provided by the user

• Information stored in enterprise applications

• The outcome of an operation carried out by the chatbot by communicating with one or more backend applications

• A disambiguous query that allows the chatbot to understand the information correctly.


ARE CHATBOTS A HUGE OPPORTUNITY?


Messaging apps now outnumber social networks. The emergence of messaging apps and chatbots is changing the way we use social media to connect and communicate.

Statistics consistently show that smartphone users have reduced their attention to some of their favorite apps, often a browser, a few calls, social apps and perhaps a few games. With dwindling chances of making money from mobile apps, developers look forward to chatbots as a new path. Chatbots are free to use and by simply sending them a post, we can chat with them as if they were a human user.


Advancements in Artificial Intelligence (AI) and Natural Language Programming (NLP) have allowed bots to use conversational language as a command-line to recognize what we want and to automate the execution of the order-making bots do what we request for. As it is applied to the messaging platform, our question can be textualized and the response can be obtained via an automated and scalable backend.


So indeed, Chatbots a strong chance in messaging apps lies in identifying people's pain and solving their problems in places where services are not available. But the difficulty here is that we need gigantic consumer behavior and preferences data. There's a high level of human interaction required at the early stages to train the AI bot about what the user is really looking for and how to better address their needs. We use messaging apps rather than social networks for making conversations.

If we want to grow a business online then logically we have to understand what people like.

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