A Survey on AI Chatbots

Abstract

Chatbots powered by artificial intelligence (AI) have become a game-changing technology with a significant impact on a number of industries, including customer service, healthcare, education, and more. This survey paper offers a thorough overview of the development, state, and potential of AI chatbots. This survey\'s main objective is to look into the many ways that AI chatbots are being used across various sectors. The use of chatbots for customer service, virtual assistants, healthcare diagnosis, language translation, and educational help is covered. Case studies and examples from the real world demonstrate how well chatbots have been incorporated into different fields.

Introduction

I. INTRODUCTION

The emergence of artificial intelligence chatbots has dramatically changed the way humans interact with computers in recent years. These skilled conversation professionals have transformed industries such as customer service, healthcare, education, and more. Powered by state-of-the-art natural language processing (NLP) algorithms and machine learning. AI chatbots are not just a passing fad; Instead, they have established themselves as essential components of our online lives, integrating into our daily routines and providing answers to a wide range of tasks and questions. Natural Language Processing (NLP) has widespread applications in various industries to generate human-like responses to user questions using natural language. Chatbots are a prime example of this, as they enable smooth communication between businesses and individuals, ultimately improving customer experience. Additionally, chatbots offer businesses the opportunity to increase customer loyalty and streamline operations by reducing additional customer service costs. To be successful, a chatbot solution must handle both of these functions well.

II. ARTIFICIAL INTELLIGENCE

A. Artificial Intelligence

At the nexus of computer science, mathematics, and cognitive science, artificial intelligence (AI) is a transformative field. It includes a wide range of tools and strategies that let computers imitate human intellect, gather knowledge from data, make choices, and resolve challenging issues. From its theoretical beginnings, AI has grown into a potent force that affects many facets of our life, from autonomous cars and medical diagnostics to personal virtual assistants and recommendation systems. Breakthroughs in machine learning, deep learning, natural language processing, and reinforcement learning, among other areas, are propelling the field's continued rapid advancement. AI is a topic of enormous relevance and attention in academia, business, and public discourse since it offers both significant ethical and societal issues as well as previously unimaginable potential.

B. Components of AI

1. Neural Network: Computational algorithms called Artificial Neural Networks (ANNs) [1] are modelled on how the human brain works. Depending on the goal, these systems can change and change their internal structure. ANNs act as the "brain" of many machine learning and deep learning applications in the field of AI. They are widely used in a variety of fields, including recommendation systems, driverless cars, natural language processing, image and audio recognition, and many more. ANNs are a key tool in the broader field of artificial intelligence because they allow AI systems to learn from data, adapt to changing circumstances, and improve their performance over time. Also, the role of advanced tools required using ML and ESPs [6-81] are becoming important in recent applications, recognition and control.
2. Natural Language Processing (NLP): Natural language processing (NLP) [2] is used to combine the capabilities of artificial intelligence (AI) systems and linguistics. This integration allows computers to efficiently produce, translate, and use human speech. NLP combined with AI enables computers to perform tasks such as textual sentiment analysis, document segmentation, language transformation, language translation, and natural dialogue between human machines through chatbots and virtual assistants.
3. Computer Vision: The revolution of visual data detection and interpre­tation is propelled by the powe­rful combination of artificial intelligence (AI) and computer vision (CV) [3]. This synergy has transformed the field, with deep learning algorithms significantly enhancing CV systems. They enable object recognition, pattern identification, and valuable insights from images and videos. The integration of AI and CV has opened up numerous applications across various domains like driverless vehicles, medical imaging, surveillance, facial recognition, industrial automation, to name a few. This convergence continues to push the boundaries of computer vision in the visual domain, offering a vast array of opportunities for academics, engineers, and innovators to explore.
4. Cognitive Computing: Cognitive computing [4], paired with AI, mimics human thinking using technologies like neural networks, machine learning, and natural language processing. This empowers machines to understand, analyse, reason, learn, and adapt independently, especially when dealing with unstructured data like text and images. These systems play a vital role in healthcare, finance, and information retrieval by processing data, identifying patterns, and advancing AI's quest to mimic human cognition.

???????C. Advantages of AI

1. AI systems have the ability to handle an amount of information and consistently adhere to rules when analysing data and making decisions. As a result, they are less prone, to errors.
2. AI systems are accessible, round the clock. Do not require any breaks. They possess the ability to effortlessly manage tasks.
3. AI has the ability to enhance decision making by making it more efficient and intelligent. It can swiftly extract insights, from processed data.

AI can make unbiased decisions

???????A. Steps Involved to Create a Chatbot

1. NLP Framework: When creating a chatbot the first step is to choose the NLP (Natural Language Processing) framework [5]. This decision depends on several factors. Firstly, it's important to assess the projects requirements considering the target audience and the level of language interaction complexity. Therefore, thorough research, into NLP frameworks becomes crucial. These options include cloud-based services such as Google Cloud Natural Language and IBM Watson as open-source tools, like NLTK and spaCy. Each framework has its advantages, which can be related to cost, ease of integration or language capabilities. It is also important to consider the language support since certain frameworks may excel in languages or dialects.
2. Dataset: The next step in creating a chatbot is to collect a dataset. This involves gathering and organizing the information to train and improve the chatbots abilities in understanding and generating language. The first customary step in this process is acquiring data, which can include obtaining text from sources, like websites, customer interactions or existing chat logs. After gathering data, the data must be cleaned up and pre-processed where processes such as eliminating duplicates, filling in missing values, and tokenize a long string of text into phrases. Handling privacy issues when working with sensitive user data is also part of this process of preparing your data.
3. Feeding data: It’s an essential step in learning for the chatbot that teaches it how to understand what users say and how to answer back, all from having fed the bots data as well. The underlying technology of machine learning and natural language processing is a fine-tuned dataset. Because the Chabot learns patterns, coherences, and contexts from the dataset, it also knows which information the customer wants (user intention), what kind of information the user is asking for, and delivers an adequate answer. The process of providing data is based on how complex the chatbot’s duty is: training machine learning models such as deep neural networks or sequence to sequence models. The capacity for these models to be able to generalize from this dataset allows the chatbot to cover a wide range of user queries and configurations.
4. Fine-Tuning: One significant step of building a chatbot involves fine-tuning, where the chatbot’s behaviour is adjusted to increase its effectiveness. Developers and data scientists use users’ feedback in addition to data coming directly from usage to further enhance the language comprehension and text generation abilities of the chatbot during fine-tuning. This might involve tweaking model hyperparameters, incorporating user input mechanisms, or using reinforcement learning techniques for helping the model pick more applicable responses given situations in which the user is interacting with the bot.
5. Integration: Bringing a chatbot to life by effortlessly integrating it into the platform or application where it will serve users is the integration of a chatbot into your interface, which is a crucial stage in chatbot development. Often, this means incorporating these conversation elements into the UI as well and making sure that the chatbot can interact with the user in a familiar way. This requires building chatbots as natural chat widgets or incorporating the chatbot with the existing channels of communication, i.e., websites, chat apps or voice Assistants. In addition, integration allows the chatbot to interact with back-end systems + databases; meaning the chatbot can get and execute on the data needed as a result of user input. These connections require safe and efficient data-exchange protocols and APIs (often).

IV. ACKNOWLEDGEMENT

I want to thank Dr. R. C. Jaiswal, for all of his help and advice while I was conducting the research. He provided this paper with the knowledge and experience it needed to be a presentable one. His counsel, business sense, and encouragement proved to be invaluable support.

Conclusion

In conclusion, this study reveals a deep understanding of the ever-changing landscape of AI-based chatbots. In this paper we have studied the basic concepts about AI, how underlying theories work, how the Chatbot is evolving continuously in the field of AI. We have discussed few key stages where chatbot development services involve more intricate processes such as selecting the perfect NLP frame work (Chatbot development services, developing a model with multiple algorithms and techniques) to train, integrate these with smart UI’s. We also discussed about the components of AI and their roles while creating a chatbot.

References

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C. and Tejveer Pratap, “ Multiparametric Monitoring of Vital Signs in Clinical and Home Settings for Patients ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 5 pp. a701-a705, May 2022. [32] Jaiswal R. C. and Sahil Nahar, “Recognition and Selection of Learning Styles to Personalize Courses for Students”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 9, Issue 2 pp. b235-b252, February 2022. [33] Jaiswal R. C. and Rushikesh Karwankar, “ Demand Forecasting for Inventory Optimization ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed in Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 8, Issue 12 pp. 121-131, January 2022. [34] Jaiswal R. C. and P. Khore, “ Exo-skeleton Arm ”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, Indexed In Google Scholar, Microsoft Academic, CiteSeerX, Thomson Reuters, Mendeley : reference manager, ISSN-2349-5162, Impact Factor:7.95, Volume 8, Issue 12 pp. 731-734, December 2021. [35] Jaiswal R. 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[47] Jaiswal R.C. and Onkar Gagare, “Head Mounted Display”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal, ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 535-541, Nov 2019. [48] Jaiswal R.C. and Nehal Borole, “Autonomous Vehicle Prototype Development and Navigation using ROS”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal, ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.177, Volume 7 Issue XI, pp. 510-514, Nov 2019. [49] Jaiswal R.C. and Vaibhav Pawar, “Voice and Android Application Controlled Wheelchair”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, ISSN-2349-5162, Volume 6, Issue 6, pp. 635-637, June 2019. [50] Jaiswal R.C. and Shreya Mondhe, “ Waste Segregation & Tracking”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Open Access, Peer Reviewed and refereed Journal , ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:7.429, Volume 8, Issue 5, pp. 2085-2087, May 2019. [51] Jaiswal R.C. and Shreya Mondhe, “Stock Market Prediction Using Machine Learning & Robotic Process Automation”, Journal of Emerging Technologies and Innovative Research (JETIR), Open Access, Peer Reviewed and refereed Journal, ISSN-2349-5162, Volume 6, Issue 6, pp. 926-929, February 2019. [52] Jaiswal R.C. and Samruddhi Sonare, “Smart Supervision Security System Using Raspberry Pi ”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 6, Issue 4, pp. 574-579, April 2019. 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[57] Jaiswal R.C. and Himanshu Mithawala, “Automatic Gate Monitoring System”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162, Volume 6, Issue 1,pp. 88-94, January 2019. [58] Jaiswal R.C. and Bernard Lewis, “Dynamic Runway and Gate Terminal Allocation for Flights”, Journal of Emerging Technologies and Innovative Research (JETIR), UGC approved Journal, ISSN-2349-5162, Volume 5, Issue 12, December 2018. [59] Jaiswal R.C. and Sakshi Jain, “Text Search Engine”,‘, Journal of Emerging Technologies and Innovative Research (JETIR), UGC approved Journal ISSN-2349-5162, Volume 5, Issue 11, November 2018. [60] Jaiswal R.C. and Arti Gurap, “Design of Different Configurations of Truncated Rectangular Microstrip Patch Antenna For 2.4 GHz And 1.6 GHz ‘, Journal of Emerging Technologies and Innovative Research (JETIR),UGC Approved Journal, ISSN-2349-5162, Volume 5, Issue 10, October 2018. [61] Jaiswal R.C. and Atharva Mahindrakar, “ Mine Warfare and Surveillance Rover”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor:6.887, Volume 6 Issue III, March 2018. [62] Jaiswal R.C. and Saloni Takawale “Multi-Client Server Communication Enhancement through Intranet”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321-9653; UGC approved Journal, IC Value: 45.98; SJ Impact Factor :6.887, Volume 6 Issue 1, January 2018. [63] Jaiswal R.C. and Nikita Kakade, “Skin disease detection and classification using Image Processing Techniques”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162; UGC approved Journal:5.87, Volume 4, Issue 12, December 2017. [64] Jaiswal R.C. and Nikita Kakade, “OMR Sheet Evaluation Using Image Processing”, Journal of Emerging Technologies and Innovative Research (JETIR), ISSN-2349-5162; UGC approved Journal:5.87, Volume 4, Issue 12, December 2017. [65] Jaiswal R.C. and Swapnil Shah, “Customer Decision Support System”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056; p-ISSN: 2395-0072; UGC approved Journal, SJ Impact Factor:5.181, Volume: 04 Issue: 10 | Oct -2017. [66] Jaiswal R.C. and Ketan Deshpande, “IOT Based Smart City: Weather, Traffic and Pollution Monitoring System”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056; p-ISSN: 2395-0072; UGC approved Journal, SJ Impact Factor:5.181, Volume: 04 Issue: 10 | Oct -2017. 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Copyright © 2023 Rahul Kulkarni, Dr. Rupesh C. Jaiswal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.