International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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Conversational AI: A Survey Yash Bhagia1, Syed Mohammad Abbas2, Shalendra Kumar3, Shwetank Maheshwari4 1,2,3,4Zakir Hussain College of Engineering and Technology, Aligarh Muslim University, Aligarh (202001) -U.P., INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Chatbots, or virtual assistants that can
conversation through text-based interfaces. Chatbots are commonly used in customer service applications, where they can provide support and assistance to customers with basic questions or problems without the need for human intervention.
communicate with humans using natural language processing, have grown in popularity in recent years. These computer programs can be used for a variety of things, including finishing duties and offering amusement and business guidance. We start by performing a bibliometric study to determine the key papers and researchers in the field. Then, to spot themes and patterns, we summarize various study articles and present studies in accordance with predetermined criteria. A chronological, thematic, and methodological summary of the related study is also provided. Researchers are also investigating the creation of chatbots that can produce interesting and coherent talks, employing deep reinforcement learning techniques to encourage sequences that exhibit informative, coherent, and simple-to-follow conversational qualities. The way we communicate with machines could be completely changed by these developments in chatbot technology, which also has the potential to completely impact a variety of sectors. An overview of the state-of-the-art in chatbot technology will be provided in this paper, along with suggestions for prospective future research areas.
Conversational AI is also finding innovative applications in various domains such as healthcare, education, and finance. For instance, AI-powered chatbots have been used to provide mental health support to patients, assist with language learning, and provide financial advice to customers. In healthcare, conversational AI can assist with medical diagnosis and decision-making by analyzing and interpreting clinical data and electronic health records. In education, AIpowered chatbots can provide personalized learning experiences and instant feedback to students, helping to improve their understanding of complex concepts. In finance, conversational AI can assist customers with banking transactions and financial planning, providing personalized recommendations based on their financial goals and needs. Overall, conversational AI has the potential to transform the way we interact with technology and make our lives more convenient, efficient, and personalized. As the technology continues to evolve, we can expect to see more innovative applications in various domains, making conversational AI an exciting area of research and development.[1]
Key Words: Natural language processing, deep learning, neural networks, chatbots, Conversational Agent, Artificial intelligence.
1.INTRODUCTION A subfield of artificial intelligence called conversational AI is concerned with speech-based or text-based AI systems that can replicate and automate verbal interactions with people. Conversational AI is an exciting and rapidly evolving field of artificial intelligence that seeks to develop systems that can engage in natural language conversations with humans. The technology leverages advanced algorithms such as natural language processing, machine learning, and deep learning to understand the meaning of human language and respond in a way that is accurate, contextually relevant, and personalized.
2. Four Components of Conversational A.I. [2] 2.1. Machine Learning A subset of artificial intelligence known as machine learning (ML) uses a range of statistical models and algorithms to find patterns and predict future outcomes. Conversational AI needs machine learning to function. It helps the system to improve its understanding of and responses to human language by allowing it to continuously learn from the data it collects. Among other ML subtypes, conversational AI commonly uses supervised learning, unsupervised learning, deep learning, and neural networks.
One of the most widely used examples of conversational AI is virtual assistants such as Siri, Alexa, and Google Assistant. These systems enable users to interact with their devices using natural language and voice commands, and the AI technology behind them interprets and processes the user’s requests to provide relevant information or complete tasks as needed.
2.2. Natural Language Processing (NLP) To create an acceptable answer, natural language processing (NLP) includes converting unstructured input into a machine-readable format. The algorithms that make up conversational AI are always being improved because to the continuous feedback loop that these NLP techniques engage
Another example of conversational AI is chatbots, which are computer programs designed to simulate human
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