Deep Learning in Artificial Intelligence: Meta-Analysis and Chatbot Applications

EasyChair Preprint 11805

Abstract

Deep learning has emerged as a powerful approach in the field of artificial intelligence, revolutionizing various applications, including natural language processing, computer vision, and speech recognition. In recent years, deep learning techniques have also been applied to enhance the capabilities of chatbots, which are intelligent conversational agents designed to interact with users in a human-like manner. This article provides a comprehensive overview of the role of deep learning in chatbot development and its potential for advancing the field of artificial intelligence. One of the key aspects discussed in this article is the use of meta-analysis in evaluating the performance of deep learning-based chatbots. Meta-analysis allows for the systematic synthesis of findings from multiple studies, enabling a comprehensive understanding of the effectiveness of different deep learning models and techniques in chatbot applications. By aggregating and analyzing data from various sources, meta-analysis provides valuable insights into the strengths, weaknesses, and overall performance of deep learning chatbots. The article also highlights the benefits of deep learning in improving various aspects of chatbot functionality, such as natural language understanding, dialogue management, and response generation. Deep learning models, such as recurrent neural networks and transformer architectures, have demonstrated remarkable capabilities in these areas, enabling chatbots to better understand user input, engage in meaningful conversations, and generate contextually relevant responses.

Keyphrases: Artificial Intelligent, Chatbots, deep learning, meta-analysis

@booklet{EasyChair:11805,
  author    = {William Jack and Maxwell Rooke},
  title     = {Deep Learning in Artificial Intelligence: Meta-Analysis and Chatbot Applications},
  howpublished = {EasyChair Preprint 11805},
  year      = {EasyChair, 2024}}