8/30/2023 0 Comments Dr chatbot rochesterINTRODUCTION " We don't just use technology we live with it " (MaCarthy& Wright, 2004, p. Keywords-Artificial Intelligence Mark Up Language (AIML) Long Short Term Memory (LSTM) Gated Recurrent Unit (GRU) I. It trains on the conversations it has had and gives more appropriate responses the next time in a similar context and as well is able to respond to more situations. The proposed chatbot Xen gives appropriate responses based on the context it is given and responds appropriately and as well understands the sentiment that is being portrayed. The generative chatbot trains itself using context classification so that it can improve its responses and expand the responses it uses. The proposed chatbots are Retrieval, AIML (Artificial Intelligence Markup Language) where it uses self-learning using K-Means, after it has collected enough data by interacting with the user and Generative chat-bot. It can be used by people to chat with or understand their problem or just to talk about when they need a flow of thought. The context can have a lot of meanings along with vagueness, incomplete and incorrect data. Lot of information needs to be analyzed to understand which is, what and what all it can mean in context classification. Generative chatbots train on data and work based on content classification or by making rules understanding the data and thus may be able to work for all scenarios. Xen learns and understands what the user is trying to convey based on conversations it has had with the users. The aim of this research is to build a counsellor chat bot as a service which responds giving the person advice based on the premise given to it. ![]() To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution (Panesar 2019a, b, 2017). This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. ![]() We will critique the knowledge representation of heavy statistical Chatbot solutions against linguistics alternatives. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. However, what is under the hood, and how far and to what extent can Chatbots/conversational artificial intelligence solutions work-is our question. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. This paper aims to demystify the hype and attention on Chatbots and its association with conversational artificial intelligence.
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