Conversational AI

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Conversational Generative AI

Conversational AI refers to technologies, like chatbots or virtual agents, which users can talk to. Conversational AI can also help you eliminate the blank sheet syndrome by providing prompting you with dialog and example content. They use large volumes of data, Machine Learning, and Natural Language Processing (NLP) to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages. Conversational AI can simulate human conversation and is made possible by Natural Language Processing (NLP), a field of AI that allows computers to understand and process human language.

Success is not about the absence of mental blocks, but the ability to overcome them.

Some examples of conversational AI include chatbots, virtual assistants, text-to-speech software, and Speech Recognition software. Conversational AI systems are trained on large amounts of data, such as text and speech. This data is used to teach the system how to understand and process human language. The system then uses this knowledge to interact with humans in a natural way. It’s constantly learning from its interactions and improving its response quality over time. Some benefits of using conversational AI include reducing costs and increasing productivity and operational efficiency through automation. It can also deliver better customer experience, achieve higher customer engagement and satisfaction .


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Chatbot is a computer program that simulates conversation with human users, especially over the Internet. It can be used to provide customer service, answer questions, and help people find information quickly and easily. Chatbots are becoming increasingly popular as they become more sophisticated in their ability to understand natural language and respond appropriately. They can also be used for entertainment purposes, such as playing games or providing humorous responses. Chatbot | Wikiwand ...Chrome Extension


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Company that specializes in creating AI-powered conversational interfaces for businesses. Their platform allows companies to build and deploy chatbots and virtual assistants that can communicate with customers through natural language processing.


This architecture consisted of a NLU model (Intents & Entities), a dialog state machine and pre-defined bot messages. In this architecture, there is not much “AI” involved. The dialog flow structure is pre-determined and hard-coded with state machine logic. The bot responses are fixed or at best a response message is concatenated with variable values inserted. As is the status quo with IVRs. Hence many chatbots were actually more of a ITR system (interactive text response). Considering that specific intents are assigned to specific sections of the flow…the only “AI” portion was the trained NLU model which extracts intents and entities from user input based on probability. Existing Rigid Chatbot Architecture Needs Large Language Model (LLM) Flexibility | Cobus Greyling - Medium

Natural Language Understanding (NLU) Design

Providing the full NLU design end-to-end capabilities; with a pluggable data pipeline it allows teams to integrate different NLU providers (for model training and evaluation) as well as ability to incorporate large language models to power the core ML-assisted workflows (like semantic search & clustering). NLU Design Landscape | Cobus Greyling - Medium