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Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities.\r\nBeyond chatbots, Conversational AI refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale.\r\nThe process of building, testing and deploying chatbots can be done on cloud-based chatbot development platforms offered by cloud Platform as a Service (PaaS) providers such as Oracle Cloud Platform SnatchBot and IBM Watson. 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In banking, chatbots and virtual assistants are some of the industry’s newest tools designed to simplify the interaction between humans and computers.\r\n<span style=\"font-weight: bold;\">What is a chatbot?</span>\r\nA chatbot is artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone.\r\n<span style=\"font-weight: bold;\">Why are chatbots important?</span>\r\nA chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. However, from a technological point of view, a chatbot only represents the natural evolution of a Question-Answering system leveraging Natural Language Processing (NLP). Formulating responses to questions in natural language is one of the most typical examples of Natural Language Processing applied in various enterprises’ end-use applications.\r\n<span style=\"font-weight: bold;\">How does a chatbot work?</span>\r\nThere are two different tasks at the core of a chatbot:\r\n<ol><li>user request analysis</li><li>returning the response</li></ol>\r\nUser request analysis: this is the first task that a chatbot performs. It analyzes the user’s request to identify the user intent and to extract relevant entities.\r\nThe ability to identify the user’s intent and extract data and relevant entities contained in the user’s request is the first condition and the most relevant step at the core of a chatbot: If you are not able to correctly understand the user’s request, you won’t be able to provide the correct answer.\r\nReturning the response: once the user’s intent has been identified, the chatbot must provide the most appropriate response for the user’s request. The answer may be:\r\n<ul><li>a generic and predefined text;</li><li>a text retrieved from a knowledge base that contains different answers;</li><li>a contextualized piece of information based on data the user has provided;</li><li>data stored in enterprise systems;</li><li>the result of an action that the chatbot performed by interacting with one or more backend application;</li><li>a disambiguating question that helps the chatbot to correctly understand the user’s request.</li></ul>\r\n<span style=\"font-weight: bold;\">In what is the benefit of chatbots?</span>\r\nChatbot applications streamline interactions between people and services, enhancing the customer experience. At the same time, they offer companies new opportunities to improve the customer's engagement process and operational efficiency by reducing the typical cost of customer service.\r\nTo be successful, a chatbot solution should be able to effectively perform both of these tasks. 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This historic innovation will unleash a wave of changes to search, e-commerce, social networks, productivity software, and devices. Semantic Machines is developing the fundamental AI technology needed to make conversational computing a reality.\r\nSemantic Machines is developing a new, language-independent technology platform that goes beyond understanding commands to understanding conversations. Our approach represents a powerful new paradigm, enabling computers to communicate, collaborate, understand goals, and accomplish tasks.\r\nLeveraging extensive proprietary machine learning technology, we are developing the following core components:\r\n<span style=\"font-weight: bold;\">Conversation Engine</span>\r\nThe Semantic Machines Conversation Engine is a revolutionary approach for modeling human discourse fluidly across speech and text. The engine extracts semantic intent from natural input (voice or text) and then spawns a self-updating learning framework for managing dialog state, context, salience, and end-user goals. The Conversation Engine's natural language generation (NLG) technology actively formulates communication with the user based on the dialog context.\r\n<span style=\"font-weight: bold;\">Deep Learning</span>\r\nSemantic Machines is developing state-of-the-art neural network systems for use across a range of critical functions from semantic analysis to dialog state to acoustic and language models, NLG and speech synthesis.\r\n<span style=\"font-weight: bold;\">Speech Synthesis</span>\r\nSpeech synthesis is critical for conversational computing. The computer’s voice takes the place of a display providing information users need. Existing speech synthesis technology, particularly the prosodic models, are not sufficient to enable effective conversational computing. 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