For each kind of question, a unique pattern must be available in the database to provide a suitable response. With lots of combination on patterns, it creates a hierarchical structure. We use algorithms to reduce the classifiers and generate the more manageable structure. Computer scientists call it a “Reductionist” approach- in order to give a simplified solution, it reduces the problem.
The bot itself is only part of a larger system that provides it with the latest data and ensures its proper operation. All of these other Azure resources — data orchestration services such as Data Factory, storage services such as Cosmos DB, and so forth — must be deployed. Azure Resource Manager provides a consistent management layer that you can access through the Azure portal, PowerShell, or the Azure CLI. For speed and consistency, it's best to automate your deployment using one of these approaches.
Another option is to integrate your own custom AI service. This approach is more complex, but gives you complete flexibility in terms of the machine learning algorithm, training, and model. For example, you could implement your own topic modeling and use algorithm such as LDA to find similar or relevant documents. A good approach is to expose your custom AI solution as a web service endpoint, and call the endpoint from the core bot logic. The web service could be hosted in App Service or in a cluster of VMs. Azure Machine Learning provides a number of services and libraries to assist you in training and deploying your models.
Another option is to integrate your own custom AI service. This approach is more complex, but gives you complete flexibility in terms of the machine learning algorithm, training, and model. For example, you could implement your own topic modeling and use algorithm such as LDA to find similar or relevant documents. A good approach is to expose your custom AI solution as a web service endpoint, and call the endpoint from the core bot logic. The web service could be hosted in App Service or in a cluster of VMs. Azure Machine Learning provides a number of services and libraries to assist you in training and deploying your models.
Smart chatbots rely on artificial intelligence when they communicate with users. Instead of pre-prepared answers, the robot responds with adequate suggestions on the topic. In addition, all the words said by the customers are recorded for later processing. However, the Forrester report “The State of Chatbots” points out that artificial intelligence is not a magic and is not yet ready to produce marvelous experiences for users on its own. On the contrary, it requires a huge work:
Interface designers have come to appreciate that humans' readiness to interpret computer output as genuinely conversational—even when it is actually based on rather simple pattern-matching—can be exploited for useful purposes. Most people prefer to engage with programs that are human-like, and this gives chatbot-style techniques a potentially useful role in interactive systems that need to elicit information from users, as long as that information is relatively straightforward and falls into predictable categories. Thus, for example, online help systems can usefully employ chatbot techniques to identify the area of help that users require, potentially providing a "friendlier" interface than a more formal search or menu system. This sort of usage holds the prospect of moving chatbot technology from Weizenbaum's "shelf ... reserved for curios" to that marked "genuinely useful computational methods".
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