Strictly speaking, chatbots, also known as communication robots, are actually software disguised as a communication partner, and which can efficiently maintain dialogue. However, a prerequisite for meaningful chats is training data tailored to the respective area of application. In other words: The chatbot must be fed relevant questions within the context of the respective chat background and must be able to recognise them in a chat situation in order to be able to provide the correct answer, which must also have been fed in.
When newly developing or refining communication robots, the primary objective is to amass the largest possible volume of data with which to train the algorithms. This is mostly rule-based and topic-specific data. The more of this there is available, the better the bot can learn. One of the main tasks here is the creation of topic-specific data tailored to the chatbot, which is required to create the essential custom knowledge base. In order to develop such a database containing the respective distinguishing features of the relevant questions, previously available data, for example from the field of customer service, can be used, as can data generated by the crowd. With the assistance of the crowd, these various topic areas can be appropriately structured and analysed. This includes among other things breaking complete or even incomplete sentences down into their constituent parts and reducing them to the core question, also known as the intent. The benefits are clear to see: Businesses not only save time and money, but can free their employees from the often very laborious and not so challenging work.
Another approach is to have the crowd simulate the intent of a chat dialogue. This delivers an enormous amount of manifold formulations that are essential for the chatbot to be able to learn, for example so it can familiarise itself with different modes of expression and therefore better recognise contexts in conversations. In this regard, maintaining a broad spectrum of possible questions is of immense importance. It has proved to be advisable and invaluable to combine both approaches, ie the client’s training data and data generated by the crowd. This input results in the creation of an artificial intelligence that is able to grasp the complexities of situations and process them independently.
The use of bots can prove useful, as well as saving time and costs, in many different areas. Communication robots have already established and proved themselves in online shops, on hotlines and in customer service, and the future promises a rapid increase in the areas in which they are used, for example in personnel recruitment. The critical argument that the use of communication robots would bring about a reduction in the workforce is countered by the fact that their involvement in various work processes would actually result in relieving customer service staff of burdens. Instead, employees would be able to concentrate, with less stress and fewer time pressures, on solving complex problems, while the chatbots would deal with answering common and frequently recurring questions. Accordingly, consumer satisfaction would increase, because customers would be spared waiting in long queues or painstakingly searching instruction manuals in order to receive answers to simple questions.
In conclusion, it is clear that in future, bots will play an ever-greater role in this world of borderless communication, and that it will become more and more advisable to harness this service technology and all the possibilities it offers. The many benefits of crowdsourcing should not be underestimated: Artificial intelligence can only be created by human intelligence.
In this regard, we can draw upon many years’ experience, and have skilled personnel at our disposal. In addition to a well-trained crowd and also outstanding quality control systems, we also offer the opportunities presented by an API interface, and can deliver training data in your desired format, along with much more.