Why are chatbots important? A 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.
Indeed, this is one of the key benefits of chatbots – providing a 24/7/365 presence that can give prospects and customers access to information no matter when they need it. This, in turn, can result in cost-savings for companies that deploy chatbots, as they cut down on the labour-hours that would be required for staff to manage a direct messaging service every hour of the week.
How far are we from building systems with commonsense? One often-heard answer is: not in the near future, while the realistic answer is: we don’t know. Last year, I spent some time trying to build a system that can do better than an information retrieval baseline in taking fourth-grade science exam (which still has a ways to go to gain a passing score of 65%). I failed hard. Here’s an example to get a sense of the difficulty of these questions.
Die Herausforderung bei der Programmierung eines Chatbots liegt in der sinnvollen Zusammenstellung der Erkennungen. Präzise Erkennungen für spezielle Fragen werden dabei ergänzt durch globale Erkennungen, die sich nur auf ein Wort beziehen und als Fallback dienen können (der Bot erkennt grob das Thema, aber nicht die genaue Frage). Manche Chatbot-Programme unterstützen die Entwicklung dabei über Priorisierungsränge, die einzelnen Antworten zuzuordnen sind. Zur Programmierung eines Chatbots werden meist Entwicklungsumgebungen verwendet, die es erlauben, Fragen zu kategorisieren, Antworten zu priorisieren und Erkennungen zu verwalten[5][6]. Dabei lassen manche auch die Gestaltung eines Gesprächskontexts zu, der auf Erkennungen und möglichen Folgeerkennungen basiert („Möchten Sie mehr darüber erfahren?“). Ist die Wissensbasis aufgebaut, wird der Bot in möglichst vielen Trainingsgesprächen mit Nutzern der Zielgruppe optimiert[7]. Fehlerhafte Erkennungen, Erkennungslücken und fehlende Antworten lassen sich so erkennen[8]. Meist bietet die Entwicklungsumgebung Analysewerkzeuge, um die Gesprächsprotokolle effizient auswerten zu können[9]. Ein guter Chatbot erreicht auf diese Weise eine mittlere Erkennungsrate von mehr als 70 % der Fragen. Er wird damit von den meisten Nutzern als unterhaltsamer Gegenpart akzeptiert.
3. Now, since ours is a conversational AI bot, we need to keep track of the conversations happened thus far, to predict an appropriate response. For this purpose, we need a dictionary object that can be persisted with information about the current intent, current entities, persisted information that user would have provided to bot’s previous questions, bot’s previous action, results of the API call (if any). This information will constitute our input X, the feature vector. The target y, that the dialogue model is going to be trained upon will be ‘next_action’ (The next_action can simply be a one-hot encoded vector corresponding to each actions that we define in our training data).
Nowadays a high majority of high-tech banking organizations are looking for integration of automated AI-based solutions such as chatbots in their customer service in order to provide faster and cheaper assistance to their clients becoming increasingly technodexterous. In particularly, chatbots can efficiently conduct a dialogue, usually substituting other communication tools such as email, phone, or SMS. In banking area their major application is related to quick customer service answering common requests, and transactional support.

“Utility gets something done following a prompt. At a higher level the more entertainment-related chatbots are able to answer all questions and get things done. Siri and Cortana you can have small talk with, as well as getting things done, so they are much harder to build. They took years and years of giant company’s efforts. Different companies that don’t have those resources, like Facebook, will build more constrained utility bots.”
This chatbot aims to make medical diagnoses faster, easier, and more transparent for both patients and physicians – think of it like an intelligent version of WebMD that you can talk to. MedWhat is powered by a sophisticated machine learning system that offers increasingly accurate responses to user questions based on behaviors that it “learns” by interacting with human beings.
Ursprünglich rein textbasiert, haben sich Chatbots durch immer stärker werdende Spracherkennung und Sprachsynthese weiterentwickelt und bieten neben reinen Textdialogen auch vollständig gesprochene Dialoge oder einen Mix aus beidem an. Zusätzlich können auch weitere Medien genutzt werden, beispielsweise Bilder und Videos. Gerade mit der starken Nutzung von mobilen Endgeräten (Smartphones, Wearables) wird diese Möglichkeit der Nutzung von Chatbots weiter zunehmen (Stand: Nov. 2016).[10] Mit fortschreitender Verbesserung sind Chatbots dabei nicht nur auf wenige eingegrenzte Themenbereiche (Wettervorhersage, Nachrichten usw.) begrenzt, sondern ermöglichen erweiterte Dialoge und Dienstleistungen für den Nutzer. Diese entwickeln sich so zu Intelligenten Persönlichen Assistenten.

One of the more talked about integrations has been Taco Bell‘s announcement that it is working on a Slackbot (appropriately named Tacobot) which will not only take your Gordita Supreme order but will do it with the same “witty personality you’d expect from Taco Bell.” Consumer demand for such a service remains to be seen, but it hints at the potential for brands to leverage Slack’s platform and growing audience.
The classic historic early chatbots are ELIZA (1966) and PARRY (1972).[10][11][12][13] More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so).[14]
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