Feine, J., Morana, S., and Maedche, A. (2019). “Leveraging Machine-Executable Descriptive Knowledge in Design Science Research ‐ The Case of Designing Socially-Adaptive Chatbots”. In: Extending the Boundaries of Design Science Theory and Practice. Ed. by B. Tulu, S. Djamasbi, G. Leroy. Cham: Springer International Publishing, pp. 76–91. Download Publication
I argued that it is super hard to scale a one-trick TODA into a general assistant that helps the user getting things done across multiple tasks. An intelligence assistant is arguably expected to hold an informal chit-chat with the user. It is this area where we are staring into perhaps the biggest challenge of AI. Observe how Samantha introduces herself to Joaquin Phoenix’s Ted in the clip below:
Reduce costs: The potential to reduce costs is one of the clearest benefits of using a chatbot. A chatbot can provide a new first line of support, supplement support during peak periods or offer an additional support option. In all of these cases, employing a chatbot can help reduce the number of users who need to speak with a human. You can avoid scaling up your staff or offering human support around the clock.
Interestingly, the as-yet unnamed conversational agent is currently an open-source project, meaning that anyone can contribute to the development of the bot’s codebase. The project is still in its earlier stages, but has great potential to help scientists, researchers, and care teams better understand how Alzheimer’s disease affects the brain. A Russian version of the bot is already available, and an English version is expected at some point this year.
Die meisten Chatbots greifen auf eine vorgefertigte Datenbank, die sog. Wissensdatenbank mit Antworten und Erkennungsmustern, zurück. Das Programm zerlegt die eingegebene Frage zuerst in Einzelteile und verarbeitet diese nach vorgegebenen Regeln. Dabei können Schreibweisen harmonisiert (Groß- und Kleinschreibung, Umlaute etc.), Satzzeichen interpretiert und Tippfehler ausgeglichen werden (Preprocessing). Im zweiten Schritt erfolgt dann die eigentliche Erkennung der Frage. Diese wird üblicherweise über Erkennungsmuster gelöst, manche Chatbots erlauben darüber hinaus die Verschachtelung verschiedener Mustererkennungen über sogenannte Makros. Wird eine zur Frage passende Antwort erkannt, kann diese noch angepasst werden (beispielsweise können skriptgesteuert berechnete Daten eingefügt werden – „In Ulm sind es heute 37 °C.“). Diesen Vorgang nennt man Postprocessing. Die daraus entstandene Antwort wird dann ausgegeben. Moderne kommerzielle Chatbot-Programme erlauben darüber hinaus den direkten Zugriff auf die gesamte Verarbeitung über eingebaute Skriptsprachen und Programmierschnittstellen.
1. AI-based: these ones really rely on training and are fairly complicated to set up. You train the chatbot to understand specific topics and tell your users which topics your chatbot can engage with. AI chatbots require all sorts of fall back and intent training. For example, let’s say you built a doctor chatbot (off the top of my head because I am working on one at the moment), it would have to understand that “i have a headache” and “got a headache” and “my head hurts” are the same intent. The user is free to engage and the chatbot has to pick things up.
As AOL's David Shingy writes in Adweek, "The challenge [with chatbots] will be thinking about creative from a whole different view: Can we have creative that scales? That customizes itself? We find ourselves hurtling toward another handoff from man to machine -- what larger system of creative or complex storytelling structure can I design such that a machine can use it appropriately and effectively?"

Say you want to build a bot that tells the current temperature. The dialog for the bot only needs coding to recognize and report the requested location and temperature. To do this, the bot needs to pull data from the API of the local weather service, based on the user’s location, and to send that data back to the user—basically, a few lines of templatable code and you’re done.
A chatbot is a computer program that simulates human conversation through voice commands or text chats or both. Chatbot, short for chatterbot, is an Artificial Intelligence (AI) feature that can be embedded and used through any major messaging applications. There are a number of synonyms for chatbot, including "talkbot," "bot," "IM bot," "interactive agent" or "artificial conversation entity."
One key reason: The technology that powers bots, artificial intelligence software, is improving dramatically, thanks to heightened interest from key Silicon Valley powers like Facebook and Google. That AI enables computers to process language — and actually converse with humans — in ways they never could before. It came about from unprecedented advancements in software (Google’s Go-beating program, for example) and hardware capabilities.
Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval.
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