One pertinent field of AI research is natural language processing.  Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required.  For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots.  Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966.  This is not strong AI, which would require sapience and logical reasoning abilities. 
As I tinker with dialog systems at the Allen Institute for Artificial Intelligence, primarily by prototyping Alexa skills, I often wonder what AI is still lacking to build good conversational systems, punting the social challenge to another day.  This post is my take on where AI has a good chance to improve and consequently, what we can expect from the next wave of conversational systems. 
Es gibt auch Chatbots, die gar nicht erst versuchen, wie ein menschlicher Chatter zu wirken (daher keine Chatterbots), sondern ähnlich wie IRC-Dienste nur auf spezielle Befehle reagieren.  Sie können als Schnittstelle zu Diensten außerhalb des Chats dienen, oder auch Funktionen nur innerhalb ihres Chatraums anbieten, z. B. neu hinzugekommene Chatter mit dem Witz des Tages begrüßen. 
Its a chat-bot — For simplicity reasons in this article, it is assumed that the user will type in text and the bot would respond back with an appropriate message in the form of text (So, we will not be concerned with the aspects like ASR, speech recognition, speech to text, text to speech etc.,  Below architecture can anyways be enhanced with these components, as required). 
Cheyer explains Viv like this.  Imagine you need to pick up a bottle of wine that goes well with lasagna on the way to your brother's house.  If you wanted to do that yourself, you'd need to determine which wine goes well with lasagna (search #1) then find a wine store that carries it (search #2) that is on the way to your brother's house (search #3).  Once you have that figured out, you have to calculate what time you need to leave to stop at the wine store on the way (search #4) and still make it to his house on time. 
A virtual assistant is an app that comprehends natural, ordinary language voice commands and carries out tasks for the users.  Well-known virtual assistants include Amazon Alexa, Apple’s Siri, Google Now and Microsoft’s Cortana.  Also, virtual assistants are generally cloud-based programs so they need internet-connected devices and/or applications in order to work.  Virtual assistants can perform tasks like adding calendar appointments, controlling and checking the status of a smart home, sending text messages, and getting directions. 
I know what you’re thinking – when will the world of marketing just stand still for a moment and let us all catch up?!?!  No such luck, dear readers.  No sooner have we all gotten to grips with the fact that we’re going to have to start building live video campaigns into our content marketing strategies, something else comes along that promises to be the next game-changer.  And so here we are with the most recent marketing phenomenon – chatbots. 
Furthermore, major banks today are facing increasing pressure to remain competitive as challenger banks and fintech startups crowd the industry.  As a result, these banks should consider implementing chatbots wherever human employees are performing basic and time-consuming tasks.  This would cut down on salary and benefit costs, improve back-office efficiency, and deliver better customer care. 
The trained neural network is less code than an comparable algorithm but it requires a potentially large matrix of “weights”.  In a relatively small sample, where the training sentences have 150 unique words and 30 classes this would be a matrix of 150x30.  Imagine multiplying a matrix of this size 100,000 times to establish a sufficiently low error rate.  This is where processing speed comes in. 
Regardless of which type of classifier is used, the end-result is a response.  Like a music box, there can be additional “movements” associated with the machinery.  A response can make use of external information (like weather, a sports score, a web lookup, etc.)  but this isn’t specific to chatbots, it’s just additional code.  A response may reference specific “parts of speech” in the sentence, for example: a proper noun.  Also the response (for an intent) can use conditional logic to provide different responses depending on the “state” of the conversation, this can be a random selection (to insert some ‘natural’ feeling). 
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. 
It didn’t take long, however, for Turing’s headaches to begin.  The BabyQ bot drew the ire of Chinese officials by speaking ill of the Communist Party.  In the exchange seen in the screenshot above, one user commented, “Long Live the Communist Party!”  In response, BabyQ asked the user, “Do you think that such a corrupt and incompetent political regime can live forever?” 

The promise of artificial intelligence (AI) has permeated across the enterprise giving hopes of amping up automation, enriching insights, streamlining processes, augmenting workers, and in many ways making our lives as consumers, employees, and customers a whole lot better.  Senior management salivates over the exponential gains AI is supposed to deliver to their business.  Kumbayah […]

Your first question is how much of it does she want?  1 litre?  500ml?  200?  She tells you she wants a 1 litre Tropicana 100% Orange Juice.  Now you know that regular Tropicana is easily available, but 100% is hard to come by, so you call up a few stores beforehand to see where it’s available.  You find one store that’s pretty close by, so you go back to your mother and tell her you found what she wanted.  It’s $2, maybe $3, and after asking her for the money, you go on your way. 
A bot is software that is designed to automate the kinds of tasks you would usually do on your own, like making a dinner reservation, adding an appointment to your calendar or fetching and displaying information.  The increasingly common form of bots, chatbots, simulate conversation.  They often live inside messaging apps — or are at least designed to look that way — and it should feel like you’re chatting back and forth as you would with a human. 

The NLP system has a wide and varied lexicon to better understand the complexities of natural language.  Using an algorithmic process, it determines what has been asked and uses decision trees or slot-based algorithms that go through a predefined conversation path.  After it understands the question, the computer then finds the best answer and provides it in the natural language of the user. 

One pertinent field of AI research is natural language processing.  Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required.  For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots.  Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966.  This is not strong AI, which would require sapience and logical reasoning abilities.