The progressive advance of technology has seen an increase in businesses moving from traditional to digital platforms to transact with consumers. Convenience through technology is being carried out by businesses by implementing Artificial Intelligence (AI) techniques on their digital platforms. One AI technique that is growing in its application and use is chatbots. Some examples of chatbot technology are virtual assistants like Amazon's Alexa and Google Assistant, and messaging apps, such as WeChat and Facebook messenger.
Before you even write a single line of code, it's important to write a functional specification so the development team has a clear idea of what the bot is expected to do. The specification should include a reasonably comprehensive list of user inputs and expected bot responses in various knowledge domains. This living document will be an invaluable guide for developing and testing your bot.
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). 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.
There are different approaches and tools that you can use to develop a chatbot. Depending on the use case you want to address, some chatbot technologies are more appropriate than others. In order to achieve the desired results, the combination of different AI forms such as natural language processing, machine learning and semantic understanding may be the best option.
However, as irresistible as this story was to news outlets, Facebook’s engineers didn’t pull the plug on the experiment out of fear the bots were somehow secretly colluding to usurp their meatbag overlords and usher in a new age of machine dominance. They ended the experiment due to the fact that, once the bots had deviated far enough from acceptable English language parameters, the data gleaned by the conversational aspects of the test was of limited value.
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.
Simplified and scripted. Chatbot technology is being tacked on to the broader AI message, and while it’s important to note that machine learning will help chatbots get better at understand and responding to questions, it’s not going to make them the conversationalists we dream them to be. No matter what the marketing says, chatbots are entirely scripted. User says x, chatbot responds y.
The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs. Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites.  Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities.
Need a Facebook bot? Well, look no further, as Chatfuel makes it easy for you to create your own Facebook and Telegram Chatbot without any coding experience necessary. It works by letting users link to external sources through plugins. Eventually, the platforms hope to open itself to third-party plugins, so anyone can contribute their own plugins and have others benefit from them.
These are one of the major tools applied in machine learning. They are brain-inspired processing tools that actually replicate how humans learn. And now that we’ve successfully replicated the way we learn, these systems are capable of taking that processing power to a level where even greater volumes of more complex data can be understood by the machine.
Les premières formes historiques de chatbots ont été utilisées sous forme d’agents virtuels mis à disposition sur les sites web et utilisant le plus souvent une image ou un avatar humain. Le terme de chatbot est désormais principalement utilisé pour désigner les chatbots proposés sur les réseaux sociaux et notamment les chatbots Facebook Messenger ou ceux intégrés au sein d’applications mobiles ou sites web. Appliqués au domaine des enceintes intelligentes et autres assistants intelligents, les chatbots peuvent devenir des voicebots.
Other companies explore ways they can use chatbots internally, for example for Customer Support, Human Resources, or even in Internet-of-Things (IoT) projects. Overstock, for one, has reportedly launched a chatbot named Mila to automate certain simple yet time-consuming processes when requesting for a sick leave. Other large companies such as Lloyds Banking Group, Royal Bank of Scotland, Renault and Citroën are now using automated online assistants instead of call centres with humans to provide a first point of contact. A SaaS chatbot business ecosystem has been steadily growing since the F8 Conference when Zuckerberg unveiled that Messenger would allow chatbots into the app.
The bottom line is that chatbots have completely transformed the way companies interact with their consumers. And guess what? This is just the very beginning. And the truth is that even though to some company leaders it may seem challenging to incorporate the omnichannel customer experience, it opens up a fantastic opportunity that allows businesses to engage with customers in a fresh, modern way. The outcome of this may prove to be an excellent opportunity to build more meaningful and long-lasting relationships with the customers.
What does the Echo have to do with conversational commerce? While the most common use of the device include playing music, making informational queries, and controlling home devices, Alexa (the device’s default addressable name) can also tap into Amazon’s full product catalog as well as your order history and intelligently carry out commands to buy stuff. You can re-order commonly ordered items, or even have Alexa walk you through some options in purchasing something you’ve never ordered before.
Once the chatbot is ready and is live interacting with customers, smart feedback loops can be implemented. During the conversation when customers ask a question, chatbot smartly give them a couple of answers by providing different options like “Did you mean a,b or c”. That way customers themselves matches the questions with actual possible intents and that information can be used to retrain the machine learning model, hence improving the chatbot’s accuracy.
There are various search engines for bots, such as Chatbottle, Botlist and Thereisabotforthat, for example, helping developers to inform users about the launch of new talkbots. These sites also provide a ranking of bots by various parameters: the number of votes, user statistics, platforms, categories (travel, productivity, social interaction, e-commerce, entertainment, news, etc.). They feature more than three and a half thousand bots for Facebook Messenger, Slack, Skype and Kik.
Students from different backgrounds can share their views and perspectives on a specific matter while a chatbot can still adapt to each one of them individually. Chatbots can improve engagement among students and encourage interaction with the rest of the class by assigning group work and projects - similarly to what teachers usually do in regular classes.
At a high level, a conversational bot can be divided into the bot functionality (the "brain") and a set of surrounding requirements (the "body"). The brain includes the domain-aware components, including the bot logic and ML capabilities. Other components are domain agnostic and address non-functional requirements such as CI/CD, quality assurance, and security.
However, the revelations didn’t stop there. The researchers also learned that the bots had become remarkably sophisticated negotiators in a short period of time, with one bot even attempting to mislead a researcher by demonstrating interest in a particular item so it could gain crucial negotiating leverage at a later stage by willingly “sacrificing” the item in which it had feigned interest, indicating a remarkable level of premeditation and strategic “thinking.”
Note — If the plan is to build the sample conversations from the scratch, then one recommended way is to use an approach called interactive learning. We will not go into the details of the interactive learning here, but to put it in simple terms and as the name suggests, it is a user interface application that will prompt the user to input the user request and then the dialogue manager model will come up with its top choices for predicting the best next_action, prompting the user again to confirm on its priority of learned choices. The model uses this feedback to refine its predictions for next time (This is like a reinforcement learning technique wherein the model is rewarded for its correct predictions).
Authentication. Users start by authenticating themselves using whatever mechanism is provided by their channel of communication with the bot. The bot framework supports many communication channels, including Cortana, Microsoft Teams, Facebook Messenger, Kik, and Slack. For a list of channels, see Connect a bot to channels. When you create a bot with Azure Bot Service, the Web Chat channel is automatically configured. This channel allows users to interact with your bot directly in a web page. You can also connect the bot to a custom app by using the Direct Line channel. The user's identity is used to provide role-based access control, as well as to serve personalized content.
Getting the remaining values (information that user would have provided to bot’s previous questions, bot’s previous action, results of the API call etc.,) is little bit tricky and here is where the dialogue manager component takes over. These feature values will need to be extracted from the training data that the user will define in the form of sample conversations between the user and the bot. These sample conversations should be prepared in such a fashion that they capture most of the possible conversational flows while pretending to be both an user and a bot.
Clare.AI is a frontend assistant that provides modern online banking services. This virtual assistant combines machine learning algorithms with natural language processing. The Clare.AI algorithm is trained to respond to customer service FAQs, arrange appointments, conduct internal inquiries for IT and HR, and help customers control their finances via their favorite messaging apps (WhatsApp, Facebook, WeChat, etc.). It can even draw a chart showing customers how they’ve spent their money.
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.