Facebook Messenger chat bots are a way to communicate with the companies and services that you use directly through Messenger. The goal of chat bots is to minimize the time you would spend waiting on hold or sifting through automated phone menus. By using keywords and short phrases, you can get information and perform tasks all through the Messenger app. For example, you could use bots to purchase clothing, or check the weather by asking the bot questions. Bot selection is limited, but more are being added all the time. You can also interact with bots using the Facebook website.
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).
This reference architecture describes how to build an enterprise-grade conversational bot (chatbot) using the Azure Bot Framework. Each bot is different, but there are some common patterns, workflows, and technologies to be aware of. Especially for a bot to serve enterprise workloads, there are many design considerations beyond just the core functionality. This article covers the most essential design aspects, and introduces the tools needed to build a robust, secure, and actively learning bot.
The most widely used anti-bot technique is the use of CAPTCHA, which is a form of Turing test used to distinguish between a human user and a less-sophisticated AI-powered bot, by the use of graphically-encoded human-readable text. Examples of providers include Recaptcha, and commercial companies such as Minteye, Solve Media, and NuCaptcha. Captchas, however, are not foolproof in preventing bots as they can often be circumvented by computer character recognition, security holes, and even by outsourcing captcha solving to cheap laborers.
Build a bot directly from one of the top messaging apps themselves. By building a bot in Telegram, you can easily run a bot in the application itself. The company recently open-sourced their chatbot code, making it easy for third-parties to integrate and create bots of their own. Their Telegram API, which they also built, can send customized notifications, news, reminders, or alerts. Integrate the API with other popular apps such as YouTube and Github for a unique customer experience.
Now, with the rise of website chatbots, this trend of two-way conversations can be taken to a whole new level. Conversational marketing can be done across many channels, such as over the phone or via SMS. However, an increasing number of companies are leveraging social media to drive their conversational marketing strategy to distinguish their brand and solidify their brand’s voice and values. When most people refer to conversational marketing, they’re talking about interactions started using chatbots and live chat – that move to personal conversations.

Specialized conversational bots can be used to make professional tasks easier. For example, a conversational bot could be used to retrieve information faster compared to a manual lookup; simply ask, “What was the patient’s blood pressure in her May visit?” The conversational bot will answer instantly instead of the user perusing through manual or electronic records.
More and more businesses are choosing AI chatbots as part of their customer service team. There are several reasons for that. Chatbots can answer customers’ inquiries cheaply, quickly, in real-time. Another reason is the ease of installation of such chatbot: once you have a fine live chat app, it takes a couple of minutes to integrate a chatbot with it.
In 1950, Alan Turing's famous article "Computing Machinery and Intelligence" was published,[7] which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the introduction to his paper presented it more as a debunking exercise:
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