With natural language processing (NLP), a bot can understand what a human is asking. The computer translates the natural language of a question into its own artificial language. It breaks down human inputs into coded units and uses algorithms to determine what is most likely being asked of it. From there, it determines the answer. Then, with natural language generation (NLG), it creates a response. NLG software allows the bot to construct and provide a response in the natural language format.
Poor user experience. The bottom line: chatbots frustrate your customers if you are viewing them as a replacement for humans. Do not ever, ever try to pass of a chatbot as a human. If your chatbot suffers from any of the issues above, you’re probably creating a poor customer experience overall and an angry phone call to a poor unsuspecting call center rep.
24/7 digital support. An instant and always accessible assistant is assumed by the more and more digital consumer of the new era. Unlike humans, chatbots once developed and installed don't have a limited workdays, holidays or weekends and are ready to attend queries at any hour of the day. It helps to the customer to avoid waiting of a company's agent to be available. Thus, the customer doesn't have to wait for the company executive to help them. This also lets companies keep an eye on the traffic during the non-working hours and reach out to them later.
Amazon’s Echo device has been a surprise hit, reaching over 3M units sold in less than 18 months. Although part of this success can be attributed to the massive awareness-building power of the Amazon.com homepage, the device receives positive reviews from customers and experts alike, and has even prompted Google to develop its own version of the same device, Google Home.
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.
The plugin aspect to Chatfuel is one of the real bonuses. You can link up to all sorts of different services to add richer content to the conversations that you're having. This includes linking up to Twitter, Instagram and YouTube, as well as being able to request that the user share their location, serve video and audio content, and build out custom attributes that can be used to segment users based on their inputs. This last part is a killer feature.
As ChatbotLifeexplained, developing bots is not the same as building apps. While apps specialise in a number of functions, chatbots have a bigger capacity for inputs. The trick here is to start with a simple objective and focus on doing it really well (i.e., having a minimum viable product or ‘MVP’). From that point onward, businesses can upgrade their bots.
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.
The process of building a chatbot can be divided into two main tasks: understanding the user's intent and producing the correct answer. The first task involves understanding the user input. In order to properly understand a user input in a free text form, a Natural Language Processing Engine can be used. The second task may involve different approaches depending on the type of the response that the chatbot will generate.
If AI struggles with fourth-grade science question answering, should AI be expected to hold an adult-level, open-ended chit-chat about politics, entertainment, and weather? It is thus encouraging to see that Microsoft’s Satya Nadella did not give up on Tay after its debacle, and Amazon’s Jeff Bezos is sponsoring an Alexa social chatbot competition. I love this below quote from Jeff:
How: this involves creating a basic content block within Chatfuel that has a discount code within it. Instead of giving all users of the bot the same experience, you can direct them through to specific parts of the conversation (or 'blocks'). Using the direct link to your content block, you'll be able to create CTAs on your website that direct people straight into Messenger to get a discount code (more info here).
2010 SIRI: Though Siri is considered colloquially to be a virtual assistant rather than a conversational bot, it was built off the same technologies and paved the way for all later AI bots and PAs. Siri is an intelligent personal assistant with a natural language UI to respond to questions and perform web-based service requests. Siri was part of apples IOS.
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).
Other bots like X.ai can help schedule your meetings for you. Simply add the bot to your email thread, and it will take over back-and-forth conversation needed to schedule a meeting, alert you once it’s been arranged and add it to your calendar. As bot technology improves, the thinking is that bots will be able to automate all kinds of things; perhaps even something as complex as your taxes.
“Bots go bust” — so went the first of the five AI startup predictions in 2017 by Bradford Cross, countering some recent excitement around conversational AI (see for example O’Reilly’s “Why 2016 is shaping up to be the Year of the Bot”). The main argument was that social intelligence, rather than artificial intelligence is lacking, rendering bots utilitarian and boring.
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.