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.
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.

Enter Roof Ai, a chatbot that helps real-estate marketers to automate interacting with potential leads and lead assignment via social media. The bot identifies potential leads via Facebook, then responds almost instantaneously in a friendly, helpful, and conversational tone that closely resembles that of a real person. Based on user input, Roof Ai prompts potential leads to provide a little more information, before automatically assigning the lead to a sales agent.
As people research, they want the information they need as quickly as possible and are increasingly turning to voice search as the technology advances. Email inboxes have become more and more cluttered, so buyers have moved to social media to follow the brands they really care about. Ultimately, they now have the control — the ability to opt out, block, and unfollow any brand that betrays their trust.
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 $3 and after asking her for the money, you go on your way.

We then ran a second test with a very specific topic aimed at answering very specific questions that a small segment of their audience was interested in. There, the engagement was much higher (97% open rate, 52% click-through rate on average over the duration of the test). Interestingly, drop-off went wayyy down there. At the end of this test, only 0.29% of the users had unsubscribed.
When considering potential uses, first assess the impact on resources. There are two options here: replacement or empowerment. Replacement is clearly easier as you don’t need to consider integration with existing processes and you can build from scratch. Empowerment enhances an existing process by making it more flexible, accommodating, accessible and simple for users.

One of the most thriving eLearning innovations is the chatbot technology. Chatbots work on the principle of interacting with users in a human-like manner. These intelligent bots are often deployed as virtual assistants. The best example would be Google Allo - an intelligent messaging app packed with Google Assistant that interacts with the user by texting back and replying to queries. This app supports both voice and text queries.
Just last month, Google launched its latest Google Assistant. To help readers get a better glimpse of the redesign, Google’s Scott Huffman explained: “Since the Assistant can do so many things, we’re introducing a new way to talk about them. We’re them Actions. Actions include features built by Google—like directions on Google Maps—and those that come from developers, publishers, and other third parties, like working out with Fitbit Coach.”
As digital continues to rewrite the rules of engagement across industries and markets, a new competitive reality is emerging: “Being digital” soon won’t be enough. Organizations will use artificial intelligence and other technologies to help them make faster, more informed decisions, become far more efficient, and craft more personalized and relevant experiences for both customers and employees.
You can structure these modules to flow in any way you like, ranging from free form to sequential. The Bot Framework SDK provides several libraries that allows you to construct any conversational flow your bot needs. For example, the prompts library allows you to ask users for input, the waterfall library allows you to define a sequence of question/answer pair, the dialog control library allows you to modularized your conversational flow logic, etc. All of these libraries are tied together through a dialogs object. Let's take a closer look at how modules are implemented as dialogs to design and manage conversation flows and see how that flow is similar to the traditional application flow.
Consumers really don’t like your chatbot. It’s not exactly a relationship built to last — a few clicks here, a few sentences there — but Forrester Analytics data shows us very clearly that, to consumers, your chatbot isn’t exactly “swipe right” material. That’s unfortunate, because using a chatbot for customer service can be incredibly effective when done […]
Aside from being practical and time-convenient, chatbots guarantee a huge reduction in support costs. According to IBM, the influence of chatbots on CRM is staggering.  They provide a 99 percent improvement rate in response times, therefore, cutting resolution from 38 hours to five minutes. Also, they caused a massive drop in cost per query from $15-$200 (human agents) to $1 (virtual agents). Finally, virtual agents can take up an average of 30,000+ consumers per month.
Developed to assist Nigerian students preparing for their secondary school exam, the University Tertiary Matriculation Examination (UTME), SimbiBot is a chatbot that uses past exam questions to help students prepare for a variety of subjects. It offers multiple choice quizzes to help students test their knowledge, shows them where they went wrong, and even offers tips and advice based on how well the student is progressing.
Web site: From Russia With Love. PDF. 2007-12-09. Psychologist and Scientific American: Mind contributing editor Robert Epstein reports how he was initially fooled by a chatterbot posing as an attractive girl in a personal ad he answered on a dating website. In the ad, the girl portrayed herself as being in Southern California and then soon revealed, in poor English, that she was actually in Russia. He became suspicious after a couple of months of email exchanges, sent her an email test of gibberish, and she still replied in general terms. The dating website is not named. Scientific American: Mind, October–November 2007, page 16–17, "From Russia With Love: How I got fooled (and somewhat humiliated) by a computer". Also available online.
I will not go into the details of extracting each feature value here. It can be referred from the documentation of rasa-core link that I provided above. So, assuming we extracted all the required feature values from the sample conversations in the required format, we can then train an AI model like LSTM followed by softmax to predict the next_action. Referring to the above figure, this is what the ‘dialogue management’ component does. Why LSTM is more appropriate? — As mentioned above, we want our model to be context aware and look back into the conversational history to predict the next_action. This is akin to a time-series model (pls see my other LSTM-Time series article) and hence can be best captured in the memory state of the LSTM model. The amount of conversational history we want to look back can be a configurable hyper-parameter to the model.

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.

Like other computerized advertising enhancement endeavors, improving your perceivability in Google Maps showcasing can – and likely will – require some investment. This implies there are no speedy hacks, no medium-term fixes, no simple method to ascend to the highest point of the pack. Regardless of whether you actualize every one of the enhancements above, it ...
Although NBC Politics Bot was a little rudimentary in terms of its interactions, this particular application of chatbot technology could well become a lot more popular in the coming years – particularly as audiences struggle to keep up with the enormous volume of news content being published every day. The bot also helped NBC determine what content most resonated with users, which the network will use to further tailor and refine its content to users in the future.
More and more companies embrace chatbots to increase engagement with their audiences in the last few years. Especially for some industries including banking, insurance, and retail chatbots started to function as efficient interactive tools to increase customer satisfaction and cost-effectiveness. A study by Humley found out 43% of digital banking users are turning to chatbots – the increasing trend shows that banking customers consider the chatbot as an alternative channel to get instant information and solve their issues.
SEO has far less to do with content and words than people think. Google ranks sites based on the experience people have with brands. If a bot can enhance that experience in such a way that people are more enthusiastic about a site – they share it, return to it, talk about it, and spend more time there, it will affect positively how the site appears in Google.
What began as a televised ad campaign eventually became a fully interactive chatbot developed for PG Tips’ parent company, Unilever (which also happens to own an alarming number of the most commonly known household brands) by London-based agency Ubisend, which specializes in developing bespoke chatbot applications for brands. The aim of the bot was to not only raise brand awareness for PG Tips tea, but also to raise funds for Red Nose Day through the 1 Million Laughs campaign.
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These days, checking the headlines over morning coffee is as much about figuring out if we should be hunkering down in the basement preparing for imminent nuclear annihilation as it is about keeping up with the day’s headlines. Unfortunately, even the most diligent newshounds may find it difficult to distinguish the signal from the noise, which is why NBC launched its NBC Politics Bot on Facebook Messenger shortly before the U.S. presidential election in 2016.

Using this method, you can manage multiple funnels of content upgrades, and even convince your users to take the next step in the buyer journey directly within Messenger. In the example below I just direct the user to subscribe to content recommendations via Messenger, but you could push them to book a meeting with a sales rep, take a free trial or directly purchase your product.
2017 was the year that AI and chatbots took off in business, not just in developed nations, but across the whole world. Sage have reported that this global trend is boosting international collaboration between startups across all continents, such as the European Commission-backed Startup Europe Comes to Africa (SEC2A) which was held in November 2017.
Have you checked out Facebook Messenger’s official page lately? Well, now you can start building your own bot directly through the platform’s landing page. This method though, may be a little bit more complicated than some of the previous ways we’ve discussed, but there are a lot of resources that Facebook Messenger provides in order to help you accomplish your brand new creation. Through full-fledged guides, case studies, a forum for Facebook developers, and more, you are sure to be a chatbot creating professional in no time.

There is a general worry that the bot can’t understand the intent of the customer. The bots are first trained with the actual data. Most companies that already have a chatbot must be having logs of conversations. Developers use that logs to analyze what customers are trying to ask and what does that mean. With a combination of Machine Learning models and tools built, developers match questions that customer asks and answers with the best suitable answer. For example: If a customer is asking “Where is my payment receipt?” and “I have not received a payment receipt”, mean the same thing. Developers strength is in training the models so that the chatbot is able to connect both of those questions to correct intent and as an output produces the correct answer. If there is no extensive data available, different APIs data can be used to train the chatbot.

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.
This was a strategy eBay deployed for holiday gift-giving in 2018. The company recognized that purchasing gifts for friends and family isn’t necessarily a simple task. For many of their customers, selecting gifts had become a stressful and arduous process, especially when they didn’t have a particular item in mind. In response to this feeling, eBay partnered with Facebook Messenger to introduce ShopBot.
Through our preview journey in the past two years, we have learned a lot from interacting with thousands of customers undergoing digital transformation. We highlighted some of our customer stories (such as UPS, Equadex, and more) in our general availability announcement. This post covers conversational AI in a nutshell using Azure Bot Service and LUIS, what we’ve learned so far, and dive into the new capabilities. We will also show how easy it is to get started in building a conversational bot with natural language.

Having a conversation with a computer might have seemed like science fiction even a few years ago. But now, most of us already use chatbots for a variety of tasks. For example, as end users, we ask the virtual assistant on our smartphones to find a local restaurant and provide directions. Or, we use an online banking chatbot for help with a loan application.
However, since Magic simply connects you with human operators who carry our your requests, the service does not leverage AI to automate its processes, and thus the service is expensive and thus may lack mainstream potential. The company recently launched a premium service called Magic+ which gets you higher level service for $100 per hour, indicating that it sees its market among business executives and other wealthy customers.
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.

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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.
These days, checking the headlines over morning coffee is as much about figuring out if we should be hunkering down in the basement preparing for imminent nuclear annihilation as it is about keeping up with the day’s headlines. Unfortunately, even the most diligent newshounds may find it difficult to distinguish the signal from the noise, which is why NBC launched its NBC Politics Bot on Facebook Messenger shortly before the U.S. presidential election in 2016.
An AI-powered chatbot is a smarter version of a chatbot (a machine that has the ability to communicate with humans via text or audio). It uses natural language processing (NLP) and machine learning (ML) to get a better understanding of the intent of humans it interacts with. Also, its purpose is to provide a natural, as near human-level communication as possible.
Two trends — the exploding popularity of mobile messaging apps and advances in artificial intelligence — are coinciding to enable a new generation of tools that enable brands to communicate with customers in powerful new ways at reduced cost. Retailers and technology firms are experimenting with chatbots, powered by a combination of machine learning, natural language processing, and live operators, to provide customer service, sales support, and other commerce-related functions.
This means our questions must fit with the programming they have been given.  Using our weather bot as an example once more, the question ‘Will it rain tomorrow’ could be answered easily. However if the programming is not there, the question ‘Will I need a brolly tomorrow’ may cause the chatbot to respond with a ‘I am sorry, I didn’t understand the question’ type response.

Improve loyalty: By providing a responsive, efficient experience for customers, employees and partners, a chatbot will improve satisfaction and loyalty. Whether your chatbot answers questions about employees’ corporate benefits or provides answers to technical support questions, users can come away with a strengthened connection to your organization.

Even if it sounds crazy, chatbots might even challenge apps and websites! An app requires space, it has to be downloaded. Websites take time to load and most of them are pretty slow. A bot works instantly. You type something, it replies. Another great thing about them is that they bypass user interface and completely change how customers interact with your business. People will navigate your content by using their natural language.
Earlier, I made a rather lazy joke with a reference to the Terminator movie franchise, in which an artificial intelligence system known as Skynet becomes self-aware and identifies the human race as the greatest threat to its own survival, triggering a global nuclear war by preemptively launching the missiles under its command at cities around the world. (If by some miracle you haven’t seen any of the Terminator movies, the first two are excellent but I’d strongly advise steering clear of later entries in the franchise.)
How: instead of asking someone to fill out a form on your website to be contacted by your sales team, you direct them straight into Messenger, where you can ask them some of their contact details and any qualification questions (for example, "How many employees does your company have?"). Depending on what they respond with you could ask if they'd like to arrange a meeting with a salesperson right there and then.
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.
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).
Morph.ai is an AI-powered chatbot. It works across messengers, websites, Android apps, and iOS apps. Morph.ai lets you automate up to 70 percent of your customer support. It can also integrate with your existing CRM and support tools. Plus, it can learn new queries and responses over time. You can add cards, carousels, and quick replies to enrich your conversations. It looks like this
ALICE – which stands for Artificial Linguistic Internet Computer Entity, an acronym that could have been lifted straight out of an episode of The X-Files – was developed and launched by creator Dr. Richard Wallace way back in the dark days of the early Internet in 1995. (As you can see in the image above, the website’s aesthetic remains virtually unchanged since that time, a powerful reminder of how far web design has come.) 
Through Amazon’s developer platform for the Echo (called Alexa Skills), developers can develop “skills” for Alexa which enable her to carry out new types of tasks. Examples of skills include playing music from your Spotify library, adding events to your Google Calendar, or querying your credit card balance with Capital One — you can even ask Alexa to “open Dominoes and place my Easy Order” and have pizza delivered without even picking up your smartphone. Now that’s conversational commerce in action.
As you roll out new features or bug fixes to your bot, it's best to use multiple deployment environments, such as staging and production. Using deployment slots from Azure DevOps allows you to do this with zero downtime. You can test your latest upgrades in the staging environment before swapping them to the production environment. In terms of handling load, App Service is designed to scale up or out manually or automatically. Because your bot is hosted in Microsoft's global datacenter infrastructure, the App Service SLA promises high availability.

In a procedural conversation flow, you define the order of the questions and the bot will ask the questions in the order you defined. You can organize the questions into logical modules to keep the code centralized while staying focused on guiding the conversational. For example, you may design one module to contain the logic that helps the user browse for products and a separate module to contain the logic that helps the user create a new order.
What if you’re creating a bot for a major online clothing retailer? For starters, the bot will require a greeting (“How can I help you?”) as well as a process for saying its goodbyes. In between, the bot needs to respond to inputs, which could range from shopping inquiries to questions about shipping rates or return policies, and the bot must possess a script for fielding questions it doesn’t understand.
Search for the bot you want to add. At the time of this writing, there are about a dozen bots available, with more being added every day. Chat bots are available for customer service, news, ordering, and more, depending on the company that releases it. For example, you could get news from the CNN bot and order flowers from the 1-800-flowers bot. The process for finding a bot varies depending on your device:[1]
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).