Let’s take a weather chat bot as an example to examine the capabilities of Scripted and Structured chatbots. The question “Will it rain on Sunday?” can be easily answered. However, if there is no programming for the question “Will I need an umbrella on Sunday?” then the query will not be understood by the chat bot. This is the common limitation with scripted and structured chatbots. However, in all cases, a conversational bot can only be as intelligent as the programming it has been given.
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.
Chatbots can direct customers to a live agent if the AI can’t settle the matter. This lets human agents focus their efforts on the heavy lifting. AI chatbots also increase employee productivity. Globe Telecom automated their customer service via Messenger and saw impressive results. The company increased employee productivity by 3.5 times. And their customer satisfaction increased by 22 percent.
One of the first stepping stones to this future are AI-powered messaging solutions, or conversational bots. A conversational bot is a computer program that works automatically and is skilled in communicating through various digital media—including intelligent virtual agents, organizations' apps, organizations' websites, social platforms and messenger platforms. Users can interact with such bots, using voice or text, to access information, complete tasks or execute transactions. 
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).

There are NLP services and applications programming interfaces that are used to build the chatbots and make it possible for all type of businesses, small. Medium and large scale. The main point here is that Smart Bots have the potential to help increase your customer base by improving the customer support services and as a result boosts the sales as well as profits. They are an opportunity for many small and mid-sized companies to reach a huge customer base.
Whilst the payout wasn't huge within the early days of Amazon, those who got in early are now seeing huge rewards, with 38% of shoppers starting their buying journey within Amazon (source), making it the number one retail search engine. Some studies are suggesting that Amazon is responsible for 80% of e-commerce growth for publicly traded web retailers (source).
Perhaps the most important aspect of implementing a chatbot is selecting the right natural language processing (NLP) engine. If the user interacts with the bot through voice, for example, then the chatbot requires a speech recognition engine. Business owners also have to decide whether they want structured or unstructured conversations. Chatbots built for structured conversations are highly scripted, which simplifies programming but restricts the kinds of things that the users can ask.
Companies most likely to be supporting bots operate in the health, communications and banking industries, with informational bots garnering the majority of attention. However, challenges still abound, even among bot supporters, with lack of skilled talent to develop and work with bots cited as a challenge in implementing solutions, followed by deployment and acquisition costs, as well as data privacy and security.
A malicious use of bots is the coordination and operation of an automated attack on networked computers, such as a denial-of-service attack by a botnet. Internet bots can also be used to commit click fraud and more recently have seen usage around MMORPG games as computer game bots.[citation needed] A spambot is an internet bot that attempts to spam large amounts of content on the Internet, usually adding advertising links. More than 94.2% of websites have experienced a bot attack.[2]

1. Define the goals. What should your chatbot do? Clearly indicate the list of functions your chatbot needs to perform. 2. Choose a channel to interact with your customers. Be where your clients prefer to communicate — your website, mobile app, Facebook Messenger, WhatsApp or other messaging platform. 3. Choose the way of creation. There are two of them: using readymade chat bot software or building a custom bot from scratch. 4. Create, customize and launch. Describe the algorithm of its actions, develop a database of answers and test the work of the chatbot. Double check everything before showing your creation to potential customers.
The educators or class organizers can opt for chatbots to simplify daily routine tasks. Chatbots may serve as a helping hand to the teacher in dealing with the daily queries by allowing bots to answer the questions of students on a daily basis, or perhaps even check their homework. Eventually, they offer teachers more time to work with their students on a one-by-one basis.
[…] But how can simple code assimilate something as complex as speech in only the span of a handful of years? It took humans hundreds of generations to identify, compose and collate the English language. Chatbots have a one up on humans, because of the way they dissect the vast data given to them. Now that we have a grip on the basics, we’ll understand how chatbots work in the next series. […]

Interestingly, the as-yet unnamed conversational agent is currently an open-source project, meaning that anyone can contribute to the development of the bot’s codebase. The project is still in its earlier stages, but has great potential to help scientists, researchers, and care teams better understand how Alzheimer’s disease affects the brain. A Russian version of the bot is already available, and an English version is expected at some point this year.


Speaking ahead of the Gartner Application Architecture, Development & Integration Summit in Sydney, Magnus Revang, research director at Gartner, said the broad appeal of chatbots stems from the efficiency and ease of interaction they create for employees, customers or other users. The potential benefits are significant for enterprises and shouldn’t be ignored.
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No one wants to download another restaurant app and put in their credit-card information just to order. Livingston sees an opportunity in being able to come into a restaurant, scan a code, and have the restaurant bot appear in the chat. And instead of typing out all the food a person wants, the person should be able to, for example, easily order the same thing as last time and charge it to the same card.
The market shapes customer behavior. Gartner predicts that “40% of mobile interactions will be managed by smart agents by 2020.” Every single business out there today either has a chatbot already or is considering one. 30% of customers expect to see a live chat option on your website. Three out of 10 consumers would give up phone calls to use messaging. As more and more customers begin expecting your company to have a direct way to contact you, it makes sense to have a touch point on a messenger.
Respect the conversational UI. The full interaction should take place natively within the app. The goal is to recognize the user's intent and provide the right content with minimum user input. Every question asked should bring the user closer to the answer they want. If you need so much information that you're playing a game of 20 Questions, then switch to a form and deliver the content another way.

The biggest benefit of having a conversational AI solution is the instant response rate. Answering queries within an hour translates into 7X increase in the likelihood of converting a lead. Customers are more likely to talk about a negative experience than a positive one. So nipping a negative review right in the bud is going to help improve your product’s brand standing.

It takes bold visionaries and risk-takers to build future technologies into realities. In the field of chatbots, there are many companies across the globe working on this mission. Our mega list of artificial intelligence, machine learning, natural language processing, and chatbot companies, covers the top companies and startups who are innovating in this space.
Designing for conversational interfaces represents a big shift in the way we are used to thinking about interaction. Chatbots have less signifiers and affordances than websites and apps – which means words have to work harder to deliver clarity, cohesion and utility for the user. It is a change of paradigm that requires designers to re-wire their brain, their deliverables and their design process to create successful bot experiences.
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.
Unfortunately, my mom can’t really engage in meaningful conversations anymore, but many people suffering with dementia retain much of their conversational abilities as their illness progresses. However, the shame and frustration that many dementia sufferers experience often make routine, everyday talks with even close family members challenging. That’s why Russian technology company Endurance developed its companion chatbot.
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.
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.
Efforts by servers hosting websites to counteract bots vary. Servers may choose to outline rules on the behaviour of internet bots by implementing a robots.txt file: this file is simply text stating the rules governing a bot's behaviour on that server. Any bot that does not follow these rules when interacting with (or 'spidering') any server should, in theory, be denied access to, or removed from, the affected website. If the only rule implementation by a server is a posted text file with no associated program/software/app, then adhering to those rules is entirely voluntary – in reality there is no way to enforce those rules, or even to ensure that a bot's creator or implementer acknowledges, or even reads, the robots.txt file contents. Some bots are "good" – e.g. search engine spiders – while others can be used to launch malicious and harsh attacks, most notably, in political campaigns.[2]
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).

ELIZA's key method of operation (copied by chatbot designers ever since) involves the recognition of cue words or phrases in the input, and the output of corresponding pre-prepared or pre-programmed responses that can move the conversation forward in an apparently meaningful way (e.g. by responding to any input that contains the word 'MOTHER' with 'TELL ME MORE ABOUT YOUR FAMILY'). Thus an illusion of understanding is generated, even though the processing involved has been merely superficial. ELIZA showed that such an illusion is surprisingly easy to generate, because human judges are so ready to give the benefit of the doubt when conversational responses are capable of being interpreted as "intelligent".
The chatbot must rely on spoken or written communications to discover what the shopper or user wants and is limited to the messaging platform’s capabilities when it comes to responding to the shopper or user. This requires a much better understanding of natural language and intent. It also means that developers must write connections to several different platforms, again like Messenger or Slack, if the chatbot is to have the same potential reach as a website.
Typically, companies applied a passive engagement method with consumers. In other words, customer support only responds to complaining consumers – but never initiate any conversations or look for feedback. While this method was fine for a long while, it doesn’t work anymore with millennials. Users want to communicate with attentive brands who have a 24/7 support system and they won’t settle for anything less.
aLVin is built on the foundation of Nuance’s Nina, the intelligent multichannel virtual assistant that leverages natural language understanding (NLU) and cognitive computing capabilities. aLVin interacts with brokers to better understand “intent” and deliver the right information 24/7; the chatbot was built with extensive knowledge of LV=Broker’s products, which accelerated the process of being able to answer more questions and direct brokers to the right products early on
Another reason is that Facebook, which has 900 million Messenger users, is expected to get into bots. Many see this as a big potential opportunity; where Facebook goes, the rest of the industry often follows. Slack, which lends itself to bot-based services, has also grown dramatically to two million daily users, which bot makers and investors see as a potentially lucrative market.
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 market shapes customer behavior. Gartner predicts that “40% of mobile interactions will be managed by smart agents by 2020.” Every single business out there today either has a chatbot already or is considering one. 30% of customers expect to see a live chat option on your website. Three out of 10 consumers would give up phone calls to use messaging. As more and more customers begin expecting your company to have a direct way to contact you, it makes sense to have a touch point on a messenger.

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.


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).[10] 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.
A chatbot that functions through machine learning has an artificial neural network inspired by the neural nodes of the human brain. The bot is programmed to self-learn as it is introduced to new dialogues and words. In effect, as a chatbot receives new voice or textual dialogues, the number of inquiries that it can reply and the accuracy of each response it gives increases. Facebook has a machine learning chatbot that creates a platform for companies to interact with their consumers through the Facebook Messenger application. Using the Messenger bot, users can buy shoes from Spring, order a ride from Uber, and have election conversations with the New York Times which used the Messenger bot to cover the 2016 presidential election between Hilary Clinton and Donald Trump. If a user asked the New York Times through his/her app a question like “What’s new today?” or “What do the polls say?” the bot would reply to the request.
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