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.
Beyond users, bots must also please the messaging apps themselves. Take Facebook Messenger. Executives have confirmed that advertisements within Discover — their hub for finding new bots to engage with — will be the main way Messenger monetizes its 1.3 billion monthly active users. If standing out among the 100,000 other bots on the platform wasn't difficult enough, we can assume Messenger will only feature bots that don't detract people from the platform.
The process of building, testing and deploying chatbots can be done on cloud based chatbot development platforms[39] offered by cloud Platform as a Service (PaaS) providers such as Yekaliva, Oracle Cloud Platform, SnatchBot[40] and IBM Watson.[41] [42] [43] These cloud platforms provide Natural Language Processing, Artificial Intelligence and Mobile Backend as a Service for chatbot development.
In so many ways I think chatbots are only just getting started – their potential is much underestimated at present. A big challenge is for chatbots mature so that they do more than is possible as a result of content entry wizards. If your content is created with a few easy clicks, it is unlikely to be much inspiration to anyone – and to date, despite much work in the field, the ability to emulated the creative open ended nature of real intellingence has seen only very partial success.
These are hardly ideas of Hollywood’s science fiction. Even when the Starbucks bot can sound like Scarlett Johansson’s Samantha, the public will be unimpressed — we would prefer a real human interaction. Yet the public won’t have a choice; efficient task-oriented dialog agents will be the automatic vending machines and airport check-in kiosks of the near future.
“There is hope that consumers will be keen on experimenting with bots to make things happen for them. It used to be like that in the mobile app world 4+ years ago. When somebody told you back then… ‘I have built an app for X’… You most likely would give it a try. Now, nobody does this. It is probably too late to build an app company as an indie developer. But with bots… consumers’ attention spans are hopefully going to be wide open/receptive again!” — Niko Bonatsos, Managing Director at General Catalyst

A basic SMS service is available via GitHub to start building a bot which uses IBM’s BlueMix platform which hosts the Watson Conversation Services. A developer can import a workspace to setup a new service. This starts with a blank dashboard where a developer can import all the tools needed to run the conversation service. The services has a dialog flow – a series of options with yes/no answers that the service uses to work out what the user’s intent is, what entity it’s working on, how to respond and how to phrase the response in the best way for the user.
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.

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.) 
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.
Forrester just released a new report on mobile and new technology priorities for marketers, based on our latest global mobile executive survey. We found out that marketers: Fail to deliver on foundational mobile experiences. Consumers’ expectations of a brand’s mobile experience have never been higher. And yet, 58% of marketers agree that their mobile services […]
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).
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.
Closed domain chatbots focus on a specific knowledge domain, and these bots may fail to answer questions in other knowledge domains. For example, a restaurant booking conversational bot will be able to take your reservation, but may not respond to a question about the price of an air ticket. A user could hypothetically attempt to take the conversation elsewhere, however, closed domain chatbots are not required, nor often programmed to handle such cases.

Conversational bots work in a similar way as an employee manning a customer care desk. When a customer asks for assistance, the conversational bot is the medium responding. If a customer asks the question, “What time does your store close on Friday?” the conversational bot would respond the same as a human would, based on the information available. “Our store closes at 5pm on Friday.”
It won’t be an easy march though once we get to the nitty-gritty details. For example, I heard through the grapevine that when Starbucks looked at the voice data they collected from customer orders, they found that there are a few millions unique ways to order. (For those in the field, I’m talking about unique user utterances.) This is to be expected given the wild combinations of latte vs mocha, dairy vs soy, grande vs trenta, extra-hot vs iced, room vs no-room, for here vs to-go, snack variety, spoken accent diversity, etc. The AI practitioner will soon curse all these dimensions before taking a deep learning breath and getting to work. I feel though that given practically unlimited data, deep learning is now good enough to overcome this problem, and it is only a matter of couple of years until we see these TODA solutions deployed. One technique to watch is Generative Adversarial Nets (GAN). Roughly speaking, GAN engages itself in an iterative game of counterfeiting real stuffs, getting caught by the police neural network, improving counterfeiting skill, and rinse-and-repeating until it can pass as your Starbucks’ order-taking person, given enough data and iterations.
The upcoming TODA agents are good at one thing, and one thing only. As Facebook found out with the ambitious Project M, building general personal assistants that can help users in multiple tasks (cross-domain agents) is hard. Think awfully hard. Beyond the obvious increase in scope, knowledge, and vocabulary, there is no built-in data generator that feeds the hungry learning machine (sans an unlikely concerted effort to aggregate the data silos from multiple businesses). The jury is out whether the army of human agents that Project M employs can scale, even with Facebook’s kind of resources. In addition, cross-domain agents will probably need major advances in areas such as domain adaptation, transfer learning, dialog planning and management, reinforcement/apprenticeship learning, automatic dialog evaluation, etc.
WeChat combines a chat-based interface with vast library of add-on features such as a mobile wallet, chat-based transactions, and chat-based media and interactive widgets, and exposes it all to businesses through a powerful API that enables businesses from mom and pop noodle shops to powerhouses such as Nike and Burberry to “friend” their customers and market to them in never before imaginable ways. Over 10MM businesses in China have WeChat accounts, and it is becoming increasingly popular for small businesses to only have a WeChat account, forgoing developing their own website or mobile app completely. US technology firms, in particular Facebook, are taking note.
Each student learns and absorbs things at a different pace and requires a specific methodology of teaching. Consequently, one of the most powerful advantages of getting educated by a chatbot is its flexibility and ability to adapt to specific needs and requirements of a particular student. Chatbots can be used in a wide spectrum, be it teaching people how to build websites, learn a new language, or something more generic like teach children Math. Chatbots are capable of adapting to the speed at which each student is comfortable - without being too pushy and overwhelming.

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.
24/7 digital support. An instant and always accessible assistant is assumed by the more and more digital consumer of the new era.[34] 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.[41]
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