Keep it conversational: Chatbots help make it easy for users to find the information they need. Users can ask questions in a conversational way, and the chatbots can help them refine their searches through their responses and follow-up questions. Having had substantial experience with personal assistants on their smartphones and elsewhere, users today expect this level of informal interaction. When chatbot users are happy, the organizations employing the chatbots benefit.
Kunze recognises that chatbots are the vogue subject right now, saying: “We are in a hype cycle, and rising tides from entrants like Microsoft and Facebook have raised all ships. Pandorabots typically adds up to 2,000 developers monthly. In the past few weeks, we've seen a 275 percent spike in sign-ups, and an influx of interest from big, big brands.”
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.
I argued that it is super hard to scale a one-trick TODA into a general assistant that helps the user getting things done across multiple tasks. An intelligence assistant is arguably expected to hold an informal chit-chat with the user. It is this area where we are staring into perhaps the biggest challenge of AI. Observe how Samantha introduces herself to Joaquin Phoenix’s Ted in the clip below:
Unfortunately the old adage of trash in, trash out came back to bite Microsoft. Tay was soon being fed racist, sexist and genocidal language by the Twitter user-base, leading her to regurgitate these views. Microsoft eventually took Tay down for some re-tooling, but when it returned the AI was significantly weaker, simply repeating itself before being taken offline indefinitely.
Operator calls itself a “request network” aiming to “unlock the 90% of commerce that’s not on the internet.” The Operator app, developed by Uber co-founder Garrett Camp, connects you with a network of “operators” who act like concierges who can execute any shopping-related request. You can order concert tickets, get gift ideas, or even get interior design recommendations for new furniture. Operator seems to be positioning itself towards “high consideration” purchases, bigger ticket purchases requiring more research and expertise, where its operators can add significant value to a transaction.
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.

In a new report from Business Insider Intelligence, we explore the growing and disruptive bot landscape by investigating what bots are, how businesses are leveraging them, and where they will have the biggest impact. We outline the burgeoning bot ecosystem by segment, look at companies that offer bot-enabling technology, distribution channels, and some of the key third-party bots already on offer.
Along with the continued development of our avatars, we are also investigating machine learning and deep learning techniques, and working on the creation of a short term memory for our bots. This will allow humans interacting with our AI to develop genuine human-like relationships with their bot; any personal information that is exchanged will be remembered by the bot and recalled in the correct context at the appropriate time. The bots will get to know their human companion, and utilise this knowledge to form warmer and more personal interactions.
Alternatively, think about the times you are chatting with a colleague over Slack. The need to find relevant information typically happens during conversations, and instead of having to go to a browser to start searching, you could simply summon your friendly Slack chatbot and get it to do the work for you. Think of it as your own personal podcast producer – pulling up documents, facts, and data at the drop of a hat. This concept can be translated into the virtual assistants we use on the daily. Think about an ambient assistant like Alexa or Google Home that could just be part of a group conversation. Or your trusted assistant taking notes and actions during a meeting.
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.
IBM estimates that 265 billion customer support tickets and calls are made globally every year, resulting in $1.3 trillion in customer service costs. IBM also referenced a Chatbots Magazine figure purporting that implementing customer service AI solutions, such as chatbots, into service workflows can reduce a business’ spend on customer service by 30 percent.

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.
Using chatbot builder platforms. You can create a chatbot with the help of services providing all the necessary features and integrations. It can be a good choice for an in-house chatbot serving your team. This option is associated with some disadvantages, including the limited configuration and the dependence on the service. Some popular platforms for building chatbots are:
Chatbots currently operate through a number of channels, including web, within apps, and on messaging platforms. They also work across the spectrum from digital commerce to banking using bots for research, lead generation, and brand awareness. An increasing amount of businesses are experimenting with chatbots for e-commerce, customer service, and content delivery.
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).
Alternatively, think about the times you are chatting with a colleague over Slack. The need to find relevant information typically happens during conversations, and instead of having to go to a browser to start searching, you could simply summon your friendly Slack chatbot and get it to do the work for you. Think of it as your own personal podcast producer – pulling up documents, facts, and data at the drop of a hat. This concept can be translated into the virtual assistants we use on the daily. Think about an ambient assistant like Alexa or Google Home that could just be part of a group conversation. Or your trusted assistant taking notes and actions during a meeting.
Some bots communicate with other users of Internet-based services, via instant messaging (IM), Internet Relay Chat (IRC), or another web interface such as Facebook Bots and Twitterbots. These chatterbots may allow people to ask questions in plain English and then formulate a proper response. These bots can often handle many tasks, including reporting weather, zip-code information, sports scores, converting currency or other units, etc.[citation needed] Others are used for entertainment, such as SmarterChild on AOL Instant Messenger and MSN Messenger.
There are a bunch of e-commerce stores taking advantage of chatbots as well. One example that I was playing with was from Fynd that enables you to ask for specific products and they'll display them to you directly within Messenger. What's more, Facebook even allows you to make payments via Messenger bots, opening up a whole world of possibility to e-commerce stores.
Oftentimes, brands have a passive approach to customer interactions. They only communicate with their audience once a consumer has contacted them first. A chatbot automatically sends a welcome notification when a person arrives on your website or social media profile making the user aware of your chatbots presence. This makes you seem more proactive, thus enhancing your brand's reputation and can even increase interactions, having a positive effect on your sales numbers, too.
1. AI-based: these ones really rely on training and are fairly complicated to set up. You train the chatbot to understand specific topics and tell your users which topics your chatbot can engage with. AI chatbots require all sorts of fall back and intent training. For example, let’s say you built a doctor chatbot (off the top of my head because I am working on one at the moment), it would have to understand that “i have a headache” and “got a headache” and “my head hurts” are the same intent. The user is free to engage and the chatbot has to pick things up.
In our work at ZipfWorks building and scaling intelligent shopping platforms and applications, we pay close attention to emerging trends impacting digital commerce such as chatbots and mobile commerce. As this nascent trend towards a more conversational commerce ecosystem unfolds at a dizzying pace, we felt it would be useful to take a step back and look at the major initiatives and forces shaping this trend and compiled them here in this report. We’ve applied some of these concepts in our current project Dealspotr, to help more shoppers save more money through intelligent use of technology and social product design.

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.
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.
This is where most applications of NLP struggle, and not just chatbots. Any system or application that relies upon a machine’s ability to parse human speech is likely to struggle with the complexities inherent in elements of speech such as metaphors and similes. Despite these considerable limitations, chatbots are becoming increasingly sophisticated, responsive, and more “natural.”
It's fair to say that I'm pretty obsessed with chatbots right now. There are some great applications popping up from brands that genuinely add value to the end consumer, and early signs are showing that consumers are actually responding really well to them. For those of you who aren't quite sure what I'm talking about, here's a quick overview of what a chatbot is:
Chatbots can strike up a conversation with any customer about any issue at any time of day. They engage in friendly interactions with customers. Besides, virtual assistants only give a bit of information at a time. This way they don’t tire customers with irrelevant and unnecessary information. Chatbots can maintain conversations and keep customers on your website longer.
But, as any human knows, no question or statement in a conversation really has a limited number of potential responses. There is an infinite number of ways to combine the finite number of words in a human language to say something. Real conversation requires creativity, spontaneity, and inference. Right now, those traits are still the realm of humans alone. There is still a gamut of work to finish in order to make bots as person-centric as Rogerian therapists, but bots and their creators are getting closer every day.
Chatbots have been used in instant messaging (IM) applications and online interactive games for many years but have recently segued into business-to-consumer (B2C) and business-to-business (B2B) sales and services. Chatbots can be added to a buddy list or provide a single game player with an entity to interact with while awaiting other "live" players. If the bot is sophisticated enough to pass the Turing test, the person may not even know they are interacting with a computer program.
Unfortunately the old adage of trash in, trash out came back to bite Microsoft. Tay was soon being fed racist, sexist and genocidal language by the Twitter user-base, leading her to regurgitate these views. Microsoft eventually took Tay down for some re-tooling, but when it returned the AI was significantly weaker, simply repeating itself before being taken offline indefinitely.

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.


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.

Interface designers have come to appreciate that humans' readiness to interpret computer output as genuinely conversational—even when it is actually based on rather simple pattern-matching—can be exploited for useful purposes. Most people prefer to engage with programs that are human-like, and this gives chatbot-style techniques a potentially useful role in interactive systems that need to elicit information from users, as long as that information is relatively straightforward and falls into predictable categories. Thus, for example, online help systems can usefully employ chatbot techniques to identify the area of help that users require, potentially providing a "friendlier" interface than a more formal search or menu system. This sort of usage holds the prospect of moving chatbot technology from Weizenbaum's "shelf ... reserved for curios" to that marked "genuinely useful computational methods".
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