Turning Documents into Chatbots

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Let’s not beat around the bush. No one wants to read large documents anymore, especially using mobile devices or cell phones. So, all the brochures, users manuals, hand books, training materials, and documents as such are becomming a majestic grave-yard of information. They are still being produced with the sad knowledge that very few people will read them.

Reading is OUT, Interacting is IN

The number of young people who declare reading as their “leasure activity” is declining in the world over the last few decades as claimed in a recent article. Technology is to be blamed. But instead of blaming technology or finding other excuses, we should look at it as a paradigm shift.

The short (recorded) history of human cognition shows tendency toward the tools of active learning (interactive) rather than old fashion, passive learning (reading).

Who wants to read a book about Abraham Lincoln if you can just talk to him. This is the new euphoria amplified with virtual reality, augmented reality, and chatbot technologies.

The Difference in a Nut Shell

The IRS publication 443, which talks about small business tax matters, is a PDF file. It is not a comfortable reading, as seen on the left below, especially when you are looking for something. On the right is a chatbot, called Terry Kohen, who prompts the user with navigatable options. Most importantly, you can ask questions at any time to see answers from the document. There are 4 more examples of how documents were replaced with chatbots at this link.

Don’t Write a Document, Write a Chatbot!

The chatbot technology is not yet matured enough to produce perfect results. However, some of the recent advances are at a point of making a difference in the enterprise world due to the fact that call centers have to answer questions that are already in such documents.

Here are the key factors that will determine the winners in the race of chatbot development platforms:

  • Creating a chatbot should be as easy as writing a document, (or copying it to the platform) without any coding requirement.
  • Chatbot development should not require instance-by-instance data entry for each step of conversation. It should be automated enough to create infinite conversation from the embedded content.
  • It should not require long deployment cycles (as in neural network training) so that content can be modifed or new content can be added instantly.
  • Chatbot solution should offer free expression of questions at every step of the way with answers (coming from the document) that are reasonably acceptable.

There are half a dozen platforms out there including Microsoft Bot Framework, IBM-Watson, Amazon-Lex, Google Chatbase, and Facebook Messenger Platform. None of them fits the requirements listed above, and they are not necessarily designed for the purpose of turning documents into chatbots. However, feel free to comment if these platforms were used for this objective (with examples), or other platforms worth mentioning for this cause.

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Why Deep Learning is Not a Good Fit For Chatbots: Combinatory Explosion Problem

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Let’s assume that you have a very simple business, and you want to deploy a chatbot for customer support. Let’s assume 100 questions and answers (Q/A)s would cover all your issues. It looks very simple, and you may be tempted to deploy one of the deep learning methods to build your chatbot. Here are the problems you are going to face:

COMBINATORY EXPLOSION IN NATURAL LANGUAGES

Unless you are a trained linguist, you might easily undermine how flexible natural language can be, and how explosive the combinations will emerge out of a single question. Let’s say your first Q/A starts with a basic complaint the users will have something like “I have a problem with my cable.” This simple statement can be expressed in more than a dozen ways as shown below, and the combinations do not end there!

If we take one of the possible expressions above, there can be another dozen combinations only by morphological and synonymous variations:

As you can see, this is only the first Q/A from your set of 100. Just imagine if some of the (Q/A)s you have are more complicated than this simple starting expression.

Your set of 100 (Q/A)s can easily mount to 10,000 different equivalent expressions the users may type which must be detected and understood by your chatbot software.

SO, WHAT IS THE PROBLEM?

The problem is not the deep learning method itself, but what it needs to function properly. You need to have a data set of 10,000 questions, if not more, that are linguistically equivalent expressions as shown above. Also, these 10,000 questions should map to 100 answers in this hypothetical case. Unless someone sits down and types them one by one, such a data set will be a nightmare to acquire.

If you already have a customer support system and collected, let’s say, 1 million (Q/A)s, there is still no guarantee that this 1 million (Q/A)s will cover the 10,000 linguistic variations to detect the 100 main (Q/A)s. Considering the Gaussian distribution of a typical user response analytics, 1 million (Q/A)s would cover less than 30% of your required data set. Your chatbot solution will remain vulnerable to undetected responses after all that trouble.

Consequently, someone who is deploying a deep learning method will find himself/herself in a data crises situation quickly. No matter what type of deep learning method you deploy, the data requirement described here holds. Neural networks cannot discover themselves equivalent variations of natural language without being provided ample examples. And I want to underline the word “ample” here.

OTHER TYPES OF DATA CRISES WITH DEEP LEARNING

Going back to the hypothetical case where you have a service operation and you can pull 1 million (Q/A)s. To make sure this data set will not cause any harm, someone must manually go through the set to clean it up. You cannot just dump data to a deep learning system without verifying it. Remember the Microsoft case, where Tay, the chatbot developed using twitter feeds, started to produce racial statements.

Learn-as-you-go approach also poses problems. Deep learning methods require a training process and convergence before deployment. This can be a long process. Once trained, the system cannot simply absorb new data in an addition mode. The entire data set must be trained again. As a result, if you plan to add new data to your chatbot every week, you need a team of AI specialists training the system every week and re-deploy. As one can imagine, this does not seem like a scalable business solution.

WILL USING BUTTONS SOLVE THE PROBLEM?

Facebook, when they launched the chatbot platform, assumed that buttonizing conversations could solve part of the combinatory explosion problem described here. First of all, let’s make one thing clear:

If the user is not allowed to enter free expressions any time during conversation, it is no longer a chatbot, or conversational AI. It is a toy.

Most Facebook chatbot developers jumped on the idea of buttonizing entire conversations, thus yielding nothing more than a toy. Most of the 30,000 plus chatbots developed in this fashion flopped big time, only few succeeded as reported in several recent articles prior to Facebook’s recent summit meeting. Entirely buttonized conversations can rarely provide successful solutions for very particular business types. If buttons are used alongside free expressions successfully detected, then this combination can be powerful.

WHERE IS THE SOLUTION?

I intend to write more about the solutions later. However, in a nut shell, solutions to the chatbot problem require independent NLP solutions before a deep learning methods can be used. One thing is for sure, deep learning alone is not a good fit, and has no future with this “silver bullet” engineering mentality.

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Is DIGITAL EMPLOYEE the Next Big Thing?

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All the technical jargon you have been hearing nowadays such as deep learning, artificial intelligence, natural language processing, etc., all converge to one single question for businesses: Can we build digital employees?

One may wonder what makes a digital employee different than all the software tools we are already using today. A digital employee may be defined as a computerized system that has superb communication skills using natural languages, and has some level of autonomy to make its own judgement and decisions.

Digital Employee represents the fine line where we delegate business responsibilities to autonomous systems, and where we communicate with them like talking to human employees.

WHAT WILL DIGITAL EMPLOYEES CONTRIBUTE TO?

Digital employees will directly contribute to business efficiency in 4 major areas as shown below. The communication at the top is essential for all other functions to perform cohesively. In other words, a digital employee starts from the core capability of communication and performing a high level dialogue.

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HOW DO WE BUILD THEM?

Creation of a digital employee cannot be a scientific project. Otherwise, it will remain very limited to a few examples based on substantial R&D budgets. This revolution will only happen when we have platforms that allow the creation of digital employees easily and fast. Here are the some of the top requirements for such transition:

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It is also important to mention that seamless integration to all communication platforms and operating systems is another key requirement.

SCIENTIFIC DISCIPLINES MUST COME TOGETHER

Creating digital employees through a platform will require many scientific disciplines and methods to amalgamate. There is no “silver bullet” solution to create such a complicated system. Below is a simplified landscape of disciplines that are most likely to contribute at least one aspect of development.

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The success will depend on who has the best cocktail of methods tucked under the platform which are literally invisible to the end user (i.e., the creator of digital employees).

BUSINESS INCENTIVES

Undoubtedly, there are several benefits of gaining digital employees as outlined below. However, their limitations compared to human employees (in certain aspects) represent a tradeoff. This trade off will exist until the technology reaches human level cognition, which may take a very long time.

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REVOLUTION TIMELINE

Estimating the timeline of the transformation from human information workers to digital employees is not an easy guess. Many businesses have adopted the IKEA model of DIY software during the last few decades, delegating tasks to clients. Banking is a prime example where you are supposed to use software to do transactions on your own. However, the current trend shows demand for command driven banking using conversational interfaces for requests like “Transfer $5,000 from checking to saving by tomorrow morning.” If we can talk to a digital employee, why bother using a software. And that’s the underlying promise for the upcoming revolution.

DIY Software model is wearing off, creating a future demand for digital employees.
We predict that the first solid evidence of this revolution will show itself by the fading away of DIY systems from our lives (including IKEA).

Creating a Business Entirely from Digital Workers (Expert Chatbots)

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We are not too far away from creating a completely digital business with a single human (the owner) setting it up. A recent Forbes article mentions the possibility of replacing managers in a futuristic tone encouraged by the advances in blockchain and IoT, A Harvard Business Review article introduces iCEO, a software that makes executive decisions. All these developments are taking us to this ultimate goal.

Autonomous Digital Business is a concept much closer to reality than chips in the brain, or self-driving cars on the streets.

Current businesses are already “digital” in so many aspects. Automated trading in stock market was a pioneering example of how buying and selling decisions can be delegated to smart computers, and they have been operating for a while now. If we can trust computers with stock trading, why not trust them with our business decisions for buying, selling, hiring, etc.?

Amazon, for example, might be rated 70% digital considering all its web operations and robotic warehouses. As conversational agents (chatbots) steadily penetrating into the CRM and sales operations, the percentage of digital business is increasing. But can it be all “digital” comprised of agents and expert chatbots? Can we delegate all decision making roles to computerized agents to run our businesses?

Expert Chatbots Making Decisions

Our current understanding of the chatbots is not sophisticated enough to make them run a business autonomously. However, “expert” chatbots are a different ball game. I had explained in an article earlier how they differ from conventional chatbots. In a nut shell, an expert chatbot communicates with humans plus makes decisions based on the expertise it has. At exClone, we have seeded the first steps of this vision.

In the short term, the following decisions can be expected to be made from AI agents/expert chatbots in the realm of digital business:

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This is a simplified view of all possible conversational decision systems applicable to businesses. In this simple model, the business owner would interact with an executive chatbot to control and manage her business.

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This picture depicts a digitalization scheme via AI in its most naive form, with a potential to signal what the future holds.

Barriers to Entry

The most important parameter in this transition is the ability to create expert chatbots easily without deploying expensive scientific projects. Here is the list of 10 requirements to win in this race:

1- No coding effort should be required.
2- Deployment should be fast (as opposed to lengthly training/learning procedures)
3- Data requirement should be limited to the content of the expertise (as opposed to vast amounts of training data to be collected)
4- Easy to fix and modify (as opposed to black box approaches that require re-training the system)
5- Building an expert chatbot must be an editorial effort, not much different than writing a blog post.
6- Builders of expert chatbots should be experts themselves without the involvement of developers or scientists.
7- Should be able to converse effectively, yield reasonable advice, and make sensible decisions.
8- Must have a certain level of awareness to be able to analyze its conversations and make deductions.
9- Must be able to learn from overall operation by evaluating its objective function.
10- It should be easily deployed in all communication channels/platforms ranging from SMS to Slack.

The winning development platform must address all the issues listed above. Most of the current platforms offered by big corporations (Microsoft, Google, IBM, Amazon, Facebook) do not meet half of these criteria, and are targeted solely to developers, not to business experts.

How will the Future of Business Look Like?

There are several measurements that apply to business valuation today such as the market cap, EBITA, gross revenue, number of employees, etc. But none of these conventional measurements indicate how close the company is to scaling upward. The rate of digitalization could be such a measurement to fill in this gap.

A new key measurement of company valuation in Wallstreet will be the “Rate of Digitalization” in the near future.

Consider Amazon again. Let’s imagine a rival, equal size, equal volume, but everything done by hand (human labor). Who would you invest in? Amazon or the rival? Knowing the degree of digitalization in Amazon, the natural choice would be her. This extreme hypothetical example emphasizes the value of this new parameter. Today, it is all blurred in the narrative interpretations of stock analysts.

Far into the future, it is fair to assume that Fortune 500 list will start to include businesses entirely digital (automated) with few human owners or controllers.

What a Chatbot can do that Search Engines cannot?

There is a very easy distinction between a chatbot and a search engine which explains almost everything: SHORT-TERM MEMORY.

A search engine, like Google, has no short-term memory. Google will take your query, and bring results. The job is done. The next query you have is completely new to it. It is a new session with no ties to the previous query.

A chatbot, on the other hand, can remember 2, 3, 4, or N steps back, which gives it a huge advantage in responding better, more focused, and with higher accuracy. Especially, applications like “advisor” chatbots can take advantage of this fact. However, remembering N steps back poses a challenging technical problem that can grow in a combinatory fashion. Without getting into such technical details, let’s see an example.

Multiple Questioning Before Presenting Answers
A showcase example is the chatbot Davis Hunter which is designed to find you new travel destinations based on your choices. The multiple questioning operation uses short-term memory which is shown below.

At the end of the questioning steps, the chatbot presents travel destinations with precision. It has used its short-term memory to remember all your inputs before making a decision on its list of destinations. Once the user selects from the options, then Davis will start to present more information about the destination using the free content from Wikivoyage.

The operation shown above is a blue print of any kind of advisory chatbot in any subject.

If you type the same query to Google: “island in spain that has festivals and good seafood restaurants” you will end up with poor results as shown below. Simply because your query is too long and falls into long-tail, a region where search engines cannot handle queries.

Search will Shift to Specialized Chatbots in the Near Future
It is fair to assume that conventional search will die out as “Google generation” is steadily replaced by “Siri generation” who are more inclined to use messaging and chatting platforms. This transformation is already at works and is expected to accelerate as chatbots get better and spread in every vertical.

The expectation that a search engine user will sift through dozens of inaccurate results is increasingly becomming obsolete and intolerable for the new generation who grew up with persistent messaging habits highly suitable for chatbot interaction.

The key point in this transformation is the ability to create quality chatbots with an easy and familiar effort (like writing a blog entry) that would accelerate the proliferation of viable chatbots in every subject.

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A New AI System Writing Its Own Code to Produce Chatbots

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If you heard AI systems that write their own computer codes, here is an example from exClone. The exclone’s platform is capable of taking any text, turn it into a special code (called CHATMATRIX), then spit out a chatbot that represents the content in a dialogue style. All in single step, with a push of a button.

The content is to be entered by subject matter experts in any particular topic. Hence the platform is called the Cloning Platform implying “digital cloning” of expertise. And the expertise can be anything, as simple as how to build a kite as long as it is articulated well. In this single step process, no coding is required to produce a chatbot.

The chatbots produced with this system have two primary functions: (1) Provide answers through presentations like how an expert would do by navigation options, (2) Provide answers to impromptu questions at any time during the conversation. There are bunch of secondary functions that make the whole experience human-like.

Machine Learning Similar to Human Learning

The human brain learns new knowledge while reading. As we all know, this process is very quick. The way to test learning is whether the person can answer questions about the sentences she/he read. exClone’s machine learning (ML) process works with the same principle, reading content and being able to answer questions. The automated step of creating a code for a given content is the byproduct of this machine learning process.

The editorial effort to create a chatbot involves few other choices by the expert. Some of these options include dialogue behavior (templates) that implement different objectives such as sales, help desk, advice, onboarding, surveying, etc. If you would like to experience the Cloning Platform, click here for free trial.

CHATMATRIX: A New Programming Language for Chatbots
Although the ML process creates a code for a given content (and template), the developers have a choice to add advanced functions and behaviors by further coding using the CHATMATRIX language. Bypassing the automation step, an entirely new chatbot can also be created. This high level language has the following features:

  • The language is comprised of predefined NLP functions for detecting user’s comments and predefined ML functions to generate response to the user.
  • Each statement handles not only the current exchange, but also the conversations took place N steps back. This enables strategizing the chatbot’s response based on history.
  • Each statement evaluates the availability of all possible response options, then chose the best one.

If you would like to learn more about CHATMATRIX, please drop a note below in the comment section.

Detecting Emotions is Key to Chatbot Performance

One of the advantages of CHATMATRIX is its ability to detect sentiments in user responses which leads to formulating emotions based on the short history of the conversation. Emotions are detected by the density of the sentiments during the conversational steps. This may seem like a humanizing effect of the chatbot experience, which is true in one sense. However, emotions are very useful for business purposes because it becomes a tool to filter out genuine users (sales leads for example) from people with no intention to engage in business.

Detecting emotions like satisfaction, confusion, boredom, curiosity, and anger allows strategic moves in dialogue flow. If the user is satisfied, chatbot can offer more options, or ask user’s contact information. If the user is confused, chatbot can ask the source of confusion. Many different strategies can be implemented based on the objective. Finally, conversation logs with emotion markers provides highly insightful information about the clientele.

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DIGITAL CLONING: How Expertise can be Commoditized by AI Driven Chatbots

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Biological cloning may be an immortal way to pass on our individual genetic information, but in digital form it offers something quite different: A new robotic society of expert chatbots as a part of our new digital life. Before digging deeper, let’s define what it is.

Digital Cloning is the duplication of a cognitive function based on the duplication of its source data (knowledge). Only duplicating data (text, image, video, sound, etc.) is not cloning just as how duplicating only DNA would not be cloning in biology.

Digital Cloning of Expertise is duplicating some particular expert knowledge along with its delivery mechanism, in this case a chatbot. In order to satisfy the criteria of digital cloning, such a chatbot is required to present expert knowledge in a useful way as well as answering related questions at a reasonable rate. Note that we are talking about “expert” chatbots here but not transactional chatbots where they book your flight or arrange your calendars. The difference between expert chatbots and transactional chatbots was explained in one of my earlier articles.

Digital cloning this way also includes persona, personal emphasis and choices since no two experts in the same field are the same. Personal variation is what makes combined expertise very powerful, always surpassing an individual expert’s opinion. Personal choices also reflect how expert content is curated to be cloned by its designer.

Expertise is the Most Valuable Commodity if it can be Shared
Expertise can be anything. It can be as simple as how to build a kite, or as complicated as how to perform a brain surgery. We all have some kind of expertise, some ideas, some vision, and stories to tell. In the enterprise world, expert knowledge is even more valuable since it is the driving force of business success. But expertise is only valuable if it is actionable. For that, expertise must be alive, always available, easy to share, easy to be consumed.

In today’s world, knowledge is shared in static (dead) forms of delivery methods: publications. Regardless of where they are published, and how long they buzzed or viewed, their destiny is ARCHIVES, which is a digital wasteland.

In Today’s World, Knowledge and Expertise Fade Away like Ocean Waves Breaking on a Beach and Retreating Back

This process resembles to ocean waves breaking on a beach (published), then retreating back (archived). They are at the mercy of Google’s ability to bring them to search queries. Worse than that, if such publications are within an enterprise environment where even Google cannot help you, their shelf-life will be even shorter comparable to how long the promoting emails are kept relevant, or how well you can query a database.

Accordingly, intellectual efforts made on a regular basis have no safe harbor to remain alive using our current digital systems other than being deposited somewhere (online or offline) including posting on social media, blogs, websites or corporate networks. You would agree with this point better when you catch yourself smiling upon discovering an old document on your computer with full of great ideas.

Immortalizing Ideas, Visions, Experiences, and Expertise
One way to keep one’s expert knowledge alive is digital cloning via chatbots. This must be a very simple process at the level of editorial effort not much different than blog writing. If it gets any more complicated than that, the demographics of digital cloning will be quite limited, and the anticipated affect will not emerge.

Digital cloning via expert chatbots is a new form of digital presence. Unlike the existing forms of digital presence such as facebook page, twitter account, or personal blog, an expert chatbot can maintain all your expert knowledge equally shareable regardless of when they were entered into the system. Every time you add some valuable knowledge, it enriches your chatbot’s response capability. People chatting with your chatbot would be presented your expertise, and would get answers to their questions. In addition, conversation logs can provide ultimate transparency to people’s concerns, curiosities, and demands relevant to the expertise served.

Community of Expert Chatbots in an Enterprise
Digital cloning concept offers tremendous advantages for enterprises in applications including, but not limited to, training, employee assistance, sales agents, help desk, and many other similar knowledge rich tasks. Managers and workers can clone themselves to offer an alternative communication channel within a company for all sorts of purposes. Expert chatbots can be created using databases and data silos for deep content (big data), converting such data driven systems into conversational experts. With such a transformation, we can start to assess the value of companies by their commoditization capability of expertise internally as well as externally.

A Society of Digitally Cloned Chatbots

Expert chatbots can refer to each other even for the same subject matter expertise very similar to how we use links in documents. This allows users to switch from one chatbot to another without a need for external search. Collaboration between chatbots in this manner will be the corner stone of the emergence of a robotic society in the mirror image of ourselves. Not to mention, a certain level of competition will emerge among the clones measured by who is the most popular expert chatbot based on number of referrals.

The emergence of such an ecosystem would redefine how we interact with computers, and would change our role from being sole digital workers to being part-time human parents of digital clones.

Such a transformation will also challenge the immortality arguments since biological death would no longer be 100% loss of one’s expertise and persona. One thing is for sure, all these possibilities are not a science fiction story any more, they are all here around the corner.

You can read my other articles on linkedin if you are patient enough to dig through, or you can wait until I clone myself (which I am in the process of doing it) and talk to my expert chatbot. You may even be able to hear my voice! Until then, stay tuned and join our CHATBOTS Group on Linkedin.

exClone in Hall of Fame of Chatbots in a Survey of 2017

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We are proud to be selected to be in top 5 chatbots in a market survey of chatbots 2017. The survey highlights the state of the chatbot market as of 2017, presented in 20 slides. There are interesting findings in this study worth reading.

A RESEARCH STUDY BY MINDBOWSER IN ASSOCIATION WITH ‘CHATBOTS JOURNAL: 300+ individuals participated from wide array of industries including Online Retail, Aviation, Logistics, Supply Chain, e-commerce, Hospitality, Education, Technology, Manufacturing and Marketing & Advertising.

Chatbots on Websites

Some interesting facts emerging from this study are listed below. The chart below shows that 80% of businesses want to launch a chatbot on their Website, which is an important data for us at exClone. Our upcoming platform allows integration to all platforms, but is mainly servicing chatbots on Websites and MS Sharepoint/Azure platforms.

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Industries: Transactional Chatbots vs Expert Chatbots

While e-commerce is strictly in transactional category, the rest of the industries in this chart is in our territory of expert chatbots.

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Assistants and Agents

The first two categories of business functions are perfectly inline with our value proposition at exClone.

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Our new Competition Ground

Our new platform will enter into this chart of competitors. We claim to be the first platform with NO coding, and NO AI experience required, the hallmark of our Digital Cloning technology.

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Future Looks Good

Chatbot technology is definitely a major disruption of the near future. Our fantasies of talking computers, which shaped up since the first episode of Start Trek, have constantly been fed with sci-fi movies during the last 5 decades that provided a concrete, historical user expectation in all demographics.

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Disclaimer: exClone has no commercial or advertorial relationship with Mindbowser or Chatbots Journal.

To download the PDF file, please click here.

 

exClone Partners with Maana: How to Turn Data into Knowledge, then Talk to it via Chatbots

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We are proud to announce a new partnership with MAANA, a pioneering tech company that turns data into actionable knowledge to accelarate enterprise profitability. The objective of this collaboration is to add a communication option where the users of the knowledge platform interact with the system via the most casual, natural, and untrained conversations. The vision of bringing  knowledge/data assistants into operational workflows is remarkable from the stand point of industrial artificial intelligence (AI).

In this article, we will share a simplified description of the problem at hand, and the path to its solution in its most general manner. The modes of conversation presented at the bottom of the article are self-descriptive value propositions as to how access to knowledge can be accelerated.

Before I do that let me briefly explain what Maana knowledge platform does and how exClone technology works with it.

The Maana Knowledge Platform is used by the largest Fortune 500 companies in the world and is uniquely designed to enable subject-matter experts to quickly build hundreds of interconnected models that encode the expertise of subject-matter experts combined with data from across silos in the context of optimizing an asset or workflow. These knowledge models provide continuous, actionable recommendations into the operations of assets and workflows enabling faster and better daily decisions by thousands of employees that result in increased enterprise profitability.

What can Chatbot Communication bring to the table?

In its most simplified strawman drawing, turning data into knowledge requires handling of 5 technical problems collectively. Going from bottom to top, they are (1) data silos and data lakes, (2) integration, (3) analysis, (4) retrieval, and (5) communication.

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At the top layer, communication function must allow the users interact with the system in the most casual, natural, and untrained manner. That’s where chatbots play an important role as being a part of the last ring of the chain.

You might as well consider chatbot as a waitress/waiter in a restaurant. Despite the massive operation in the kitchen and abundance of meal options, the only interface you would have with this system would be her/him. A typical customer – waitress interaction lays out all possible combinations that a chatbot must handle.

  • Going over the menu: A typical and most common interaction where the customer is presented options to chose from and the questions about the options are answered.
  • Questions without the menu: An interaction where customer choses to investigate options without looking at the menu, and such questions must be answered impromptu which may sometimes lead to a menu item.
  • Specials: An introductory style of interaction where the waitress takes an active step to assist the customer about the best options for that particular moment in time before any conversation starts.
  • Remembering the customer: An interaction with a known customer where previously used options are brought up, and the customer is alerted to the variations from the previous state of affairs.

These 4 types of interactions are the range of information service applicable to all chatbot applications. Bringing these on the table of knowledge platform, however, has some unique challenges.

How Does a Chatbot Serve Knowledge Platforms?

There are 5 main functions of a chatbot to bridge the gap between the end-user and a knowledge platform as shown in the simplified diagram below.

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  • Interface: Chatbot interfaces are usually simple and SMS like, which are enriched with image and video insertions. They are particularly popular for their advantages in the ever growing mobile device usage. No special requirements would apply to interface design that could be attributable to the knowledge platform.
  • Decision: This is the state information of the ongoing conversation where the system (1) qualifies if the user’s response needs to be sent to the knowledge platform, (2) if there are options to present for navigating to the next level, (3) if the current response is the continuation of the previous response via short-term memory, (4) if the user must be helped according to his/her historical choices, and (5) if the user’s response is related to the answers in the short-term memory. This list is actually much larger with details that belong to the type of application and nature of the knowledge operation.
  • Translation: This step translates a qualified English sentence (qualified by the Decision step) to a system specific retrieval command(s) within the knowledge platform. This translation requires ontological parsing of the English sentence then formation of it in the new language.
  • Generation: Once the result is retrieved, it has to be put into an English sentence for better communication. This seemingly simple task can actually be quite challenging, and is also known as the generation problem in computational linguistics. Bite-size information is a concept that has to be decided for the generation step according to the objective of the chatbot. For example, if the chatbot is helping with federal regulations, then the bite-size information can be a full sentence, or sometimes a paragraph, of unedited original text, with very little generation challenge. However, with numerical data, diagrams, tables, images, or video, the bite-size determination can be a difficult task.
  • Navigation: This step requires an independent cycle of communication with the knowledge platform to harvest available options of navigation. Usually, the options within the knowledge domain are mirrored at the chatbot level so that the users can access knowledge with less effort.

Modes of Conversation

There are various modes of conversations that a chatbot can conduct, making this interaction much more valuable than using a static, single-step search box. Here are some of these possibilities using a hypothetical content.

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Case A above shows an impromptu answer (4,788 tons) presented with the generation capability. Case B and C show how chatbots can utilize their short-term memory to suggest alternatives and navigation options while remembering important aspects of the original query that was asked 2, 3, and sometimes 4 steps earlier. While the knowledge platform was utilized in A, the responses B and C may come from the chatbot itself depending on the availability of embedded (learned) content. Also note that the clickable options are very useful especially when the chatbot is used on a mobile device.

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Case D shows a possible exchange to narrow down the type of analysis available in the knowledge platform domain before it has been retrieved. Case E illustrates long-term memory to continue the previous conversation which can eliminate unnecessary steps to reach back to the same point. Case F shows that knowledge platform and chatbot can create alerts to indicate updates in the back end.

These are some of the possible exchanges that could be achieved from chatbot – knowledge platform combination.

 

 

 

 

 

 

 

 

 

 

 

 

Conversational Analytics … Enough Reason to Launch a Chatbot for your Business.

It is fair to say that “conversational analytics” does not exist today. Even if some companies may already be using it via Twitter type feeds, it is still an untouched territory in the mass scale of the Internet business.

Conversational analytics is a gold mine of user feedback, and there is nothing better than seeing praises and complaints articulated openly in their most naked form.

Web Analytics Versus Conversational Analytics

If you have a business relying on the Internet presence, you are most likely familiar with these terms of Web analytics: Bounce Rate, Conversion Rate, Time Spent, etc. In the absence of a recorded conversation with visitors, these measures are the only tool to guess the visitors’ intent and feedback. Hence, the concept of growth hacking has emerged to launch a trial and error process to improve presentations one step at a time. If you had a chatbot recording conversations about your products and services, then you would experience a major advantage:

Web analytics only shows you how your existing presentation performs (in time). Conversational analytics shows you what you are missing (instantly).

Conversational analytics, even before forming a statistical result, would constantly indicate the weaknesses of your presentation through user feedback like this: I cannot find your address … why should I buy this… when will this be available… where is the information related to….

Human Assisted Chatlines Versus Chatbots

Recently, human-assisted chatlines have been increasing in numbers deployed for marketing and support operations. It can obviously be very expensive to maintain such operations and conversational analytics obtained from them can be too few in quantity. In a manner of self-fulfilling prophecy, the meaning of conversational analytics is often reduced to managing FAQs where the identified FAQs would still keep the same human labor busy, perhaps 90% of the time. Chatbot effect is shown in the diagram below. Note that a Chatbot is not an FAQ machine, however if designed properly it can handle most of the FAQs while reserving human labor for more in-depth conversations.

Not only the bulk of the human labor can be eliminated by chatbots, but also the conversational analytics would have a different information embedded in them. For example, people may be asking “what is the price?” repeatedly. If this information was handled, they would move on to ask “Do you have any volume discounts?” If that was handled, they could move on to ask another more detailed question. Before human operator is engaged a chatbot can pave the way for more in depth questions to arise from the visitors which would otherwise emerge while keeping human labor busy. Since human labor is expensive, and slow, some of such questions would never reach the business without a chatbot implementation.

Conversational Analytics Behind a Firewall

Remember the old way of maintaining a complaint box in the entrance of offices? For internal operations, a chatbot assisting employees in an anonymous manner could be a real value, sometimes priceless feedback to the management. Otherwise, for obvious reasons, many suggestions and complaints may not surface to maintain the employee-employer relationships, which would progressively result in blind operations.

Upper management can show its confidence by deploying an anonymous chatbot for suggestions and complaints.

Anonymous or not, and internal chatbot could provide valuable information to the upper management to include, but not limited to, these factors:

  • Suggestions for operational and business matters to upper management
  • Information needs to conduct mission critical operations
  • Satisfaction levels with compensation and benefits
  • Promotional expectations and ambitions
  • Readiness for emergencies, safety, and security tutorials.
  • Maintenance support, remembering training knowledge quickly on the spot

All these needs of the employees would become transparent in conversational analytics as a byproduct of using an internal chatbot.

Stay tuned for exClone‘s upcoming announcements of chatbot development for enterprises. Join our linkedin group of CHATBOTS for similar articles.