The Rise of Virtual Experts via Machine Learning


By now, you must have heard the term “virtual assistants.” The natural evolution of virtual assistants is the virtual experts. Going from former to the latter is a substantial technical challenge not many companies are willing to meet yet, because the simpler “assistant” version has untapped commercial potential and a quick ROI. Nevertheless, virtual experts is the real game changer – a paradigm shift – that will have social and economic impact beyond our wildest imagination.

The rise of virtual experts is just hiding behind the puzzle of the most effective machine learning approach.

Investing in the most effective machine learning approach holds the key for commercial success. It has to be practical, transparent, agile, and quick to deploy. Our process is explained in simple terms in my previous articles: “Deep Cloning Versus Deep Learning” and “Can Machine Learning Use Knowledge …

Virtual Doctor for Women’s Health – DrCHAT

Some examples of virtual experts coming off our conveyor belt include DrCHAT which is a virtual doctor for women’s health (in Beta). DrCHAT encapsulates physicians’ expertise following the ACOG guidelines for evidence-based care, and is further described in the article “Artificial Intelligence (AI) in Medicine …

Virtual Spokesperson

Companies needing to interact with clients beyond Website presentations can launch a virtual company spokesperson. Vera is an example where she has absorbed several layers of company information via machine learning. Although her conversation skills do not match a real human, she is highly effective with genuine visitors who are looking for information by chatting instead of surfing Web pages.

Virtual Tax Helper

As an example of converting documents into chatbots, the virtual expert Terry Kohen chats about IRS Small Business Tax guide (Publication 347). The conversation with Terry is somewhat limited to the scope of the IRS document, thus it does not replicate the expertise of a human tax expert.

Virtual Guide – Smart Cities and Travel Safety

Geographic expertise is always in demand for travellers. The two most prominent areas for virtual guides include smart city and travel safety applications. Before these specialties become virtual experts, we have been testing a destination finder, Davis Hunter, using a limited-scope wikivoyage data.


New Opportunity to Monetize Expertise
The commercial impact of virtual experts will be driven by the scalability offered by chatbots.

While human experts monetize only by face-to-face consultations, their virtual counterparts will be able to monetize by one-to-thousands consultations, simultaneously.

Eventhough such electronic consultations may require small payments, high volume will push the revenues to levels only determined by server capacity and market demand. That’s the critical value point.


This article is brought to you by exClone, a chatbot technology provider.

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Consulting with a Virtual Doctor for Women’s Health


One of the biggest impacts chatbots are expected to make on society will be in the medical field. The newly launched (in beta) is a prime example. DrCHAT provides patients with medical consultations prior to initial doctor’s visits, or a second opinion afterwards. Free usage and the ubiquitous availability of DrCHAT allows patients to continue consultation at every stage of treatment. This empowers women, with a number of benefits to the entire health ecosystem, and it presents unlimited potential for the use of technology such as DrChat to improve the nexus between patients and care.

The only obstacle for chatbots becoming virtual doctors is the ability to handle consultation dialogue similar to what occurs in a doctor’s office.

The dialogue obstacle is a major challenge, and solving it will determine who wins the race to claim this value service space.

Knowledge-driven Machine Learning as the Backbone
While most machine learning methods are data-driven, they all suffer the problems of data availability and reliability. However, volumes of medical knowledge are readily available that may be turned into a dialogue system. Knowledge-based machine learning accomplishes just that without the rigorous requirements of a data-driven approach. The expertise of a medical doctor, as depicted below, is converted into a conversational system through the knowledge-driven machine learning method (as indicated by the blue arrow). This process is explained in simple terms in two linkedin articles “Deep Cloning Versus Deep Learning” and “Can Machine Learning Use Knowledge …


In the case of DrCHAT, the expertise is derived from certified Ob/Gyn physicians who have laid out over 30 different clinical flows – following American College of Obstetricians & Gynecologists guidelines for evidence-based care. Although the machine learning process continues its growth, some beta-testers have been granted early access to DrCHAT.

Compared to Flat Search Systems
One of the striking differences between flat (single-step) searches using Google, WebMD, or Wikipedia and a medical chatbot such as DrCHAT is the consultation dialogue, in which clinical work flows are utilized to allow a step-by-step conversation to diagnose illnesses and suggest treatment options. Considering the popular usage of mobile devices and messaging apps, consultation dialogue offers the richest and quickest experience compared to opening documents and sifting through large volumes of text on a narrow screen.

Single-step search engines fall short for health problems that require multi-step interaction with a patient to suggest diagnosis and treatment options.

Current Health Apps are Not Chatbots
Some current health apps, including ADA, Babylon, and YourMD, offer valuable services such as scheduling visits or video conferencing with doctors. However, their chatbot interactions are imitations of a single-step search with no genuine dialogue capability. The fact that these apps are geared toward “general medicine” to cover everything without specialization makes them less capable of delivering the requested consultation. Medicine is such a vast topic that automated consultation is best handled by specialized expertise.

Professional Version

Another important feature of DrCHAT is that it comes in two versions, one for patients and the other for professionals. Although derived from the same expertise (IP), the professional version lays out the clinical flows for decision-making which is a valuable reminder, fact-checker, and a quick guide for practitioners. The complexity of the medical terminology used during a dialogue also differs between the two versions.


Anonymity is a Big Plus for Women’s Health Chatbot

Most Ob/Gyn specialists agree that women do not always feel comfortable talking about their intimate problems, and sometimes skip mentioning critical details during face-to-face consultation. DrCHAT’s approach of anonymous dialogue, without any registration, will break down some of these barriers and further empower women during these exchanges. In return, conversation logs (without identity information) become a valuable source of information to analyze women’s behavior under a regular clinical examination.

The Future of Health Chatbots
Where we go from here will be determined by the engagement and acceptance level of health chatbots such as DrCHAT. It is clear, however, that once the concept has been validated, other specialty areas may be replicated quickly by deploying the underlying technology – which focuses on automated knowledge acquisition from experts. Cardiology, Emergency Medicine, Pediatrics, and Urology (men’s health) are some of the specialties to be launched under DrCHAT following Women’s Health. If you want to be a tester, just talk to the chatbot and ask to become a tester. Stay tuned for more on health chatbots.



CHAT WITH DrCHAT ABOUT women’s health.







Chatbots for Monetizing Expertise

A New Era for SMEs to Monetize Their Knowledge!

Every person can be some sort of subject-matter-expert (SME) regardless of the complexity of the subject. Knowledge acquired over the years (thru education, work, etc.) is unfortunately bottle-necked by the limited communication bandwidth of our biological framework. We can only tutor one or two person at a time with the highest level of participation. We may give speeches to a group of people with less level of interaction. Finally, writing essays, books, blogs, videos, or social media gives us broader audience, but with minimal interaction. As a result, expertise is highly limited in its interactive exposure.

Chatbots can scale up interactive exposure of expertise 1000s of times more than peer to peer alternative, which is like one-on-one tutoring 1000s of users, simultaneously.

Editorial Platform to Create Expert Chatbots
Building SME chatbots must be refined to content curation and persona creation without any coding or AI training requirements. Otherwise, SME chatbots will become very expensive and lenghtly to develop that will jeopardize their ROI. The challenge is to turn content into a sense-making chatbot with dialogue and short-term memory skills. For this, various machine learning platforms may be used. exClone’s platform is an example where SME chatbots can be build fast using samples loaded in users’ accounts.

An example to interactive exposure of a subject matter expertise is shown below for tax advice for small business owners derived from the IRS Publication 334.

Expert Chatbot is a Conversational Flow Chart
Some people do not fully appreciate the difference between a chat interaction (dialogue) and flat document search. This is also wrongfully encouraged by products like Siri and Alexa where the conversation is a single-step question/answer. Human dialogue, which is the ultimate form of learning/teaching, is beyond a single step communication. It requires rather complicated short-term memory ability, because the user has many steps to describe his/her problem. Here is a very simple example, a flow chart for handling your taxes involving health insurance.

There are five different actions you can take according to the flow chart about how to handle your taxes involving health insurance. It is instantly obvious that you could not state your problem in one Google query. Flat document search will not work by any means. Rather, you either need a diagram like this, or an expert who walks you through a similar work flow. A chatbot embedded with this expertise could be just as useful especially if more information is tucked under each block for impromptu questions.

Most expert solutions involve multiple steps through a flow of information exchange that cannot be replicated by any Google Query.

Flow charts define a dialogue template in exClone platform some of which are pre-defined whereas others are customized.
Knowledge-based Machine Learning Challenge
The scientific challenge in creating expert chatbots is the ability to convert flat documents, (like the IRS Publication 334), and flow charts (like the one shown above) into proper dialogue flow in the form of a chatbot. Because the knowledge is already available, the machine learning challenge is to automate this process without any coding effort. Obviously, data-driven machine learning methods (such as deep learning) do not apply here since knowledge is already available. This crucial different was explained in one of my earlier articles titled “Can Machine Learning Use Knowledge instead of Data? Deep Cloning vs Deep Learning“.


This article is brought to you by exClone, a chatbot technology provider.

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Can Machine Learning Use Knowledge instead of Data? Deep Cloning vs Deep Learning


Machine Learning (ML) field is defined by most people to be exclusively a field of data science, which is incorrect in principle. The main goal is to make computers perform cognitive skills similar to human brain and to immitate how human brain learns and thinks. Why use data only? Isn’t most of our learnings based on knowledge consumption?

Human brain learns mostly from knowledge, not from data!

As a result, we need machine learning methods that use knowledge directly. This area of research has not been explored as much as its data-driven counterpart (deep learning) because of the challenge of Knowledge Representation (KR) and the difficulty of computerized ontology creation.

KR methods such as semantic nets and logico-linguistic modeling have a long history of R&D using static/given knowledge but not in the context of “learning”. So, the question is how can we extend KR methods into a “learning” method? This brings us to the new idea of deep cloning where KR is molded into a neural-network-like structure poised for learning by reading.

Can Computers Learn by Reading?


Knowledge-based learning methods make it possible for computers to learn by reading similar to how we educate ourselves. Once a deep cloning system is set, then a computer can start reading books (text) to learn a subject and answer questions about it. The trade off is between the difficulty of ontological (knowledge-based) learning versus the advantages of independence from training large data (corpus) and dealing with issues like convergence and generalization.

Advantages of Knowledge-based Learning
There are a number of advantages of this approach in comparison to data-driven methods as outlined below:

  • One-shot Machine Learning: Since knowledge does not require a supervised reference point, learning becomes one-shot machine learning devoid of convergence problems encountered in deep learning.
  • Not Stuck in the Past: Data-driven models require data collected from the past experiences. This makes them vulnerable in application to new things (i.e., new car, new plane, new drug, new house, new neighborhood, new disaster.) Knowledge-based systems are not biased by the past, and can employ new knowledge immediately.
  • Knowledge is Less Limited than Data: Availability and abundance of data do not guarantee its completeness, and data can still be limited in explain the process it comes from. Weather prediction is a good example. Knowledge, on the other hand, represents the best data experience available.

Fundamental Differences
In processing natural language and representing knowledge (after reading a text), deep cloning network (shown on the left) is comprised of layers with different objectives and different neuron functions. In contrast, deep learning (shown on the right) is a homogenous architecture of neurons dedicated to minimize the error at the output in a supervised mode of learning. Despite variations of deep learning, no neuron activity is designated for any linguistic role.


Knowledge representation on the left can be a one-shot process using only the text of the knowledge whereas learning on the right requires long training cycles using corpus way larger than what is needed on the left.

Answering Questions


Knowledge-based machine learning can answer questions from the content it learned with utmost precision using the ontological connections shown in the network picture above. Shown aboveis a hypothetical case, where a question presented to the network finds its most relevant answer using those connections. In case of partial connections, the network puts more emphasis on target, event, and instrument (in this order) and produces answers with an accuracy score. Based on the type of application, a threshold can be set to declare “no answer” if the best scoring sentence is below the threshold. With such a capability, the chatbot becomes self-aware of its performance, and can report how well it did in answering questions. This can be further expanded to social learning where chatbots can ask for feedback to learn how to answer particular questions.

Knowledge Breeding


More impressive than answering questions, deep cloning machine learning can breed new knowledge from the content it learned as shown on the right. This is logic resolution using existing knowledge to produce possible new knowledge using the ontological connections. Obviously, breeding new knowledge is one of the most exciting aspects of learning algorithms that are not as straight forward as it looks when using data-driven models such as deep learning. One of the advantages of knowledge-driven machine learning is that the “new knowledge” is transparent (can be verified by human inspection) whereas the same cannot be said for data-driven deep learning.


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Most Chatbots don’t Use AI, are Misrepresenting AI


This title is the summary of what is happening in the market today, mostly encouraged by Facebook’s move for Messenger bots.

The ChatbotConf 2017 revealed this sad truth. There are 200,000 Messenger bots today, most likely none of them have a real AI backbone. A recent article summarizing the conference draws a similar conclusion.

End users of chatbots would not really care whether there is AI backbone or not if the chatbot they are using solves their problem. In a small fraction of cases, chatbots without AI can be helpful, especially in e-commerce transactions where buying and selling options are rudimentary, and the conversations can be buttonized. However, the AI issue surfaces when chatbots try to service higher complexity tasks. The way chatbots can be used in real life, this corresponds to, maybe, 90% of the cases. So, what is the AI backbone that is required?

The AI Backbone

Chatbots that represent AI must have some (if not all) of the capabilities listed below:

  • NLP: Capability to understand users’ responses in their most variant form.
  • Answering Questions: Ability to communicate with the user about a subject matter by absorbing knowledge and answering questions about it.
  • Asking Questions: Ability to ask questions to navigate the user to solve a problem.
  • Dialogue Behavior: Ability to engage users in certain behavior in concert with the chatbot’s objective (sales, transactions, advice, training, story telling, idea sharing, etc.)
  • Learning from Conversations: Ability to ask users for answers and to learn from them. This should be optional since social input may not be desirable for certain objectives.
  • Short-term Memory: Ability to remember the topic of conversation and interpret pronouns correctly. This requires chatbot to take into account what was said 2, 3, or 4 steps earlier.
  • Long-term Memory: Remembering previous chat sessions and starting conversation from where it was left of.
  • Emotions and Attitude: Ability to detect unproductive conversations, change strategy, or abort not to waste resources.
  • Awareness: Ability to self-assess its performance, produce reports about its performance, and suggest bot builders the weaknesses encountered.
  • Infinite Speech: Not to be restricted by a pre-defined steps of conversation.

Canning Responses Instance-by-Instance is not AI

Most chatbot platforms today are requiring instance-by-instance input from its builder to develop every step of the intended conversation in a rigid sequence. This approach is feasible for banking transactions, travel bookings, or other similar interactions where dialogue is restricted to solid options. Obviously, there is no AI backbone needed for such chatbots.

Chatbot science is at its infancy while most developers are expecting adult behavior.

Deep Learning is not a Silver Bullet

One of the latest misconceptions emerged in the market is that if there is enough data thrown at deep learning system, all the requirements listed above as AI backbone can be satisfied. Deep learning can only handle some parts of the required list, and the rest must be called the “chatbot science”. The only way to produce a chatbot development platform in the scope of AI backbone is to offer data-driven tools and/or knowledge-driven tools with certain level of built-in functions, where those functions define the secrets of the chatbot science.


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#chatbot #chatbots #AI #artificialintelligence #ConversationalAI #Virtualassistants #bots #machinelearning #NLP #DL #deeplearning

Build a Chatbot Impersonating Yourself

Impersonating chatbots is one of those concepts that are around the corner. They will add one more option to our online digital presence with social networks, personal blogs, etc. An immediate question is why would anyone build his/her own chatbot? Here are five reasons why impersonating chatbots may take off sooner than later.

1. Share Your Ideas

A chatbot impersonating you is like your personal messenger that can tell others about your ideas, expertise, interpretations, and status. You can pack as much information as you want inside your chatbot and update it as frequent as you can. When you review the conversational logs, you can see how people are reacting to your ideas.

Anonymous conversations with your chatbot can test your ideas by real feedback devoid of social pressure to please.

2. Managerial Communication

If you are managing a group in your business, you can build your chatbot to remind your workers of the rules, regulations, milestones, visions, expectations, and much more. Usually, one-on-one conversations between a manager and a worker is an awkward one if the subject matter is rules, regulations, etc.

Chatbots can be a polite way to fully inform your workers about rules, regulations, and what is expected of them.

3. Chatbot as Your Talking Resume

If you are looking for a job, your conventional resume may fall short of explaining who you really are. Your impersonating chatbot, on the other hand, can contain more social knowledge of your life, pictures, videos, and those appropriately selected “personal touch” bits of information. Whilst it can be considered annoying to toot your horn during an actual interview, your chatbot can do that for you.

A chatbot as your talking resume can fill an important gap of personal touch which may otherwise not be appropriate to share with a future employer during an interview.

4. Dating Game

Impersonating chatbots can easily be a vehicle to increase our social engagement by presenting ourselves in a unique manner. While many dating sites use personal information to make matches, a chatbot may be a new way for both chatbot owner and the people talking to it. In one end, the anonymous talker can ask tough and private questions freely. On the other end chatbot owner can make selection from conversational logs.

Social selection based on chatbot presentation, and chatbot conversation can be a new avenue for dating.

5. Digital Life After Death

Either for personal reasons, or for educational purposes, life after death may be possible in a digital form. Impersonating chatbots are the first step in this direction.

Chatting with dead people via chatbots may keep us better acknowledged and aware of our heritage and history.


All these avenues will become possible only if chatbot creation is reduced to a mere editorial effort. It should not include any coding, corpus training, or AI experience. Everyone should be able to build it just by writing and curating content. Here is an example of my impersonating chatbot which I built using our editorial platform. The whole process is straighforward and fast as long as you have your content ready.

Another example is a chatbot impersonating Abraham Lincoln. That was built in the same manner for educational purposes.

The deployment is automatic: a public URL is created for your chatbot which you can share. Let us know what other creative reasons you can come up with for impersonating chatbots.

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I Cloned Myself (into a Chatbot)


I cloned myself on digital domain and created a duplicate of me in the form of a chatbot who can chat with visitors about the topics I loaded into my clone. My clone, who presents himself in the beginning as Riza’ Clone, can handle conversations within its objective. The trade-off is between its limited knowledge versus its capacity to disseminate my ideas and expertise to the masses at an incredible volume and speed. I also get to view all the conversations when I wanted to, and contact people of my chosing. If you want to talk to my clone, or try making your own clone, please click here. The process involves editorial effort only. No coding, no AI experience are required. Once you have your content ready, it takes 10 to 20 minutes to enter it and create your clone chatbot.

My clone chatbot can talk about my ideas, current projects, past experiences, some of my expertise, and any other subject I chose to share. Also a touch of personal life, likes and dislikes are included. I promoted some subjects as topics of conversations, others appear only if a relevant question is asked. I can also update it on a regular basis with new information using the editorial platform. Obviously, my clone chatbot cannot talk as good as myself, however it has enough juice to be effective and fun.

This could be your New (AI) Presence on the Internet

If you have a Facebook page, Twitter acount, and/or a Linkedin Profile, you have created some form of your existence on the Internet. Cloning yourself in the manner described here will be another form of your digital existence, and a unique one. One that talks like you with your persona and knowledge. So my contact information (email footer, website, article footer, etc.) has one more line now giving the direct URL to my cloned chatbot.

What Does Your Grey Zone Look Like?

There are several reasons to make your clone chatbot. In reference to the circle of people you have, there might be a large grey zone as shown in the picture here. These are the people who would like to talk to you, but cannot due to lack of connection. They may be the followers or your blog, recruitment professionals, fans, or people who want to talk anonymously. They can also be your employees, students, clients, or future customers. Sure they can leave messages here and there, but nothing compares to a chat interaction where questions can be answered. Also keep in mind, a chatbot can talk 100s of people simultaneously while you could only chat with one or 2 people at a time.

Your Clone can be Your Talking Resume

Your clone chatbot can have the personal touch you could otherwise not deliver in your conventional resume. You could use this tool to impress your future employer, and give them a unique, personal information which would otherwise not be suitable in a formal application or even during an interview. From the recruiters point of view, they may feel more comfortable asking certain questions, and judge you by how you present yourself. More articles are coming about the recruitment opportunities soon. Until then, stay tuned.

#chatbots #bots #chatbot #bot #machinelearning #AI #artificialiintelligence #ML #DL #smartassitants #personalassistants #botplatform #helpdesk #CRM #healthbots #medicalbots #digitalcloning

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Cloning Chatbots for Education


In this context, cloning is an advanced form of impersonating where the chatbot can talk about the person’s life experiences and his/her expertise as curated by the chatbot maker. Compared to impersonating a person just using his/her image and name, cloning is obviously more involved and more challenging. As an example, you can chat with Abraham Lincoln and see how it was developed via one-shot machine learning technology with no-coding requirement. This chatbot uses Wikipedia content as its main source of conversation.

As one can easily deduce, all historical characters can be cloned into chatbots for educational purposes. But cloning goes beyond that as it allows creating chatbots of teachers themselves.

Top 6 Reasons Why Cloning Chatbots are Inevitable Tools for Education

  1. Control: Interactive content gives students much more control over what they want to focus on.
  2. Fun: Talking/messaging/chatting is always more fun than just reading.
  3. Ease: Use of small screen devices are ideal fit for chatbots which add to their educational role.
  4. New Teaching Methods: Chatbots can be a great summarization tool offering students main points to remember and option to dive deeper. Various new teaching strategies can be implemented.
  5. Creativity: Creation of chatbots can also be an educational experience.
  6. Feedback: Conversational analytics obtainable from chatbot interactions provide valuable clues to teachers as to how students learn, or fail to learn.

Profiliration of Chatbots Require Editorial Platforms

For chatbots to take a serious role in education, their development and profiliration must be fast and effective. Here are the three most important requirements for such a progress:

  1. No Coding: Chatbot creation should migrate from a coding effort to an editorial effort. This will enable students and teachers to develop education chatbots by curating content only.
  2. No Corpus Training: Underlying technology should not require large corpus training, and no experience in AI. One-shot machine learning techniques must drive these platforms processing the content for chat interaction while working silently in the background.
  3. Effective Communicator: Chatbots created for education must be effective, being able to answer improptu questions and offer topics of discussion. Although no chatbot today is expected to match human level dialogue, the educational effectiveness can be achieved by presenting chatbots for the specific goals they are designed for.

If you come across cloning/impersonating chatbots, please drop a note below. We may create a list of educational chatbots here.

How I made Abraham Lincoln CHATBOT in Less Than 10 Minutes


In our quest for turning static knowledge (documents) into interactive knowledge (chatbots) via the chatbot Platform, we have experimented creating a chatbot from scratch to completion. The main question was, how long would it take? We first downloaded Lincoln’s content from Wikipedia (16,000+ words), cleaned the content, made editorial changes, and curated some images. Then, it took less than 10 minutes to create a fully functional chatbot through the platform. Its one-shot machine learning technology (learning by reading) took less than 1 minute, and the previous 9 minutes were spent on entering the content into the platform. You can test this chatbot at this link and examine how it was developed.

It is a fully functional chatbot with short-term memory, answering impromptu questions any time, topical suggestions, detecting user behavior, and providing infinite speech. Its knowledge is limited to what the historians said as compiled in the Wikipedia page.


For chatbots to spread and flourish in the future depends on how quickly they can be developed. This would mean development by editorial effort rather than by coding effort. In other words, chatbot platforms should only require content curation and selecting dialogue features. Everything else should be automated underneath (invisible to the developer), including machine learning and NLP capabilities.

Developers of chatbots in the future will be the writers not the computer programmers.

Current platforms offered by big companies (Microsoft Bot Framework, IBM-Watson, Amazon-Lex, Google API, and Facebook Messenger Platform) all require coding skills and/or AI experience. Obviously, developing the same chatbot for Abraham Lincoln would take much longer than 10 minutes when hands-on AI skills and coding are involved.

Considering the document stockpiles of enterprises, a quick and easy conversion to chatbots can be valuable for training, help desk, and other vital operations.


The second reason for this initiative was to assess the value proposition of chatbots for the education sector. Here are the top 6 reasons why chatbots (conversational AI) will be inevitable tools for education:

  1. Control: Interactive content gives students much more control over what they want to focus on.
  2. Fun: Talking/messaging/chatting is always more fun than just reading.
  3. Ease: Use of small screen devices are ideal fit for chatbots which add to their educational role.
  4. New Teaching Methods: Chatbots can be a great summarization tool offering students main points to remember and option to dive deeper. Various new teaching strategies can be implemented.
  5. Creativity: Creation of chatbots can also be an educational experience.
  6. Feedback: Conversational analytics obtainable from chatbot interactions provide valuable clues to teachers as to how students learn, or fail to learn.

There is no doubt that one of the most active areas of conversational AI will be education. We will report how Abraham Lincoln chatbot was received in a follow up article.

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Turning Documents into Chatbots


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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