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

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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?

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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.

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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

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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

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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

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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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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.

CHATBOT CREATION by EDITORIAL EFFORT

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)

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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.

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Cloning Chatbots for Education

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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

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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.

WHY IS THIS IMPORTANT?

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.

EDUCATIONAL CHATBOTS ARE HERE

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

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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.