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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Since the recent upsurge of chatbots, a lot of confusion has surfaced about chatbots in general. Similar to the emergence of laser technology when no one was quite sure whether we were entering the star wars era with military satellites shooting each other using laser guns, some people mistakenly expect that the chatbot technology can deliver results close to the fantasies portrayed in Hollywood movies. To make things more explicable, we first need this basic distinction: Transactional chatbots versus expert chatbots.

TRANSACTIONAL Chatbot is a simple interactive system delivered in a dialogue style interface where the main objective can be as simple as organizing calendars, booking reservations, helping check-out process, or presenting retail options.

EXPERT Chatbot is a sophisticated conversational system that actually engages in a dialogue with the user about the expert knowledge it embodies. The objectives are more ambitious such as help desk, tech support, employee assistant, or online advisory systems.

The Difference
The blue line in between these two categories of chatbots is actually not thin, if not very thick. The thickness comes from the scientific and technological challenges involved comparing one to another.

Transactional chatbots operate in a single dimension: Natural Language Processing (NLP). Using NLP, whatever the user says (or types) is to be detected in order to perform some form of transactional response. If the chatbot is designed to operate for a specific task, the sphere of language detection is nicely confined within a narrow band. As a result, some basic NLP technology would be enough to power these chatbots as long as the users’ dialogue do not diverge away from the chatbots main objective. Many messenger chatbots fall into this category, and some has real commercial future.

The situation with expert chatbots quickly gets much more complicated because they must operate at least in 2 dimensions. The second dimension, machine learning (ML), is needed to absorb content (expert knowledge) into the system. As shown in the diagram above, ML will also help the NLP capability (green line) with increased content and training. The problem here is that the conventional ML algorithms (like neural networks) are complicated, rely on data volume and quality, and are black-box approach. They are mainly an “engineering” approach to knowledge processing devoid of innovations in computational linguistics, semantics, and even in psychology. New approaches to ML are needed to fit this particular operation before expert chatbots can flourish.

Yet There is the 3rd Dimension
One of the largely misunderstood aspects of chatbots is the missing 3rd dimension: dialogue behavior. This 3rd dimension gives “life” to a chatbot which would otherwise act lifeless without any active speech or goal-oriented behavior.

To give you an example, if the purpose of the chatbot is to be a sales person, then it should have a salesmanship behavior. Behavior modeling is another untapped territory that involves disciplines as far away from engineering as it can be: psychology. Game-theoretic, behavioral algorithms can actually provide a framework for NLP and ML processes to fill in the dialogue engine with knowledge and language detection.

We, at exClone, are pioneering this combination using an umbrella term “digital cloning technology” that can be accessed via our Creators’ Platform coming out soon. The good news is that chatbot developers will not need any coding, or even experience in any of these disciplines. Although “advanced” options will be provided for those technical geniuses, the platform will aim enterprise clients who want to solve the problems of communication quality, scalability and cost across enterprise operations.

The Last Category: Purposeless Chatbots
I would originally call this group of chatbots as “useless chatbots” because they are. Unfortunately, there is an alarming rate of them being advertised (surprisingly) by the misguided PR departments of tech giants like IBM, Microsoft, Apple, and Google. Let me give you two examples, then you can fill the rest.

What is Siri? Apple adopted Siri, which could be a good move, but then deployed it without any clear “purpose” by hijacking an important button functionality. As a result, 9 out of 10 things you ask is not fulfilled by Siri. Siri does not ask you any question, it does not guide you to any specific problem solving. Why not deploy Siri with an actual purpose, even if the purpose is limited to, say organizing your calendar? A clear lack of vision (after Jobs) manifests itself by not even being a platform (unlike Amazon’s Echo), so that purposeful chatbots could have been launched from a Siri platform.

Another example is Microsoft’s Tay, now its derivative Zo. Using a pure engineering approach of machine learning, they are training the system using tweets to create a chatbot that has no clear purpose. What problem does it solve? If it is a social experiment, Zo will become an average teenager, with knowledge of teenager tweets, without any distinct behavior, and no contribution to society beyond chit chat engagement. Such projects will eventually come to a realization that knowledge representation, natural language, and human-like dialogue cannot be handled solely by an engineering approach, and such purposeless, “do-it-all” attempts will fall short.

Purposeless chatbots are destined to suffer from the lack of usefulness, and they will fail to fulfill a promise that has been advertised beyond reality. Although we are accustomed to see these companies shooting themselves in the foot, it is somewhat interesting to see that while they are promoting “learning” technology, they are failing to learn themselves not to hype the tech markets for short-term sensationalism.

Your DNA determines everything about you: eye color, height, body shape, skin type, etc. But it does not determine one thing about you. Your mind.

At birth, human brain is nothing but an empty storage tank with 30 billion neurons in it. In contrast to your wonderfully choreographed body with details from toe nails to hair thickness, there will be nothing special about this most important vital organ. The brain needs to be filled. It is a process. The process of learning and maturing via various life experiences results in the final description of who you are, and yet it continues to change in time with increasingly smaller amounts and slower pace.

You may wonder why it is important to know the DNA of your Mind (DNAM). Although it may sound like an original idea, it is actually nothing new. For example, tracking and profiling Facebook users based on their likes is some rudimentary form of DNAM.  Obviously, such a thing is perceived as a dark enterprise nowadays due to privacy concerns.

When we move from the present gloomy picture and imagine what can happen in the future, the meaning of DNAM may change drastically. If DNA cloning ensures the eternal continuation of your body, then DNAM may ensure the immortality of your mind, in a peculiar and exciting manner. The truthfulness of this statement very much depends on how DNAM will evolve from being just a commercial “profile” to something much spectacular.

Psychological studies have several, somewhat debatable, human personality theories. Creating a model for DNAM must use something like the  Raymond Cattell’s 16 Personality Factors. Marking them on 1 to 10 scale (either by measurements or self determination) shows your behavior such as reasoning, emotional stability, sensitivity, patience, and other factors as shown in the blue chart. Mathematically speaking, if we had Steve Jobs’ blue chart like this one, there could be another 20 million people out there with the same blue chart. As a result, psychological profiling is never unique enough to claim your DNAM.

The exClone process takes the blue chart into consideration by determining several factors such as dialogue behavior, curiosity, openness, patience, and eagerness to learn. All these traits easily reflect themselves in one’s speech patterns. However, the degree of accuracy in replicating your personality will always be debatable.

The classical approach to human personality modeling omits the role of a 2^nd important element, which we call it “expertise.” In the same scale of 1 to 10, now we can mark the level of knowledge in various fields as shown in the green chart. This list could be as long as it needs to be depending on each person. The expertise can be anything ranging from how to boil an egg to how to launch a nuclear missile. The blue chart combined with green chart will now have a better chance to depict a unique DNAM of Steve Jobs as well as you.

The digital cloning of human expertise, undertaken by the exClone project, attacks this basic problem by the creation process of exClones.  To make exClones useful to society, the main emphasis is given to the expertise part (green chart.) The expertise in various fields and subfields are entered into the system by the creator. To leverage the potential of organic growth of knowledge, an exClone continues to learn after his/her birth by following the personality traits of its creator (blue chart) by means of social conversations and by accessing the Internet sources.

The uniqueness of the green chart is in its identification and prioritization of knowledge. For example, between the two dentists who went to the same school, it would be impossible for them to have the same expertise in real life. Each would have a different clinical experience over time. This unique experience combined with the personality traits (blue chart) is what makes up the final definition of our minds and DNAM. The screenshot below shows how Micheal, the first exClone ever born, displays his expertise in a conversation with a somewhat nerdy attitude.

The exClone project is significant in its role to be the first comprehensive attempt to model deep artificial intelligence at a personal level. Should the computers we create have personalities and knowledge prioritization? The short answer is “absolutely yes”. Differences fill all the gaps, and avoid common blind spots. That is the power of group thinking and the corner stone of human civilization. The future of computerized human societies will be more successful with human-like variety as opposed to a single, “can do all,” generic, emotionless computers.