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


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.


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.


  • 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.


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.


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.

The Thick Blue Line Between Chatbots

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.

LiveTiles Presentation of Chatbots by exClone

We had an exciting gathering last Friday at the LiveTiles office to explore future directions via LiveTiles platform, digital workplace. LiveTiles offers a unique tool to curate and create content that is relevant to vertical needs of an enterprise without any coding requirement. Now, through the same channel, artificial intelligence applications can reach Sharepoint users one of which is exClone’s expert chatbots.

We have demonstrated Wendy, an example of employee assistant that can be launched by any enterprise using their own content. The example included simple interaction of Wendy bringing health benefits information and answering questions. Wendy’s expertise can include health insurance policies as well as all other different benefit information.

There is literally no limit to the type of content that can be curated as part of Wendy’s expertise. For example, a derivative of Wendy can be created to include training material, compliance, in-house surveys, and employee testing. One of the most important properties of exClone is to allow chatbot creators to develop a chatbot in a single-step machine learning process. Similar to LiveTiles feature, chatbot creation with exClone does not require any coding or any working knowledge of AI. This is how Wendy looks like through LiveTiles platform.

More about Wendy will be announced soon. Stay tuned for an exciting future!


Chatbots Obey the Two Principles of the Human Brain: (1) Laziness, (2) Stimulus Junkie

Let’s start with the laziness aspect. If I flash two pictures in front of your eyes in a split second, you will recognize one picture instantly, and you will have no clue of the other. Guess which one is which?

For evolutionary reasons, the human brain’s cognitive capacity is largely reserved for image recognition to detect dangers instantly. Obviously, a tiger would not send you a text message before attacking; therefore, “reading” is not a biological priority. Since humans started to read only for a few thousand years, we are not yet evolved to balance the picture above. As a result, “reading” is a painful and tiresome activity. We all know this from school days. Hence the saying “a picture is worth thousand words.”

Now the same comparison can be made with these two images. The image replaced by tiger is still not as easily recognizable, however, it is much easy on the eye. And the most importantly, it promotes focus that is one screen, one place, one single action for interaction. The reason for messaging platforms to be so widespread and popular is this basic principle of FOCUS that plays into the hands of a lazy brain. Probably, half of the messaging activity includes pictures and videos, satisfying the hunger of a lazy brain through this focused interaction.

The second principle is that the human brain is a STIMULUS junkie. In a boring environment, a human brain will always steer toward something more exciting. Curiosity and learning have strong ties to the evolutionary instincts of survival. It is “in our nature.” Stimuli can now be delivered instantly by mobile devices. Chatting/texting with a friend on a mobile phone while socializing with others has recently become a widely acceptable form of social interaction. Everybody silently agrees that we all need to be stimulated even during the short, dull moments of our social gathering. It may actually improve our social relationships since we no longer have to endure boredom when we get together.

If people have already chosen chat/text as one of the most precious priorities in their lives, then why not use the same tool (Chatbots) to interact with computers, databases, websites, machines, and even with books?

That is the point of departure of this new wave of realization across the tech world. The only problem, though, is that chatbots are not as easy to develop as many people assume so. It requires the culmination and curation of machine learning, natural language processing, and the psychology of human dialogue. These are not easy skills to deploy, and the market will eventually filter out its natural selection of the fittest. Chatbots are here to stay and occupy our lives in the next decades to come.

exClone Chatbot, Debate Guide, on The Wall Street Journal’s Politics Front Page

exClone’s chatbot, called Debate Guide, which is developed for The Wall Street Journal, has made it to the Politics front page last night following the 1st presidential debate. It is titled as “DEBATE DATABASE” positioned on the right top corner.
WSJ Front Page 9-28-2016The exClone, Debate Guide, is an example of how chatbots deliver a simple conversational search function. It has received over 60,000 conversations so far, showing high engagement rate. We will report conversational analytics of this operation after the election in November 8th, 2016. You can try Debate Guide and examine what the presidential candidates said during prime-time debates.


6 Reasons Why Chatbots are the Next Gold Rush in Tech World

The term “chatbot” is on its way to become a household concept sooner than anyone has expected. Although chatbots have been around nearly three decades, their promise have just recently started to accelerate in the media. The emergence of mobile devices equipped with voice recognition apps contributed to this upsurge. Also, a more diluted name “digital assistant” helped people’s perception. However, the user acceptance bar is still very high with chatbots unlike many other applications. Is the science and technology finally caught up to pass this bar? Here are the six reasons that summarize the gold rush nature of the chatbot business.


ex1Talking computers have been in the movies since the first few episodes of Star Trek. An intelligent computer, that talks with a sexy voice, knowing everything possible to know, advising us what to do, has been the central theme of almost every science fiction movie. Finally, we hit a point where it has become such an expected functionality, that less than a perfect talking robot is unthinkable. Hollywood effect is real, and shapes consumer expectation perhaps beyond what is usually acceptable from a computer program. We are very accustomed to Windows OS messing up in the middle of an important work, but we will not tolerate less than a perfect chatbot. WE ARE LONG OVERDUE FOR SOME REAL LIFE EXAMPLES OF TALKING COMPUTERS THAT DELIVER SENSIBLE CONVERSATIONS, THANKS TO HOLLYWOOD.


dewey2Do you remember Microsoft’s Ms. Dewey? This chatbot with nice graphical interface was released almost a decade ago and failed quickly afterwards. Perhaps she was too early for her time. Nevertheless, Microsoft showed its soft underbelly by proving that they could release high tech products without actually understanding the challenges behind the technology and its underlying science. A decade later, Cortana is not any better, making people wonder what did Microsoft learn during the last decade? Almost to the point, another blunder with Tay Twitter app does not seem to dampen any spirits in Microsoft. Chatbot technology has become a shameless trial-and-error game, maybe because Apple could stomach Siri, a seriously limited gadget, a joking material. We can add Amazon’s Echo to the list, and now Facebook’s API release. At least, Facebook is moving more cautiously by spreading the responsibility to independent developers. IBM’s stake in this game is only to be seen in TV commercials. There is no public launch of any chatbot to avoid public scrutiny. Google is also experimenting with chatbots without a convincing public demo. Despite the blunders and overpromised hype, the entrance of the big players into the Chatbot game is a positive development. SOME OF THE BIG PLAYERS ARE TAKING THE RISK OF PUBLIC MOCKERY AND DAMAGE TO THEIR BRAND JUST TO BE IN THE CHATBOT WORLD. That means the gold rush is on!


We may have landed on the moon several times, but the state of the customer service today is based on sheer human labor. The nature of this job is boring, repetitious, and stressful, not much different than the rowers in this Charlton Heston movie. If we round up helpdesk, tech support, and corporate training industries, we are talking about $50 billion market segment that is a sitting duck for computerized automation. Only if the computers can talk and solve problems adequately.


THE CHATBOT USAGE IN CUSTOMER SERVICE MARKET IS LESS THAN 0.01% INDICATING AN UNTAPPED COMMERCIAL TERRITORY. The current suppliers are half-dozen startups none of which is worth mentioning at this stage. There are, however, chat-line providers that connect visitors to human agents. Obviously, chatting live with human agents does not change the human labor requirement, its mathematics remain the same. Therefore, we should not confuse chat-line technology with chatbot technology although they can complement each other.


hiltonThere is an unstoppable wave of robotic products coming to the market. Led by Japan, and South Korea these robots can do incredible tricks, except for less than decent conversation skills. CHATBOTS ARE A NATURAL FIT TO ROBOTICS. Therefore, a skillful chatbot technology can flourish in this market segment. You may run into Connie, Hilton’s new robot concierge with primitive skills. IBM’s Watson was not impressive with this example, yet it still showed us what to expect in the near future. Then, there are vehicles that can talk to its driver. In-car voice recognition still has some way to go. However, a few automakers are taking the lead in creating in-car interfaces that are easy to use. Among the favorites are Acura and Honda, Infiniti, Mercedes, and Ford. It would make sense to keep your hands on the steering wheel, and eyes on the road while you talk to a chatbot for various purposes. But this is only the opening act. There is a huge realm of industrial machines. Operating each one of them require expertise and knowledge, and chatting with them could improve safety and enhance efficiency. This list goes on and on. Bottom line, every machine can talk to humans sometime in the future if it makes commercial sense.


vera11It all started with a Website concept in 1990s. A person could have his/her own Website. Then, it was automated by MySpace. Then came the personal blogs. Social networks like Facebook and Linkedin, followed by YouTube and Twitter, they all presented a new form of digital presence of one’s self image on the Internet. Now, what if you had your own chatbot?

Your chatbot could represent who you are, your skills, perhaps the products you sell, services you offer. Is that possible?  The answer is yes, of course. CHATBOTS WILL BECOME THE ULTIMATE FORM OF SELF EXPRESSION, AND PERSONAL PROMOTION. The only stumbling block on the way is the accuracy of chatbot technology and ability to automate creation. Personal chatbots are likely to appear in the celebrity circles first, then spread to professionals like lawyers, doctors, dentists, and financial advisors. The commercial aspect of this development is strongly related to the c-commerce potential, which is my next point below.


Sending messages has become a landmark behavior in the timeline of human evolution. Billions of messages are sent daily across the globe. Facebook Messenger, Google Chat, Skype, Instagram, WhatsApp, Slate, and many other platforms allow sending and receiving messages, none of which has been commercialized. CHATBOTS ARE THE NEW SALES CHANNEL AND DEFINE C-COMMERCE.

Facebook’s Zuckerberg recently said “You never have to call 1-800 FLOWERS anymore” referring to Facebook’s Chatbot APIs mainly targeted for ecommerce and retail operations over the social network. Perhaps, we will remember Facebook down the road as being the visionary company to open up this new channel.

Out of 6 reasons listed above, the C-commerce virtue of chatbots is perhaps the strongest argument for expecting a gold rush. Sales dialogue is relatively easier for chatbots to handle, and there is money in the electronic commerce world floating back and forth. C-commerce is likely to be the fastest realization of ROI in this segment.


This article is brought to you by exClone.com, a new chatbot technology provider. You can follow exClone in Facebook, and LinkedIn. To follow chatbot related discussions and news, please join CHATBOTS group in linkedin.


What is the DNA of your Mind?

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 2nd 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.