Instant Learning vs Deep Learning

In the context of conversational AI, instant learning refers to a cognitive function we are too familiar with: learning instantly from conversations.

If someone tells you “beware of the dog when you enter the yard“, your brain will process it immediately, and you will absorb that knowledge. Once learned, you may warn another person saying “be careful, there is a dog in the yard.” Why is it so difficult to teach a computer to do the same? Actually, instant learning technology is already here as explained below.

Instant Learning Example

ALIXD is a conversational system that switches to learning mode by entering a password anytime during conversation (see the bottom of the article for testing ALIXD) As shown below, ALIXD learns new knowledge about Bitcoin and Ethereum from its human teacher.

The important point here is that the system will bring this answer to approximately 960 different semantic variations of a relevant question, which is computed by a simple equation using ontological parameters: V = T x N x E x I where V is the variations, T is the question type (typically 20), N is the number of onomasticon (6), E is the number of events (2), and I is the number of instruments (4). Both N, E and I include words in the answer as well as the question entered by the teacher.

Being able to bring an answer to hundreds of different meaningful variations of a single question is highly similar to the human brain’s cognitive skill in instant learning during conversations.

Two examples (out of possible 960) are shown below. If there is no other knowledge entered into the system, these variations are easy to track. If some new and relevant knowledge is added, then the system will pick an answer that is semantically best match to the embedded meaning.

Mechanics of Instant Learning

The departure point of instant learning is to represent knowledge by a group of linguistic neurons as shown below (left). When another knowledge is entered, a new group of linguistic neurons appear and they connect (right) based on ontological properties. If a property is identical (such as the same event), then neurons fuse into one. As more knowledge added to the system, a vast network of ontological relationships emerges. Entire documents can be learned in a single step of fusion process. This approach allows answering questions with great ease and deducting new knowledge by logic resolution. The inner workings of this method is proprietary.

Compared to Deep Learning (DL)

There is nothing “instant” about deep learning as the name implies. In short, the DL approach to conversational AI goes against our natural life experiences. For example, the instant learning example shown above cannot be replicated by DL.

First of all, DL method requires a vast amount of data (more than a Q&A pair) to climb the ladder of language proficiency. Then, a training process and convergence are needed. After a time consuming process, a DL network can be claimed to function as intended, however any new addition of knowledge would require expanding the training data set, and re-training the network.

While current DL methods are producing impressive results in image processing and kinematics, there are serious problems in application to conversational AI mainly caused by the uniformity of neurons (no neurons with linguistic role), limitation of vector space modulation, and statistical bias. More explanations can be found in my previous articles listed below.

Instant learning comes from knowledge science whereas deep learning is rooted to data science. Considering the pyramid of hierarchy, knowledge science works from top to bottom whereas data science works from bottom to top. Going from bottom to top in this hierarchy suits well for image processing, for example, yet it becomes impractical and misfit for natural language processing at least for the current approaches of deep learning.

Instant Learning API

Instant learning enables conversational systems (chatbots) to continue learning after they are deployed. This allows organic growth of knowledge by designated teachers, or sometimes by the end users. ALIXD API can be integrated into any conversational system, and will be available soon. Interested parties can contact me for notification.


You can test it at this link by entering temporary password 0014. Note that if other people are adding knowledge about the same subject, you may find the system more versatile.


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Learning by Conversations in Chatbots, and Why it is Important


I have published several examples of chatbots with embedded expertise (Virtual Experts) under the exClone umbrella. This time, the chatbot I want to talk about is ALIX, which has no embedded expertise, but she has something very unique: Social Learning capability. With this capability also comes curiosity and emotions, which are essential parts of a cognitive picture.

Chatbots which can learn instantly from social conversations will be one step ahead in the realm of AI


Social learning is the capability of teaching a chatbot new content by having a conversation. As seen below, ALIX will ask to learn if she cannot answer a question. In this case, ALIX learned the answer to the question “why does spring bloom aggravate my allergies?”


The system will simply produce the answer if the same question was asked again by anyone using the system. However, this is not the extent of the learning occurred in the system. To understand the depth of learning in ALIX, there are four cases shown below.

(1) The original query is asked in a different way using different word senses

An example is shown here where the original query WHY is changed to WHEN and the words (IRRITATE, SINUSES) are different. The system is able to make the associations and bring the answer it learned previously. This expands the answering capability of the system many folds since the users will rarely be able to replicate the original question.


(2) The new query is referring to the knowledge embedded inside the answers previously taught, no question matching involved

More importantly, ALIX is able to analyze ON-THE-FLY the previously entered answers to bring the relevant one without matching to the original query. In this case, the query has no matching segments to the original question. As a result, the content taught to the system is utilized to the maximum extend in answering questions.


For novice readers, it is important to point out that all other systems in the market today (Google, Wikipedia, Quora, etc.) are basically “question matching” systems, and none of them have on-the-fly capability to analyze their content embedded in answers. Not to mention, none can be taught, nor can engage in dialogue.

(3) Learning is not limited to Questions & Answers

ALIX is able to learn from regular statements (non-question) as shown here when she has no relevant knowledge to chat about. This further promotes the organic growth of knowledge by contributions from the end users. As more knowledge captured by the system, a two way dialogue about a certain subject becomes more frequent in a fashion similar to two human beings exchanging opinions.


(4) Curiosity and Self-awareness

As part of an essential element of learning, ALIX gets curious about certain subjects and asks to learn more. In a way, ALIX is aware of her lack of knowledge in such topics. In the example shown here, ALIX had not heard anything about Star Wars, and asking to learn about it.


Currently, curiosity is triggered in ALIX for onomasticons (Proper names) to manage the memory load, which is a temporary limitation. ALIX also exhibits some basic emotions like joy and annoyance (she may quit if the conversation is fruitless.)


The learning function described above can be open only to a group of designated users (teachers). In an enterprise set up (such as help desk), or in any other Virtual Expert application, the initial loading of content (learning by reading) can be augmented by social learning (learning by conversations).


Social learning allows chatbots to be updated with new or modified answers instantly (on-the-fly) after they are deployed.


ALIX is, by no means, at the level of human learning, however a few milestone capabilities are accomplished, probably the first time ever. While we will improve ALIX’s understanding capabilities, an important next milestone will be generating new knowledge from existing knowledge by reasoning. This is depicted in the example in which the system figures out why United Airlines shares fell by examining other evidence and by logic inferencing. Accordingly, the question “Why did United Airlines shares fall?” will find an answer from the new knowledge generated. We will make an announcement when this milestone is achieved.



The technology behind ALIX is a proprietary machine learning technique that utilizes knowledge directly (knowledge science as opposed to data science). More about this approach was published in the article titled “Deep Cloning vs Deep Learning” and was further elaborated in another article titled: “Can Machine Learning Use Knowledge instead of Data?”

In creating virtual experts, the same backbone technology drives “learning by reading” from documents curated by experts, and “learning by conversations” with the end users (of all or designated) after deployment.

Knowledge-driven Machine Learning replaces decades-old technology of question matching and indexing

Machine Learning by Reading, a Path to Paul Allen’s Common Sense AI


A recent article about Paul Allen’s project Alexandria mentions the need for computers being able to have common sense. This means computers reaching somewhat human level cognition, which is a super ambitious goal. This level of achievement is most likely infeasible in the short run, and funding judgments for this goal is encouraged by the availability of super computers and vast amount of data. However, the assessment of this goal and the possible routes to success require us to define a measurable (or perceivable) scale. Hence, let’s start defining such a scale.


The easiest scale to follow this argument is the basic definitions of data, information, knowledge, and logic as shown here in the figure below. Detection of the difference among data creates information. Same hierarchy applies to knowledge, logic, and common sense reasoning. If there is no difference detected, there can be no information, knowledge, logic, or common sense reasoning. This is the very basic premise of processing intelligence. For computers to operate at the “common sense” level, they are required to resolve (1) common sense resoning from logic, (2) logic from available knowledge, (3) knowledge from available information, and (4) information from available data. The question is how can we shorten this path for a feasible solution for common sense reasoning in the foreseeable future?


Methods of data science, such as deep learning, are useful for anaylzing data to extract new information. These methods can sometimes go one step further to produce knowledge with limitations (For example, stock market analysis using data-driven methods can never justify the knowledge produced). There is a natural barrier of conversion from information to knowledge by sheer data analysis. Knowledge science is an entirely different realm. The difference between data science and knowledge science is as striking as the difference beteen Newtonian physics versus Quantum physics.

The challenge of knowledge science is to deploy correct models of knowledge, whereas data science crunches numbers without assuming a model.

The attractiveness of “no model” in deep learning, for example, causes a misconception such that it can be applied to higher domains (i.e., CNN applied to any problem). One particular direction is natural languages where tensor flow and vectorized words are assumed to cross that barier. One of my earlier articles titled “Why Deep Learning and NLP don’t Get Along Well?” explain why this is nothing but wishfull thinking.

As shown in the figure above, knowledge-driven machine learning will undoubtedly be the shorter path to reach common sense reasoning. Because the existing knowledge (millions of books for example) can be processed by a computer just like reading them to learn. Here is another article, titled “Can Machine Learning Use Knowledge instead of Data?” that sheds light to this subject.


Knowledge-driven approach does not treat sentences in natural language as data. Instead, it assumes them as part of its initial model. The basic premise is that the initial model assumed for knowledge representation can be corrected iteratively as more sentences are processed. This hypothesis is supported by our own human experiences as our understanding improves by reading more books.

The idea of lifting knowledge from a source curated by a human experts (authors), and implanting to a computer is, in one sense, similar to cloning knowledge. Hence, the method is called Deep Cloning, and explained in this article titled “Deep Cloning vs Deep Learning“.

The figure below shows one of our experiments with deep cloning for logic resolution. The system resolves the question “Is Mike in good shape?” by following a path through its knowledge representation from the sentences acquired earlier. As more sentences learned, the logic improves, and it may strengthen of reverse its conclusion. This demo will be open to public in coming months.


It will clearly be a very long path for implanting common sense reasoning to a computer. The knowledge-driven methods offer a shorter path to reach the goal while subject to more challenging and creative solutions.

If we can make computers read and learn like we do, then there is a good chance to expect higher level cognitive functions from them in the near future.


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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.


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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.



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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“.


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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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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.

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

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For exClone’s Chatbot Platform, click here for free trial via LinkedIn access.

Join our CHATBOTS linkedin group

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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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For exClone’s Chatbot Platform, click here for free trial via LinkedIn access.

Join our CHATBOTS linkedin group

Follow exClone in Linkedin or on Facebook