Enterprise IQ and Virtual Experts

In its simplest form, the Enterprise IQ concept assumes an imaginary brain of an organization where all know-how and expertise are gathered, then distributed at maximum scale so that the workers can utilize it rapidly and effectively. The AI application of Virtual Experts helps this vision to become a reality as summarized below.

Cloning Experts to yield Virtual Expert


Cloning experts refers to capturing the knowledge of an expert and being able to apply it when appropriate. The curation of the expertise may include documents, reference materials (such as books), articles, news feeds, conversation logs, and media sources like videos. The curator can be an expert person, or a group of experts. Personal choices and dialogue behavior can also be adjusted. The expertise captured by digital cloning is delivered to the end user by a conversational (chatbot) interface. An example is shown below for the particular expertise of Crystallography.

Teaching the virtual expert can continue after the deployment by allowing designated users to teach it via conversations. Such a cloning process makes the in-house expertise captured, preserved, and protected in case of experts leaving the enterprise. An enterprise can launch as many virtual experts as necessary to help its workers and/or customers, or launch a master virtual expert to handle all the subjects.

Expertise Accessible by the Masses


The most important function of a virtual expert is its scalability where 1000s of people (workers and/or customers) can converse with it simultaneously. The value is realized when critical questions are answered instantly without the need to talk to the human expert. The only alternative to virtual experts, today, is going through document stockpiles to find answers manually (or by rudimentary search engines which are notoriously ineffective).

Being able to access expert knowledge instantly via natural language dialogue is an enhancement to Enterprise IQ and improves bottom line.


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Cortana Out exClone In

recent article reports ” Come the end of January, it appears the Cortana app’s getting booted to the Microsoft assistant graveyard. At least poor Clippy will have some company now. That’s according to a support article Microsoft posted to several regional markets this week.” After Siri becoming a laughing stock, Cortana’s departure is not surprising. These “can-do-it-all” voice assistants are simply not delivering the AI promise. Their utility have been questioned with the exception of Alexa due to its clever commercial use attached to playing songs.

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Instead of “can-do-it-all”single voice assistant, exClone has just released an app (experimental) that is an ecosystem of virtual experts (chatbots). Starting with 8 examples, each virtual expert is focused on a different subject as designed by its owner. Contributors to the ecosystem can be anyone or any organization who can clone themselves into a virtual expert via exClone’s platform. The cloning process requires nothing but documents (Word or PDF) that contain the knowledge of potential conversation with the visitors. Contrary to “can-do-it-all” attitude, this environment offers number of virtual experts (conversational agents with voice). Once the ecosystem reaches its maturity, finding the relevant virtual expert will be easy via a category search function (i.e. HEALTH, LAW, ART, etc.)

Download the App

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Please download the exClone app from the links below. If you already have it, you should update it with the latest version. Please bear in mind, this is an ALPHA test.

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New clones/virtual experts are added regularly without the need for you to update the app. Notifications will highlight the newly added clones and their content changes.

Practical AI: Deep Learning Costs Reduced 100 times via Instant Learning Yielding Industry Level Performance

In reference to my earlier post about the exClone Case Study reported by Forbes, the future of conversational AI signals a shift from data-driven, expensive, and lengthily methods to knowledge-driven, affordable, and fast methods. In short, it boils down to Deep Learning (DL) versus Instant Learning (or its derivatives).

The two different approaches are summarized in the diagram below.

Linguistic NNET

Practical AI, the right side of the diagram above, uses the existing knowledge on linguistics, ontological semantics, psychology, neuro-sciences, and other cognitive sciences. The resulting hybrid method reduces the load of a machine learning algorithm, turning it into a mere knowledge absorption step from documents (similar to how we read and learn). These documents are about the subject matter of which the conversational system talks about, but nothing more. Simplicity and speed give it the name, instant learning.

Conventional AI, the left side of the diagram, dismisses most (if not all) the existing knowledge, and assumes to solve everything by data crunching. The required data set is assumed to contain examples of all cognitive skills in language processing which is an overly optimistic (if not impossible) expectation.

The Cost Issue

When you have to acquire, validate, and process data, the costs can sky rocket. In my earlier article, the example of Morgan Stanley’s AskResearch system, which is reported to bring answers to somewhat mediocre questions like “What is Morgan Stanley’s standpoint on gold?”, took 1 year to train the system. Obviously, the costs associated with such a process, and data services, would wind up in 7 figures. Not to mention the cost of the required staffing, and the repeating cost cycle in every correction attempt.

In contrast, the practical AI example of exClone’s deployment of Virtual Experts for enterprises can cost 100 or 1000 times less. Because, no data is utilized, no AI staff is required, no coding is necessary, no long training cycles are endured. The only required effort centers around editorial, document management, and curation.

If engineering means finding the most practical and affordable solution, then the sole DL application to NLP may be the worst engineered systems to date!

The Difficulty Scale of NLP

One common problem I see in the DL community is the unawareness of the difficulties of different NLP problems which reminds me of the saying “if the only tool is a hammer…” I made a conceptual scale as shown in the diagram below. Some may argue the ranking of few items. Nevertheless, the exponential nature of the complexity involved in these different problems is indisputable. For example, if your problem at hand is Text Labeling, you are light years away from handling Abstraction. Accordingly, if a DL approach proves successful in the former, it does not mean its readiness in the latter. The differences are huge.

NLP Scale

More drastically, data-driven methods have inherent limitations to handle higher level NLP problems no matter the size of the corpus. At the lower end of the scale, most DL applications can be duplicated by statistical linguistics (such as in sentiment analysis) which begs the question “how much better is the conventional AI?”


Data is expensive and risky. Data driven methods make sense to attack problems of high level complexity where (1) the underlying principles are not well known, such is in stock market analysis, or (2) the complexity arises from multi-body nature of the problems, such as in atmospheric modeling or image processing. Applying DL to NLP is treating NLP like atmospheric modeling. More sensible approach is to utilize available knowledge at its maximum, then apply machine learning for the remainder of the problem. This requires innovation of hybrid systems. exClone’s instant learning technology is one good example, however more hybrid solutions are expected to emerge in the near future.


This article is brought to you by exClone, a Virtual Expert & Chatbot technology provider via its proprietary Instant Learning technology.

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exClone Launches Virtual Experts at Black & Veatch to Enhance Knowledge Utilization by Artificial Intelligence


NEW YORK–(BUSINESS WIRE)–Today, exClone Inc. announced the launch of its AI-based virtual experts at Black & Veatch as an enhancement to enterprise knowledge capture, utilization, communication, and search functionality.

exClone’s virtual experts open a new, unprecedented window of communication between experts and employees in an enterprise. In addition to the documents of expertise written in the conventional manner, experts now may be represented virtually through a conversational AI system (chatbot) where the embedded knowledge comes from exClone’s platform that converts documents, such as MS Word or PDF, straight into chatbots. The conversational interaction delivered by such virtual experts helps workers access critical knowledge in a more productive way than by other conventional means such as search engines.

exClone’s technology of converting documents into chatbots does not require any coding, availability of large data sets, long training cycles, or experience in AI. After deployed, the technology also allows “on-the-fly” teaching of virtual experts through conversations undertaken by designated teachers. As a result, virtual experts remain dynamic sources of knowledge updated as often as needed without a redeployment process. Workers’ unanswered questions beyond the scope of the deployed knowledge may be quickly answered by designated teachers thus introducing a new social connection and communication paradigm across the enterprise.

Alan Young, the CEO of exClone, said, “If messaging tools can be used to get answers from friends, we should be able to get answers from virtual experts embedded with knowledge from enterprise documents.” He added: The connection between experts and workers in an enterprise is elevated to a new dimension with virtual experts, and we are proud to lead this new paradigm with visionary companies like Black & Veatch. “We’re excited to deploy and leverage this new connectivity tool for our professionals and capitalize on the efficiencies we believe it will bring to our business,” said Mike Etheridge, Global Chief Engineer for Black & Veatch’s water business. “This tool will help our professionals to find information quicker and harness knowledge and expertise from our global workforce to drive efficiency and effectiveness in new ways moving toward the future.”

About Black & Veatch
Black & Veatch is an employee-owned, global leader in building critical human infrastructure in energy, water, telecommunications and government services. Since 1915, we have helped our clients improve the lives of people in more than 100 countries through consulting, engineering, construction, operations and program management. Our revenues in 2017 were US$3.4 billion. Follow us on bv.com and in social media.

About exClone
exClone, Inc. is a New York City-based technology company specializing in virtual experts, chatbots and conversational AI systems to enhance enterprise knowledge utilization, communication, and search functionality.


Turn your MS Word, PDF Documents Straight into Chatbots: Virtual Experts


It is finally here. You can now convert your MS Word/PDF documents into Chatbots and Virtual Experts with exClone technology. No coding involved, no data sets to mingle with, no long training cycles, no experience in AI. This is the highest level of automation in the market today where all AI functions are tucked under the hood, invisible to a chatbot builder. As a result, the path between an expert and his/her virtual version involves no other process/developer in between.

A Chatbot Learning from Documents Becomes a Virtual Expert
Siri, Cortana, Hey Google, or Alexa, lack any expertise they can chat about. If a question is asked with some complexity, they point you to search results. exClone’s process yields a virtual expert where questions are answered about the particular subject. Here is an example, Frank, who is a virtual expert on crystallography solutions using Phenix software system. Frank was built straight from MS Word documents in a single step process (we call it Instant Learning). The documents were written and curated by a real expert.



On-the-fly Learning by Conversations with Teachers After Deployment
In addition to learning from documents for deployment, exClone offers teaching virtual experts on-the-fly through conversations by designated teachers after deployment. This has a number of advantages one of which is the ability to update the system with new or modified knowledge anytime without the need for re-deployment.

Answering Questions at the Concept Level


The most powerful feature of the exClone system is its ontological answering capability where words of the question and its answer don’t match, but the concepts they refer to do match. The proprietary machine learning algorithm (Instant Learning) is able to achieve an almost human level of understanding when answering questions. This means that the system can handle hundreds of various forms of a single question, which points to the same meaning, thus can bring the same relevant answer. This is the ultimate goal in making computers understand language and learn knowledge correctly.

What does this Mean for Enterprises?
Documents in the world of enterprise are the main asset to encapsulate and preserve organizational expertise. Being able to create virtual experts out of these documents easily, with no specialized effort, means that it is now scalable and inexpensive to launch enhancements to enterprise searchhelp deskcall center, and training systems.

With virtual experts, the workers and customers of an enterprise can access critical information via conversational (messaging) type interface, rapidly, accurately, and efficiently. Such an efficiency directly improves bottom line.


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Machine/Deep Learning to Include Evolutionary, Experiential, and Instant Learning Components

In our quest to understand and replicate the cognitive capabilities of the human brain, the AI discipline has focused on the subject of learning rather unevenly. Regardless of the non-scientific reasons, I felt compelled to raise awareness of the most important 3 components of learning mainly distinguished by the “time” factor. Evolutionary, Experiential, and Instant learning. Without taking into account all 3 forms of learning, it is unlikely to achieve ambitious goals in AI regardless of how much computing power, or data collection is available to us. The diagram below summarizes this concept.



When a wildebeest calf is born, it takes only a few minutes for her to run fast enough to escape from predators. It is obvious, evolution has hard-wired some of its learning in the blue print of a new born calf in terms of motor skills. Evolutionary learning is also obvious from the distinct regions of a biological brain which is almost always utilized in a predetermined manner. We can argue that human specie has developed a unique neuron structure suitable for language and logic in response to survival pressure through evolution. If this is true, then the idea of “linguistic neuron” could be what separates us from animals.

Has human specie developed language sensitive neurons in the brain through an evolutionary process so that some neurons take on linguistic roles?

Evolutionary learning is like a factory setting, initial condition, or starting assumptions of any model we want to build for specific learning task. This initial condition step is what is missing in today’s deep learning methods.



Once a biological system is born, experiential learning starts along with the growth of the brain. In case of humans, many activities like walking, speaking, learning how to ride a bicycle, or playing piano fall into this type of learning where repetition is the key. Today’s deep learning methods heavily focus on this model using artificial neural networks. Unfortunately, the network types and learning algorithms do not start from any biological inspiration, and there are no initial assumptions targeted to a certain type of learning. Consequently, most applications turn into a nonlinear mapping exercise rather than modeling a real learning process.



One of the most obvious, yet mysteriously ignored form of learning is instant learning. In case of humans, cognitive activities like reading, conversing, deducing, summarizing, abstracting, and conceptualizing require very small number of iterations to learn. If you ask directions on the street, repeating it twice would be more than enough to learn it. If we are studying a subject, we may have to read it a few times. That is instant learning. You cannot replicate this type of learning using today’s deep learning methods. Assuming the evolutionary learning has yielded a hard-wired design of linguistic neurons, we are experimenting with instant learning at exClone with promising results. In applications involving natural languages and human like dialogue, we believe that the 3 forms of learning is essential to complete the picture. More details are in my previous article about instant learning.

One of the examples that I have come across recently is the RBF learning which is another form of instant learning without mentioning the arguments described above. Their point of departure in RBF Learning is the industrial demand for instant learning systems.

If you know a new learning algorithm relevant to the arguments above, please mention it in the comments below.


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eTravelSafety Signs on exClone’s Virtual Expert AI Technology


eTravelSafety, a UK firm based in Hereford, who provides corporate travel safety training and technology solutions, have signed on the Virtual Expert technology offered by exClone. The project aims at providing the first ever interactive travel safety training to their user by a virtual expert (an Artificial Intelligence application) who is able to deliver relevant training videos and answers to questions on demand. This approach of utilizing cutting-edge technology is a visionary step where a high-degree of interactivity makes a competitive difference. The product launch will be announced soon.

James Barton, CTO of eTravelSafety says “Working with exClone on our new first-in-the-market Travel Safety Virtual Expert represents a quantum leap in Travel Safety training, allowing travelers to quickly access and interact with powerful training in a way that meets their needs. After looking for partners, The exClone platform provided us with the very best in AI technology, and the best partners to support our desire to provide Travel Safety to everyone”


We all know virtual assistants like Siri, Alexa, Hey Google, Cortana, etc. Virtual assistants do not contain any specific expertise, nor can they converse about any particular knowledge. Their tasks are rudimentary in the category of pointing, arranging, organizing, playing songs, or scheduling. Virtual Expert is the next step-up where the conversational AI system can talk about a particular expertise. It is technologically much more challenging than its counterparts.


A corporation may have a specific expertise captured in a bundle of videos, much like eTravelSafety do. There may be tens of thousands of answers embedded within these videos that a user can benefit from. There are two distinct advantages of virtual experts in such cases.

  • The BUNDLE EFFECT: Ability to locate the most relevant video from a bundle in response to a question asked by the user.
  • The INTERACTIVE VIDEO EFFECT: Ability to answer a question promptly relevant to the content presented in a video.

An example of a Virtual Expert is shown below where a video (on the left) is wrapped with a conversational interface (on the right) and loaded with knowledge that can (in some cases) go beyond what is included in the original video itself. Not only can this system bring answers from the video content, but it can also suggest other videos more appropriate for the question.



Interactivity is the future. Elevating any content to the level of instant conversational engagement holds the obvious key to competitive edge.

You cannot ask a question to a video, slide deck, document, image, diagram, podcast, Web page, etc. But you can ask to its virtual expert as an interactive wrapper.


Creating a virtual expert is mainly an editorial process via the Instant Learning technology offered by the exClone platform. The process involves curating documents of expertise and rendering them into the system. There is no coding involved. Training the system is a single step machine learning process using the content only, devoid of large data requirements.

But the creation process can continue after deployment. Designated teachers can chat with a virtual expert to add more knowledge without re-deployment. This allows organic growth and instant modifications/additions to the system.


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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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How does IBM Watson Compare to Google’s Hype of Deep Learning for NLP?(10,843 views)

Why Deep Learning is Not a Good Fit For Chatbots: Combinatory Explosion Problem(4,465 views)

Why Deep Learning and NLP Don’t Get Along Well? (6,596 views)

Can Machine Learning Use Knowledge instead of Data? Deep Cloning vs Deep Learning (5,823 views)

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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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