Cloning Chatbots for Education

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In this context, cloning is an advanced form of impersonating where the chatbot can talk about the person’s life experiences and his/her expertise as curated by the chatbot maker. Compared to impersonating a person just using his/her image and name, cloning is obviously more involved and more challenging. As an example, you can chat with Abraham Lincoln and see how it was developed via one-shot machine learning technology with no-coding requirement. This chatbot uses Wikipedia content as its main source of conversation.

As one can easily deduce, all historical characters can be cloned into chatbots for educational purposes. But cloning goes beyond that as it allows creating chatbots of teachers themselves.

Top 6 Reasons Why Cloning Chatbots are Inevitable Tools for Education

  1. Control: Interactive content gives students much more control over what they want to focus on.
  2. Fun: Talking/messaging/chatting is always more fun than just reading.
  3. Ease: Use of small screen devices are ideal fit for chatbots which add to their educational role.
  4. New Teaching Methods: Chatbots can be a great summarization tool offering students main points to remember and option to dive deeper. Various new teaching strategies can be implemented.
  5. Creativity: Creation of chatbots can also be an educational experience.
  6. Feedback: Conversational analytics obtainable from chatbot interactions provide valuable clues to teachers as to how students learn, or fail to learn.

Profiliration of Chatbots Require Editorial Platforms

For chatbots to take a serious role in education, their development and profiliration must be fast and effective. Here are the three most important requirements for such a progress:

  1. No Coding: Chatbot creation should migrate from a coding effort to an editorial effort. This will enable students and teachers to develop education chatbots by curating content only.
  2. No Corpus Training: Underlying technology should not require large corpus training, and no experience in AI. One-shot machine learning techniques must drive these platforms processing the content for chat interaction while working silently in the background.
  3. Effective Communicator: Chatbots created for education must be effective, being able to answer improptu questions and offer topics of discussion. Although no chatbot today is expected to match human level dialogue, the educational effectiveness can be achieved by presenting chatbots for the specific goals they are designed for.

If you come across cloning/impersonating chatbots, please drop a note below. We may create a list of educational chatbots here.

How I made Abraham Lincoln CHATBOT in Less Than 10 Minutes

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In our quest for turning static knowledge (documents) into interactive knowledge (chatbots) via the chatbot Platform, we have experimented creating a chatbot from scratch to completion. The main question was, how long would it take? We first downloaded Lincoln’s content from Wikipedia (16,000+ words), cleaned the content, made editorial changes, and curated some images. Then, it took less than 10 minutes to create a fully functional chatbot through the platform. Its one-shot machine learning technology (learning by reading) took less than 1 minute, and the previous 9 minutes were spent on entering the content into the platform. You can test this chatbot at this link and examine how it was developed.

It is a fully functional chatbot with short-term memory, answering impromptu questions any time, topical suggestions, detecting user behavior, and providing infinite speech. Its knowledge is limited to what the historians said as compiled in the Wikipedia page.

WHY IS THIS IMPORTANT?

For chatbots to spread and flourish in the future depends on how quickly they can be developed. This would mean development by editorial effort rather than by coding effort. In other words, chatbot platforms should only require content curation and selecting dialogue features. Everything else should be automated underneath (invisible to the developer), including machine learning and NLP capabilities.

Developers of chatbots in the future will be the writers not the computer programmers.

Current platforms offered by big companies (Microsoft Bot Framework, IBM-Watson, Amazon-Lex, Google API, and Facebook Messenger Platform) all require coding skills and/or AI experience. Obviously, developing the same chatbot for Abraham Lincoln would take much longer than 10 minutes when hands-on AI skills and coding are involved.

Considering the document stockpiles of enterprises, a quick and easy conversion to chatbots can be valuable for training, help desk, and other vital operations.

EDUCATIONAL CHATBOTS ARE HERE

The second reason for this initiative was to assess the value proposition of chatbots for the education sector. Here are the top 6 reasons why chatbots (conversational AI) will be inevitable tools for education:

  1. Control: Interactive content gives students much more control over what they want to focus on.
  2. Fun: Talking/messaging/chatting is always more fun than just reading.
  3. Ease: Use of small screen devices are ideal fit for chatbots which add to their educational role.
  4. New Teaching Methods: Chatbots can be a great summarization tool offering students main points to remember and option to dive deeper. Various new teaching strategies can be implemented.
  5. Creativity: Creation of chatbots can also be an educational experience.
  6. Feedback: Conversational analytics obtainable from chatbot interactions provide valuable clues to teachers as to how students learn, or fail to learn.

There is no doubt that one of the most active areas of conversational AI will be education. We will report how Abraham Lincoln chatbot was received in a follow up article.

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Is DIGITAL EMPLOYEE the Next Big Thing?

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All the technical jargon you have been hearing nowadays such as deep learning, artificial intelligence, natural language processing, etc., all converge to one single question for businesses: Can we build digital employees?

One may wonder what makes a digital employee different than all the software tools we are already using today. A digital employee may be defined as a computerized system that has superb communication skills using natural languages, and has some level of autonomy to make its own judgement and decisions.

Digital Employee represents the fine line where we delegate business responsibilities to autonomous systems, and where we communicate with them like talking to human employees.

WHAT WILL DIGITAL EMPLOYEES CONTRIBUTE TO?

Digital employees will directly contribute to business efficiency in 4 major areas as shown below. The communication at the top is essential for all other functions to perform cohesively. In other words, a digital employee starts from the core capability of communication and performing a high level dialogue.

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HOW DO WE BUILD THEM?

Creation of a digital employee cannot be a scientific project. Otherwise, it will remain very limited to a few examples based on substantial R&D budgets. This revolution will only happen when we have platforms that allow the creation of digital employees easily and fast. Here are the some of the top requirements for such transition:

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It is also important to mention that seamless integration to all communication platforms and operating systems is another key requirement.

SCIENTIFIC DISCIPLINES MUST COME TOGETHER

Creating digital employees through a platform will require many scientific disciplines and methods to amalgamate. There is no “silver bullet” solution to create such a complicated system. Below is a simplified landscape of disciplines that are most likely to contribute at least one aspect of development.

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The success will depend on who has the best cocktail of methods tucked under the platform which are literally invisible to the end user (i.e., the creator of digital employees).

BUSINESS INCENTIVES

Undoubtedly, there are several benefits of gaining digital employees as outlined below. However, their limitations compared to human employees (in certain aspects) represent a tradeoff. This trade off will exist until the technology reaches human level cognition, which may take a very long time.

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REVOLUTION TIMELINE

Estimating the timeline of the transformation from human information workers to digital employees is not an easy guess. Many businesses have adopted the IKEA model of DIY software during the last few decades, delegating tasks to clients. Banking is a prime example where you are supposed to use software to do transactions on your own. However, the current trend shows demand for command driven banking using conversational interfaces for requests like “Transfer $5,000 from checking to saving by tomorrow morning.” If we can talk to a digital employee, why bother using a software. And that’s the underlying promise for the upcoming revolution.

DIY Software model is wearing off, creating a future demand for digital employees.
We predict that the first solid evidence of this revolution will show itself by the fading away of DIY systems from our lives (including IKEA).

Creating a Business Entirely from Digital Workers (Expert Chatbots)

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We are not too far away from creating a completely digital business with a single human (the owner) setting it up. A recent Forbes article mentions the possibility of replacing managers in a futuristic tone encouraged by the advances in blockchain and IoT, A Harvard Business Review article introduces iCEO, a software that makes executive decisions. All these developments are taking us to this ultimate goal.

Autonomous Digital Business is a concept much closer to reality than chips in the brain, or self-driving cars on the streets.

Current businesses are already “digital” in so many aspects. Automated trading in stock market was a pioneering example of how buying and selling decisions can be delegated to smart computers, and they have been operating for a while now. If we can trust computers with stock trading, why not trust them with our business decisions for buying, selling, hiring, etc.?

Amazon, for example, might be rated 70% digital considering all its web operations and robotic warehouses. As conversational agents (chatbots) steadily penetrating into the CRM and sales operations, the percentage of digital business is increasing. But can it be all “digital” comprised of agents and expert chatbots? Can we delegate all decision making roles to computerized agents to run our businesses?

Expert Chatbots Making Decisions

Our current understanding of the chatbots is not sophisticated enough to make them run a business autonomously. However, “expert” chatbots are a different ball game. I had explained in an article earlier how they differ from conventional chatbots. In a nut shell, an expert chatbot communicates with humans plus makes decisions based on the expertise it has. At exClone, we have seeded the first steps of this vision.

In the short term, the following decisions can be expected to be made from AI agents/expert chatbots in the realm of digital business:

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This is a simplified view of all possible conversational decision systems applicable to businesses. In this simple model, the business owner would interact with an executive chatbot to control and manage her business.

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This picture depicts a digitalization scheme via AI in its most naive form, with a potential to signal what the future holds.

Barriers to Entry

The most important parameter in this transition is the ability to create expert chatbots easily without deploying expensive scientific projects. Here is the list of 10 requirements to win in this race:

1- No coding effort should be required.
2- Deployment should be fast (as opposed to lengthly training/learning procedures)
3- Data requirement should be limited to the content of the expertise (as opposed to vast amounts of training data to be collected)
4- Easy to fix and modify (as opposed to black box approaches that require re-training the system)
5- Building an expert chatbot must be an editorial effort, not much different than writing a blog post.
6- Builders of expert chatbots should be experts themselves without the involvement of developers or scientists.
7- Should be able to converse effectively, yield reasonable advice, and make sensible decisions.
8- Must have a certain level of awareness to be able to analyze its conversations and make deductions.
9- Must be able to learn from overall operation by evaluating its objective function.
10- It should be easily deployed in all communication channels/platforms ranging from SMS to Slack.

The winning development platform must address all the issues listed above. Most of the current platforms offered by big corporations (Microsoft, Google, IBM, Amazon, Facebook) do not meet half of these criteria, and are targeted solely to developers, not to business experts.

How will the Future of Business Look Like?

There are several measurements that apply to business valuation today such as the market cap, EBITA, gross revenue, number of employees, etc. But none of these conventional measurements indicate how close the company is to scaling upward. The rate of digitalization could be such a measurement to fill in this gap.

A new key measurement of company valuation in Wallstreet will be the “Rate of Digitalization” in the near future.

Consider Amazon again. Let’s imagine a rival, equal size, equal volume, but everything done by hand (human labor). Who would you invest in? Amazon or the rival? Knowing the degree of digitalization in Amazon, the natural choice would be her. This extreme hypothetical example emphasizes the value of this new parameter. Today, it is all blurred in the narrative interpretations of stock analysts.

Far into the future, it is fair to assume that Fortune 500 list will start to include businesses entirely digital (automated) with few human owners or controllers.

A New AI System Writing Its Own Code to Produce Chatbots

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If you heard AI systems that write their own computer codes, here is an example from exClone. The exclone’s platform is capable of taking any text, turn it into a special code (called CHATMATRIX), then spit out a chatbot that represents the content in a dialogue style. All in single step, with a push of a button.

The content is to be entered by subject matter experts in any particular topic. Hence the platform is called the Cloning Platform implying “digital cloning” of expertise. And the expertise can be anything, as simple as how to build a kite as long as it is articulated well. In this single step process, no coding is required to produce a chatbot.

The chatbots produced with this system have two primary functions: (1) Provide answers through presentations like how an expert would do by navigation options, (2) Provide answers to impromptu questions at any time during the conversation. There are bunch of secondary functions that make the whole experience human-like.

Machine Learning Similar to Human Learning

The human brain learns new knowledge while reading. As we all know, this process is very quick. The way to test learning is whether the person can answer questions about the sentences she/he read. exClone’s machine learning (ML) process works with the same principle, reading content and being able to answer questions. The automated step of creating a code for a given content is the byproduct of this machine learning process.

The editorial effort to create a chatbot involves few other choices by the expert. Some of these options include dialogue behavior (templates) that implement different objectives such as sales, help desk, advice, onboarding, surveying, etc. If you would like to experience the Cloning Platform, click here for free trial.

CHATMATRIX: A New Programming Language for Chatbots
Although the ML process creates a code for a given content (and template), the developers have a choice to add advanced functions and behaviors by further coding using the CHATMATRIX language. Bypassing the automation step, an entirely new chatbot can also be created. This high level language has the following features:

  • The language is comprised of predefined NLP functions for detecting user’s comments and predefined ML functions to generate response to the user.
  • Each statement handles not only the current exchange, but also the conversations took place N steps back. This enables strategizing the chatbot’s response based on history.
  • Each statement evaluates the availability of all possible response options, then chose the best one.

If you would like to learn more about CHATMATRIX, please drop a note below in the comment section.

Detecting Emotions is Key to Chatbot Performance

One of the advantages of CHATMATRIX is its ability to detect sentiments in user responses which leads to formulating emotions based on the short history of the conversation. Emotions are detected by the density of the sentiments during the conversational steps. This may seem like a humanizing effect of the chatbot experience, which is true in one sense. However, emotions are very useful for business purposes because it becomes a tool to filter out genuine users (sales leads for example) from people with no intention to engage in business.

Detecting emotions like satisfaction, confusion, boredom, curiosity, and anger allows strategic moves in dialogue flow. If the user is satisfied, chatbot can offer more options, or ask user’s contact information. If the user is confused, chatbot can ask the source of confusion. Many different strategies can be implemented based on the objective. Finally, conversation logs with emotion markers provides highly insightful information about the clientele.

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DIGITAL CLONING: How Expertise can be Commoditized by AI Driven Chatbots

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Biological cloning may be an immortal way to pass on our individual genetic information, but in digital form it offers something quite different: A new robotic society of expert chatbots as a part of our new digital life. Before digging deeper, let’s define what it is.

Digital Cloning is the duplication of a cognitive function based on the duplication of its source data (knowledge). Only duplicating data (text, image, video, sound, etc.) is not cloning just as how duplicating only DNA would not be cloning in biology.

Digital Cloning of Expertise is duplicating some particular expert knowledge along with its delivery mechanism, in this case a chatbot. In order to satisfy the criteria of digital cloning, such a chatbot is required to present expert knowledge in a useful way as well as answering related questions at a reasonable rate. Note that we are talking about “expert” chatbots here but not transactional chatbots where they book your flight or arrange your calendars. The difference between expert chatbots and transactional chatbots was explained in one of my earlier articles.

Digital cloning this way also includes persona, personal emphasis and choices since no two experts in the same field are the same. Personal variation is what makes combined expertise very powerful, always surpassing an individual expert’s opinion. Personal choices also reflect how expert content is curated to be cloned by its designer.

Expertise is the Most Valuable Commodity if it can be Shared
Expertise can be anything. It can be as simple as how to build a kite, or as complicated as how to perform a brain surgery. We all have some kind of expertise, some ideas, some vision, and stories to tell. In the enterprise world, expert knowledge is even more valuable since it is the driving force of business success. But expertise is only valuable if it is actionable. For that, expertise must be alive, always available, easy to share, easy to be consumed.

In today’s world, knowledge is shared in static (dead) forms of delivery methods: publications. Regardless of where they are published, and how long they buzzed or viewed, their destiny is ARCHIVES, which is a digital wasteland.

In Today’s World, Knowledge and Expertise Fade Away like Ocean Waves Breaking on a Beach and Retreating Back

This process resembles to ocean waves breaking on a beach (published), then retreating back (archived). They are at the mercy of Google’s ability to bring them to search queries. Worse than that, if such publications are within an enterprise environment where even Google cannot help you, their shelf-life will be even shorter comparable to how long the promoting emails are kept relevant, or how well you can query a database.

Accordingly, intellectual efforts made on a regular basis have no safe harbor to remain alive using our current digital systems other than being deposited somewhere (online or offline) including posting on social media, blogs, websites or corporate networks. You would agree with this point better when you catch yourself smiling upon discovering an old document on your computer with full of great ideas.

Immortalizing Ideas, Visions, Experiences, and Expertise
One way to keep one’s expert knowledge alive is digital cloning via chatbots. This must be a very simple process at the level of editorial effort not much different than blog writing. If it gets any more complicated than that, the demographics of digital cloning will be quite limited, and the anticipated affect will not emerge.

Digital cloning via expert chatbots is a new form of digital presence. Unlike the existing forms of digital presence such as facebook page, twitter account, or personal blog, an expert chatbot can maintain all your expert knowledge equally shareable regardless of when they were entered into the system. Every time you add some valuable knowledge, it enriches your chatbot’s response capability. People chatting with your chatbot would be presented your expertise, and would get answers to their questions. In addition, conversation logs can provide ultimate transparency to people’s concerns, curiosities, and demands relevant to the expertise served.

Community of Expert Chatbots in an Enterprise
Digital cloning concept offers tremendous advantages for enterprises in applications including, but not limited to, training, employee assistance, sales agents, help desk, and many other similar knowledge rich tasks. Managers and workers can clone themselves to offer an alternative communication channel within a company for all sorts of purposes. Expert chatbots can be created using databases and data silos for deep content (big data), converting such data driven systems into conversational experts. With such a transformation, we can start to assess the value of companies by their commoditization capability of expertise internally as well as externally.

A Society of Digitally Cloned Chatbots

Expert chatbots can refer to each other even for the same subject matter expertise very similar to how we use links in documents. This allows users to switch from one chatbot to another without a need for external search. Collaboration between chatbots in this manner will be the corner stone of the emergence of a robotic society in the mirror image of ourselves. Not to mention, a certain level of competition will emerge among the clones measured by who is the most popular expert chatbot based on number of referrals.

The emergence of such an ecosystem would redefine how we interact with computers, and would change our role from being sole digital workers to being part-time human parents of digital clones.

Such a transformation will also challenge the immortality arguments since biological death would no longer be 100% loss of one’s expertise and persona. One thing is for sure, all these possibilities are not a science fiction story any more, they are all here around the corner.

You can read my other articles on linkedin if you are patient enough to dig through, or you can wait until I clone myself (which I am in the process of doing it) and talk to my expert chatbot. You may even be able to hear my voice! Until then, stay tuned and join our CHATBOTS Group on Linkedin.

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

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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.

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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.

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  • 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.

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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.

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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.

 

 

 

 

 

 

 

 

 

 

 

 

The Thick Blue Line Between Chatbots

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

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

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

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

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

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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.

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.

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.

DNAM3

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.

micheal-Screen

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.