A Forbes article published last week reported that Chinese chatbots (WeChat and Alipay) were used to suppress the spread of the Covid-19 virus. Although the chatbots did something unacceptable in Western societies (i.e., using the assigned color codes like red, green, or yellow to indicate individuals’ freedom level for travel and social activities), they produced effective results in a short period. And the article mentioned nine other ways that AI can be useful in such circumstances.

When it comes to disseminating credible, useful, and unique information, chatbot technologies can deliver valuable insights promptly and accurately. For example, the question “Is plasma donation approved by the FDA?” produces a clear answer by exClone’s virtual expert, Dr. Margo, compared to Google search results.

On the other hand, asking Google the same questions produced contradictory results written in a language that is not easy to understand (April 26, 2020, New York.)

The virtual expert technology delivered by exClone does not only produce more precise answers to single-step questions, but also engages the user in a multi-step dialogue investigation, very similar to how a human expert would deliver advice. The important point here is the capability of the computer to ask the user questions to provide better answers as shown in the example below.

Google, Alexa, or other similar assistants do not ask the user questions for a more refined answer. This is the fundamental difference between the exClone’s dialogue technology (based on AI) versus short cut coding efforts devoid of AI.

exClone’s Dr Margo is a virtual expert that encapsulates knowledge from credible sources (curated by human experts) then disseminates accurate answers in a multi-step, human-like dialogue. The human expert can continue teaching the system after deployment by just talking to it.

The BETA version of Dr. Margo is available via mobile apps (with voice interaction like Alexa) or by the Web app at this link.

Enterprise IQ is the ability of an organization to harvest, curate, and utilize its expertise to improve its bottom line.

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.

__________________________________________________________

This article is brought to you by exClone.

Join CHATBOTS group in linkedin.

You can follow exClone in Facebook, and in LinkedIn.

Request a Demo from exClone

Download exClone App (iOS) — Ecosystem of virtual experts (beta)

Download exClone App (Android) — Ecosystem of virtual experts (beta)

___________________________________________________________

#AI #Experts #EnterpriseIQ #Virtualexperts #Voiceassistants #practicalAI #chatbot #chatbots #artificialintelligence #ConversationalAI #Virtualassistants #bots #machinelearning #NLP #DL #deeplearning

Originally published at https://www.linkedin.com.

A 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.

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

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 BETA test.

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.

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.

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.

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?”

Conclusion

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.

• Request for a demo at exClone.com
• Join CHATBOTS group in linkedIn.
• You can follow exClone in Facebook, and in LinkedIn.

#AI #ArtificialIntelligence #virtualexperts #chatbots #conversationalAI #ML #DL #deeplearning #Machinelearning #exclone #NLP #humandialoguetheory #instantlearning #virtualreality #VR #StanleyMorgan

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.