It's a great idea to do so. Moreover, the recent upcoming AI systems have improved their accuracy of prediction.

Thank you Ankur Priyadarshi for your opinion.

Dear vadym,

Intergration NLP and DL, and it's trends are among solutions for multi task learning which each of these aspects could extract specific information and integrate them,

Here is a good paper which could be useful :

"Trends in Integration of Vision and Language Research:

A Survey of Tasks, Datasets, and Methods"

Dear Amin Honarmandi Shandiz , thank you for your contribution. It is very interesting paper. On the other hand, the Integration of Vision and Language Processing is only one part of the way to implementation into AI the understanding of meaning. In this context need to integrate NLP not only with video but with other Deep Learning areas as well.

Dear Vadym,

I can tell you about the most common uses of integration deep learning and NLP.

For example, sentiment analysis and text classification, and bots. These methods process the text and then feed it to deep neural networks. The area of General Artificial Intelligence is new and involves more sophisticated neural networks and a lot of data. IBM showed a case of debate (currently on specific subjects), between AI and professional humans, with remarkable results. This breakthrough debate can be a good starting point for General Artificial Intelligence.

Thank you, Barak Avisar , yes, such examples confirm that we have the start of the integration process between NLP and AI as a means to the creation of General Artificial Intelligence.

Dear Vadym Slyusar , thank you for your question. NLP and Deep Learning will certainly have some nice applications. This article on Arxiv is quite interesting Recent Trends in Deep Learning Based Natural Language Processing, https://arxiv.org/pdf/1708.02709.pdf%C2%A0. Theoretically there is I think much work to do. I am still puzzeling how to model meaning. At the moment I am working on equilibrium semantics as proposed by Prashant Parikh to model meaning in natural languages. May be this area might be of interest. Kind regards.

Dear Rob Christiaanse , this article from Arxiv is an important conceptual approach. Thank you for this link. I think that this link will be useful for all members of this discussion.

Similar to You I am still puzzling how to model meaning. In this regard, your research of equilibrium semantics as proposed by Prashant Parikh to model meaning in natural languages will be a big step to future progress in this area.

Hi Vadym ,

Natural language processing together with Neural Networks and deep neural networks are used to process the unstructured language text data, and to build applications like Smiley analysis, Smiley Prediction,Chatbots that talk in regional languages,Domain-specific Chatbots

In the book "Constructing Models of Being by Cognizing" there is an attempt to reduce the highest cognizing, including communication, to its roots , which , it was argued, are 1-/2- place classifiers/relationships.

In turn, 1-/2- place classifiers can be formed by ANN , as well as by other means, say DNF.

Recalling that any English clause is a combination of 1-/2- place classifiers , in the work it is outlined how NL texts can be reduced to algorithmic representation in computers and vise versa/ extended compilators!.

Some steps to realization of these ideas are presented in S. Grigoryan "Automating Acquisition and Explanation of Strategy Knowledge" , CSIT 21 , [email protected].

Thank you, Pratijnya Ajawan and Edward Pogossian . Your answers are useful for this discussion.

I may provide a more detailed response later, while General Intelligence is a common and some might say noble pursuit it cannot be achieved without considerable work to represent human knowledge and human language. I recommend that you take time to consider human and machine readable domain specific synthetic languages. I believe that I started some discussion Feb 1, 2021.

Ok, Dear Barry Fox , it is a nice idea! Maybe I saw your discussion, I will search this in ResearchGate

What is the impact of NLP in the medical emergency assistance?

Barry Fox

Feb 1, 2021

Answer

I have been working in the field of domain specific artificial intelligence and domain specific synthetic languages for several years. This is a very important question. I discussed this subject with a colleague just yesterday. A few notes from our discussion follows.

There are some jobs where a person’s eyes and hands must be free to perform their work, yet they must be able to communicate with other people and computer systems that help them perform that work. In some of these jobs, safety is most important.

Although systems like Alexa and Siri are already in wide use, they do not today provide the degree of reliability and safety that would be required for use in an emergency room or neonatal ICU.

In such cases, it would be prudent to a create a task specific synthetic language designed for communication between humans and computer systems while humans perform their work, and computers answers questions, provide information, control task specific equipment, and other functions, thereby enabling a person to perform their work with their hands and eyes free to focus on the task at hand.

In short summary this would require implementation of computer systems that are able to:

· reliably hear and process spoken words

· translate spoken words into human and machine-readable form (text)

· verify that the spoken words, in human and machine-readable, form comply with the specification (grammar) of the synthetic language

· determine what question or command has been voiced by the human,

· invoke the procedure that will answer the question, provide requested information, or perform the command using task specific equipment.

· reply in some audible or visible form if the spoken words were not recognized or if the spoken words in human and machine-readable form do not comply with the specification of the synthetic language, or if they are used in the wrong context.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands both correct and incorrect in their formation to verify that both correct and incorrect commands can be successfully categorized as such.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands and to verify that the correct procedures are invoked, and through simulation, verify that the procedures safely perform the intended action.

Simply stated, a synthetic language is a set of words and set of rules for how to create requests or commands using the words of that language that can be spoken by a human, recognized by a computer, properly interpreted by a computer, and safely translated into the intended actions.

View

What is the impact of NLP in the medical emergency assistance?

Barry Fox

Feb 1, 2021

Answer

I have been working in the field of domain specific artificial intelligence and domain specific synthetic languages for several years. This is a very important question. I discussed this subject with a colleague just yesterday. A few notes from our discussion follows.

There are some jobs where a person’s eyes and hands must be free to perform their work, yet they must be able to communicate with other people and computer systems that help them perform that work. In some of these jobs, safety is most important.

Although systems like Alexa and Siri are already in wide use, they do not today provide the degree of reliability and safety that would be required for use in an emergency room or neonatal ICU.

In such cases, it would be prudent to a create a task specific synthetic language designed for communication between humans and computer systems while humans perform their work, and computers answers questions, provide information, control task specific equipment, and other functions, thereby enabling a person to perform their work with their hands and eyes free to focus on the task at hand.

In short summary this would require implementation of computer systems that are able to:

· reliably hear and process spoken words

· translate spoken words into human and machine-readable form (text)

· verify that the spoken words, in human and machine-readable, form comply with the specification (grammar) of the synthetic language

· determine what question or command has been voiced by the human,

· invoke the procedure that will answer the question, provide requested information, or perform the command using task specific equipment.

· reply in some audible or visible form if the spoken words were not recognized or if the spoken words in human and machine-readable form do not comply with the specification of the synthetic language, or if they are used in the wrong context.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands both correct and incorrect in their formation to verify that both correct and incorrect commands can be successfully categorized as such.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands and to verify that the correct procedures are invoked, and through simulation, verify that the procedures safely perform the intended action.

Simply stated, a synthetic language is a set of words and set of rules for how to create requests or commands using the words of that language that can be spoken by a human, recognized by a computer, properly interpreted by a computer, and safely translated into the intended actions.

View

What is the impact of NLP in the medical emergency assistance?

Barry Fox

Feb 1, 2021

Answer

I have been working in the field of domain specific artificial intelligence and domain specific synthetic languages for several years. This is a very important question. I discussed this subject with a colleague just yesterday. A few notes from our discussion follows.

There are some jobs where a person’s eyes and hands must be free to perform their work, yet they must be able to communicate with other people and computer systems that help them perform that work. In some of these jobs, safety is most important.

Although systems like Alexa and Siri are already in wide use, they do not today provide the degree of reliability and safety that would be required for use in an emergency room or neonatal ICU.

In such cases, it would be prudent to a create a task specific synthetic language designed for communication between humans and computer systems while humans perform their work, and computers answers questions, provide information, control task specific equipment, and other functions, thereby enabling a person to perform their work with their hands and eyes free to focus on the task at hand.

In short summary this would require implementation of computer systems that are able to:

· reliably hear and process spoken words

· translate spoken words into human and machine-readable form (text)

· verify that the spoken words, in human and machine-readable, form comply with the specification (grammar) of the synthetic language

· determine what question or command has been voiced by the human,

· invoke the procedure that will answer the question, provide requested information, or perform the command using task specific equipment.

· reply in some audible or visible form if the spoken words were not recognized or if the spoken words in human and machine-readable form do not comply with the specification of the synthetic language, or if they are used in the wrong context.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands both correct and incorrect in their formation to verify that both correct and incorrect commands can be successfully categorized as such.

For added safety, before such capability is put into use, it would be necessary to generate many requests and commands and to verify that the correct procedures are invoked, and through simulation, verify that the procedures safely perform the intended action.

Simply stated, a synthetic language is a set of words and set of rules for how to create requests or commands using the words of that language that can be spoken by a human, recognized by a computer, properly interpreted by a computer, and safely translated into the intended actions.

View