Novel applications in finance, national security, health care, criminal justice, transportation, and smart cities

There is no doubt that 1 dimensional (sound) and 2 dimensional (image) analysis using convolution neural networks has proven invaluable in medicine and industry. Domain specific diagnostic logic and operational reasoning is less recognized and used but equally important. However there is growing consensus that current AI technologies have a plateau of capability and relevance. I have provided my thoughts in another short essay: Deleted research item The research item mentioned here has been deleted

Tilo Strutz - sure, but that is another very important field and use case of ethical AI. how to remove the inherent bias in the data, this is the challenge as its used for algos to learn.

We are putting a survey together, to understand the business use cases and AI capability used in real world by corporates. vs the investment we make in academia to make it happen.

please see and fill this survey, so that we have enough sample size from corporate and academia to analyze the situation better.

https://www.linkedin.com/posts/snigdha-gupta-88a31a45_ai-artificialintelligence-transformation-activity-6818794419295203328-3VxA

Please fill.

I think AI has taken centre stage in our lives and has become more important as the IoT, 5G networks and people continue to seek scientific information about everything.

Application of AI has significantly increased lately however currently the application of AI in smart cities and sustainability are amongst the HOT topics.

Serious social problems arise if people become uncritically compliant with unethical and indiscriminate applications of AI.

Article Do Less Teaching, Do More Coaching: Toward Critical Thinking...

* Reference: Do Less Teaching, Do More Coaching: Toward Critical Thinking for Ethical Applications of Artificial Intelligence

Automated transformation of empirical data into information, and its into knowledge and automatic use of this knowledge to identify and solve problems of identification, forecasting and research of the simulated subject area by studying its intellectual model

This is another aspect of what I have called "domain specific artificial intelligence" that is based upon "knowledge" of the subject matter. Eugene recommends an important step! We might call it "domain specific knowledge acquisition and conversion into useful capability" I will cite his work and include his suggestion when I write of "domain specific artificial intelligence"

Barry Fox!

I advise you to read the entire strategy of Russia in the field of artificial intelligence:

https://cset.georgetown.edu/wp-content/uploads/Decree-of-the-President-of-the-Russian-Federation-on-the-Development-of-Artificial-Intelligence-in-the-Russian-Federation-.pdf

It contains a lot of things that you are talking about.

You can also view my works.

*The Eidos-X++ system differs from other artificial intelligence systems in the following parameters:

*- it was developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);

*- it is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm) and has all the relevant source texts (http://lc.kubagro.ru/__AidosALL.txt);

*- it is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not take special training in the field of technologies of artificial intelligence from the user (there is an act of introduction of system "Eidos" in 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);

*- it provides stable identification in a comparable form of strength and direction of cause-effect relationships in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements to the data that cannot be performed, and processes the data that can);

*- it contains a large number of local (supplied with the installation) and cloud educational and scientific applications (currently 31 and 290 (http://aidos.byethost5.com/Source_data_applications/WebAppls.htm), respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);

*- it supports on-line environment of knowledge accumulation and is widely used all over the world (http://aidos.byethost5.com/map5.php);

*- it provides multilingual interface support in 51 languages. The language databases are included in the installation and can be replenished automatically;

*- the most time-consuming, computationally, are the operations of the synthesis models and implements recognition using graphic processing unit (GPU) where some tasks can only support up to several thousand times; the solution of these tasks is intelligent processing of big data, big information and big knowledge;

*- it provides transformation of the initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf);

*- it well imitates the human style of thinking: gives the results of the analysis, understandable to experts according to their experience, intuition and professional competence.

*- instead of making almost impossible demands on the source data (such as the normality of distribution, absolute accuracy and complete repetitions of all combinations of factor values and their complete independence and additivity), the  automated system-cognitive analysis (ASC-analysis) offers to process this data without any preliminary processing and thereby transform it into information, and then transform this information into knowledge by applying it to achieve goals (i.e. for the management) and solving problems of classification, decision support, and meaningful empirical research of the domain being modeled.

*What is the strength of the approach implemented in Eidos system? The strength is implementing an approach whose effectiveness does not depend on what we think about the subject area or whether we think at all. It generates models directly based on empirical data, rather than based on our understanding of the mechanisms for implementing patterns in this data. This is why Eidos models are effective, even if our understanding of the subject area is incorrect or totally absent.

*And this as well is the weakness of this approach implemented in Eidos system. Models of the Eidos system are phenomenological models, i.e. they do not reflect the mechanisms of determination, but only the fact and nature of determination.

See also: https://robotics.ua/news/ai/4244-the_third_generation_of_the_ai_which_independently_examines_the_rules_of_any_game

Thank you Sir. I will start reading this weekend and may have questions. Barry.

thanks for sharing Eugene Veniaminovich Lutsenko - we are compiling and aggregating information from different sources to build on an exhaustive list of capabilities. we have tried to put a framework to measure the ROI of AI holistically, but we would need to establish one to measure the capability effectiveness model too.

Snigdha Gupta! I built models of the influence of various factors and the dynamics of their changes on various results in physical and monetary terms. I am the developer of the models themselves and the software tools that allow them to be created and used in an adaptive mode.

cool. what is the methodology you have used here, regresssion? structural constructs?

we too have put an entire system/ investment evaluation framework for a business.

Please see the paper here. your recommendation and feedback will be valuable:

Article ROI of AI: Effectiveness and Measurement

I used automated system-cognitive analysis and system generalization of information theory, which I developed (http://www.famous-scientists.ru/school/1608). I have a lot of work in this field, including investment management. Managing an investment portfolio is not the same as managing an investment object with the help of investments. I have been doing a lot of management by using the volume and direction of investments as management factors. This is completely different from managing investment portfolios. But, if necessary, you can also manage portfolios.

sure Eugene Veniaminovich Lutsenko thanks will take a look. this is helpful.

Learning the hidden information in real time datasets related to economics and health. Both predicting and feature interpretation.