Will the combination of AI technology, Big Data Analytics and the high power of quantum computers allow the prediction of multi-faceted, complex macroprocesses?
Will the combination of generative artificial intelligence technology, Big Data Analytics and the high power of quantum computers make it possible to forecast multi-faceted, complex, holistic, long-term economic, social, political, climatic, natural macroprocesses?
Generative artificial intelligence technology is currently being used to carry out various complex activities, to solve tasks intelligently, to implement multi-criteria processes, to create multi-faceted simulations and generate complex dynamic models, to creatively perform manufacturing processes that require processing large sets of data and information, etc., which until recently only humans could do. Recently, there have been attempts to create computerized, intelligent analytical platforms, through which it would be possible to forecast complex, multi-faceted, multi-criteria, dynamically changing macroprocesses, including, first of all, long-term objectively realized economic, social, political, climatic, natural and other macroprocesses. Based on the experience to date from research work on the analysis of the development of generative artificial intelligence technology and other technologies typical of the current Fourth Technological Revolution, technologies categorized as Industry 4.0/5.0, the rapidly developing various forms and fields of application of AI technologies, it is clear that the dynamic technological progress that is currently taking place will probably increase the possibilities of building complex intelligent predictive models for multi-faceted, complex macroprocesses in the years to come. The current capabilities of generative artificial intelligence technology in the field of improving forecasting models and carrying out forecasts of the formation of specific trends within complex macroprocesses are still limited and imperfect. The imperfection of forecasting models may be due to the human factor, i.e., their design by humans, the determination by humans of the key criteria and determinants that determine the functioning of certain forecasting models. In a situation where in the future forecasting models will be designed and improved, corrected, adapted to changing, for example, environmental conditions at each stage by artificial intelligence technology then they will probably be able to be much more perfect than the currently functioning and built forecasting models. Another shortcoming is the issue of data obsolescence and data limitation. There is currently no way to connect an AI-equipped analytical platform to the entire resources of the Internet, taking into account the processing of all the data and information contained in the Internet in real time. Even today's fastest quantum computers and the most advanced Big Data Analytics systems do not have such capabilities. However, it is not out of the question that in the future the dynamic development of generative artificial intelligence technology, the ongoing competition among leading technology companies developing technologies for intelligent chatbots, robots equipped with artificial intelligence, creating intelligent control systems for machines and processes, etc., will lead to the creation of general artificial intelligence, i.e. advanced, general artificial intelligence that will be capable of self-improvement. However, it is important that the said advanced general advanced artificial intelligence does not become fully autonomous, does not become completely independent, does not become out of the control of man, because there would be a risk of this highly advanced technology turning against man which would involve the creation of high levels of risks and threats to man, including the risk of losing the possibility of human existence on planet Earth.
I have described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:
OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT
Article OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL I...
In view of the above, I address the following question to the esteemed community of scientists and researchers:
Will the combination of generative artificial intelligence technology, Big Data Analytics and the high power of quantum computers make it possible to forecast multi-faceted, complex, holistic, long-term economic, social, political, climatic, natural macro-processes?
Will the combination of AI technology, Big Data Analytics and high-powered quantum computers allow forecasting of multi-faceted, complex macro-processes?
And what is your opinion about it?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
As most complex processes are highly dynamic, this seems to be a question of the time scale in mind.
With respect to forest dynamics, advanced computer modeling yields landscape scale perspectives that can't be obtained locally. I doubt there is any disagreement there. But extrapolations are always risky. I'm not sure that we can tell when the current models don't project well down to local levels. That's where ground-truthing becomes critical, which has become my specialty. Lots that can be shared there.
Sí, la combinación de la tecnología de inteligencia artificial generativa, el análisis de Big Data y la alta potencia de las computadoras cuánticas tiene el potencial de permitir la predicción de macroprocesos complejos y multifacéticos en diferentes ámbitos, incluyendo lo económico, social, político, climático y natural. Estas tecnologías pueden ayudar a analizar grandes cantidades de datos de manera más eficiente y extraer patrones y tendencias que de otro modo podrían pasar desapercibidos. Sin embargo, es importante tener en cuenta que la predicción de macroprocesos complejos y de largo plazo sigue siendo un desafío y puede estar sujeta a una serie de incertidumbres y limitaciones.
I doubt that QC will be helpful. Theoretical there are at least 3 different types, only one being developed to be useful in a very special field. Quantum algorithms are totally different from classic algorithms, and i doubt, that more than 1% of computer scientist know what they are speaking about when they mention QC.
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