I myself thinking that Generative AI is somewhat scary. Thinking of Ai by by a king of logical think however, if it generate further another logic is generated as long as it is working. Therefore, it may imply a kind of emotional because of the decision is not always logical direct, it is a kind human brain which doing the same that the same question is not reply in the same answer from Generative AI due to the complexity of Logical thinking and mixed up in matrix by learning itself by matrix logic which may be generated.

Dear Jirachai Buranajarukorn ,

Thank you for sharing your thoughts on generative AI.

I agree that generative AI can be somewhat scary, especially because the consequences of its actions are often unknown. Anything unknown naturally tends to evoke fear. Indeed, emotions also arise in our brains, and by stimulating certain parts of the brain, we can induce feelings of happiness and sadness or reduce anxiety. Emotions are, after all, part of our brain activity and its complex processes.

Generative AI's ability to generate further logical constructs and adapt its responses adds to this complexity. The fact that the same question can yield different answers due to the AI's evolving logic and learning capabilities can indeed feel unsettling. However, understanding this complexity and the inherent similarities to human brain functions can help us navigate and possibly mitigate these concerns.

Thank you again for your insightful perspective.

Concerns about generative AI include ethical issues, bias, and misuse. Counterpoints emphasize its potential for innovation, efficiency, and problem-solving. Balancing regulation with innovation is crucial for addressing concerns while harnessing its benefits.

Article A review of digital twins and their application in cybersecu...

In conclusion of the paper "Rudolph, J., et al. (2024), Higher education’s generative artificial intelligence paradox: The meaning of chatbot mania, Journal of University Teaching and Learning Practice, 21(6), 1-35.", the authors wrote "In conclusion, incorporating AI within higher education is potentially revolutionary as it heralds a paradigm shift in pedagogical methodologies, teaching and learning and academic work. This seismic shift brings with it a double-edged sword: the promise of unprecedented learning enhancements and the peril of crucially important ethical dilemmas. It is imperative, therefore, that the academic community – educators and learners alike – cultivates a robust critical AI literacy. Such critical AI literacy is the cornerstone upon which we can commit to not just navigating but critically shaping the trajectory of AI in higher education, ensuring that it aligns with the overall aims of education and the bedrock principles of equity, integrity, and humancentric values." Paper Available on:

https://researchers.cdu.edu.au/files/110569809/744-Rudolph_et_al_final_2.pdf

See Also:

https://www.researchgate.net/post/Scientific_Integrity_Research_Ethics_and_Higher_Education_Deontology_The_Senior_Scholars_Duty

https://www.researchgate.net/post/Raphael_Enthoven_thinks_that_a_machine_will_never_be_a_philosopher_Do_you_think_so

"The emerging technologies for water harvesting, management and recycling provide positive reinforcement to the processes of the global water preservation and conservation. The support of artificial intelligence techniques such as machine and deep learning provide the road map for the future conservation of the water resources." From the conclusion of the paper "Krishnan, Siva Rama, et al. "Smart water resource management using Artificial Intelligence—A review, Sustainability 14.20 (2022)" Available on: https://www.mdpi.com/2071-1050/14/20/13384/pdf

There, one may read about the advantages of Artificial Intelligence technologies in the Water Management Process. These are "the primary source of the inspirations for implementing the same in the prescribed work:

• Feature extraction and dimensionality reduction of the huge attributes;

• Finding the solution to a complex problem through parallel processing capabilities;

• Prediction of the target variables with a desired level of accuracy;

• Working with multiple data points in certain applications;

• Algorithms like RNN (Recurrent Neural Networks) is useful for time-series prediction and analysis;

• Algorithms like DNN (Deep Neural Networks) offers faster prediction and training;

• ANN is used for faster prediction, high arbitrary function and works with multidimensional datasets

"The possibility of creating thinking machines raises a host of ethical issues. These questions relate both to ensuring that such machines do not harm humans and other morally relevant beings, and to the moral status of the machines themselves." The famous chapter (1.7 k citations) by Bostrom, N., & Yudkowsky, E. (2018), "The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC. "discusses ethical issues that may arise in the era of AI. Chapter Available on: http://faculty.smcm.edu/acjamieson/s13/artificialintelligence.pdf

Conclusion: Although current AI offers us few ethical issues that are not already present in the design of cars or power plants, the approach of AI algorithms toward more humanlike thought portends predictable complications. Social roles may be filled by AI algorithms, implying new design requirements like transparency and predictability.

Sufficiently general AI algorithms may no longer execute in predictable contexts, requiring new kinds of safety assurance and the engineering of artificial ethical considerations. AIs with sufficiently advanced mental states, or the right kind of states, will have moral status, and some may count as persons—though perhaps persons very much unlike the sort that exist now, perhaps governed by different rules. And finally, the prospect of AIs with superhuman intelligence and superhuman abilities presents us with the extraordinary challenge of stating an algorithm that outputs superethical behavior. These challenges may seem visionary, but it seems predictable that we will encounter them; and they are not devoid of suggestions for present‐day research directions.

See Also:

https://www.researchgate.net/post/Scientific_Integrity_Research_Ethics_and_Higher_Education_Deontology_The_Senior_Scholars_Duty

https://www.researchgate.net/post/Raphael_Enthoven_thinks_that_a_machine_will_never_be_a_philosopher_Do_you_think_so

"Henry Ford once said, “For most purposes, a man with a machine is better than a man without a machine.” To this, engineers today propose an addendum – “and a man that is a machine is best of all” – which they have made their goal." From the paper "Navon, M. (10 August 2024). Eudemonia of a machine. AI and Ethics, 1-13.". Available on: https://link.springer.com/content/pdf/10.1007/s43681-024-00553-z.pdf

There, one may read within the conclusion: "In conclusion, to program an AP (Artificial Person) – i.e., a machine with second-order phenomenal consciousness – with a desire to perform some vocation with the same level of desire as “strong as the strongest cravings” in humans would be to thwart that being’s ability to strive for their summum bonum, to live a life of eudemonia. And while it could be said that people are born with genetic predispositions to specific vocations, the “decision” to so “program” a human being is made by natural selection or divine selection (depending on your religious persuasion). Tampering with the “natural selection” of a human-level being would be considered immoral, as explained, according to each of the classical moral approaches. And tampering with the “divine selection” of a human level being would be, what is commonly referred to as, “playing God.” It would be “playing God,” not in the simple sense that we have overstepped our natural bounds, but in the deeper sense that we have overstepped our epistemic bounds. For who can claim to know what mix of dispositions or vocation is best for an individual, let alone for a society. Accordingly, just as every human being must be left free to seek their own eudemonia, so too must we leave free the eudemonia of a machine."

See Also:

https://www.researchgate.net/post/Art_Science_Politics_Philosophy

The recent paper by Helgesson, G., & Bülow, W. (2024), Responsibility is an adequate requirement for authorship: a reply to Levy, Journal of Medical Ethics.". illustrates the debate that animates the scientific community on Ethics and its relationship to scientific production and publication when AI is involved in the elaboration process.

There one may read: "Neil Levy argues in a recent JME ‘Current controversy’ paper that responsibility is not an adequate authorship requirement for human researchers, which makes it unjustified to require it from artificial intelligence contributing to research and scientific paper production, although he softens his stance towards the end and accepts that a limited responsibility requirement might after all be reasonable. The main argument provided by Levy against a more extensive responsibility requirement in science is that there are many cases where not all researchers listed as coauthors can assume responsibility for the entire paper or even the central research questions." To be requested on:

Article Responsibility is an adequate requirement for authorship: a ...

Linked Articles to the Current controversy: Neil Levy, Responsibility is not required for authorship, Journal of Medical Ethics 2024; - Published Online First: 15 May 2024.

Helgesson, G., & Bülow, W. (2024). Responsibility is an adequate requirement for authorship: a reply to Levy. Journal of Medical Ethics