Is ‘global’ warming concept consistent with thermodynamics according to ChatGPT?
DOI:
https://doi.org/10.15503/emet2023.70.78Słowa kluczowe:
Computation and language, Artificial Intelligence, Machine Learning, Global warmingAbstrakt
Aim. Taking care of the environment on Planet Earth is still important and has be- come popular exceeding boundaries of science. Therefore description of climate changes has evolved into the concept of Global Warming since the 20th century. On the other hand, principles of thermodynamics where temperature is defined locally (not globally) are still valid. In my research I have applied tools like ChatGPT to detect possible contra- dictions and propose bridging gaps between statistical physics and the concept of global warming.
Methods. The concept of temperature based on calculations of local particles’ kinetic energy in a microcanonical ensemble has been confronted with the concept of so called ‘average’ temperature in ‘global’ warming. The principles of Kinetic Theory and Statisti- cal Ensembles provided by statistical physics have been applied to ChatGPT based on natural language Results. Contradictions between the concept of average temperature in global warming and the concept of temperature based on local particle kinetic energy arise due to the different scales, heterogeneity, presence of feedback mechanisms, and boundary conditions. According to the chatGPT’s verdict, the concept of temperature based on calculations of local particles kinetic energy in the microcanonical ensemble is statistically more accurate.
Conclusion. The concept of so called ‘global’ warming (confronted with the physics- based concept of temperature, providing a more accurate and nuanced understanding of temperature changes in different scales and contexts) should be replaced by the idea of local warming which is consistent with the concept of temperature based on calcula- tions of local particles kinetic energy in the microcanonical ensemble and experimental measurements provided by NASA. Therefore, talking about local (not global) warming is more reasonable when our goal is to prevent our coexistence with environment on Planet Earth from disasters.
##plugins.generic.usageStats.downloads##
Bibliografia
Atkins P., de Paula J.(2006). Physical Chemistry (8 ed.). Oxford University Press. p.9.
Feng G., Zhang B., Gu Y., Ye H., He D., Wang L. (2023) Towards revealing the mystery behind
chain of thought: A theoretical perspective, 2023.
Gibbs, J.W. (1902). Elementary principles in statistical mechanics, Charles Scribner’s Sons.
New York.
Hesse W. (2001) Division is in uniform tc0. International Colloquium on Automata, Lan-
guages, and Programming, Springer, 104–114.
Horgan J. (2023) A 25-year-old bet about consciousness has finally been settled, Scientifi-
camerican.
Kappeler, P.M., Silk J.B. (2010) Mind the gap: Tracing the origins of human universal, Spring-
er, doi:10.1007/978-3-642-02725-3. ISBN 978-3-642-02724-6.
Koch C.,Chalmers D.(1998), Can science explain consciousness? Scientific American, Vol-
ume 271, Issue 1.
Sheurer J, Balesini M., Hobbhan M. (2023) Technical report: Large language models can strate-
gically deceive their users when put under pressure. https://arxiv.org/abs/2311.07590v2. Wei C., Chen Y., Ma T. (2022) Statistically meaningful approximation: a case study on approximating turing machines with transformers. Advances in Neural Information Pro- cessing Systems, volume 35, (pp. 12071–12083). https://www.scientificamerican.com/
article/a-25-year-old-bet-about-consciousness-has- finally-been-settled/.
West D.B. (1996), Introduction to graph theory.
Prentice-Hall, Englewood Cliffs NJWong, F., Zheng, E.J., Valeri, J.A. et al. (2023). Discov-
ery of a structural class of antibiotics with explainable deep learning. Nature. https:// doi.org/10.1038/s41586-023-06887-8
Pobrania
Opublikowane
Jak cytować
Numer
Dział
Licencja
Prawa autorskie (c) 2024 Andrzej Buda
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa 4.0 Międzynarodowe.
