A comment on both the Nobel Prize in Physiology or Medicine and in Physics 2024: RNA Intelligence and AI
Heartfelt
Congratulations to Victor Ambros and Gary Ruvkun being awarded the 2024 Nobel
Prize in Physiology or Medicine “for the discovery of microRNA and its role in
post-transcriptional gene regulation” and Heartfelt Congratulations to John J. Hopfield and Geoffrey E. Hinton being awarded The Nobel Prize in Physics 2024 “for
foundational discoveries and inventions that enable machine learning with
artificial neural networks” from Germany.
A comment on both the Nobel Prize in Physiology or Medicine and in Physics 2024: RNA Intelligence and AI
I want to present the theory that RNA evolution on the microRNA level and AI share at least two common principles:
1. The factor ½, and
2. Summation.
This is
due to:
A) The consideration we presented on 7. October 2024[1]:
It's noteworthy that the lin-4 microRNA is 21 nucleotides long and 21 is a FIBONACCI number (F8) and thus the microRNA
concept is (strictly) consistent with our considerations[2].
Please, note furthermore that with the LUCAS numbers 18 and 29 we
get
= {21 + [18 +29]/2}/2 = {21 + 23.5}/2 = = 22.25 (nt,
nucleotides, "base pairs") being a good approximation for the
microRNA length of more advanced organisms.
The equation (1) includes the factor ½
and the principle of summation.
B) The basic equation of backpropagation [3] as the basic tool of neural networks
and artificial networks is the equation
½
Sum(xi - εi)2 (2).
C) Equation (1) and (2) contain the factor ½ and the principle of Summation.
D) Therefore, I want to present the theory that
microRNA and AI share a common principle which is based on Summation and the factor ½.
This means that the principles of most neural networks and RNA evolution share a common principle [of A) nature and B) of human intellgence].
Critique and discussion welcome!
Yours Stefan Geier
Gerhart Hauptmann Straße
6
83071 Haidholzen, Germany
Additum:
1. With F(x+1)/F(x) ≈ L(x+1)/L(x) → Φ for x → ∞ in an both alternating manner we can include the deviation component xi - εi as deviation from Φ and loss reduction related to Φ in our considerations on RNA. Thus systematic deviation and loss reduction is a third principle for both RNA and AI.
[1] https://humanistischebetrachtungen1.blogspot.com/2024/10/microrna-fibonacci-numbers-quantum.html
[2]
https://www.researchgate.net/publication/381659767_Coiled_Coil_Helices_Including_Alpha-Keratin_and_Leucine_Zippers_are_Related_to_the_Golden_Ratio_Concept_by_the_Omega_Constant_O_and_are_Related_to_Tetrahedra_Helices_and_to_Quantum_Physics?utm_source=twitter&rgutm_meta1=eHNsLXJ1OXdzR3Mwc2ZRRTJVSXEzN2ZwNHQ1dEQ2MXRFcWtSLzAxMENLenNoSCtKYzJrNFROcGJCaFd2UEN2dmFVc3BJZ1VXSUpqQXVDak9wdlBCWnpTam9aVT0%3D
[3] Werner Kinnebrock: Neuronale Netze: Grundlagen, Anwendungen, Beispiele. R. Oldenbourg Verlag, München 1994, ISBN 3-486-22947-8.
My Warmest Congratulations for being awarded the 2024 Nobel Prize in Chemistry to David Baker “for computational protein design” and to Demis Hassabis and John M. Jumper “for protein structure prediction.”*
AntwortenLöschenMay I note that my comment on RNA intelligence and AI from today morning (above) is able to explain the enormous efficacy of AlphaFold2 at least in part.
Yours Stefan Geier
#nobelprize2024 #NobelPrize #alphafold #KI #AI #artificialintelligence
*https://www.youtube.com/live/bPjB9NRu8Jc?si=fxjljKg6cDpLJUO2