Acta Scientific Nutritional Health (ASNH)(ISSN: 2582-1423)

Review Article Volume 8 Issue 3

Artisanal Foods: What can be Learned from Mind Genomics Coupled with AI Synthesis

Howard Moskowitz1*, Stephen D Rappaport2 and Taylor Mulvey3

1Cognitive Behavioral Insights, LLC, Albany, New York
2SDR Consulting, Inc., Norwalk, CT
3St. Thomas More School, Oakdale, CT

*Corresponding Author: Moskowitz, Cognitive Behavioral Insights, LLC, Albany, New York.

Received: March 01, 2024; Published: March 11, 2024

Abstract

This paper presents the application of artificial intelligence to the task of synthesizing and then more profoundly understand different mentalities (mind-sets) in relation to the subject of artisanal foods derived from animals. The approach of combining Mind Genomics thinking about mind-sets with the power of AI might serve as a great beginning point for growing scientific knowledge of this field. Furthermore, AI has the potential to assist businesses and researchers in gaining a more in-depth understanding of the decision-making processes and motivations of individuals working in the artisanal food industry. This can be accomplished through the analysis of data pertaining to consumer behavior, preferences, and attitudes. Despite the fact that there are some restrictions associated with the utilization of artificial intelligence in this particular setting, the advantages of utilizing AI technology to gain an understanding of consumer behavior and preferences in the artisanal food industry are substantial. This can result in more informed decision-making and a more profound comprehension of this intricate and ever-changing subject matter.

Keywords: Artisanal Foods; Mind Genomics; Coupled; AI Synthesis

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Citation

Citation: Howard Moskowitz. “Artisanal Foods: What can be Learned from Mind Genomics Coupled with AI Synthesis".Acta Scientific Nutritional Health 8.3 (2024): 39-51.

Copyright

Copyright: © 2024 Howard Moskowitz. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Impact Factor1.316

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