A Comparative Review of Traditional AI, Generative AI, and Organoid Intelligence

Author

Dr. K.R. Martin, Jegan G


Abstract

 Computers are transforming dramatically. For years, AI has been extremely good at completing difficult computational tasks as well as pattern recognition, but only using a rule-based approach. A breakthrough happened a year ago in the form of Generative AI, which enables computers to produce new text and images and gives machines the appearance of being creative. The challenge of artificial intelligence tools, though, is that neither type runs on regular silicon computer chips, which means they consume prodigious amounts of electricity and are rapidly approaching the physical limits of conventional hardware.

This is where Organoid Intelligence (OI) emerges as a wholly new approach: Instead of a computer chip, it uses a network of tiny, lab-grown human brain cells connected to machines to compute. With living cells, OI can learn as it goes and consume a fraction of the power of a conventional computer. In this review paper, we compare the structure, learning model, and power consumption of standard AI, Generative AI, and OI and examine how the use of biological cells could fix AI's issues and revolutionize technology.



Keywords

Traditional Artificial Intelligence, Generative AI, Organoid Intelligence, Machine Learning, Deep Learning, Foundation Models, Large Language Models, Brain Organoids, Biocomputing, Neuroplasticity, Energy Efficiency, Silicon Computing



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References


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