Connecting human mind with machine learning: from a platonic approach

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Abstract

The human mind has the superintelligence to instigate and provoke to do much scientific research and discover mysteries in the ancient world and the modern world. Human thoughts, feelings, and behaviors are the human mind’spowers that originate from the human brain.A brain has a network of cells that process information from both the internal and external environment to create our perception of who we are, how the world works, and how we interact with it. It is an endless searchand researchers are still investigating how this occurs.The humanbrain-like modelsdeveloped to behave like the human (human intelligence), called Artificial Intelligence, and Machine Learning. Human intelligence adapts to new settings by fusing different cognitive processes, whereas Artificial Intelligence is the machines builtto mimic human behavior and carry out human-like activities. This research paper aims to find the connection between humans(mind and brain) and Artificial Intelligence (Artificial Deep Neural Networks) from the method of the problem of universals. This has been explained with the platonic concepts for the concept of human intelligence andArtificial Neural Networks for Machine Intelligence, using traditional, mathematical, and real-time scenarios, with the factors like Properties, Uniqueness, Resemblance, and Relations, and the factors used to determine how humans and machines use intelligence in a known and unknown environment. Learning is necessary for intelligent behavior for the act of understanding things and events based on learning. A long-standing philosophical problem relates to induction or learning. Therefore, combining the intelligence of humans and machine learning is beneficial to understand the superiority of creation.

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APA

Mary, S. S. C., Ross, D. S., & Arun, J. (2024). Connecting human mind with machine learning: from a platonic approach. In Multidisciplinary Science Journal (Vol. 6). Malque Publishing. https://doi.org/10.31893/multiscience.2024ss0426

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