On the Knowledge of Neural Networks: The Genesis, Characterization, and Justification

Huikang Jiang

doi:10.36948/ijfmr.2024.v06i05.29195

On the Knowledge of Neural Networks: The Genesis, Characterization, and Justification

Author(s)	Huikang Jiang
Country	China
Abstract	This paper systematically analyzes the knowledge generation process of neural networks through the lens of Philosophy for Science and Epistemology. This paper first argues that for neural network’s knowledge to be possible, it is necessary that the truth of the world can be represented by mathematics, since neural networks are, by their first nature, a mathematical model, and this paper provides few arguments that support the mathematical nature of truth. For a central characterization of neural network’s knowledge, this paper argues that neural networks bypass the three inherent limits of traditional sciences, such as physics and chemistry, suggested by Eugene P. Wigner (1995). The three limits are: 1) Traditional sciences are inevitably approximations of the transcendental truth; 2) Scientists will never stop pursuing deeper, more profound scientific theories that encompass more information than previous theories, despite every theory being an approximation; 3) The increasing complexity and depth of the successive scientific theories poses a significant challenge to human intellect. Neural networks bypass these limits by adaptively learning any underlying function to the given data space, without creating any fundamental hypothesis, which are inevitably not transcendental, like that of traditional sciences. This is also another distinction that supports the differentiation between “machine knowledge” and “human knowledge”, as first proposed by Wheller (2016). This paper argues that the only way humans can justify machine knowledge is through Reliabilism, the epistemic justification through trust in the knowledge generation process. Although reliabilism may currently seem unacceptable, this paper predicts that scientists will take control over machine knowledge in the future, as testified by scientists exploring the chemical world, which is similarly beyond direct comprehension. In the end, this paper suggests the necessity of discussing how AI can justify its own knowledge as conscious beings.
Keywords	Epistemology, Philosophy for Science, Artificial Intelligence, Neural Networks
Field	Sociology > Philosophy / Psychology / Religion
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-22
Cite This	On the Knowledge of Neural Networks: The Genesis, Characterization, and Justification - Huikang Jiang - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29195
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.29195
Short DOI	https://doi.org/g8pnn2

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On the Knowledge of Neural Networks: The Genesis, Characterization, and Justification

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