ML is data to data. It can conceivably make great achievement if huge data and computing resources can be used, just like our current era.
The question is whether the achievement made by the ML can be helpful to improve our understanding of nature. I think this is very difficult before some solid experiments or mathematical proof are provided. Just like the quote “every rode leads to Rome”, there can be lots of ways on connecting the starting point and the end point. If the way used by ML is quite different from the physical laws we are obeying, then how can the significant "precursors" extracted from the ML but "overlooked" by the physical models (with inevitable biases) be used to improve our understanding and the models? These "precursors" might not work in the models like the way in the ML framework. Then how can we interpret its achievements using the physical language?
Yes, machine learning can significantly contribute to improving our understanding of nature across various scientific disciplines. Machine learning techniques have the ability to analyze vast amounts of data, recognize patterns, and make predictions, which can lead to valuable insights and advancements in our understanding of natural phenomena.
Here are some ways in which machine learning can aid in improving our understanding of nature:
Overall, machine learning can be a powerful tool in advancing our understanding of nature by enabling data-driven analyses, pattern recognition, and predictive modeling. By harnessing the capabilities of machine learning, researchers and scientists can gain deeper insights into natural processes, address environmental challenges, and develop more effective strategies for conservation and sustainable resource management.
Machine Learning (ML), a paradigm of data-to-data correlations, holds potential to foster substantial advancements, especially in this age of ubiquitous computing and vast data reservoirs. However, the real conundrum lies in the applicability of ML-derived insights to bolster our understanding of nature.
Unraveling this quandary demands rigorous experimental validations or mathematical substantiation. The adage "All roads lead to Rome" posits the existence of multiple paths connecting an origin to a destination. In this context, if the path traversed by ML is deviant from the known physical laws, the implementation of "precursors" or significant signals gleaned from ML but disregarded by the standard physical models becomes challenging.
Inevitable biases exist in physical models, and these "precursors" might not function within the confines of these models as they do in an ML ecosystem. Consequently, the interpretation of ML's accomplishments using the lexicon of physics presents a formidable challenge. The process could be akin to drawing parallels between two disparate languages or systems that follow distinct sets of rules and structures.
This conundrum signifies the larger philosophical question regarding the intersection of data-driven insights and our understanding of natural phenomena. The path to reconciliation likely involves iterative refinement of both ML algorithms and physical models, as well as the development of new methodologies to interpret ML outputs in the light of physics and vice versa. The true potency of ML in comprehending nature might be realized when we devise mechanisms to harmonize these two disparate paths.
Hi, please check the recent study regarding the unsupervised machine learning applications in early plant diseases detection:
Article Abaxial leaf surface-mounted multimodal wearable sensor for ...
In my old-fashioned opinion，from a practical perspective，ML could be viewed as an alternative fast way of numerical modeling if our physical control equations dot not hold quite well in real nature ( e.g. shallow water equation ). ML may work in compensating for the residual term of control equations if it can really give the analytical expressions described by elementary functions.
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