Artificial intelligenceand astrology might seem, at first glance, to be mutually incompatible domains. AI is the epitome of scientific progress and technological advancement, whereas astrology is often viewed as a field of pseudoscience, superstition, and even magic and mysticism. And yet, both share a striking similarity: they are deeply embedded in the activity of making predictions and finding meaning by reading patterns in the world. Artificial intelligence has reinvigorated public trust in the notion that the future may, after all, be grasped and renewed the framework of speculative thinking.
In the ancient tradition, the human astrologers perform all the mathematical parts of astrology related to divination. On the other hand, the artificial machines use speculative calculation through the idea of mimicking the human process in a computable manner. It is made possible by algorithms that automatically search through large collections of data sometimes termed as big data, particularly with deep learning algorithms.
Artificial intelligence, considered as an extension of human intelligence, is used as a predictive tool to improve decision-making, predict potential risks, and influence behavior in a wide range of human activities.
This shows a great likeness to artificial intelligence in the role of speculation; it is able to rationalize and predict a pattern of behavior among humans and societal events by observing and recording patterns, either celestial in astrology or data in artificial intelligence. The underlying assumption of machine learning algorithms and statistical modeling is actually based on a similar set of beliefs that is found in practices of astrology-about knowing the future and controlling unpredictability.
Astrology, an institutionalized system of divination system from ancient traditions of pattern recognition, operated upon medieval scholars and was also the forerunner to sciences such as astronomy and astrometeorology. With the help of imperial and military explorations, the tongue of the heavenly horizon transformed to climatology and meteorology as scientific disciplines. The study of climate and weather required the collection of a lot of data, statistical computation, and powerful computing systems.
Artificial intelligence emerged from the algorithms and neural networks of computers that were designed to mimic the working of the human brain.
But the use of artificial intelligence (AI) in astrological predictions is limited by several factors:
- Subjective Interpretations: Astrology is highly based on subjective interpretations of celestial positions and their assumed impacts on individuals and events. AI works based on objective data and algorithms, which makes it incapable of understanding subjective beliefs or emotions.
- Data Quality: AI systems require high-quality, reliable data to make accurate predictions. The data used in astrology often consists of birth dates, celestial positions, astrological charts, and astrological texts, which do not have a solid empirical foundation and are inconsistent from one astrologer to another.
- There is no consensus in the interpretations or methodologies that most astrologers have themselves. Therefore, they tend to get conflicting predictions, and it may be a very difficult job to ensure consistency and clear rules when creating an AI model that produces predictions.
- Overfitting: The tendency of AI models to fit very closely to noise in the training data instead of generalizing towards new, unseen data. That is, astrological models trained on some astrological data are likely to result in a fitting pattern to some noise in that data and possibly fail to be effective for some new data sets.
- Ethical Issues: Application of AI to astrological prediction may give rise to some ethical issues. This can create loopholes through prediction without the involvement of any scientific principle or proof, spreading misconceptions in the process.
- Limited Predictive Power: Even if AI is used to analyze astrological data, it has a fundamental issue. Astrology lacks strong predictive power in controlled scientific studies. The effectiveness of AI depends on the quality of the data that AI is trained with, and if the underlying data lacks predictive validity, AI predictions will lack the same.
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