Astrology and Algorithms
Posted: Sat Jun 18, 2022 4:31 am
The concept of algorithm has existed since antiquity. Arithmetic algorithms, such as a division algorithm, were used by ancient Babylonian mathematicians c. 2500 BC and Egyptian mathematicians c. 1550 BC. Greek mathematicians later used algorithms in 240 BC in the sieve of Eratosthenes for finding prime numbers, and the Euclidean algorithm for finding the greatest common divisor of two numbers. Arabic mathematicians such as al-Kindi in the 9th century used cryptographic algorithms for code-breaking, based on frequency analysis.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
The word algorithm is derived from the name of the 9th-century Persian mathematician Muḥammad ibn Mūs?? al-Khw??rizmī, whose name was Latinized as Algoritmi.
Its simplest definition is 'set of rules that precisely defines a sequence of operations'.
The earliest evidence of algorithms is found in the Babylonian mathematics of ancient Mesopotamia (modern Iraq). A Sumerian clay tablet found in Shuruppak near Baghdad and dated to circa 2500 BC described the earliest division algorithm. During the Hammurabi dynasty circa 1800-1600 BC, Babylonian clay tablets described algorithms for computing formulas. Algorithms were also used in Babylonian astronomy. Babylonian clay tablets describe and employ algorithmic procedures to compute the time and place of significant astronomical events.
Algorithms for arithmetic are also found in ancient Egyptian mathematics, dating back to the Rhind Mathematical Papyrus circa 1550 BC. Algorithms were later used in ancient Hellenistic mathematics. Two examples are the Sieve of Eratosthenes, which was described in the Introduction to Arithmetic by Nicomachus and the Euclidean algorithm, which was first described in Euclid's Elements (c. 300 BC).
The first cryptographic algorithm for deciphering encrypted code was developed by Al-Kindi, a 9th-century Arab mathematician, in A Manuscript On Deciphering Cryptographic Messages. He gave the first description of cryptanalysis by frequency analysis, the earliest codebreaking algorithm.
Indian mathematics was predominantly algorithmic. Algorithms that are representative of the Indian mathematical tradition range from the ancient Śulbasūtr??s to the medieval texts of the Kerala School. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu. It begins with:
Haec algorismus ars praesens dicitur, in qua
Talibus Indorum fruimur bis quinque figuris.
which translates to:
Algorism is the art by which at present we use those Indian figures, which number two times five.
The poem is a few hundred lines long and summarizes the art of calculating with the new styled Indian dice (Tali Indorum), or Hindu numerals.
(Wikipedia)
The evolution of algorithms can be traced back to the evolution of mini-computers in the 60's with the Uranus-Pluto conjunction in Virgo, the expansion of the mind (Mercury).
With the first sextile from 1995 to 1997, the first version of Windows was released.
The quintile phase in the early 2000 saw the emergence of Peer-to-Peer connections like Napster that modulated connections through different planes.
But it really took off during the square phase between 2012-2015 with the Bitcoin being released approaching this phase and the large scale use by political organisations that led to the Facebook–Cambridge Analytica data scandal. Personal data belonging to millions of Facebook users was collected without their consent by British consulting firm Cambridge Analytica, predominantly to be used for political advertising and it was revealed by The Guardian and The New York Times that they helped in the election of Donald Trump.
Pluto enters Aquarius in March 2023 and Uranus enters Gemini in July 2025. The trine phase will be prominent between 2026 and 2028 in Air Signs.
What future for astrology?
Like divinatory techniques, algorithmic systems of prediction should be considered as modes of knowledge aimed at ordering the world. Despite their differences, they do indeed share a common ambition:
The seemingly irresistible need to explain, to bring order to things, to discover in them (or to give them) a meaning by seeing in them (or by establishing) relationships that make a disparate set of diverse sensations a fabric organized, unified in space, and, perhaps above all, in time.
(Atlan H., 1986, À tort ou à raison. Intercritique de la science et du mythe.)
In a paper published by Christophe Lazaro, he explains citing Chandler that 'Big data is therefore generally considered to generate a different type of “knowledge???: closer to the translation or interpretation of signs than to the understanding of chains of causality.'
He goes on to explain that “The spectacular development of algorithmic systems capable of collecting, analyzing and processing massive quantities of data have arguably conferred on humans a new device of prediction allowing to optimize decision-making processes, to anticipate risks and to exercise control over individuals. In the era of Big data, applications based on new machine learning methods are developed in various fields such as security, marketing and entertainment. These algorithmic systems, we contend, have to be understood as original modes of prediction and preemption of the future. Drawing inspiration from divinatory practices of classical Antiquity as described in Cicero’s Treatise De Divinatione, we shed light on the contemporary beliefs in the predictive power of algorithms. “
DOI : https://doi.org/10.7202/1052640ar
In a near future, it will be possible via algorithms to have a computer machine who will start to talk the language of astrology. Launching an algorithm which will first read all the literature written by astrologers around the world in all languages, listening to phone calls, messages, chats, forums, emails and websites, it will be become an 'astrologer' with deep-learning.
Just like computers which have started to create and publish cookbooks based on the knowledge they have acquired by reading recipes.
Christophe Lazaro warns us of 3 forms of opacity with this new data-driven science.
1) The first form of opacity relates to their status as “black boxes??? computing (Kallinikos 2002) including computational complexity and logic can only be grasped by specialists. In this regard, the knowledge of the computer scientist is similar to that of the diviner. Indeed, to take the auspices or decipher the viscera, it is necessary to have learned, through training or an initiation, the meaning of the signs and, moreover, to have a certain experience of the discipline (Guillaumont 2006: 90). The opacity characterizes here especially the perception that the layman has of algorithms.
2) In this line, the second form of opacity implicated in the literature concerns algorithmic systems based on systems of artificial intelligence. Far beyond the problem of acquiring a culture digital, the operation of the self-learning algorithms used in the predictive analysis systems can be obscure even for specialists:
As we have already mentioned, the intelligibility of correlations and categorizations made by the algorithm is sometimes lacking, so it is difficult to trace the different steps that led to a decision. Because this one has no grounds or clear explanation, it requires then a particularly delicate work of interpretation comparable to what goes through divinatory practices.... This second level of technical opacity is also problematic because it has the effect of masking any errors and biases resulting from the use of some algorithmic systems. In terms of predictive models, this can lead to serious problems of discrimination (Zarsky 2013) or detection of “false positives???, as in the often dramatic cases of identification of people with no real terrorist intent (Munk 2017).
Note: When I was working for a tech company in audio-video, I personally witnessed some of the errors the new technology can bring. One day, my colleagues were installing a new smart camera able to detect who is talking in a boardroom. Now mind you, there was an African black man with a white tie in the room and every time he was talking, the camera was focusing on his tie instead of his face!!! The camera had been programmed to detect the 'light' of the face, which the camera could not do and was ending up on the tie.
3) Finally, a third form of opacity associated with algorithmic systems contemporaries takes on a more institutional dimension. She participates of a kind of culture of secrecy, deployed intentionally by certain organizations, through not only legal provisions in terms of of intellectual property closing access to the code, but more generally through a set of insidious economic and political practices and crafty (Manovich 2009). These practices, sometimes aimed at misleading users, to steal data from them, to observe their behavior for sometimes malicious, end up constituting what Pasquale calls in a book become an essential “black box society??? (Pasquale 2015).
When we reach the Uranus-Pluto opposition in 2046, I think the definition of an astrologer and the tools that we use will be totally different.
Not to mention the advent of the 3rd web generation (www3) which will be using the blockchain like Cryptos and a unique way of securely recording and transferring information. For the creators and the copyright this will be well received but I am not sure that Big Corp will loose in the battle.
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
The word algorithm is derived from the name of the 9th-century Persian mathematician Muḥammad ibn Mūs?? al-Khw??rizmī, whose name was Latinized as Algoritmi.
Its simplest definition is 'set of rules that precisely defines a sequence of operations'.
The earliest evidence of algorithms is found in the Babylonian mathematics of ancient Mesopotamia (modern Iraq). A Sumerian clay tablet found in Shuruppak near Baghdad and dated to circa 2500 BC described the earliest division algorithm. During the Hammurabi dynasty circa 1800-1600 BC, Babylonian clay tablets described algorithms for computing formulas. Algorithms were also used in Babylonian astronomy. Babylonian clay tablets describe and employ algorithmic procedures to compute the time and place of significant astronomical events.
Algorithms for arithmetic are also found in ancient Egyptian mathematics, dating back to the Rhind Mathematical Papyrus circa 1550 BC. Algorithms were later used in ancient Hellenistic mathematics. Two examples are the Sieve of Eratosthenes, which was described in the Introduction to Arithmetic by Nicomachus and the Euclidean algorithm, which was first described in Euclid's Elements (c. 300 BC).
The first cryptographic algorithm for deciphering encrypted code was developed by Al-Kindi, a 9th-century Arab mathematician, in A Manuscript On Deciphering Cryptographic Messages. He gave the first description of cryptanalysis by frequency analysis, the earliest codebreaking algorithm.
Indian mathematics was predominantly algorithmic. Algorithms that are representative of the Indian mathematical tradition range from the ancient Śulbasūtr??s to the medieval texts of the Kerala School. Another early use of the word is from 1240, in a manual titled Carmen de Algorismo composed by Alexandre de Villedieu. It begins with:
Haec algorismus ars praesens dicitur, in qua
Talibus Indorum fruimur bis quinque figuris.
which translates to:
Algorism is the art by which at present we use those Indian figures, which number two times five.
The poem is a few hundred lines long and summarizes the art of calculating with the new styled Indian dice (Tali Indorum), or Hindu numerals.
(Wikipedia)
The evolution of algorithms can be traced back to the evolution of mini-computers in the 60's with the Uranus-Pluto conjunction in Virgo, the expansion of the mind (Mercury).
With the first sextile from 1995 to 1997, the first version of Windows was released.
The quintile phase in the early 2000 saw the emergence of Peer-to-Peer connections like Napster that modulated connections through different planes.
But it really took off during the square phase between 2012-2015 with the Bitcoin being released approaching this phase and the large scale use by political organisations that led to the Facebook–Cambridge Analytica data scandal. Personal data belonging to millions of Facebook users was collected without their consent by British consulting firm Cambridge Analytica, predominantly to be used for political advertising and it was revealed by The Guardian and The New York Times that they helped in the election of Donald Trump.
Pluto enters Aquarius in March 2023 and Uranus enters Gemini in July 2025. The trine phase will be prominent between 2026 and 2028 in Air Signs.
What future for astrology?
Like divinatory techniques, algorithmic systems of prediction should be considered as modes of knowledge aimed at ordering the world. Despite their differences, they do indeed share a common ambition:
The seemingly irresistible need to explain, to bring order to things, to discover in them (or to give them) a meaning by seeing in them (or by establishing) relationships that make a disparate set of diverse sensations a fabric organized, unified in space, and, perhaps above all, in time.
(Atlan H., 1986, À tort ou à raison. Intercritique de la science et du mythe.)
In a paper published by Christophe Lazaro, he explains citing Chandler that 'Big data is therefore generally considered to generate a different type of “knowledge???: closer to the translation or interpretation of signs than to the understanding of chains of causality.'
He goes on to explain that “The spectacular development of algorithmic systems capable of collecting, analyzing and processing massive quantities of data have arguably conferred on humans a new device of prediction allowing to optimize decision-making processes, to anticipate risks and to exercise control over individuals. In the era of Big data, applications based on new machine learning methods are developed in various fields such as security, marketing and entertainment. These algorithmic systems, we contend, have to be understood as original modes of prediction and preemption of the future. Drawing inspiration from divinatory practices of classical Antiquity as described in Cicero’s Treatise De Divinatione, we shed light on the contemporary beliefs in the predictive power of algorithms. “
DOI : https://doi.org/10.7202/1052640ar
In a near future, it will be possible via algorithms to have a computer machine who will start to talk the language of astrology. Launching an algorithm which will first read all the literature written by astrologers around the world in all languages, listening to phone calls, messages, chats, forums, emails and websites, it will be become an 'astrologer' with deep-learning.
Just like computers which have started to create and publish cookbooks based on the knowledge they have acquired by reading recipes.
Christophe Lazaro warns us of 3 forms of opacity with this new data-driven science.
1) The first form of opacity relates to their status as “black boxes??? computing (Kallinikos 2002) including computational complexity and logic can only be grasped by specialists. In this regard, the knowledge of the computer scientist is similar to that of the diviner. Indeed, to take the auspices or decipher the viscera, it is necessary to have learned, through training or an initiation, the meaning of the signs and, moreover, to have a certain experience of the discipline (Guillaumont 2006: 90). The opacity characterizes here especially the perception that the layman has of algorithms.
2) In this line, the second form of opacity implicated in the literature concerns algorithmic systems based on systems of artificial intelligence. Far beyond the problem of acquiring a culture digital, the operation of the self-learning algorithms used in the predictive analysis systems can be obscure even for specialists:
As we have already mentioned, the intelligibility of correlations and categorizations made by the algorithm is sometimes lacking, so it is difficult to trace the different steps that led to a decision. Because this one has no grounds or clear explanation, it requires then a particularly delicate work of interpretation comparable to what goes through divinatory practices.... This second level of technical opacity is also problematic because it has the effect of masking any errors and biases resulting from the use of some algorithmic systems. In terms of predictive models, this can lead to serious problems of discrimination (Zarsky 2013) or detection of “false positives???, as in the often dramatic cases of identification of people with no real terrorist intent (Munk 2017).
Note: When I was working for a tech company in audio-video, I personally witnessed some of the errors the new technology can bring. One day, my colleagues were installing a new smart camera able to detect who is talking in a boardroom. Now mind you, there was an African black man with a white tie in the room and every time he was talking, the camera was focusing on his tie instead of his face!!! The camera had been programmed to detect the 'light' of the face, which the camera could not do and was ending up on the tie.
3) Finally, a third form of opacity associated with algorithmic systems contemporaries takes on a more institutional dimension. She participates of a kind of culture of secrecy, deployed intentionally by certain organizations, through not only legal provisions in terms of of intellectual property closing access to the code, but more generally through a set of insidious economic and political practices and crafty (Manovich 2009). These practices, sometimes aimed at misleading users, to steal data from them, to observe their behavior for sometimes malicious, end up constituting what Pasquale calls in a book become an essential “black box society??? (Pasquale 2015).
When we reach the Uranus-Pluto opposition in 2046, I think the definition of an astrologer and the tools that we use will be totally different.
Not to mention the advent of the 3rd web generation (www3) which will be using the blockchain like Cryptos and a unique way of securely recording and transferring information. For the creators and the copyright this will be well received but I am not sure that Big Corp will loose in the battle.