Astrology is a collection of belief systems that assert that there is a connection between astrological phenomena and events or personality traits in the human world. The scientific community has dismissed astrology as having no explanatory power for describing the universe. Scientific testing has discovered no evidence to back up the astrological traditions’ premises or alleged effects.
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Is astrology considered a science?
Is astrology accurate? Reading horoscopes is a popular pastime, but is there any scientific evidence that they are accurate?
When you’re enticed by a familiar interruption and your willpower weakens, problems can occur.
Every day, up to 70 million Americans consult their horoscopes. At least, that’s what the American Federation of Astrologers claims. According to a Pew Forum on Religion & Public Life poll conducted twenty years ago, 25% of Americans believe that the positions of the stars and planets have an impact on our daily life. In 2012, the General Social Survey indicated that 34% of Americans think astrology is “extremely” or “kind of scientific,” with the percentage of individuals who think astrology is “not at all scientific” dropping from two-thirds to about half.
Astrology is the concept that astronomical phenomena, such as the stars over your head when you were born or the fact that Mercury is retrograde, have the potential to influence our daily lives and personality traits. Of course, this is distinct from astronomy, which is the scientific study of celestial objects, space, and the physics of the cosmos.
A particular facet of astrology, the foretelling of a person’s future or the provision of daily counsel via horoscopes, is gaining in popularity. The Cut, for example, recorded a 150 percent rise in horoscope page views in 2017 compared to 2016.
Clearly, a lot of people are trying to figure out how to read the stars for guidance. Understanding the positions of the stars is the foundation of astrology, which appears to be a scientific discipline in and of itself. Is there any scientific evidence that astrology has an impact on our personalities and lives?
But, since I still have five minutes of this six-minute podcast to fill, let’s take a look at how astrology has been put to the test.
Is it true that astrology is a data science?
“What good is studying astrology if I don’t believe in it?” I wondered. “Astrology is a tremendously mathematical science, and you excel at it. As a result, you’ll have a great time with it “She retorted, avoiding the question.
We got into a long conversation about the beginnings of astrology and how it led to early breakthroughs in astronomy (required for precisely determining the position of planets) and other topics. The debate became engrossing, with numerous digressions, as such discussions frequently do. And, as is customary in such situations, Priyanka threw a curveball: “You say you’re launching a company based on data analysis. Isn’t astrology similar to data analysis?”
I was perplexed (OK, I realize I’m mixing sports metaphors here), and that was the end of the conversation. However, I remained unconvinced, and I eventually forgot about the conversation. For the record, I spent my newly acquired spare time learning Western classical music.
Machine learning is, at its most basic level, a pattern recognition exercise. The system modifies a set of parameters in a mathematical formula given a set of inputs and outputs so that the outputs may be anticipated as accurately as possible given the inputs (I’m massively oversimplifying here, but this captures the spirit for this topic).
Machine learning has the advantage of being able to spot patterns that are not readily obvious to the human eye. The most prominent example of this has been in the realm of medical imaging, where algorithms have repeatedly been demonstrated to beat human experts in picture analysis.
In February of last year, a group of Stanford University researchers demonstrated that a deep learning algorithm they developed could detect skin cancer as well as a team of professional doctors. Another Stanford team developed an algorithm to diagnose heart arrhythmia using electrocardiograms in July, and shown that it surpassed the average cardiologist. More recently, algorithms have been demonstrated to outperform expert clinicians in detecting pneumonia and breast cancer.
With huge collections of photos including both positive and negative cases of the condition to be identified, all of these techniques calibrate parameters of a mathematical formula so that patterns leading to positive and negative cases may be discriminated. Then, when they’re fed fresh images, they use these formulae along with the calibrated parameters to classify them.
While applications such as medical imaging may lead us to believe that machine learning will take over the world, we must remember that if left to their own devices, machines can go horribly wrong. Google, for example, got into problems in 2015 when it labeled photographs of black women as “gorillas.”
Google admits to making a mistake and that the classification was “inappropriate,” but it appears that its engineers were powerless to stop it. The Guardian reported last month that Google has solved the problem by eliminating tags like “gorilla,” “chimp,” and “monkey” from its database.
The ability of machine learning to detect patterns that are not visible to humans might also be its undoing. Aside from the gorilla issue, the challenge with recognizing patterns that aren’t obvious or natural to humans is that meaningless patterns can also be picked up and exaggerated. This is referred to as “spurious correlations” in statistics.
And when we work with larger and larger data sets, the chances of misleading correlations arising from pure chance rise. In 2013, Nassim Nicholas Taleb, author of Fooled by Randomness and The Black Swan, predicted that “big data” would result in “huge errors.”
This is also true of modern machine learning algorithms that rely on a large amount of data. A group of researchers from New York University, for example, demonstrated that traffic sign detection algorithms may be deceived by simply introducing an additional set of training photos with an unclear pattern in the corner. More crucially, they demonstrated how a hostile opponent might carefully select these additional photos so that the misclassification would go undetected while the model was being trained.
The most common way of avoiding misleading associations has traditionally been for statisticians (or data scientists) to evaluate their models and ensure that they make “intuitive sense.” In other words, the models are permitted to uncover patterns, which are subsequently validated by a domain expert. In the event that the patterns don’t make sense, data scientists have altered their algorithms to provide more meaningful outcomes.
Another strategy taken by statisticians is to select models that are most appropriate for the data at hand. Different mathematical models excel in detecting patterns in various types of data, and choosing the proper technique for the data minimizes spurious pattern recognition.
Modern machine learning algorithms, on the other hand, have models that are difficult to verify and understand. It’s impossible, for example, to examine the calibrated parameters of a deep learning system to determine whether the patterns it detects are meaningful. Indeed, the “explainability” of artificial intelligence algorithms has piqued the curiosity of academics.
Because the algorithms are difficult to explain, the average data scientist treats them as black boxes. Furthermore, selecting the best model for a given data set is more of an art than a science, requiring visual inspection of the data as well as a comprehension of the arithmetic behind the models.
Data scientists have gotten past this “issue” by utilizing standardized packages to do the maths in a “cheap” manner (a Python module called Scikit Learn allows data scientists to create just about any machine learning model using three very similar appearing lines of code).
A huge number of data scientists tackle an issue by taking a data collection and applying all of the available machine learning approaches to it. They then choose the model that produces the best results on the given data set. There is no attempt to comprehend why the provided inputs lead to the outputs or whether the patterns “physically make sense.” This is analogous like stirring a pile of answers until they start looking right, as this XKCD cartoon puts it.
And this isn’t that dissimilar to how astrology works. There are several predictor factors (the positions of several “planets” in various sections of the “sky”) as well as observed variables (whether some disaster happened or not, in most cases). Then some of our forefathers did some data analysis on it, attempting to uncover combinations of predictors that predicted the outcome (unfortunately, they lacked the power of statistics or computers, so the models were limited in this regard). Then they just accepted the results without questioning why it makes sense that Jupiter’s location at the time of a wedding determines how a marriage will go.
With this knowledge, I recently brought up the subject of astrology and data science with my wife, informing her that “after careful consideration, I admit that astrology is the earliest type of data science.”
“I didn’t say that,” Priyanka responded. “I stated data science is new-age astrology, not the other way around,” said the author.
What is the basis of astrology’s science?
This isn’t the first time astrology has had a moment like this, and it won’t be the last. For thousands of years, the practice has existed in various forms. More recently, the New Age movement of the 1960s and 1970s included a heavy dose of astrology. (Some refer to the New Age as the “Age of Aquarius,” referring to the 2,000-year period after the Earth’s passage through the Aquarius sign.)
While astrology didn’t go completely in the decades between the New Age boom and nowyou could still read horoscopes in the back pages of magazinesit “got back to being a little bit more in the background,” says Chani Nicholas, an astrologer in Los Angeles. “Then there’s something that’s happened in the last five years that’s given it an edge, a relevancy for this time and place that it hasn’t had in 35 years.” The millennial generation has taken it and run with it.
Many of the people I spoke to for this article felt that, while the stigma surrounding astrology still exists, it has faded as the discipline has gained traction in online culture, particularly among young people.
“We’ve seen a reframing of New Age activities over the last two years, very much tailored toward a Millennial and young Gen X component,” says Lucie Greene, global director of J. Walter Thompson’s Intelligence Group, which studies and predicts cultural trends.
Broadly’s horoscope traffic, according to Callie Beusman, a senior editor, “has increased very dramatically.” The Cut’s president and editor-in-chief, Stella Bugbee, claims that a typical horoscope article on the site received 150 percent more traffic in 2017 than the previous year.
Astrology is well-suited to the digital age in some aspects. If you feel like plunging into a Google-research rabbit hole, there’s a low barrier to admission and practically infinite depths to plumb. The availability of more detailed information on the internet has given this cultural wave of astrology a level of sophistication. There will be more jokes about Saturn returns and less “Hey baby, what’s your sign?” questions. lines for a pick-up
A quick refresher: Astrology is not a science, and there is no proof that one’s zodiac sign has anything to do with personality. However, the system has its own logic. The positioning of the sun, moon, and planets within 12 parts of the sky, known as the zodiac signs, is given significance in astrology. Even if you’re not an astrology fan, you’re probably aware of your sun sign, the most well-known zodiac sign. It’s determined by the position of the sun on your birthday. However, the position of the moon and each of the other planets at the time and place of your birth adds more shades to the portrait of you that your “birth chart” paints.
Horoscopes are designed to tell you what the planets are doing right now and in the future, as well as how all of this influences each sign.
Susan Miller, the popular astrologer who developed the Astrology Zone website, describes the planets as a cocktail party. “You could have three individuals chatting at the same time, two people arguing in the corner, and Venus and Mars kissing.” I need to figure out what’s going on in those monthly talks for you.
“Astrologers are continually attempting to break down these massive concepts into manageable chunks of information,” Nicholas explains.
These days’ kids and their memes provide an ideal setting for astrology.
Astrology uses the planets and zodiac symbols to express complex ideas about personality, life cycles, and relationship patterns. That shorthand also works well online, where symbols and shorthand are frequently used.
Bertram Malle, a social cognitive scientist at Brown University, wrote me in an email, “Let me say first that I consider astrology a cultural or psychological phenomenon, not a scientific one.” However, “full-fledged astrology,” which goes beyond newspaper-style sun-sign horoscopes, gives you a powerful vocabulary to describe not only your personality and temperament, but also your life’s obstacles and prospects. To the extent that one just learns this vocabulary, it may appeal as a rich method of reflecting (rather than explaining or forecasting) human feelings and life events, as well as identifying some potential coping paths.
In times of stress, people frequently consult astrology. According to a short 1982 research by psychologist Graham Tyson, “those who contact astrologers do so in response to pressures in their lives, notably stress related to the individual’s social duties and connections.” “Under high stress, the individual is willing to employ astrology as a coping mechanism, even though he does not believe in it under low stress.”
Millennials have been the most stressed generation since 2014, according to American Psychological Association survey data, and they are also the group most likely to claim their stress has increased in the past year since 2010. Since 2012, Millennials and Gen Xers have been much more anxious than previous generations. Since the 2016 presidential election, Americans have been experiencing greater stress as a result of the political turmoil. According to the APA’s 2017 survey, 63 percent of Americans are “extremely concerned” about their country’s future. Reading the news stresses out 56% of individuals, with Millennials and Gen Xers being substantially more likely than older people to say so. Political infighting, climate change, global problems, and the prospect of nuclear war have all been prominent in recent news. If stress makes astrology look more appealing, it’s no surprise that more people are interested in it now.
What makes you think astrology isn’t scientific?
I thought the discussion about the moon’s influence on my mind was over until YouTube oddly offered a video about my Capricorn sun and Sagittarius moon combination. According to an astrologer, while Capricorn is realistic and goal-oriented, the spontaneity that Sagittarius desires can make these goals more difficult to achieve due to an aversion to routine. I’d always felt astrology was excessively pessimistic, but as she spoke, I understood it wasn’t. The astrologer clarified that each sign’s characteristics and tendencies are possibilities, not absolute truths. These options might help you figure out how you learn and express emotion, as well as what you want in a relationship and what motivates you. As I listened to her describe the aspects of myself that had previously gone unnamed, I realized that astrology isn’t meant to categorize people into stereotypes, but rather to provide a language and structure for getting to know oneself better. Rather of succumbing to the whims of the planet, astrology encourages us to take more control of our lives.
The adorable power of pop astrology reacted to this newfound admiration. While memes and witty horoscopes have helped to popularize the practice, they also contribute to its numerous myths. “Sun sign astrology became popular in the 1930s when newspapers began publishing overly simplified horoscopes to save money. Since then, every nuance has been reduced to a Buzzfeed quiz response, cementing the zodiac’s place in culture’s kitschy, infantile underbelly.
At a time when the wellness industry wants us to believe that our mental health can be bought, astrology can help us reconnect with our inner strength. While Plato’s ties to astrology have been downplayed, he was a firm believer in it “Celestial bodies’ ensouled nature has a direct impact on humans, and we must be conscious of these elements in order to be completely present and in control of our lives.
People have used astrology to help them achieve personal and societal empowerment across cultures. Those in authority, however, have actively attempted to shut it down from the 2nd millennium BC, according to Babylonian archives. As Christianity became more popular, astrology was outlawed as a heretic. Astronomers like Galileo had to hide astrology’s impact even as it helped them make scientific achievements like the discovery of a heliocentric solar system. God was replaced by rationalism during the Enlightenment, and astrology had no place in the religion of Logic and Reason.
Scientists believe that astrology lacks scientific foundations, and while some astrologers remain defensive, the majority are unconcerned. Astrology was never meant to be a science, and it never will be. Astrology can create spiritual significance for life’s patterns and inspire reflection in the same way that religion can be used in a healthy, nondogmatic fashion.
Many people believe that the traits of each sign can be applied to everybody. This is correct, and it is the entire point. While astrology shows our own characteristics, it also reveals universal patterns. A common astrological mythology that has evolved over time from Greece to China demonstrates that all individuals have a fundamental drive to find meaning in their surroundings.
Is astronomy considered a science course?
Astronomy is a discipline of natural science that investigates the elements of the universe, such as galaxies, planets, the Solar System, celestial objects, stars, comets, and other out-of-this-world phenomena.
What was Jesus’ take on astrology?
I believe that God created astrology as a tool for us to better understand ourselves and to use as a spiritual tool. Numerous bible texts, in my opinion, support astrology. As a Christian, I try to remember what Jesus said. “There shall be signs in the sun, moon, and stars,” Christ predicted in Luke 21:25, referring to the importance of astrology. He explains the value of astrology with his pupils, as well as how it might be used as a sign of his return. Why would Jesus provide us this critical knowledge if we are not intended to understand the energies of the planets and signs, and if he was actually against it? Just as the three wise men knew Jesus would be born under the star in the sky that led them to him lying in the manger, Jesus warned us that when he returns, there will be signals in the sky.
Introduction
What is Data Science, and how does it work? Data Science, like “Big Data,” is a term of art from industry that is used so broadly that no one knows what it means. This article does not attempt to describe Data Science for everyone; rather, it delves into what it means at the Institute and what it could mean for astronomical science institutes around the world. It’s worth mentioning that the Institute’s Data Science Mission Office isn’t only about data science; we’re also concerned with all elements of data management. At the Institute, we’ll discuss three different fields of Data Science: (1) Data-Driven Astronomy, (2) Statistics, Machine Learning, and Algorithms, and (3) Analytics and Data Mining.
Data-Driven astronomy
The generation of astronomical knowledge based on archive data sets is known as Data-Driven Astronomy (DDA). DDA is related to industrial data science in that the data sets are collected as a consequence of other operations or studies, rather than specifically for the experiment. DDA is commonly used on large homogeneous data sets, such as the Sloan Digital Sky Survey or the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS), but it can also be used on heterogeneous data sets, such as the Hubble Legacy Archive. These scientific investigations frequently use tools that are similar: astronomical data awareness, a thorough understanding of selection biases, and sophisticated multipoint statistics. DDA experts examine minor systematic effects that only become dominant over Poisson noise with bigger amounts of data, and create data analysis procedures to suppress these effects, because DDA approaches frequently use very large data sets. This contradicts the popular belief that Data Scientists are unconcerned about the minutiae of the data they work with; good DDA necessitates a tight relationship with the data, which frequently necessitates collaboration with instrument and survey professionals. At the Institute, which was based on one of the most prolific “Guest Observer” telescopes in history, DDA is not the traditional style of astronomy. By improving DDA at the Institute, we will be able to make even better use of MAST, massive data sets from throughout the world, and, in the future, WFIRST.