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Nostradamus prediction on King Charles sparks fresh theories

An eerie prediction on King Charles made by 16th century astrologer Nostradamus has resurfaced following the royal's cancer diagnosis. The French physician is known for his uncannily accurate predictions which he wrote in a tome called Les Propheties in 1555. Les Propheties contains 942 predictions which have been analysed over the years, and some have claimed that it foresaw major events including the Great Fire of London in 1666 , the French Revolution and even 9/11. It is also believed to have accurately predicted Queen Elizabeth's death, as in his book Nostradamus said that the second Queen Elizabeth would die in "22" at "around" the age of 96. She passed away on the 8th of September 2022 at the age of 96. The astrologer also predicted that in 2024, the royal family would face turmoil with a King “driven out by force”. “King of the Isles driven out by force ... replaced by one who will have no mark of a king,” the passage in the book read. Nostradamus expert Mario Reading initially interpreted the prophecy to refer to an “unworthy” and unpopular King who would be driven out by the wishes of the people and replaced by someone who "never expected to be King". But now, with King Charles' cancer diagnosis, many have shared their own predictions on what might happen to the royal, mainly him being forced from the throne because of his illness. Others have raised the question on who is the man they “never expected to be King”? If Charles abdicates, and Prince William declines the throne and the role of Prince Regent as he faces his own battle with Kate's cancer diagnosis, then would William’s eldest son George, 10, become king? In today's monarchy, what are the implications of a 10-year-old becoming King and would they instead consider an adult, aka the fifth in line or “spare to the heir,” Prince Harry as a better option for King? There are so many possibilities floating around. Image: Shutterstock/ Instagram

Caring

The alarmingly accurate predictions made 100 years ago

A university researcher has uncovered predictions made in 1924 about what the world would look like 100 years in the future, with some of the predictions proving to be scarily accurate. The Canadian professor Paul Fairie shared a selection of headlines and articles made by newspapers in the 1920s on his X account, showing in what ways the world has changed and how it has remained the same. While some predictions made in 1924 hit the nail on the head, others couldn't be further from reality. Accurate predictions Car speedways One newspaper clipping claimed that in the future, cars would be travelling on super "speedways" that allowed them to travel faster. While cars first hit the roads in the 1880s, it wasn't until the 1920s that vehicles became more common, with cities quickly trying to build safe roads to accommodate the growth in motor vehicles. “In the city of 2024, this authority predicts there will be three-deck roads; speedways through the heart of town,” the newspaper article predicted. The article also envisaged monorail express services to the suburbs replacing cars and buses and moving sidewalks (similar to airport travelators) that moved people in all directions, “serving all railroad stations and business districts”. Women getting tattooed and dyeing their hair In the 1920s, tattoos were exclusively reserved for sailors, criminals and gangland figures until they became more commonplace. But one 1924 article predicted, incredibly accurately, that by 2024 women would be getting tattoos and dyeing their hair “all the colours of the rainbow”. “Debutantes of 1924 are shingle-haired, sleek-looking maidens with delicately rouged cheeks and provocative red lips,” the article stated. “The 1924 debutante successfully conceals her personality under paint and power." “The debutante of 100 years hence may revert to type and frankly copy her ancestors, who dyed their skins with woad; only, with the modern instinct for progress." “She may go still further and dye her complexion and hair all the colours of the rainbow.” Women becoming stronger The newspapers of 1924 also predicted that women of the future would be “physically strong, vital and alert.” One article predicted that women would spend more time in the outdoors, participate in sports, and would be “engaging by choice” in activities that were historically restricted to men. Intellectually, women would be “quick at wit and keen of judgment,” while spiritually, she would “radiate love and good will”. Remote viewing One of the most accurate predictions from 1924 was the invention of technology that would allow people to view entertainment "remotely". “Many inconveniences which the touring artist now has to suffer will no doubt be eliminated,” one prediction read. “It will not be necessary to travel great distances. The strain of the concert tour will be dispensed with. Artists may not even have to leave their homes (to perform).” Not so accurate predictions Horses becoming extinct One prediction from a 1924 newspaper believed that as cars took over as the main form of transportation, there would be no more use for horses and they would soon becoming extinct. “If a house would decrease in the same ratio as in the last ten or twenty years, it might be easy to tell when the last horse would give up his stall to an automobile,” the prediction read. Self-launching beds Another not-so accurate prediction claimed that beds would soon come with a feature that would override the use for an alarm clock, but would come with a mechanism to launch you out of bed in the morning. Describing this futuristic scenario, the reporter wrote: “My bed turns over automatically and I am deposited on the floor”. “Eight o’clock and the switch operating above the fiendish substitute for an alarm clock is operated from school, so at the moment, I am in the same predicament as the rest of the 450 scholars.” Image credits: Shutterstock

Technology

Nostradamus’ 2024 predictions revealed

Nostradamus, a 16th-century French astrologer known as “the prophet of doom,” has a warning about the potential upcoming events of 2024. The astrologer's visions and predictions in the past have credited with foreseeing the rise of Adolf Hitler, the assassination of John F. Kennedy, the September 11 attacks and Covid, to name a few. Now, according to his 1555 text Les Propheties (The Prophecies), 2024 is set to bring continued global strife, including war on the seas, royal turmoil and humanitarian disaster. One of the passages in the text mentions that a “King of the Isles” will be “driven out by force”, with many taking this to mean King Charles III will be ousted from the throne. A following passage reads “soon afterwards [a disastrous war] a new King shall be anointed / Who, for a long time, will appease the earth,” with British author and Nostradamus commentator Mario Reading deciphering this to mean Prince Harry will become King. According to the Daily Mail, Reading analysed that King Charles III would be abdicating due to “persistent attacks on both himself and his second wife,” and Harry would replace him instead of William because he — the “Spare” heir — has “no mark of a king.” Nostradamus predicted “combat and naval battle” and said that a “red adversary will become pale with fear, putting the great ocean in dread”. Many have guessed the "red adversary" is in reference to China, with suggestions the global superpower could wage war on the rest of the world, given the growing tensions between China and Taiwan. For many years, climate change has been a topic at the front of people's mind, with Nostradamus predicting global warming will get worse over the next 12 months. “The dry earth will grow more parched, and there will be great floods when it is seen,” he wrote. He also predicted extreme weather events and world hunger, writing about a “Very great famine through pestiferous wave”. While some of Nostradamus’ doomsday predictions seem bleak, fear not, for his most terrifying 2023 predictions have yet to come true with just a few weeks left before the new year. This year, Nostradamus predicted the coming of the antichrist as well as a full-blown World War III. Image credits: Getty Images

Mind

Here’s how a new AI tool may predict early signs of Parkinson’s disease

In 1991, the world was shocked to learn actor <a href="https://www.theguardian.com/film/2023/jan/31/still-a-michael-j-fox-movie-parkinsons-back-to-the-future">Michael J. Fox</a> had been diagnosed with Parkinson’s disease. He was just 29 years old and at the height of Hollywood fame, a year after the release of the blockbuster Back to the Future III. This week, documentary <a href="https://www.imdb.com/title/tt19853258/">Still: A Michael J. Fox Movie</a> will be released. It features interviews with Fox, his friends, family and experts. Parkinson’s is a debilitating neurological disease characterised by <a href="https://www.mayoclinic.org/diseases-conditions/parkinsons-disease/symptoms-causes/syc-20376055">motor symptoms</a> including slow movement, body tremors, muscle stiffness, and reduced balance. Fox has already <a href="https://www.cbsnews.com/video/michael-j-fox-on-parkinsons-and-maintaining-optimism">broken</a> his arms, elbows, face and hand from multiple falls. It is not genetic, has no specific test and cannot be accurately diagnosed before motor symptoms appear. Its cause is still <a href="https://www.apdaparkinson.org/what-is-parkinsons/causes/">unknown</a>, although Fox is among those who thinks <a href="https://www.cbsnews.com/video/michael-j-fox-on-parkinsons-and-maintaining-optimism">chemical exposure may play a central role</a>, speculating that “genetics loads the gun and environment pulls the trigger”. In research published today in <a href="https://pubs.acs.org/doi/10.1021/acscentsci.2c01468">ACS Central Science</a>, we built an artificial intelligence (AI) tool that can predict Parkinson’s disease with up to 96% accuracy and up to 15 years before a clinical diagnosis based on the analysis of chemicals in blood. While this AI tool showed promise for accurate early diagnosis, it also revealed chemicals that were strongly linked to a correct prediction. <h2>More common than ever</h2> Parkinson’s is the world’s <a href="https://www.who.int/news-room/fact-sheets/detail/parkinson-disease">fastest growing neurological disease</a> with <a href="https://shakeitup.org.au/understanding-parkinsons/">38 Australians</a>diagnosed every day. For people over 50, the chance of developing Parkinson’s is <a href="https://www.parkinsonsact.org.au/statistics-about-parkinsons/">higher than many cancers</a> including breast, colorectal, ovarian and pancreatic cancer. Symptoms such as <a href="https://www.apdaparkinson.org/what-is-parkinsons/symptoms/#nonmotor">depression, loss of smell and sleep problems</a> can predate clinical movement or cognitive symptoms by decades. However, the prevalence of such symptoms in many other medical conditions means early signs of Parkinson’s disease can be overlooked and the condition may be mismanaged, contributing to increased hospitalisation rates and ineffective treatment strategies. <h2>Our research</h2> At UNSW we collaborated with experts from Boston University to build an AI tool that can analyse mass spectrometry datasets (a <a href="https://www.sciencedirect.com/topics/neuroscience/mass-spectrometry">technique</a> that detects chemicals) from blood samples. For this study, we looked at the Spanish <a href="https://epic.iarc.fr/">European Prospective Investigation into Cancer and Nutrition</a> (EPIC) study which involved over 41,000 participants. About 90 of them developed Parkinson’s within 15 years. To train the AI model we used a <a href="https://www.nature.com/articles/s41531-021-00216-4">subset of data</a> consisting of a random selection of 39 participants who later developed Parkinson’s. They were matched to 39 control participants who did not. The AI tool was given blood data from participants, all of whom were healthy at the time of blood donation. This meant the blood could provide early signs of the disease. Drawing on blood data from the EPIC study, the AI tool was then used to conduct 100 “experiments” and we assessed the accuracy of 100 different models for predicting Parkinson’s. Overall, AI could detect Parkinson’s disease with up to 96% accuracy. The AI tool was also used to help us identify which chemicals or metabolites were likely linked to those who later developed the disease. <h2>Key metabolites</h2> Metabolites are chemicals produced or used as the body digests and breaks down things like food, drugs, and other substances from environmental exposure. Our bodies can contain thousands of metabolites and their concentrations can differ significantly between healthy people and those affected by disease. Our research identified a chemical, likely a triterpenoid, as a key metabolite that could prevent Parkinson’s disease. It was found the abundance of triterpenoid was lower in the blood of those who developed Parkinson’s compared to those who did not. Triterpenoids are known <a href="https://www.sciencedirect.com/topics/neuroscience/neuroprotection">neuroprotectants</a> that can regulate <a href="https://onlinelibrary.wiley.com/doi/10.1002/ana.10483">oxidative stress</a> – a leading factor implicated in Parkinson’s disease – and prevent cell death in the brain. Many foods such as <a href="https://link.springer.com/article/10.1007/s11101-012-9241-9#Sec3">apples and tomatoes</a> are rich sources of triterpenoids. A synthetic chemical (a <a href="https://www.cdc.gov/biomonitoring/PFAS_FactSheet.html">polyfluorinated alkyl substance</a>) was also linked as something that might increase the risk of the disease. This chemical was found in higher abundances in those who later developed Parkinson’s. More research using different methods and looking at larger populations is needed to further validate these results. <h2>A high financial and personal burden</h2> Every year in Australia, the average person with Parkinson’s spends over <a href="https://www.hindawi.com/journals/pd/2017/5932675/">A$14,000</a>in out-of-pocket medical costs. The burden of living with the disease can be intolerable. Fox acknowledges the disease can be a “nightmare” and a “living hell”, but he has also found that “<a href="https://www.cbsnews.com/video/michael-j-fox-on-parkinsons-and-maintaining-optimism">with gratitude, optimism is sustainable</a>”. As researchers, we find hope in the potential use of AI technologies to improve patient quality of life and reduce health-care costs by accurately detecting diseases early. We are excited for the research community to try our AI tool, which is <a href="https://github.com/CRANK-MS/CRANK-MS">publicly available</a>. This research was performed with Mr Chonghua Xue and A/Prof Vijaya Kolachalama (Boston University). Image credits: Getty Images This article originally appeared on <a href="https://theconversation.com/heres-how-a-new-ai-tool-may-predict-early-signs-of-parkinsons-disease-205221" target="_blank" rel="noopener">The Conversation</a>.

Mind

Do you use predictive text? Chances are it’s not saving you time – and could even be slowing you down

Typing is one of the most common things we do on our mobile phones. A recent survey suggests that millenials spend <a href="https://www.provisionliving.com/blog/smartphone-screen-time-baby-boomers-and-millennials/">48 minutes</a> each day texting, while boomers spend 30 minutes. Since the advent of mobile phones, the way we text has changed. We’ve seen the introduction of autocorrect, which corrects errors as we type, and word prediction (often called predictive text), which predicts the next word we want to type and allows us to select it above the keyboard. Functions such as autocorrect and predictive text are designed to make typing faster and more efficient. But research shows this isn’t necessarily true of predictive text. A <a href="https://dl.acm.org/doi/10.1145/2858036.2858305">study</a> published in 2016 found predictive text wasn’t associated with any overall improvement in typing speed. But this study only had 17 participants – and all used the same type of mobile device. In 2019, my colleagues and I published <a href="https://doi.org/10.1145/3338286.3340120">a study</a> in which we looked at mobile typing data from more than 37,000 volunteers, all using their own mobile phones. Participants were asked to copy sentences as quickly and accurately as possible. Participants who used predictive text typed an average of 33 words per minute. This was slower than those who didn’t use an intelligent text entry method (35 words per minute) and significantly slower than participants who used autocorrect (43 words per minute). <h2>Breaking it down</h2> It’s interesting to consider the poor correlation between predictive text and typing performance. The idea seems to make sense: if the system can predict your intended word before you type it, this should save you time. In my most <a href="https://doi.org/10.1145/3411764.3445566">recent study</a> on this topic, a colleague and I explored the conditions that determine whether predictive text is effective. We combined some of these conditions, or parameters, to simulate a large number of different scenarios and therefore determine when predictive text is effective – and when it’s not. We built a couple of fundamental parameters associated with predictive text performance into our simulation. The first is the average time it takes a user to hit a key on the keyboard (essentially a measure of their typing speed). We estimated this at 0.26 seconds, based on <a href="https://dl.acm.org/doi/10.1145/2470654.2466180">earlier research</a>. The second fundamental parameter is the average time it takes a user to look at a predictive text suggestion and select it. We fixed this at 0.45 seconds, again based on <a href="https://dl.acm.org/doi/10.1145/1240624.1240723">existing data</a>. Beyond these, there’s a set of parameters which are less clear. These reflect the way the user engages with predictive text – or their strategies, if you like. In our research, we looked at how different approaches to two of these strategies influence the usefulness of predictive text. The first is minimum word length. This means the user will tend to only look at predictions for words beyond a certain length. You might only look at predictions if you’re typing longer words, beyond, say, six letters – because these words require more effort to spell and type out. The horizontal axis in the visualisation below shows the effect of varying the minimum length of a word before the user seeks a word prediction, from two letters to ten. The second strategy, “type-then-look”, governs how many letters the user will type before looking at word predictions. You might only look at the suggestions after typing the first three letters of a word, for example. The intuition here is that the more letters you type, the more likely the prediction will be correct. The vertical axis shows the effect of the user varying the type-then-look strategy from looking at word predictions even before typing (zero) to looking at predictions after one letter, two letters, and so on. A final latent strategy, perseverance, captures how long the user will type and check word predictions for before giving up and just typing out the word in full. While it would have been insightful to see how variation in perseverance affects the speed of typing with predictive text, even with a computer model, there were limitations to the amount of changeable data points we could include. So we fixed perseverance at five, meaning if there are no suitable suggestions after the user has typed five letters, they will complete the word without consulting predictive text further. Although we don’t have data on the average perseverance, this seems like a reasonable estimate. <h2>What did we find?</h2> <img src="https://oversixtydev.blob.core.windows.net/media/2022/12/graph-text.jpg" alt="" />Above the dashed line there’s an increase in net entry rate while below it, predictive text slows the user down. The deep red shows when predictive text is most effective; an improvement of two words per minute compared to not using predictive text. The blue is when it’s least effective. Under certain conditions in our simulation, predictive text could slow a user down by as much as eight words per minute. The blue circle shows the optimal operating point, where you get the best results from predictive text. This occurs when word predictions are only sought for words with at least six letters and the user looks at a word prediction after typing three letters. So, for the average user, predictive text is unlikely to improve performance. And even when it does, it doesn’t seem to save much time. The potential gain of a couple of words per minute is much smaller than the potential time lost. It would be interesting to study long-term predictive text use and look at users’ strategies to verify that our assumptions from the model hold in practice. But our simulation reinforces the findings of previous human research: predictive text probably isn’t saving you time – and could be slowing you down. Image credits: Getty Images This article originally appeared on <a href="https://theconversation.com/do-you-use-predictive-text-chances-are-its-not-saving-you-time-and-could-even-be-slowing-you-down-170163" target="_blank" rel="noopener">The Conversation</a>.

Technology

Volcano breath test helps scientists predict deadly eruptions

Humanity has a long history of living in the shadows of active volcanoes. Prized for their rich, fertile soils – ideal for cultivating crops – and their local topography, it isn’t hard to see why living in active volcanic regions remains a worthwhile gamble. Volcanic eruptions, however, are notoriously difficult to predict but improving our diagnostic abilities is crucial for developing early warning procedures and evading disaster. External indicators such as earthquakes and deformation of the Earth’s crust are traditional methods of identifying an imminent eruption, however, not all eruptions give these early warning signs. But now a research team from the University of Tokyo has gained better insight into the relationship between changes in the magma composition and eruption, by studying the ratio of specific chemical isotopes in gas and steam emitted from fumaroles — holes and cracks in the earth’s surface. “When you compare a volcano with a human body, the conventional geophysical methods represented by observations of earthquakes and crustal deformation are similar to listening to the chest and taking body size measurements”, said Professor Hirochika Sumino from the Research Centre for Advanced Science and Technology, who led the study. “In these cases, it is difficult to know what health problem causes some noise in your chest or a sudden increase in your weight, without a detailed medical check. On the other hand, analysing the chemical and isotope composition of elements in fumarolic gases is like a breath or blood test. This means we are looking at actual material directly derived from magma to know precisely what is going on with the magma.” Previous research on gas associated with an eruption from a volcano in the Canary Islands in 2011 showed an increase in the ratio of heavier helium isotopes which are typical of mantle material. “We knew that the helium isotope ratio occasionally changes from a low value, similar to the helium found in the Earth’s crust, to a high value, like that in the Earth’s mantle, when the activity of magma increases,” said Sumino. “But we didn’t know why we had more mantle-derived helium during magmatic unrest.” Sumino and team sought the answers in fumerole gas around Kusatsu-Shirane, an active volcano 150 km northwest of Tokyo. Taking samples of the gas back to the lab every few months between 2014 and 2021, the researchers were able to ascertain precise measurements of the isotopic components, discovering a relationship between the ratio of argon-40 to helium-3 ( a ‘high value’ isotope of helium) and magmatic unrest. “Using computer models, we revealed that the ratio reflects how much the magma underground is foaming, making bubbles of volcanic gases which separate from the liquid magma,” explained Sumino. The extent to which the magma is foaming “controls how much magmatic gas is provided to the hydrothermal system beneath a volcano and how buoyant the magma is. The former is related to a risk of phreatic eruption, in which an increase in water pressure in the hydrothermal system causes the eruption. The latter would increase the rate of magma ascent, resulting in a magmatic eruption.” The research collaboration is now developing a portable type of mass spectrometer which could be used in the field for real time analysis, reducing the need to constantly collect and transport samples back to the lab – a challenging a time-consuming process. “Our next step is to establish a noble gas analysis protocol with this new instrument, to make it a reality that all active volcanoes — at least those which have the potential to cause disaster to local residents — are monitored 24 hours a day, seven days a week,” said Sumino. This article originally appeared on cosmosmagazine.com and was written by <a href="https://cosmosmagazine.com/earth/volcano-breath-test-predict-eruptions/" target="_blank" rel="noopener">Clare Kenyon</a>. Image: Getty

Travel Trouble

Can big data really predict what makes a song popular?

Music is part of our lives in different ways. We listen to it on our commutes and it resounds through shopping centres. Some of us seek live music at concerts, festivals and shows or rely on music to set the tone and mood of our days. While we might understand the genres or songs we appreciate, it’s not clear precisely why a certain song is more appealing or popular. Perhaps the lyrics speak to an experience? Perhaps the energy makes it appealing? These questions are important to answer for music industry professionals, and <a href="https://theconversation.com/how-data-is-transforming-the-music-industry-70940">analyzing data</a> is a key part of this. At Carleton University, a group of data science researchers sought to answer the question: “What descriptive features of a song make it popular on music/online platforms?” <h2>Revenue in the music industry</h2> Revenue in the music industry <a href="https://doi.org/10.1509/jm.14.0473">is derived from two sources that are affected by different factors: live music and recorded music</a>. During the pandemic, although live music income dropped due to the cancellation of in-person performances, the <a href="https://doi.org/10.1371/journal.pone.0267640">income from streaming</a> rose. As digital platforms like Spotify and TikTok have grown, <a href="https://doi.org/10.5753/sbcm.2019.10436">the majority of music revenue has come to be contributed by digital media, mostly music streaming</a>. How and whether this <a href="https://theconversation.com/artists-spotify-criticisms-point-to-larger-ways-musicians-lose-with-streaming-heres-3-changes-to-help-in-canada-176526">revenue reaches singers and songwriters at large</a> is another matter. <h2>Popularity on digital platforms</h2> The popularity of a song on digital platforms is considered a measure of the revenue the song may generate. As such, producers seek to answer questions like “<a href="https://doi.org/10.1098/rsos.171274">How can we make the song more popular?</a>” and “<a href="https://doi.org/10.1109/ICMLA.2019.00149">What are the characteristics of songs that make it the top charts?</a>” With collaborators <a href="https://www.linkedin.com/in/laura-colley/">Laura Colley</a>, <a href="https://www.linkedin.com/in/andrew-dybka/">Andrew Dybka</a>, Adam Gauthier, Jacob Laboissonniere, Alexandre Mougeot and Nayeeb Mowla, we produced a systematic study that collected data from YouTube, Twitter, TikTok, Spotify and Billboard (<a href="https://www.billboard.com/charts/hot-100">Billboard Hot-100</a>, sometimes also denoted by data researchers as “<a href="https://data.world/bigml/association-discovery">Billboard hot top</a>” or in our work and others’ work, “Billboard Top-100”). We linked the datasets from the different platforms with Spotify’s acoustic descriptive metric or “descriptive features” for songs. These features have been derived <a href="https://www.billboard.com/music/music-news/echo-nest-columbia-university-launch-million-song-dataset-1178990/">from a dataset which yielded categories for measuring and analyzing qualities of songs</a>. Spotify’s <a href="https://www.theguardian.com/technology/2014/mar/06/spotify-echo-nest-streaming-music-deal">metrics capture</a> <a href="https://doi.org/10.1098/rsos.171274">descriptive features such as</a>acousticness, energy, danceability and instrumentalness (the collection of instruments and voices in a given piece). We sought to find trends and analyze the relationship between songs’ descriptive features and their popularity. The rankings on the weekly <a href="https://www.billboard.com/charts/hot-100/">Billboard Hot-100</a> are based on sales, online streams and radio plays in the United States. The analysis we performed by looking at Spotify and Billboard revealed insights that are useful for the music industry. <h2>What predicts a Billboard hit?</h2> To perform <a href="https://ieeexplore.ieee.org/document/9842568">this study</a>, we used two different data sets pertaining to songs that <a href="https://www.npr.org/sections/therecord/2013/08/16/207879695/how-the-hot-100-became-americas-hit-barometer">were Billboard hits</a> <a href="https://data.world/kcmillersean/billboard-hot-100-1958-2017">from the early 1940s to 2020</a> and Spotify data related to over 600,000 tracks and over one million artists. Interestingly, we found no substantial correlations between the number of weeks a song remained on the charts, as a measure of popularity, and the acoustic features included in the study. Our analysis determined that newer songs tend to last longer on the charts and that a song’s popularity affects how long it stays on the charts. In a related study, researchers collected data for Billboard’s Hot 100 from 1958 to 2013 and found that <a href="https://doi.org/10.1007/978-3-319-13734-6_36">songs with a higher tempo and danceability often get a higher peak position on the Billboard charts</a>. <h2>Predicting Spotify song popularity</h2> We also used the songs’ features to generate machine learning models to predict Spotify song popularity. Preliminary results concluded that features are not linearly correlated, with some expected exceptions including songs’ energy. This indicated that the Spotify metrics we studied — including acousticness, danceability, duration, energy, explicitness, instrumentalness, liveness, speechiness (a measure of the presence of spoken words in a song), tempo and release year — were not strong predictors of the song’s popularity. The majority of songs in the Spotify dataset were not listed as explicit, tended to have low instrumentalness and speechiness, and were typically recent songs. Although one may think that some features that are innate to certain songs make them more popular, our study revealed that popularity can not be attributed solely to quantifiable acoustic elements. This means that song makers and consumers must consider other contextual factors beyond the musical features, as captured by Spotify’s measurables, that may contribute to the song’s success. <h2>Elements affecting popularity shift</h2> Our study reinforces that elements affecting the popularity of songs change over time and should be continuously explored. For example, <a href="https://doi.org/10.1098%2Frsos.171274">in songs produced between 1985 and 2015 in the United Kingdom, songs produced by female artists were more successful</a>. Other aspects may substantially contribute to the success of a song. Data scientists have proposed <a href="https://doi.org/10.1371/journal.pone.0244576">simplicity of the lyrics</a>, the advertising and <a href="https://www.ipr.edu/blogs/audio-production/what-are-the-elements-of-popular-music/">distribution plans</a> as potential predictors of songs’ popularity. <h2>Attached listeners</h2> Many musicians and producers make use of popular events and marketing strategies to advertise songs. Such events create social engagements and <a href="https://doi.org/10.3389/fpsyg.2018.02682">audience involvement</a> which attaches the listener to the song being performed. For the public, <a href="https://www.osheaga.com/en">live music events</a>, following long lockdowns, have been opportune for reuniting friends, and <a href="https://ottawabluesfest.ca/">enjoying live artistry and</a> entertainment. While attending a music event or listening to a song, we invite you to reflect on what it is about the song that makes you enjoy it. Image credits: Getty Images This arctic originally appeared on <a href="https://theconversation.com/can-big-data-really-predict-what-makes-a-song-popular-189052" target="_blank" rel="noopener">The Conversation</a>.

Music

Your eyes could predict your risk of heart disease

As well as being windows to the soul, <a href="https://www.oversixty.com.au/health/body/could-an-eye-test-predict-your-risk-of-heart-disease" target="_blank" rel="noopener">your eyes</a> could indicate your risk of developing heart disease according to new research. Scientists have developed imaging powered by Artificial Intelligence (AI) that can predict cardiovascular disease and death just by looking at the network of veins and arteries in your retina. Their findings could pave the way for a non-invasive and highly effective test that could replace the blood tests and blood pressure measurements currently used. With previous studies finding that the width of the tiny veins and arteries in the retina may be an accurate, early indicator for circulatory diseases including heart disease, cardiovascular disease, stroke, and heart failure. But, it was unclear whether these findings apply to both men and women, prompting the researchers to develop an AI-enabled algorithm called QUARTZ (QUantitative Analysis of Retinal vessels Topology and siZe) to develop models for assessing whether combining imaging of the retina with known risk factors could predict vascular health and death. They then applied models the algorithm created to retinal images of 88,052 people that are stored in the UK’s BioBank, as well as 7411 participants in the European Prospective Investigation into Cancer (EPIC)-Norfolk study, which tracked the health of participants for seven to nine years. The predictive model used known risk factors, including smoking, medical history, and age and was able to identify two-thirds of the participants who later died of circulatory disease who were most at risk. With retinal imaging already being common practice in the UK and US, the researchers argue that using changes to the retina and AI has the potential to reach a greater portion of the population than current testing methods. “[Retinal vasculature]is a microvascular marker, hence offers better prediction for circulatory mortality and stroke compared with [heart attack] which is more macrovascular, except perhaps in women,” they write. “In the general population it could be used as a non-contact form of systemic vascular health check, to triage those at medium-high risk of circulatory mortality for further clinical risk assessment and appropriate intervention.” Drs Ify Mordi and Emanuele Trucco of Scotland’s University of Dundee wrote in <a href="https://bjo.bmj.com/content/early/2022/09/12/bjo-2022-322517" target="_blank" rel="noopener">a separate editorial</a> that using changes to the retina to inform overall cardiovascular risk is “certainly attractive and intuitive” but is yet to form part of clinical practice. “Using retinal screening in this way would presumably require a significant increase in the number of ophthalmologists or otherwise trained assessors,” they write. “What is now needed is for ophthalmologists, cardiologists, primary care physicians and computer scientists to work together to design studies to determine whether using this information improves clinical outcome, and, if so, to work with regulatory bodies, scientific societies and healthcare systems to optimise clinical workflows and enable practical implementation in routine practice.” The study was published in the <a href="https://bjo.bmj.com/content/early/2022/08/23/bjo-2022-321842" target="_blank" rel="noopener">British Journal of Ophthalmology</a>. Image: Getty Images

Caring

Leo DiCaprio scolded by fans after "predictable" split with model girlfriend

Leonard DiCaprio fans everywhere are having the last laugh as the famed actor has ended his four-year relationship with model girlfriend Camilla Morrone. Having a track record of exclusively dating women under 25, the 47-year-old ended his relationship quietly, earlier this summer, according to a source close to the couple. DiCaprio and Morrone began dating in 2017 when she was 20 years old, making their awards show debut at the 2020 Oscars. Morrone at the time defended their 23-year age difference, telling the Los Angeles Times in 2019, “There’s so many relationships in Hollywood — and in the history of the world — where people have large age gaps. I just think anyone should be able to date who they want to date.” This recent widely publicised breakup has only reinforced predictions made that the relationship wouldn't last much longer, after Camilla celebrated her 25th birthday in early June. Of course the breakup sent social media into a frenzy as fans couldn’t wait to post jokes about the couple's "doomed" relationship. One fan tweeted: "3 things are certain in life: - death, - taxes, - Leonardo Dicaprio breaking up with his girlfriend before her pre-frontal cortex has fully developed." <blockquote class="twitter-tweet"> 3 things are certain in life: - death - taxes - leonardo dicaprio breaking up with his girlfriend before her pre-frontal cortex has fully developed <a href="https://t.co/5bw3uLWlQ3">https://t.co/5bw3uLWlQ3</a> — Erin Brockobić (@erinbrockobic) <a href="https://twitter.com/erinbrockobic/status/1564744270950633472?ref_src=twsrc%5Etfw">August 30, 2022</a></blockquote> "Leonardo DiCaprio is actually dating all the under-25s he possibly can to spread the word about climate change to those who will be the most impacted," another user joked. <blockquote class="twitter-tweet"> leonardo dicaprio is actually dating all the under-25s he possibly can to spread the word about climate change to those who will be the most impacted — flamin nora (@katierpacker) <a href="https://twitter.com/katierpacker/status/1564888223205269505?ref_src=twsrc%5Etfw">August 31, 2022</a></blockquote> A third added: "there's no phenomenon on this planet more reliable than leonardo dicaprio breaking up with his girl by age 25, the stats are breathtaking". <blockquote class="twitter-tweet"> there's no phenomenon on this planet more reliable than leonardo dicaprio breaking up with his girl by age 25, the stats are breathtaking <a href="https://t.co/A9czRJo56Q">https://t.co/A9czRJo56Q</a> — no (@zedonarrival) <a href="https://twitter.com/zedonarrival/status/1564730774485704704?ref_src=twsrc%5Etfw">August 30, 2022</a></blockquote> Meanwhile, others have tried to figure out how DiCaprio might have ended the romance as one stated: "i need to know how leonardo dicaprio conducts these breakups. does he start picking fights months before they turn 25 to be less conspicuous?" <blockquote class="twitter-tweet"> i need to know how leonardo dicaprio conducts these breakups. does he start picking fights months before they turn 25 to be less conspicuous? do they wake up on their birthday to a note that says “sorry i can’t don’t hate me” someone pls investigate — megan (@weganmilliamson) <a href="https://twitter.com/weganmilliamson/status/1564756315276279808?ref_src=twsrc%5Etfw">August 30, 2022</a></blockquote> Other A-list women DiCaprio has dated who were 25 or younger at the time of their relationships include Kate Moss, Helena Christensen, Naomi Campbell, Bar Refaeli, Nina Agdal and Gisele Bündchen, who he dated for six years. What a complete Morrone! Image: Twitter

Relationships

Chilling note reveals Princess Diana may have predicted her fatal car crash

Two years before the car crash that claimed the life of Princess Diana, the royal predicted she would "either end up dead or be seriously injured" in a car accident that "could be staged". The eerie revelation, which became known as the "Mishcon Note", is discussed in detail in the upcoming docuseries, The Diana Investigations, of which <a href="https://pagesix.com/2022/08/17/princess-diana-predicted-fatal-car-crash-in-chilling-note/" target="_blank" rel="noopener">The Daily Beast </a>obtained a preview. In October 1995, Diana requested a private meeting with her personal legal adviser, Victor Mishcon, allegedly to “tell him about something that was on her mind.” Mishcon took diligent notes during their conversation, in which Diana allegedly said “reliable sources,” whom she refused to name, had informed her “that a car accident might be staged.” Diana apparently predicted she would “either end up dead or be seriously injured.” Two years later, in August 1997, Diana died after her driver Henri Paul slammed their Mercedes into a pillar at 104km/h in Paris’ Pont de l’Alma tunnel. Paul was under the influence of alcohol and prescription drugs, but he was also trying to dodge the relentless paparazzi, who were trailing them on motorcycles. According to the experts in the docuseries, Mishcon gave the notes of his meeting with Diana to the London’s Metropolitan Police commissioner at the time, Sir Paul Condon. It wasn’t until after Condon’s successor, John Stevens, assumed the commissioner role that the public found out about the note, as Condon had locked it away in a safe. “When the coroner announced his inquest, I made sure that letter was immediately given to the royal coroner, who at that time was Michael Burgess and then subsequently became Lord Justice Scott Baker,” Lord Stevens told Daily Beast. “I saw Lord Mishcon about a month before he died, in about the spring of 2005, and he held course to the fact that he thought [Diana] was paranoid, and he hadn’t held much credence to [the note].” Image credits: Getty Images

Could an eye test predict your risk of heart disease?

A team of scientists believe a routine eye test, combined with genetic information, could accurately predict an individual’s risk of coronary artery disease and its potential outcome, heart attacks. The researchers examined the patterns of blood vessels in the retina using data from the UK Biobank (UKB), which includes demographic, epidemiological, clinical and genetic data from 500,000 UK participants. They found that simpler patterns were related to coronary heart disease (CAD) and myocardial infarctions (MI), also known as heart attacks, and developed a model that they say is able to predict an individual’s risk of MI based on multiple factors, including images of their eyes. “Strikingly, we discovered that our model was able to better classify participants with low or high MI risk in UKB when compared with established models that only include demographic data. The improvement of our model was even higher if we added a score related to the genetic propensity of developing MI,” Ana Villaplana-Velasco, a PhD student at the University of UK, <a href="https://www.scimex.org/newsfeed/the-eyes-could-be-a-window-into-our-heart-risk" target="_blank" rel="noopener">says</a>. The team also found that several regions of our genes drive branching of vessels in the eye - four of which are also involved in cardiovascular disease genetics. “In particular, we found that these common genetic regions are involved in processes related to MI severity and recovery,” Ms Villaplana-Velasco says. They also believe their findings could be useful for identifying risk of other diseases, with retinal vascular patterns potentially reflecting the development of diseases such as diabetic retinopathy and stroke. “We would like to investigate this further, as well as undertaking a sex-specific analysis. We know that females with a higher MI or CAD risk tend to have pronounced retinal vascular deviations when compared to the male population. We would like to repeat our analysis separately in males and females to investigate if a sex-specific model for MI completes a better risk classification,” Ms Villaplana-Velasco says. With the average age for an MI being 60, the team found that their model was most accurate at predicting risk more than five years before a person experiences an MI. ”So the calculation of an individualised MI risk from those over 50 years old would seem to be appropriate,” says Ms Villaplan-Velasco. ”This would enable doctors to suggest behaviours that could reduce risk, such as giving up smoking and maintaining normal cholesterol and blood pressure. Our work once more shows the importance of comprehensive analysis of data that is routinely collected and its value in the further development of personalised medicine.” Their findings were presented at the annual conference of the European Society of Human Genetics. Image: Getty Images

Body

Predictions for the Oscars 2022 best picture

There were some extraordinary films released in 2021: Ninja Thyberg’s <a href="https://www.imdb.com/title/tt8550054/">Pleasure</a>, an electric neon romp through the porn world, Ildikó Enyedi’s period epic, <a href="https://www.imdb.com/title/tt8205028/">The Story of My Wife</a>, Paul Verhoeven’s nun-film, <a href="https://www.imdb.com/title/tt6823148/">Benedetta</a>. It should come as no surprise that this is not reflected in the Oscar nominees for Best Picture. <h2>Dune: Best Picture?</h2> A pulpy science-fiction narrative, not that far removed from an L. Ron Hubbard work, it seems strange to suggest that <a href="https://www.imdb.com/title/tt1160419/">Dune is 2021’s “Best Picture”</a>. It isn’t – but it is my choice for the Oscar, out of a ho-hum bunch of nominees. Denis Villeneuve’s latest film is certainly the most cinematic of the selection, and it works exceptionally well at a formal level. It’s a striking, hypnotic work – and totally coherent (unlike the previous <a href="https://www.denofgeek.com/movies/dune-2021-vs-dune-1984-the-differences/">Lynch/Smithee version!</a>) – a realisation of Herbert’s world both surreal and appropriately full of dread. The whole thing is anchored by the excellent production design of Patrice Vermette: desert planet Arakis seems ominously sparse, its weathered concrete monoliths straight from a deranged modernist’s dream. The sound design is also first rate (as is Hans Zimmer’s score, perfect as usual), and the combination of the surreal images and intense sounds creates a wholly immersive cinematic experience. Villeneuve effectively converts Herbert’s convoluted narrative of the power machinations of the Atreides, Harkonnen, etc. into a staggeringly clear – and relatively short, these days, for this kind of thing – genre film. Angsty heartthrob Timothée Chalamet, as main character Paul Atreides, has neither the presence or chops to carry an epic fantasy film like this, and, thankfully, he doesn’t have to – Josh Brolin and Jason Mamoa are suitably brutal, Oscar Isaac brings an air of stately gravity to the role of Duke Leto Atreides, father of Paul (though they look pretty close in age!), and Rebecca Ferguson, as Lady Jessica Atreides, does the mother-witch thing with a humane and slightly off-kilter bent, reminding one of a more benevolent Lady Macbeth. Villeneuve is a powerhouse director, one of the more interesting working in mainstream film today, and Dune plays well as a trashy fantasy film. That being said, it is only Part I of two, so we’ll see how the second part holds up now most of the compelling characters have been killed. <h2>The Power of the Dog: Worst Picture?</h2> At the opposite end of the list is another genre film, Jane Campion’s <a href="https://www.imdb.com/title/tt10293406/">The Power of the Dog</a>. Made for Netflix, Campion’s Western has received wide acclaim and has been film of the year on multiple lists. It’s hard to see why. Campion has made some good films in the past – <a href="https://www.imdb.com/title/tt0107822/">The Piano</a> is a deliciously silly bodice ripper in the clothing of a serious film, and deserved its accolades (naked Harvey Keitel alone would warrant the acclaim!). The Power of the Dog, in contrast, is strained and tiresome, a long epic without an interesting story, meaningful panoramic cinematography (though I watched it on a TV screen, maybe it would have appeared spectacular in the cinema) or anything significant and original to say about its topic of interest: the American West and the Western genre. Its one point – revealing the <a href="https://offscreen.com/view/homosexuality_western">homoerotic impulses</a> often underpinning the masculine bravado of Western cowboy narratives – has been made numerous times before, with Westerns, American and otherwise, frequently making a similar statement since at least the 1940s, in films that are more dynamic, look better, and don’t suffer from the air of self-importance surrounding this one. Jesse Plemons is a good actor, but he’s wasted in a fairly thankless part; Kirsten Dunst seems lost, struggling to convince us as a hapless alcoholic woman trapped in a thankless man’s world; and Benedict Cumberbatch, in the lead part, gives a typically commanding but unnuanced performance, playing Phil Burbank with stagy gusto. While Australian Kody Smit-McPhee is fine as Peter Gordon, the point is that all of the characters are caricatures – unoriginal inversions of genre archetypes – and, in a serious film in which not much happens – that is, in a film that is supposed to be buoyed by emotional authenticity and emotional realism – this is fatal. That being said, it will not surprise me if this wins the Oscar for Best Picture. <h2>And… the rest</h2> Campion’s film is the weakest of the nominees; there are some others that are quite good. <a href="https://theconversation.com/comedies-in-serious-clothing-an-introduction-to-the-films-of-paul-thomas-anderson-174608">Paul Thomas Anderson’s Licorice Pizza</a> is a madcap LA teen romp, a 1970’s picaresque nostalgia film featuring stellar newcomers Alana Haim and Cooper Hoffman in the lead roles. It’s long and unwieldy, and would not be to everyone’s taste, but has the advantage of actually capturing an original, albeit fractured and fractious, vision of the world. Unlike some of P.T. Anderson’s other recent films, it is surprisingly good-natured. <a href="https://www.imdb.com/title/tt7740496/">Nightmare Alley</a>, directed by Guillermo del Toro, also offers solid genre fare. It’s a remake of Edmund Goulding’s (considerably better) <a href="https://www.imdb.com/title/tt0039661/">film-noir of the same name from 1947</a>, and follows the rise and fall of Stanton Carlisle, a mentalist who begins his trade in the circus before becoming a popular nightclub act. Like most of del Toro’s films, it is dripping in period and genre nostalgia, but this adds to, rather than detracts from, the colour, here. <a href="https://www.imdb.com/title/tt11286314/">Don’t Look Up</a> is the global warming equivalent of Barry Levinson’s 1990s satire, <a href="https://www.imdb.com/title/tt0120885/">Wag the Dog</a>, and, like Wag the Dog, it is, at times, a little too clever for its own good. It comes across as irritatingly pontificating in places, with Jonah Hill’s characterisation as a Presidential son (a la Trump’s family) heavy-handedly hammering home its point. But – like all of Adam Mackay’s films – it’s well made and funny, a media-literate romp satirising (or maybe just demonstrating) life in the social media age. The rest of the nominees are watchable if unspectacular films, including the Irish nostalgia film <a href="https://www.imdb.com/title/tt12789558/">Belfast</a>, the coming of age drama <a href="https://www.imdb.com/title/tt10366460/">CODA</a>, <a href="https://www.imdb.com/title/tt9620288/">King Richard </a>a biopic about the struggle of the Williams family to make it in the tennis world, <a href="https://www.imdb.com/title/tt14039582/">Drive My Car</a> a beautifully shot version of the eponymous Murakami story, and Spielberg’s latest, an un-engaging remake of <a href="https://www.imdb.com/title/tt3581652/?ref_=nv_sr_srsg_1">West Side Story</a>. Which movie will win? Dune? The Power of the Dog? King Richard? In any case, I can guarantee it won’t be the best picture of 2021. This article originally appeared on<a href="https://theconversation.com/from-dune-to-the-power-of-the-dog-our-predictions-for-the-oscars-2022-best-picture-179660"> The Conversation</a>.

Did the Simpsons predict Russia’s war on Ukraine?

Fans of the long-running cartoon sitcom claim it predicted the current crisis between Russia and Ukraine in an episode way back in 1998. A clip from the episode “Simpson Tide” shows a United Nations meeting between Russia and the United States that quickly turns into Russia announcing the return of the Soviet Union. Complete with hidden tanks, barbed-wired walls and even a zombified Lenin raising from the dead to declare he “must crush Capitalism”. Check out this clip from Twitter: <blockquote class="twitter-tweet"> The Simpsons predicted the Crisis of <a href="https://twitter.com/hashtag/Putin?src=hash&ref_src=twsrc%5Etfw">#Putin</a>, <a href="https://twitter.com/hashtag/Russia?src=hash&ref_src=twsrc%5Etfw">#Russia</a>,<a href="https://twitter.com/hashtag/Ukraine?src=hash&ref_src=twsrc%5Etfw">#Ukraine</a> and <a href="https://twitter.com/hashtag/Donetsk?src=hash&ref_src=twsrc%5Etfw">#Donetsk</a> and<a href="https://twitter.com/hashtag/Luhansk?src=hash&ref_src=twsrc%5Etfw">#Luhansk</a> way before it was cool 🤯😆<a href="https://twitter.com/hashtag/RussiaUkraineCrisis?src=hash&ref_src=twsrc%5Etfw">#RussiaUkraineCrisis</a><a href="https://twitter.com/hashtag/Ukraina?src=hash&ref_src=twsrc%5Etfw">#Ukraina</a> <a href="https://twitter.com/hashtag/ukrainerussia?src=hash&ref_src=twsrc%5Etfw">#ukrainerussia</a> <a href="https://twitter.com/hashtag/USA?src=hash&ref_src=twsrc%5Etfw">#USA</a> <a href="https://t.co/Hqd2icRj6w">pic.twitter.com/Hqd2icRj6w</a> — Genius Manushya (@GeniusManushya) <a href="https://twitter.com/GeniusManushya/status/1496003007313772547?ref_src=twsrc%5Etfw">February 22, 2022</a></blockquote> While it is not an exact match for the war Russia is currently waging in Ukraine, it comes close enough for some fans to chalk it up as yet another Simpsons prediction. “At what point are we gonna make Matt Groening tell us how he does this?” one fan tweeted, referring to the show’s creator. “THE SIMPSONS ALWAYS PREDICTS EVERYTHING,” wrote another. “Pretty sure most world events were predicted by the Simpsons at some point,” said another. It comes just weeks after another Simpsons prediction appeared to come true, this one from the show’s 2007 big screen outing, The Simpsons Movie. In the film, A-list actor Tom Hanks appears in animated form as a spokesman in an ad for the US government, with this memorable line during his brief cameo: “Hello, I’m Tom Hanks. The US government has lost its credibility so it’s borrowing some of mine.” Here we are, 15 years later, and Tom Hanks is working as a spokesman for the US government in real life, narrating a new White House video promoting the achievements of the Biden administration during its first year in office. It’s the latest in a long line of alleged predictions the writers of The Simpsons have unwittingly made about future events. Images: The Simpsons (1998)

Big travel deals predicted to come soon following Jetstar's $22 flights

Travel is slowly returning to normal and our major airlines are slashing fares, with Jetstar offering flights from major cities to our favourite holiday destinations for just $22. "They really want to entice people back into the air and they want to give you an offer that's almost too good to be true," Australian Traveller co-founder, Quentin Long, told A Current Affair. The destinations are far and wide and if a tropical climate is what you're looking for, Queensland's Cairns and the Whitsundays could be an option for you. Jetstar is getting in early, ahead of the launch of budget airline Bonza, which is set to enter the market mid-year. It's offering flights on more than 25 routes to 16 destinations and fares will average $50 one way on shorter flights. Qantas is launching its Asia Fly Away sale tomorrow which includes return flights from Sydney, Melbourne, Brisbane and Adelaide to Singapore starting at $699. A flight to Delhi from Sydney, Melbourne or Adelaide will cost $119 and a flight to Manila from Brisbane or Adelaide will cost $829. Meanwhile, Virgin Australia has $69 flights until May 10, with its Book Early Fares Sale. They're also running a weekly Happy Hour sale every Thursday with fares as low as $49. Experts believe this is just the beginning of sales to be seen both domestically and internationally, with big deals predicted in March and April - with the hope of getting more passengers back in the air. "You'll see aviation plus tours, plus accommodation deals coming to the market about then," Mr Long said. "Having competitive and very good value airfares is really important to the tourist market, whether it's in Australia or overseas," Flight Centre chief executive Graham Turner said. "Everything that we can do to help that inbound and outbound travel is certainly good for the hundreds of thousands of people employed in the travel and tourism industry. "And good value airfares are a really important part of it. "The domestic travel, tourism, business travel, will pick up steadily over the next 12 months and I think we will be back to around that 100 per cent mark in around 12 months' time." Jetstar's sale closes at midnight on Thursday unless sold out prior. <span style="font-size: 12pt; font-family: Arial; color: #000000; background-color: transparent; font-weight: 400; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Image: Getty

Travel Tips

Algorithms predicting parole outcomes

<div> <div class="copy"> The US has the highest incarceration rate in the world, which results in overcrowded prisons and all the additional violence that implies. Funnelling felons back onto the street through granting parole is thus a critical safety mechanism and management tool – but assessing which inmates will likely not reoffend when granted liberty is a difficult and troubling task. For some years now, the people responsible for calculating the chances of someone reoffending have been assisted in their decision-making by computational frameworks known as risk-assessment instruments (RAIs). The validity of these algorithms was thrown into question in 2018 after a <a rel="noopener" href="https://advances.sciencemag.org/content/4/1/eaao5580" target="_blank">major study</a> tested their predictive power against that of untrained humans. The machines and the people were given brief information on 400 inmates, including sex, age, current charge and prior convictions, and asked to make a determination. Both cohorts made the correct call in 65% of cases, which was pretty perceptive on the part of the untrained humans, but rather ordinary for the algorithms, given what was at stake. Now a new <a rel="noopener" href="https://advances.sciencemag.org/content/6/7/eaaz0652" target="_blank">study</a>, led by Sharad Goel, a computational social scientist at Stanford University, US, has repeated and extended the earlier research, and finds in favour of the software. In the first phase of the research, Goel and colleagues replicated the previous work, and came up with similar results. They then repeated the exercise with several additional variables in play – a situation, they suggest, that much better resembles real-world conditions. With the extra information, the algorithms performed much better, correctly predicting recidivism in 90% of cases. The humans got it right only 60% of the time. “Risk assessment has long been a part of decision-making in the criminal justice system,” says co-author Jennifer Skeem. “Although recent debate has raised important questions about algorithm-based tools, our research shows that in contexts resembling real criminal justice settings, risk assessments are often more accurate than human judgment in predicting recidivism. That’s consistent with a long line of research comparing humans to statistical tools.” In their paper, published in the journal Science Advances, the researchers say the more accurate RAI results will be helpful in the management of the over-burdened US penal system. The algorithm will be useful not only in helping to decide which inmates can be safely released into the community but will also assist in allocating prisoners too low or high security facilities. Image credit: Shutterstock</div> <div id="contributors"> This article was originally published on <a rel="noopener" href="https://cosmosmagazine.com/technology/algorithms-getting-better-at-predicting-parole-outcomes/" target="_blank">cosmosmagazine.com</a> and was written by Barry Keily. </div> </div>

Technology

Could AI predict the next pandemic?

<div> <div class="copy"> Most of the emerging infectious diseases that threaten humans – including coronaviruses – are zoonotic, meaning they originate in another animal species. And as population sizes soar and urbanisation expands, encounters with creatures harbouring potentially dangerous diseases are <a rel="noreferrer noopener" href="https://theconversation.com/urbanisation-brings-animals-and-diseases-closer-to-home-34415" target="_blank">becoming ever more likely</a>. Identifying these viruses early, then, is becoming vitally important. A <a rel="noreferrer noopener" href="http://10.1371/journal.pbio.3001390" target="_blank">new study</a> out today in PLOS Biology from a team of researchers at the University of Glasgow, UK, has identified a novel way to do this kind of viral detective work, using machine learning to predict the likelihood of a virus jumping to humans. According to the researchers, a major stumbling block for understanding zoonotic disease has been that scientists tend to prioritise well-known zoonotic virus families based on their common features. This means that there is potentially myriad viruses unrelated to known zoonotic diseases that have not been discovered, or are not well known, which may hold zoonotic potential – the ability to make the species leap. More reading: <a rel="noreferrer noopener" href="https://cosmosmagazine.com/health/cosmos-qa-predicting-the-next-pandemic/" target="_blank">Cosmos Q&A: Predicting the next pandemic</a> In order to circumvent this problem, the team developed a machine learning algorithm that could infer the zoonotic potential of a virus from its genome sequence alone, by identifying characteristics that link it to humans, rather than looking at taxonomic relationships between the virus being studied and existing zoonotic viruses. The team found that viral genomes may have generalisable features that enable them to infect humans, but which are not necessarily taxonomically closely related to other human-infecting viruses. They say this approach may present a novel opportunity for viral sleuthing. “By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterisation to be targeted more effectively,” the authors write. “These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques,” says co-author Simon Babayan. “A genomic sequence is typically the first, and often only, information we have on newly discovered viruses, and the more information we can extract from it, the sooner we might identify the virus’s origins and the zoonotic risk it may pose. “As more viruses are characterised, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritised for pre-emptive vaccine development.” Image credit: Shutterstock This article was originally published on <a rel="noopener" href="https://cosmosmagazine.com/technology/ai/could-ai-predict-next-pandemic/" target="_blank">cosmosmagazine.com</a> and was written by Amalyah Hart. </div> </div>

Technology

Could AI predict the next pandemic?

Most of the emerging infectious diseases that threaten humans – including coronaviruses – are zoonotic, meaning they originate in another animal species. And as population sizes soar and urbanisation expands, encounters with creatures harbouring potentially dangerous diseases are <a style="font-size: 14px;" rel="noreferrer noopener" href="https://theconversation.com/urbanisation-brings-animals-and-diseases-closer-to-home-34415" target="_blank">becoming ever more likely</a>. <div class="copy"> Identifying these viruses early, then, is becoming vitally important. A <a rel="noreferrer noopener" href="http://10.1371/journal.pbio.3001390" target="_blank">new study</a> out today in PLOS Biology from a team of researchers at the University of Glasgow, UK, has identified a novel way to do this kind of viral detective work, using machine learning to predict the likelihood of a virus jumping to humans. According to the researchers, a major stumbling block for understanding zoonotic disease has been that scientists tend to prioritise well-known zoonotic virus families based on their common features. This means that there is potentially myriad viruses unrelated to known zoonotic diseases that have not been discovered, or are not well known, which may hold zoonotic potential – the ability to make the species leap. In order to circumvent this problem, the team developed a machine learning algorithm that could infer the zoonotic potential of a virus from its genome sequence alone, by identifying characteristics that link it to humans, rather than looking at taxonomic relationships between the virus being studied and existing zoonotic viruses. The team found that viral genomes may have generalisable features that enable them to infect humans, but which are not necessarily taxonomically closely related to other human-infecting viruses. They say this approach may present a novel opportunity for viral sleuthing. “By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterisation to be targeted more effectively,” the authors write. “These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques,” says co-author Simon Babayan. “A genomic sequence is typically the first, and often only, information we have on newly discovered viruses, and the more information we can extract from it, the sooner we might identify the virus’s origins and the zoonotic risk it may pose. “As more viruses are characterised, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritised for pre-emptive vaccine development.”  <img id="cosmos-post-tracker" style="opacity: 0; height: 1px!important; width: 1px!important; border: 0!important; position: absolute!important; z-index: -1!important;" src="https://syndication.cosmosmagazine.com/?id=167020&title=Could+AI+predict+the+next+pandemic%3F" alt="" width="1" height="1" /> </div> <div id="contributors"> <a rel="noopener" href="https://cosmosmagazine.com/technology/ai/could-ai-predict-next-pandemic/" target="_blank">This article</a> was originally published on <a rel="noopener" href="https://cosmosmagazine.com" target="_blank">Cosmos Magazine</a> and was written by <a rel="noopener" href="https://cosmosmagazine.com/contributor/amalyah-hart" target="_blank">Amalyah Hart</a>. Amalyah Hart is a science journalist based in Melbourne. Image: Getty Images </div>

Technology

Powerful books that predicted the future

As we were told by countless teachers and school librarians during our childhoods, a good book can transport you to another time and place, letting you briefly inhabit another world – or a different version of the one you’re living in. And whether the books are set in the past, present or future, the authors of fiction can create their own societies, and the rules, technologies, and social and political situations that come with it. Given how much literature has been written throughout history, it makes sense that some of it would include events or inventions that were not around (or maybe even possible) when the author wrote about them. Here are nine examples of books that predicted the future. The Parable Series Though she died before completing the third book in the trilogy, science fiction writer Octavia E. Butler created a dystopian world in Parable of the Sower (1993) and Parable of the Talents (1998) that featured the rise of a populist demagogue. While the books were well-received when they were published, they have struck a chord with readers more recently, given some stark similarities between the society Butler created and our reality today, including global warming, extremely influential corporations, and social inequality. But the strangest parallel came in Parable of the Talents, where she writes about a conservative evangelist who runs for president using the slogan “Make America Great Again”. 1984 George Orwell’s dystopian novel 1984 predicted so many aspects of the future that referring to it has become shorthand for any situations where technology threatens to control aspects of society. In fact, the term ‘Big Brother’, which refers to abuse of government power – specifically involving surveillance – originated in the book. Though it was published in 1949, Orwell described multiple technological advancements that now exist in some form. An article in Insider published in June 2019 discusses two of his sci-fi creations that are eerily similar to technology that exists today. The first example is the ‘telescreen’, which is essentially a large television used to monitor people’s private lives and is able to identify a person based on their facial expressions and heart rate: ie facial recognition software. The second example is the ‘Versificator’: a machine that can automatically produce music and literature – much like some of the artificial intelligence technology used today. The Machine Stops In his 1909 book The Machine Stops, E.M. Forster imagined a future in which people live and work exclusively in their own rooms, communicating with each other entirely through electronic means, says professor of humanities and legal studies, Kenneth Schneyer. The people in the book create and sustain their ‘friendships’, ‘groups’ or ‘teams’ entirely through electronic communications, and eventually become positively phobic about leaving their rooms or meeting other people in the flesh. And while the telephone did exist at this point, radio was virtually unknown and television not yet invented, Schneyer explains. “Until the internet and social media, I don’t think anyone thought of Forster’s novella as prophetic,” he says. “But by the time I first taught it to students five years ago, I was able to say with a straight face, ‘We are all living the nightmare that Forster is dreaming in Hell.’ In the world of commerce-via-Zoom, it’s even more true.” When the Sleeper Wakes Science-fiction writer H.G. Wells had a knack for predicting the future of warfare – including the atom bomb – in his 1914 novel The World Set Free, according to Professor Andrew Peck, an interdisciplinary researcher and educator. “Wells’ habit for seeing the future of armed conflict extends to his visions of the use and importance of airpower in warfare in his 1899 story When the Sleeper Wakes,” says Peck. “[This was] a feat of foresight some 12 years before the first military aerial reconnaissance mission (1911, Italians over Turkey) and four years before the Wright brothers first got off the ground with a manned, heavier-than-air, plane.” Fahrenheit 451 When Ray Bradbury’s book Fahrenheit 451 was published in 1953, television was already a form of entertainment. At that time, most of the programming consisted of scripted comedies and mysteries, game shows, news programmes and variety shows. But the book featured what sounds a lot like modern reality TV. “Bradbury, who was more interested in the way humans would react to technology than technology itself, imagined a world in which wall-sized televisions involved viewers directly in the action of the programs, anticipating not only our widescreen media but also reality TV,” Schneyer explains. “More than this, he foresaw how people would become increasingly devoted to their television programmes even in preference to their home lives and personal relationships. Although this book did not imagine the election of a reality TV star as president, I doubt that Montag (its protagonist) would be surprised.” The Wreck of the Titan Even though The Wreck of the Titan is one of the most well-known examples of books that predicted the future, it’s still hard to believe. Written by Morgan Robertson and originally published under the title Futility in 1898, the novella tells the tale of a massive passenger ship named the Titan that hit an iceberg and sank in the frigid waters of the Atlantic Ocean, killing thousands of people. “Like the Titanic, the Titan was also described in Robertson’s book as ‘the largest ship of its time,” says Lewis Keegan, creator of the online course resource website SkillScouter. The Titan was also glorified and called ‘unsinkable’ before it sank, he says – just like the Titanic. A Song for a New Day A more recent, and tragically timely example, is Sarah Pinsker’s novel A Song for a New Day. Published on September 10, 2019 – and written two or three years earlier – the book takes place in a society dealing with a combination of domestic terrorism and a lethal pandemic. “That causes the government to outlaw gatherings beyond a certain size, and to radically alter the economy, such that nearly everyone works full-time from home, wearing protective gear at all times when away from home,” Schneyer explains. “One of the two protagonists is a singer/songwriter whose livelihood depends on live gatherings of audiences, and who is now unable to do what she was born to do. Another protagonist is a young woman – a child during the pandemic – who is terrified of any other person or any public space.” This one hits a little too close to home right now. The Foundation trilogy First published in the early 1950s, Isaac Asimov’s Foundation trilogy predicted a science called ‘psychohistory’, in which the future could be predicted by accurately measuring current developments and trends in human behaviour and life, says professor of communication and media studies, and non-fiction and science-fiction author Paul Levinson. “Although statistics as a way of gauging the public existed back then, they were very rudimentary in comparison to today’s surveys and statistics, which are used every day to measure and predict everything from consumer behaviour and voting preferences to the impact of COVID-19,” he explains. “In other words, the psychohistory in Asimov’s science fiction has become a crucial way of life in our world.” The Foundation series is being brought to the small screen by Apple TV as a major television series for streaming in 2021. The Sultana’s Dream In her 1905 book The Sultana’s Dream, Rokeya Sakhawat Hussain – a Muslim feminist social reformer from Bengal – described a place called ‘Ladyland’ in which men were locked away so women could actually get stuff done without having to deal with annoying distractions like violence and war. Though that part hasn’t happened (yet), Hussain does predict a variety of technological developments, including solar power and video calls. With so much additional time in their schedules, thanks to the lack of men, the women of Ladyland have the opportunity to invent other useful things, like flying cars, weather control, and labour-less farms. Written by Elizabeth Yuko. This article first appeared on <a href="https://www.readersdigest.co.nz/book-club/powerful-books-that-predicted-the-future">Reader’s Digest</a>. For more of what you love from the world’s best-loved magazine, <a href="http://readersdigest.co.nz/subscribe">here’s our best subscription offer</a>.

Books

Chinese “batwoman” scientist issues chilling prediction

Before the coronavirus pandemic took the world by surprise, a Chinese scientist who was the first in the world to discover the genetic sequence of the virus issued a terrifying public prediction. She’s named the “Batwoman” of Wuhan by those who work with her as she has been studying the potential viruses’ bats carry for years, even going on expeditions to bat caves which she describes as “spellbinding”. Shi Zenghli has warned the world for years that the wildlife trade of bats, civets and other animals was only going to result in disaster. She co-authored a paper five years ago that contained a warning for the public that the SARS virus outbreak "heralded a new era in the cross-species transmission of severe respiratory illness with globalisation leading to rapid spread around the world and massive economic impact." "Although public health measures were able to stop the SARS-CoV outbreak, recent metagenomics studies have identified sequences of closely related SARS-like viruses circulating in Chinese bat populations that may pose a future threat,'' the paper states. During that time, Dr Shi gave a Ted Talk, discussing the history of bat-bourne viruses which included the Hendra outbreak in Australia where she worked with the CSIRO. In the presentation, she mentioned that more SARS-style viruses were lurking in bat caves and humans were to blame for putting “pig farms next to bat colonies”. “Even though we have been looking for so many viruses for so many years, SARS didn’t come back,” she said. “But in fact, in nature, these viruses similar to SARS … actually it’s still there. "If we humans do not become vigilant, the next time the virus gets infected, either directly or through other animals. This possibility is entirely possible." Now, amid the coronavirus outbreak, Dr Shi is at the centre of a diplomatic war of words between the US and China, as the US claims the Chinese government “covered up” her COVID-19 findings during a critical week in January. On December 30, authorities in Wuhan approached Dr Shi and asked her team to analyse blood samples, making her the first scientist in the world to learn about COVID-19. On February 3, her team publicly reported for the first time that the virus was born from bats. "Here we report on a series of cases caused by an unidentified pneumonia disease outbreak in Wuhan, Hubei province, central China,'' the paper states. "This disease outbreak - which started from a local seafood market - has grown substantially to infect 2761 people in China, is associated with 80 deaths and has led to the infection of 33 people in 10 additional countries as of 26 January 2020. Typical clinical symptoms of these patients are fever, dry cough, breathing difficulties (dyspnoea), headache and pneumonia. Disease onset may result in progressive respiratory failure owing to alveolar damage and even death. "Samples from seven patients with severe pneumonia (six of whom are sellers or deliverymen from the seafood market), who were admitted to the intensive care unit of Wuhan Jin Yin-Tan Hospital at the beginning of the outbreak, were sent to the laboratory at the Wuhan Institute of Virology (WIV) for the diagnosis of the causative pathogen. As a laboratory investigating CoV, we first used pan-CoV PCR primers to test these samples13, given that the outbreak occurred in winter and in a market - the same environment as SARS infections." But according to the Mail on Sunday, the director of the Wuhan Institute of Virology Yanyi Wang ordered Dr Shi and other key officials to not disclose information on the disease in January. She warned “inappropriate and inaccurate information” was causing “general panic” and warned the National Health Commission "unequivocally requires that any tests, clinical data, test results, conclusions related to the epidemic shall not be posted on social media platforms, nor shall [it] be disclosed to any media outlets including government official media". Speaking to Scientific American, Dr Shi insisted that COVID-19 came from wet markets, but that her first fear was it escaped from her own lab. But she said this is not possible because the genetic code of COVID-19 does not match the coronaviruses her team was working on. However, she allegedly released a strange statement through a Chinese social messaging app in early February, saying those claiming the virus came from her Wuhan lab should “shut their stinking mouths.” "The novel 2019 coronavirus is nature punishing the human race for keeping uncivilised living habits,'' it said. "I, Shi Zhengli, swear on my life that it has nothing to do with our laboratory," she wrote on a Chinese social messaging app in early February, according to Caixin Global. "I advise those who believe and spread rumours from harmful media sources … to shut their stinking mouths."

Travel Trouble

Apps may soon be able to predict your life expectancy, but do you want to know?

When will I die? This question has endured across cultures and civilisations. It has given rise to a plethora of religions and spiritual paths over thousands of years, and more recently, <a href="https://apps.apple.com/us/app/when-will-i-die/id1236569653">some highly amusing apps</a>. But this question now prompts a different response, as technology slowly brings us closer to accurately predicting the answer. Predicting the lifespan of people, or their “Personal Life Expectancy” (PLE) would greatly alter our lives. On one hand, it may have benefits for policy making, and help optimise an individual’s health, or the services they receive. But the potential misuse of this information by the government or private sector poses major risks to our rights and privacy. Although generating an accurate life expectancy is currently difficult, due to the complexity of factors underpinning lifespan, emerging technologies could make this a reality in the future. How do you calculate life expectancy? Predicting life expectancy is not a new concept. <a href="http://www.bbc.com/travel/story/20170807-living-in-places-where-people-live-the-longest">Experts do this</a> at a population level by classifying people into groups, often based on region or ethnicity. Also, tools such as <a href="https://www.nature.com/articles/s41598-018-23534-9">deep learning</a> and <a href="https://mipt.ru/english/news/scientists_use_ai_to_predict_biological_age_based_on_smartphone_and_wearables_data">artificial intelligence</a> can be used to consider complex variables, such as biomedical data, to predict someone’s biological age. Biological age refers to how “old” their body is, rather than when they were born. A 30-year-old who smokes heavily may have a biological age closer to 40. <a href="https://www.mdpi.com/2227-7080/6/3/74/htm">Calculating a life expectancy reliably</a> would require a sophisticated system that considers a breadth of environmental, geographic, genetic and lifestyle factors – <a href="https://www1.health.gov.au/internet/publications/publishing.nsf/Content/oatsih-hpf-2012-toc%7Etier1%7Elife-exp-wellb%7E119">all of which have influence</a>.<a href="https://www.shutterstock.com/image-vector/healthy-lady-run-away-angel-death-329261456" class="source"></a> With <a href="https://builtin.com/artificial-intelligence/machine-learning-healthcare">machine learning</a> and artificial intelligence, it’s becoming feasible to analyse larger quantities of data. The use of deep learning and cognitive computing, such as with <a href="https://www.ibm.com/watson-health">IBM Watson</a>, helps doctors make more accurate diagnoses than using human judgement alone. This, coupled with <a href="https://www.cio.com/article/3273114/what-is-predictive-analytics-transforming-data-into-future-insights.html">predictive analytics</a> and increasing computational power, means we may soon have systems, or even apps, that can calculate life expectancy. There’s an app for that Much like <a href="https://www.mdanderson.org/for-physicians/clinical-tools-resources/clinical-calculators.html">existing tools</a> that predict cancer survival rates, in the coming years we may see apps attempting to analyse data to predict life expectancy. However, they will not be able to provide a “death date”, or even a year of death. Human behaviour and activities are so unpredictable, it’s almost impossible to measure, classify and predict lifespan. A personal life expectancy, even a carefully calculated one, would only provide a “natural life expectancy” based on generic data optimised with personal data. The key to accuracy would be the quality and quantity of data available. Much of this would be taken directly from the user, including gender, age, weight, height and ethnicity. Access to real-time sensor data through fitness trackers and smart watches could also monitor activity levels, heart rate and blood pressure. This could then be coupled with lifestyle information such as occupation, socioeconomic status, exercise, diet and family medical history. <hr /> Read more: <a href="https://theconversation.com/your-local-train-station-can-predict-health-and-death-54946">Your local train station can predict health and death</a> <hr /> All of the above could be used to classify an individual into a generic group to calculate life expectancy. This result would then be refined over time through the analysis of personal data, updating a user’s life expectancy and letting them monitor it. <img src="https://images.theconversation.com/files/308303/original/file-20191230-11891-nswi58.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" alt="" /> This figure shows how an individual’s life expectancy might change between two points in time (F and H) following a lifestyle improvement, such as weight loss. Two sides of a coin Life expectancy predictions have the potential to be beneficial to individuals, health service providers and governments. For instance, they would make people more aware of their general health, and its improvement or deterioration over time. This may motivate them to make healthier lifestyle choices. They could also be used by insurance companies to provide individualised services, such as how some car insurance companies use <a href="https://www.theguardian.com/money/2017/dec/16/motoring-myths-black-boxes-telematics-insurance">black-box technology</a> to reduce premiums for more cautious drivers. Governments may be able to use predictions to more efficiently allocate limited resources, such as social welfare assistance and health care funding, to individuals and areas of greater need. That said, there’s a likely downside. People <a href="https://www.theatlantic.com/health/archive/2017/11/the-existential-slap/544790/">may become distressed</a> if their life expectancy is unexpectedly low, or at the thought of having one at all. This raises concerns about how such predictions could impact those who experience or are at risk of mental health problems. Having people’s detailed health data could also let insurance companies more accurately profile applicants, <a href="https://www.abc.net.au/news/2019-07-08/fitness-tracker-used-to-set-health-insurance-premiums/11287126">leading to discrimination against groups or individuals</a>. Also, pharmaceutical companies could coordinate targeted medical campaigns based on people’s life expectancy. And governments could choose to tax individuals differently, or restrict services for certain people. When will it happen? Scientists have been working on ways to <a href="https://towardsdatascience.com/what-really-drives-higher-life-expectancy-e1c1ec22f6e1">predict human life expectancy</a> for many years. The solution would require input from specialists including demographers, health scientists, data scientists, IT specialists, programmers, medical professionals and statisticians. While the collection of enough data will be challenging, we can likely expect to see advances in this area in the coming years. If so, issues related to data compliance, as well and collaboration with government and state agencies will need to be carefully managed. Any system predicting life expectancy would handle highly sensitive data, raising ethical and privacy concerns. It would also attract cybercriminals, and various other security threats. Moving forward, the words of Jurassic Park’s Dr Ian Malcolm spring to mind: <blockquote> Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.<img style="border: none !important; box-shadow: none !important; margin: 0 !important; max-height: 1px !important; max-width: 1px !important; min-height: 1px !important; min-width: 1px !important; opacity: 0 !important; outline: none !important; padding: 0 !important; text-shadow: none !important;" src="https://counter.theconversation.com/content/129068/count.gif?distributor=republish-lightbox-basic" alt="The Conversation" width="1" height="1" /> </blockquote> <a href="https://theconversation.com/profiles/james-jin-kang-903030">James Jin Kang</a>, Lecturer, <a href="https://theconversation.com/institutions/edith-cowan-university-720">Edith Cowan University</a> and <a href="https://theconversation.com/profiles/paul-haskell-dowland-382903">Paul Haskell-Dowland</a>, Associate Dean (Computing and Security), <a href="https://theconversation.com/institutions/edith-cowan-university-720">Edith Cowan University</a> This article is republished from <a href="http://theconversation.com">The Conversation</a> under a Creative Commons license. Read the <a href="https://theconversation.com/dont-die-wondering-apps-may-soon-be-able-to-predict-your-life-expectancy-but-do-you-want-to-know-129068">original article</a>.

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Apps may soon be able to predict your life expectancy, but do you want to know?