Phys .org: Predictive algorithms are no better at telling the future than a crystal ball . “Predictive analytics powered by algorithms are designed to help managers make decisions that favourably impact the bottom line. The global market for this technology is expected to grow from US$3.9 billion in 2016 to US$14.9 billion by 2023. Despite the promise, predictive algorithms are as mythical as the crystal ball of ancient times.”
The Guardian: Brazil’s biggest newspaper pulls content from Facebook after algorithm change. “Brazil’s biggest newspaper, the Folha de S Paulo, has announced that it will no longer publish content on its Facebook page, accusing the social media giant of encouraging fake news with an overhaul of its news feed algorithm.”
Phys .org: Algorithm identifies vulnerable people during natural disasters. “A new algorithm developed at the University of Waterloo will help first responders and home care providers better help the elderly during natural disasters. According to the World Health Organization, older adults who live at home face disproportionally high fatality rates during natural disasters as evidenced by Hurricane Katrina where 71 per cent of the deaths resulting from that disaster involved people over 60 years of age.”
The Guardian: ‘Fiction is outperforming reality’: how YouTube’s algorithm distorts truth. “Company insiders tell me the algorithm is the single most important engine of YouTube’s growth. In one of the few public explanations of how the formula works – that sketches the algorithm’s deep neural networks, crunching a vast pool of data about videos and the people who watch them – YouTube engineers describe it as one of the ‘largest scale and most sophisticated industrial recommendation systems in existence’. Lately, it has also become one of the most controversial.” Long and substantive article.
Ars Technica: Is “Big Data” racist? Why policing by data isn’t necessarily objective. “Algorithmic technologies that aid law enforcement in targeting crime must compete with a host of very human questions. What data goes into the computer model? After all, the inputs determine the outputs. How much data must go into the model? The choice of sample size can alter the outcome. How do you account for cultural differences? Sometimes algorithms try to smooth out the anomalies in the data—anomalies that can correspond with minority populations. How do you address the complexity in the data or the ‘noise’ that results from imperfect results? The choices made to create an algorithm can radically impact the model’s usefulness or reliability. To examine the problem of algorithmic design, imagine that police in Cincinnati, Ohio, have a problem with the Bloods gang—a national criminal gang, originating out of Los Angeles, that signifies membership by wearing the color red.”
Wired: 2017 Was The Year We Fell Out Of Love With Algorithms. “WE OWE A lot to 9th century Persian scholar Muhammad ibn Musa al-Khwarizmi. Centuries after his death, al-Khwarizmi’s works introduced Europe to decimals and algebra, laying some of the foundations for today’s techno-centric age. The latinized version of his name has become a common word: algorithm. In 2017, it took on some sinister overtones.”
Quanta Magazine: Best-Ever Algorithm Found for Huge Streams of Data. “It’s hard to measure water from a fire hose while it’s hitting you in the face. In a sense, that’s the challenge of analyzing streaming data, which comes at us in a torrent and never lets up. If you’re on Twitter watching tweets go by, you might like to declare a brief pause, so you can figure out what’s trending. That’s not feasible, though, so instead you need to find a way to tally hashtags on the fly.”