An anonymous reader quotes a report from The Globe and Mail: Canada's best and brightest computer engineering graduates are leaving for jobs in Silicon Valley at alarmingly high rates, fueling a worse "brain drain" than the mass exodus by Canadian doctors two decades ago, according to a new study. The study, led by Zachary Spicer, a senior associate with the Munk School of Global Affairs' Innovation Policy Lab at University of Toronto, found one-in-four recent science, technology, engineering and math (STEM) graduates from three of the country's top universities -- University of Waterloo, University of British Columbia and U of T -- were working outside Canada. The numbers were higher for graduates of computer engineering and computer science (30 percent), engineering science (27 percent) and software engineering, where two out three graduates were working outside Canada, mostly in the United States. Nearly 44 percent of those working abroad were employed as software engineers, with Microsoft, Google, Facebook and Amazon listed as top employers.

An amateur mathematician has made the first breakthrough in more than 60 years towards solving a well-known maths problem. From a report: Aubrey de Grey, who is more widely known as a maverick biologist intent on extending the human lifespan, has taken the academic world by surprise after announcing a new solution to the so-called Hadwiger-Nelson problem. The problem sounds deceptively simple, but despite some professionals spending years trying to crack it, progress has stalled since shortly after the puzzle was first posed in 1950. "Literally, this is the first progress in more than 60 years," said Gil Kalai, a mathematician at Hebrew University of Jerusalem.

The problem is as follows. Imagine a collection of dots connected by lines. The dots can be arranged any way at all, the only rule is that all the connecting lines must be of equal length. For instance, in a square the diagonal would not be joined up, but the outer edges would be. Now, colour in all the dots so that no two connected points have the same colour. How many colours are required. For a square, the answer would be two. But the Hadwiger-Nelson problem asks what the minimum would be for any configuration -- even one that extends across a plane of infinite size.

An anonymous reader quotes a report from Gizmodo: Back in 2012, a Reddit user posted a map claiming to show the longest straight line that could be traversed across the ocean without hitting land. Intrigued, a pair of computer scientists have developed an algorithm that corroborates the route, while also demonstrating the longest straight line that can be taken on land. The researchers, Rohan Chabukswar from United Technologies Research Center Ireland, and Kushal Mukherjee from IBM Research India, created the algorithm in response to a map posted by reddit user user kepleronlyknows, who goes by Patrick Anderson in real life. His map showed a long, 20,000 mile route extending from Pakistan through the southern tips of Africa and South America and finally ending in an epic trans-Pacific journey to Siberia. On a traditional 2D map, the path looks nothing like a straight line; but remember, the Earth is a sphere.

Anderson didn't provide any evidence for the map, or an explanation for how the route was calculated. In light of this, Chabukswar and Mukherjee embarked upon a project to figure out if the straight line route was indeed the longest, and to see if it was possible for a computer algorithm to solve the problem, both for straight line passages on water without hitting land or an ice sheet, and for a continuous straight line passage on land without hitting a major body of water. Their ensuing analysis was posted to the pre-print arXiv server earlier this month, and has yet to go through peer review. "There would be 233,280,000 great circles to consider to find the global optimum, and each great circle would have 21,600 individual points to process -- a staggering 5,038,848,000,000 points to verify," the researchers wrote in their study.

Google co-founder Sergey Brin has warned that the current boom in artificial intelligence has created a "technology renaissance" that contains many potential threats. In the company's annual Founders' Letter, the Alphabet president struck a note of caution. "The new spring in artificial intelligence is the most significant development in computing in my lifetime," writes Brin. "Every month, there are stunning new applications and transformative new techniques." But, he adds, "such powerful tools also bring with them new questions and responsibilities." From a report: When Google was founded in 1998, Brin writes, the machine learning technique known as artificial neural networks, invented in the 1940s and loosely inspired by studies of the brain, was "a forgotten footnote in computer science." Today the method is the engine of the recent surge in excitement and investment around artificial intelligence. The letter unspools a partial list of where Alphabet uses neural networks, for tasks such as enabling self-driving cars to recognize objects, translating languages, adding captions to YouTube videos, diagnosing eye disease, and even creating better neural networks.

Brin nods to the gains in computing power that have made this possible. He says the custom AI chip running inside some Google servers is more than a million times more powerful than the Pentium II chips in Google's first servers. In a flash of math humor, he says that Google's quantum computing chips might one day offer jumps in speed over existing computers that can be only be described with the number that gave Google its name, a googol, or a 1 followed by 100 zeroes.

As you might expect, Brin expects Alphabet and others to find more uses for AI. But he also acknowledges that the technology brings possible downsides. "Such powerful tools also bring with them new questions and responsibilities," he writes. AI tools might change the nature and number of jobs, or be used to manipulate people, Brin says -- a line that may prompt readers to think of concerns around political manipulation on Facebook. Safety worries range from "fears of sci-fi style sentience to the more near-term questions such as validating the performance of self-driving cars," Brin writes.

The universe will end with a bang -- and not a whimper -- reports The New York Post, citing a new study by Harvard Researchers predicting exactly when (and how) the universe will end. But Gizmodo's science writer takes issue with the media coverage: That paper predicts that the universe's lifetime would be between 10**88 and 10**241 years, but probably probably around 10**139 years. "I think people don't have a sense as to how big these numbers are," study author and physicist Matthew Schwartz from Harvard told Gizmodo. "It's such an enormous out of time. But they think 10**139 years is 139."

The universe is around 10 billion, or 10**10 years old. 10**139 is a completely unfathomable number of years... It's more than the amount of time it would take to count every atom in the universe, if you had to wait from the Big Bang until now in between counting each atom. That number of years eludes any rational attempt to understand it (Which is probably why it sounds so close -- our heads just short circuit and say, threat!!!). It is forever.

"The BBC reports on something sure to impress your next date -- and possibly your last -- when you explain it," writes Slashdot reader dryriver. From the report: Scientists have turned their attention to investigating that most annoying of human habits -- the sound made when you crack your knuckles. The characteristic pop can be explained by three mathematical equations, say researchers in the US and France. Their model confirms the idea that the cracking sound is due to tiny bubbles collapsing in the fluid of the joint as the pressure changes. Surprisingly, perhaps, the phenomenon has been debated for around a century. Science student Vineeth Chandran Suja was cracking his knuckles in class in France when he decided to investigate.

"The first equation describes the pressure variations inside our joint when we crack our knuckles," he told BBC News. "The second equation is a well-known equation which describes the size variations of bubbles in response to pressure variations. And the third equation that we wrote down was coupling the size variation of the bubbles to ones that produce sounds." The equations make up a complete mathematical model that describes the sound of knuckle cracking, said Chandran Suja, who is now a postgraduate student at Stanford University in California. "When we crack our knuckles we're actually pulling apart our joints," he explained. "And when we do that the pressure goes down. Bubbles appear in the fluid, which is lubricating the joint -- the synovial fluid. "During the process of knuckle cracking there are pressure variations in the joint which causes the size of the bubbles to fluctuate extremely fast, and this leads to sound, which we associate with knuckle cracking.'' The study has been published in the journal Scientific Reports.

Kevin Hartnett, writing for Quanta magazine: Albert Einstein released his general theory of relativity at the end of 1915. He should have finished it two years earlier. When scholars look at his notebooks from the period, they see the completed equations, minus just a detail or two. "That really should have been the final theory," said John Norton, an Einstein expert and a historian of science at the University of Pittsburgh. But Einstein made a critical last-second error that set him on an odyssey of doubt and discovery -- one that nearly cost him his greatest scientific achievement. The consequences of his decision continue to reverberate in math and physics today.

Here's the error. General relativity was meant to supplant Newtonian gravity. This meant it had to explain all the same physical phenomena Newton's equations could, plus other phenomena that Newton's equations couldn't. Yet in mid-1913, Einstein convinced himself, incorrectly, that his new theory couldn't account for scenarios where the force of gravity was weak -- scenarios that Newtonian gravity handled well. "In retrospect, this is just a bizarre mistake," said Norton. To correct this perceived flaw, Einstein thought he had to abandon what had been one of the central features of his emerging theory. Einstein's field equations -- the equations of general relativity -- describe how the shape of space-time evolves in response to the presence of matter and energy. To describe that evolution, you need to impose on space-time a coordinate system -- like lines of latitude and longitude -- that tells you which points are where. Another interesting read on Quanta: Why Stephen Hawking's Black Hole Puzzle Keeps Puzzling.

Long-time Slashdot reader hey! writes: Benjamin D. Morrison of Beaver Dam Wisconsin was killed on March 5 while synthesizing explosives in his apartment... The accident has left the apartment building so contaminated that it will be demolished in a controlled burn, and residents are not being allowed in to retrieve any of their belongings.
It was just five years ago that Morrison graduated from Pensacola Christian College in Florida with a degree in pre-pharmacy and minors in chemistry and math. Though a local reverend believes 28-year-old Morrison was "not a bomb maker," USA Today's site FDL Reporter notes that "Officials assume he was making bombs that accidentally exploded and killed him... They have not publicly disclosed what chemicals were in apartment 11 where Morrow lived, only describing them as 'extremely volatile and unstable explosives.'"

Last week, an MIT study using data from more than 1,100 Uber and Lyft drivers concluded they're earning a median pretax profit of just $3.37 per hour. Uber was less than pleased by their findings and used a blog post to highlight problems with the researchers' methodology. "Now the lead researcher behind the draft paper has admitted that Uber's criticism was actually pretty valid -- while also asking Uber and Lyft to make more data available, in order to improve his analysis," reports Fortune. From the report: The issue with the draft paper from MIT's Center for Energy and Environmental Policy Research (CEEPR), Uber's chief economist Jonathan Hall said, was this: The researchers asked drivers how much money they made on average each week from such services, but then asked "How much of your total monthly income comes from driving" -- without specifying that such income must relate to on-demand services. Of course, many people driving for Uber and Lyft also earn money from regular jobs and other income sources. And this, Hall alleged, skewed the researchers' results.

"Hall's specific criticism is valid," wrote Stephen Zoepf, the executive director of Stanford's Center for Automotive Research, who led the MIT study, on Monday. "In re-reading the wording of the two questions, I can see how respondents could have interpreted the two questions in the manner Hall describes." Zoepf said he would be updating the CEEPR paper, but in the meantime he recalculated the figures using a methodology suggested by Hall, and found that the median profit was $8.55 per hour, rather than $3.37, and only 8% of drivers lose money on on-demand platforms. Using another methodology, he added, the median rises to $10 per hour and only 4% of drivers lose money.

dmoberhaus writes: An international team of mathematicians has found that there are theoretical black holes that would allow an observer to survive passage through the event horizon. This would result in the breakdown of determinism, a fundamental feature of the universe that allows physics to have predictive power, and result in the destruction of the observer's past and present them with an infinite number of futures. The findings were detailed in a report published last week in Physical Review Letters.

Jason Pontin, writing for Wired: Sundar Pichai, the chief executive of Google, has said that AI "is more profound than ... electricity or fire." Andrew Ng, who founded Google Brain and now invests in AI startups, wrote that "If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future." Their enthusiasm is pardonable.

[...] But there are many things that people can do quickly that smart machines cannot. Natural language is beyond deep learning; new situations baffle artificial intelligences, like cows brought up short at a cattle grid. None of these shortcomings is likely to be solved soon. Once you've seen you've seen it, you can't un-see it: deep learning, now the dominant technique in artificial intelligence, will not lead to an AI that abstractly reasons and generalizes about the world. By itself, it is unlikely to automate ordinary human activities.

To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works. Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. [...] Deep learning's advances are the product of pattern recognition: neural networks memorize classes of things and more-or-less reliably know when they encounter them again. But almost all the interesting problems in cognition aren't classification problems at all.

Key findings from a Gallup poll: Nearly three-quarters of adults (73%) say an increased use of AI will eliminate more jobs than it creates (PDF). Results are consistent across most demographic groups. However, those with blue-collar jobs are particularly pessimistic, with 82% saying the transition will result in a net job loss, compared with 71% of those with white-collar jobs.

Nearly half of Americans (49%) say "soft" skills, such as teamwork, communication, creativity and critical thinking, are the most important for U.S. workers to cultivate to avoid losing their jobs to AI. Alternatively, 51% say learning "hard" skills, including math, science, coding and the ability to work with data, are the most important to maintain a job in the face of new technology adoption.

When asked to choose among seven options concerning who should pay for retraining, a clear majority of U.S. adults overall (61%) say employers should fund these programs. The federal government comes in second at 50%.

Fifth-graders in China's Shunqing district were recently asked to answer this question: "If a ship had 26 sheep and 10 goats on board, how old is the ship's captain?" The Washington Post: The apparently unsolvable question sparked a debate over the merits of the Chinese education system and the value it places on the memorization of information over the importance of developing critical thinking skills. "Some surveys show that primary school students in our country lack a sense of critical awareness in regard to mathematics," a statement by the Shunqing Education Department posted Jan. 26 reportedly said. One student offered a pragmatic law-abiding answer: "The captain is at least 18 because he has to be an adult to drive the ship." Meanwhile on Twitter, some have gone with 42, a reference to the science fiction novel "A Hitchhiker's Guide to the Galaxy," by Douglas Adams, in which 42 is the "Answer to the Ultimate Question of Life, The Universe, and Everything." BBC: "If a school had 26 teachers, 10 of which weren't thinking, how old is the principal?" another asked. Some however, defended the school -- which has not been named -- saying the question promoted critical thinking. "The whole point of it is to make the students think. It's done that," one person commented. "This question forces children to explain their thinking and gives them space to be creative. We should have more questions like this," another said.

overheardinpdx quotes HPCwire: Wednesday a company called Bounded Floating Point announced a "breakthrough patent in processor design, which allows representation of real numbers accurate to the last digit for the first time in computer history. This bounded floating point system is a game changer for the computing industry, particularly for computationally intensive functions such as weather prediction, GPS, and autonomous vehicles," said the inventor, Alan Jorgensen, PhD. "By using this system, it is possible to guarantee that the display of floating point values is accurate to plus or minus one in the last digit..."

The innovative bounded floating point system computes two limits (or bounds) that contain the represented real number. These bounds are carried through successive calculations. When the calculated result is no longer sufficiently accurate the result is so marked, as are all further calculations made using that value. It is fail-safe and performs in real time.
Jorgensen is described as a cyber bounty hunter and part time instructor at the University of Nevada, Las Vegas teaching computer science to non-computer science students. In November he received US Patent number 9,817,662 -- "Apparatus for calculating and retaining a bound on error during floating point operations and methods thereof." But in a followup, HPCwire reports: After this article was published, a number of readers raised concerns about the originality of Jorgensen's techniques, noting the existence of prior art going back years. Specifically, there is precedent in John Gustafson's work on unums and interval arithmetic both at Sun and in his 2015 book, The End of Error, which was published 19 months before Jorgensen's patent application was filed. We regret the omission of this information from the original article.

chalsall writes: Persistence pays off. Jonathan Pace, a GIMPS volunteer for over 14 years, discovered the 50th known Mersenne prime, 2^77,232,917 -- 1 on December 26, 2017. The prime number is calculated by multiplying together 77,232,917 twos, and then subtracting one. It weighs in at 23,249,425 digits, becoming the largest prime number known to mankind. It bests the previous record prime, also discovered by GIMPS, by 910,807 digits. You can read a little more in the press release.

A new study in IEEE Transactions on Intelligent Transportation Systems mathematically suggests that if you and everyone else on the road kept an equal distance between the cars ahead and behind, traffic would move twice as quickly. From a report: Now sure, you're probably not going to convince everyone on the road to do that. Still, the finding could be a simple yet powerful way to optimize semi-autonomous cars long before the fully self-driving car of tomorrow arrives. Traffic is perhaps the world's most infuriating example of what's known as an emergent property. Meaning, lots of individual things forming together to create something more complex. Emergent properties are usually quite astounding. You've probably seen video of starlings forming a murmuration, a great shifting blob of thousands upon thousands of birds. Bats flying en masse out of a cave is another example, swarming sometimes by the millions through a small exit. And scientists are just beginning to understand how they do so.

If the United States were more like the rest of the world, a McDonald's Quarter Pounder might be known as the McDonald's 113-Grammer, John Henry's 9-pound hammer would be 4.08 kilograms, and any 800-pound gorillas in the room would likely weigh 362 kilos. NPR explores: One reason this country never adopted the metric system might be pirates. Here's what happened: In 1793, the brand new United States of America needed a standard measuring system because the states were using a hodgepodge of systems. "For example, in New York, they were using Dutch systems, and in New England, they were using English systems," says Keith Martin, of the research library at the National Institute of Standards and Technology. This made interstate commerce difficult. The secretary of state at the time was Thomas Jefferson. Jefferson knew about a new French system and thought it was just what America needed. He wrote to his pals in France, and the French sent a scientist named Joseph Dombey off to Jefferson carrying a small copper cylinder with a little handle on top. It was about 3 inches tall and about the same wide. This object was intended to be a standard for weighing things, part of a weights and measure system being developed in France, now known as the metric system. The object's weight was 1 kilogram. Crossing the Atlantic, Dombey ran into a giant storm. "It blew his ship quite far south into the Caribbean Sea," says Martin. And you know who was lurking in Caribbean waters in the late 1700s? Pirates.

2017 began with an AI named "Libratus" defeating four of the world's best poker players. Now the AI's creators reveal how exactly they did it. An anonymous reader quotes the Pittsburgh Tribune-Review: First, the AI made the game easier to understand. There are 10**161 potential outcomes in the game of poker -- that's a one followed by 161 zeros, potential outcomes in a game of poker. Libratus grouped similar hands, like a King-high flush and a Queen-high flush, and similar bet sizes to cut down that number. Libratus then created a detailed strategy for how it would play the early rounds of the game and a less-refined strategy for the final rounds. As the game nears the end, Libratus refined the second strategy based on how the game had gone.

A third strategy was at work as well. In real-time, Libratus created another model based on how its play stacked up against the play of the humans. If the humans did something unexpected to Libratus, the AI accounted for it and built it into the strategy. Instead of trying to exploit weaknesses in the play of the human, Libratus focused on improving its play.
The AI was created by a computer science professor at Carnegie Mellon University and his Ph.D. student, who argue in a new paper that "The techniques that we developed are largely domain independent and can thus be applied to other strategic imperfect-information interactions, including non-recreational applications."

"Due to the ubiquity of hidden information in real-world strategic interactions, we believe the paradigm introduced in Libratus will be critical to the future growth and widespread application of AI."

Science_afficionado writes: By measuring the flicker pattern of light from distant stars, astronomers have developed a new and improved method for measuring the masses of millions of solitary stars, especially those hosting exoplanets. Stevenson Professor of Physics and Astronomy Keivan Stassun says, "First, we use the total light from the star and its parallax to infer its diameter. Next, we analyze the way in which the light from the star flickers, which provides us with a measure of its surface gravity. Then we combine the two to get the star's total mass." Stassun and his colleagues describe the method and demonstrate its accuracy using 675 stars of known mass in an article titled "Empirical, accurate masses and radii of single stars with TESS and GAIA" accepted for publication in the Astronomical Journal.

David Salisbury via Vanderbilt University explains the other methods of determining the mass of distant stars, and why they aren't always the most accurate: "Traditionally, the most accurate method for determining the mass of distant stars is to measure the orbits of double star systems, called binaries. Newton's laws of motion allow astronomers to calculate the masses of both stars by measuring their orbits with considerable accuracy. However, fewer than half of the star systems in the galaxy are binaries, and binaries make up only about one-fifth of red dwarf stars that have become prized hunting grounds for exoplanets, so astronomers have come up with a variety of other methods for estimating the masses of solitary stars. The photometric method that classifies stars by color and brightness is the most general, but it isn't very accurate. Asteroseismology, which measures light fluctuations caused by sound pulses that travel through a star's interior, is highly accurate but only works on several thousand of the closest, brightest stars." Stassun says his method "can measure the mass of a large number of stars with an accuracy of 10 to 25 percent," which is "far more accurate than is possible with other available methods, and importantly it can be applied to solitary stars so we aren't limited to binaries."

An anonymous reader shares an article: Bitcoin wasn't intended to be an investment instrument. Its creators envisioned it as a replacement for money itself -- a decentralized, secure, anonymous method for transferring value between people. But what they might not have accounted for is how much of an energy suck the computer network behind bitcoin could one day become. Simply put, bitcoin is slowing the effort to achieve a rapid transition away from fossil fuels. What's more, this is just the beginning. Given its rapidly growing climate footprint, bitcoin is a malignant development, and it's getting worse. Digital financial transactions come with a real-world price: The tremendous growth of cryptocurrencies has created an exponential demand for computing power. As bitcoin grows, the math problems computers must solve to make more bitcoin (a process called "mining") get more and more difficult -- a wrinkle designed to control the currency's supply. Today, each bitcoin transaction requires the same amount of energy used to power nine homes in the U.S. for one day. And miners are constantly installing more and faster computers. Already, the aggregate computing power of the bitcoin network is nearly 100,000 times larger than the world's 500 fastest supercomputers combined. The total energy use of this web of hardware is huge -- an estimated 31 terawatt-hours per year. More than 150 individual countries in the world consume less energy annually. And that power-hungry network is currently increasing its energy use every day by about 450 gigawatt-hours, roughly the same amount of electricity the entire country of Haiti uses in a year.