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Lawrence Hrubes

Louis C.K. Against the Common Core : The New Yorker - 1 views

www.newyorker.com/...k-against-the-common-core.html

education testing tests prediction standards human sciences TOK maths math

shared by Lawrence Hrubes on 01 May 14 - No Cached
  • “Students who already believe they are not as academically successful as their more affluent peers, will further internalize defeat,” Carol Burris, a principal from Rockville Centre, wrote in the Washington Post last summer, calling on policymakers to “re-examine their belief that college readiness is achieved by attaining a score on a test, and its corollary—that is possible to create college readiness score thresholds for eight year olds.” This week, teachers at International High School at Prospect Heights, which serves a population of recently arrived immigrants from non-English-speaking countries, announced that they would not administer an assessment required by the city. A pre-test in the fall “was a traumatic and demoralizing experience for students,” a statement issued by the teachers said. “Many students, after asking for help that teachers were not allowed to give, simply put their heads down for the duration. Some students even cried.”
Lawrence Hrubes

Most People Can’t Multitask, But a Few Are Exceptional. : The New Yorker - 0 views

www.newyorker.com/...multitask-masters.html

TOK psychology neuroscience cognition exceptions assumptions

shared by Lawrence Hrubes on 08 May 14 - No Cached
  • In 2012, David Strayer found himself in a research lab, on the outskirts of London, observing something he hadn’t thought possible: extraordinary multitasking. For his entire career, Strayer, a professor of psychology at the University of Utah, had been studying attention—how it works and how it doesn’t. Methods had come and gone, theories had replaced theories, but one constant remained: humans couldn’t multitask. Each time someone tried to focus on more than one thing at a time, performance suffered. Most recently, Strayer had been focussing on people who drive while on the phone. Over the course of a decade, he and his colleagues had demonstrated that drivers using cell phones—even hands-free devices—were at just as high a risk of accidents as intoxicated ones. Reaction time slowed, attention decreased to the point where they’d miss more than half the things they’d otherwise see—a billboard or a child by the road, it mattered not.
  • What, then, was going on here in the London lab? The woman he was looking at—let’s call her Cassie—was an exception to what twenty-five years of research had taught him. As she took on more and more tasks, she didn’t get worse. She got better. There she was, driving, doing complex math, responding to barking prompts through a cell phone, and she wasn’t breaking a sweat. She was, in other words, what Strayer would ultimately decide to call a supertasker.
  • Cassie in particular was the best multitasker he had ever seen. “It’s a really, really hard test,” Strayer recalls. “Some people come out woozy—I have a headache, that really kind of hurts, that sort of thing. But she solved everything.
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  • Their task was simple: keep your eyes on the road; keep a safe difference; brake as required. If they failed to do so, they’d eventually collide with their pace car. Then came the multitasking additions. They would have to not only drive the car but follow audio instructions from a cell phone. Specifically, they would hear a series of words, ranging from two to five at a time, and be asked to recall them in the right order. And there was a twist. Interspersed with the words were math problems. If they heard one of those, the drivers had to answer “true,” if the problem was solved correctly, or “false,” if it wasn’t. They would, for instance, hear “cat” and immediately after, “is three divided by one, minus one, equal to two?” followed by “box,” another problem, and so on. Intermittently, they would hear a prompt to “recall,” at which point, they’d have to repeat back all the words they’d heard since the last prompt. The agony lasted about an hour and a half.
markfrankel18

Maryam Mirzakhani: 'The more I spent time on maths, the more excited I got' | Science |... - 0 views

www.theguardian.com/...lds-medal-winner-mathematician

TOK math

shared by markfrankel18 on 13 Aug 14 - No Cached
  • Of course, the most rewarding part is the "Aha" moment, the excitement of discovery and enjoyment of understanding something new – the feeling of being on top of a hill and having a clear view. But most of the time, doing mathematics for me is like being on a long hike with no trail and no end in sight.
Lawrence Hrubes

Mathematicians and Blue Crabs - NYTimes.com - 1 views

www.nytimes.com/...ematicians-and-blue-crabs.html

math science ecology models prediction environment statistics animals

shared by Lawrence Hrubes on 03 May 15 - No Cached
  • The math behind these formulas may be elegant, but applying them is more complicated.
  • Although a definitive cause has yet to be identified, one thing is clear: Mathematical models failed to predict it.
  • For instance, it was long believed that a blue crab’s maximum life expectancy was eight years. This estimate was used, indirectly, to calculate crab mortality from fishing. Derided by watermen, the life expectancy turned out to be much too high; this had resulted in too many crab deaths being attributed to harvesting, thereby supporting charges of overfishing.
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  • Randomness is built into biological processes, so predicting a population is never going to be like calculating the interest on a bank account. The best we can do is use available science to make educated guesses about various outcomes. “The models we use are not universally predictive in the sense that Newton’s laws are; they are more like the weather forecast,” says Dr. Miller.
markfrankel18

Facebook math problem: Why PEMDAS doesn't always give a clear answer. - 0 views

www.slate.com/...ive_a_clear_answer.single.html

TOK math

shared by markfrankel18 on 08 Dec 15 - No Cached
  • You might expect 10 ÷ 5 is the same as 10/5 is the same as 10 over a 5 with a vinculum between them, but each has its own eccentricities. We’ve already noted that ÷ can mean “divide the number on the left by the number on the right” or “divide the expression on the left by the expression on the right.” But it gets really tricky when people assume that a slash replaces a vinculum. Does ab/cd = (ab)÷(cd) or ((ab)÷c)÷d? Does a/b/c mean (a)÷(b)÷(c) or a÷(b/c) or (a/b)÷c? (Answer: Use some parentheses!)
  • The bottom line is that “order of operations” conventions are not universal truths in the same way that the sum of 2 and 2 is always 4. Conventions evolve throughout history in response to cultural and technological shifts.
Lawrence Hrubes

The Great A.I. Awakening - The New York Times - 1 views

www.nytimes.com/...the-great-ai-awakening.html

language translation technology AI brain

shared by Lawrence Hrubes on 22 Dec 16 - No Cached
  • Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate “minister of agriculture” as “priest of farming.” Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled “general intelligence” better than math and chess.
  • A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new — a version of it dates to the earliest stages of modern computing, in the 1940s — but for much of its history most computer scientists saw it as vaguely disreputable, even mystical. Since 2011, though, Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts. Speech recognition didn’t work very well until Brain undertook an effort to revamp it; the application of machine learning made its performance on Google’s mobile platform, Android, almost as good as human transcription. The same was true of image recognition. Less than a year ago, Brain for the first time commenced with the gut renovation of an entire consumer product, and its momentous results were being celebrated tonight.
markfrankel18

The case against big data: "It's like you're being put into a cult, but you don't actua... - 0 views

www.salon.com/...ou-dont-actually-believe-in-it

TOK ethics math

shared by markfrankel18 on 09 Sep 16 - No Cached
  • in the very worst manifestation it was actually kind of a weaponized mathematical algorithm. I was working in online advertising. Most of the people working online advertising represented it as a way of giving people opportunities. That’s true for most technologists, most educated people, most white people. On the other side of the spectrum you have poor people, who are being preyed upon, by the same kinds algorithms.
  • people need to stop trusting mathematics and they need to stop trusting black box algorithms. They need to start thinking to themselves. You know: Who owns this algorithm? What is their goal and is it aligned with mine? If they’re trying to profit off of me, probably the answer is no.
Lawrence Hrubes

Fighting ISIS With an Algorithm, Physicists Try to Predict Attacks - The New York Times - 0 views

www.nytimes.com/...ts-try-to-predict-attacks.html

science math human sciences SocialScience prediction terrorism social networks

shared by Lawrence Hrubes on 20 Jun 16 - No Cached
  • And with the Islamic State’s prolific use of social media, terrorism experts and government agencies continually search for clues in posts and Twitter messages that appear to promote the militants’ cause.A physicist may not seem like an obvious person to study such activity. But for months, Neil Johnson, a physicist at the University of Miami, led a team that created a mathematical model to sift order from the chaotic pro-terrorism online universe.
  • The tracking of terrorists on social media should take a cue from nature, Dr. Johnson said, where “the way transitions happen is like a flock of birds, a school of fish.”
  • The researchers also said there might be a spike in the formation of small online groups just before an attack takes place.
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  • Both Mr. Berger and Ms. Patel noted a tricky question raised by the research: When is it best to try to suppress small groups so they do not mushroom into bigger groups, and when should they be left to percolate? Letting them exist for a while might be a way to gather intelligence, Ms. Patel said.
Lawrence Hrubes

How a mathematician dissects a coincidence - YouTube - 0 views

www.youtube.com/watch

math luck coincidence probability

shared by Lawrence Hrubes on 17 Feb 17 - No Cached
  • Can you unknot a twist of fate with logic? Vox's Phil Edwards asked mathematician Joseph Mazur about his book, Fluke, and one of its most incredible stories.
markfrankel18

Links 2013 - 1 views

worrydream.com/Links2013

books arts science ethics language perception human sciences reason emotion psychology models paradigm math digital

shared by markfrankel18 on 10 Jan 14 - No Cached
  • markfrankel18 on 10 Jan 14
     
    I love his Reading Tips in the sidebar!
Lawrence Hrubes

BBC World Service - More or Less, The death toll in Syria - 0 views

www.bbc.co.uk/p01fyvkc

TOK maths statistics SocialScience BBCRadioMoreOrLess podcast

shared by Lawrence Hrubes on 08 Sep 13 - No Cached
  • Lawrence Hrubes on 08 Sep 13
     
    As global leaders remain divided on whether to carry out a military strike against Syria in response to the apparent use of chemical weapons against its people, Tim Harford looks at the different claims made about how many people have been killed. The United States, the UK and France are sharing intelligence, but all quote different estimates of how many people they think died in the attack by Syrian President Bashar al-Assad's forces. Tim speaks to Kelly Greenhill, a professor of political science at Tufts University in the US, and co-author of Sex, Drugs and Body Counts about why the numbers vary so widely. And he speaks to Megan Price from the Human Rights Data Analysis Group, who has been trying to keep a tally of the deaths in Syria since the conflict began.
markfrankel18

Without Language, Large Numbers Don't Add Up : NPR - 0 views

www.npr.org/...or-understanding-large-numbers

TOK math

shared by markfrankel18 on 30 Jan 14 - No Cached
  • A study of people in Nicaragua has concluded that humans need language in order to understand large numbers.
  • He says the brains of all people — and some animals — can tell the difference between, say, two cookies and three cookies on a plate. The human brain is also very good at assessing approximate values, like the difference between 10 and 20 cookies, Casasanto says. But he says the brain needs some sort of counting system to tell the difference between 10 cookies and 11. "What language does is give you a means of linking up our small, exact number abilities with our large, approximate number abilities," Casasanto says. And for people in developed countries, that's essential.
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