Trinidad and Tobago’s national motto is “Together we aspire. Together we achieve.” Education has always been the tool to turn those aspirations into achievements. We are excited to work with Trinidad to make that tool more accessible beyond classroom walls through a country-wide initiative called
A computer program called the Never Ending Image Learner (NEIL) is running 24 hours a day at Carnegie Mellon University, searching the Web for images, doing its best to understand them on its own and, as it builds a growing visual database, gathering common sense on a massive scale.
The Optimod’Lyon project in Lyon, east-central France, comprises of 450 sensors spread all over the city to measure the density of traffic. The data they transmit allows the city to predict traffic for the next hour with a reliability rate of over 90%. The idea is that in the future traffic light management and information for public transport users can be adapted accordingly.
Within 24 hours of quitting the drug, your withdrawal symptoms begin. Initially, they’re subtle: The first thing you notice is that you feel mentally foggy, and lack alertness. Your muscles are fatigued, even when you haven’t done anything strenuous, and you suspect that you’re more irritable than usual.
Over time, an unmistakable throbbing headache sets in, making it difficult to concentrate on anything. Eventually, as your body protests having the drug taken away, you might even feel dull muscle pains, nausea and other flu-like symptoms.
This isn’t heroin, tobacco or even alcohol withdrawl. We’re talking about quitting caffeine, a substance consumed so widely (the FDA reports thatmore than 80 percent of American adults drink it daily) and in such mundane settings (say, at an office meeting or in your car) that we often forget it’s a drug—and by far the world’s most popular psychoactive one.
Like many drugs, caffeine is chemically addictive, a fact that scientists established back in 1994. This past May, with the publication of the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM), caffeine withdrawal was finally included as a mental disorder for the first time—even though its merits for inclusion are symptoms that regular coffee-drinkers have long known well from the times they’ve gone off it for a day or more.
Why, exactly, is caffeine addictive? The reason stems from the way the drug affects the human brain, producing the alert feeling that caffeine drinkers crave.
Soon after you drink (or eat) something containing caffeine, it’s absorbed through the small intestine and dissolved into the bloodstream. Because the chemical is both water- and fat-soluble (meaning that it can dissolve in water-based solutions—think blood—as well as fat-based substances, such as our cell membranes), it’s able to penetrate the blood-brain barrier and enter the brain.
Structurally, caffeine closely resembles a molecule that’s naturally present in our brain, called adenosine (which is a byproduct of many cellular processes, including cellular respiration)—so much so, in fact, that caffeine can fit neatly into our brain cells’ receptors for adenosine, effectively blocking them off. Normally, the adenosine produced over time locks into these receptors and produces a feeling of tiredness.
When caffeine molecules are blocking those receptors, they prevent this from occurring, thereby generating a sense of alertness and energy for a few hours. Additionally, some of the brain’s own natural stimulants (such as dopamine) work more effectively when the adenosine receptors are blocked, and all the surplus adenosine floating around in the brain cues the adrenal glands to secrete adrenaline, another stimulant.
For this reason, caffeine isn’t technically a stimulant on its own, says Stephen R. Braun, the author or Buzzed: the Science and Lore of Caffeine and Alcohol, but a stimulant enabler: a substance that lets our natural stimulants run wild. Ingesting caffeine, he writes, is akin to “putting a block of wood under one of the brain’s primary brake pedals.” This block stays in place for anywhere from four to six hours, depending on the person’s age, size and other factors, until the caffeine is eventually metabolized by the body.
In people who take advantage of this process on a daily basis (i.e. coffee/tea, soda or energy drink addicts), the brain’s chemistry and physical characteristics actually change over time as a result. The most notable change is that brain cells grow more adenosine receptors, which is the brain’s attempt to maintain equilibrium in the face of a constant onslaught of caffeine, with its adenosine receptors so regularly plugged (studies indicate that the brain also responds by decreasing the number of receptors for norepinephrine, a stimulant). This explains why regular coffee drinkers build up a tolerance over time—because you have more adenosine receptors, it takes more caffeine to block a significant proportion of them and achieve the desired effect.
This also explains why suddenly giving up caffeine entirely can trigger a range of withdrawal effects. The underlying chemistry is complex and not fully understood, but the principle is that your brain is used to operating in one set of conditions (with an artificially-inflated number of adenosine receptors, and a decreased number of norepinephrine receptors) that depend upon regular ingestion of caffeine. Suddenly, without the drug, the altered brain chemistry causes all sorts of problems, including the dreaded caffeine withdrawal headache.
The good news is that, compared to many drug addictions, the effects are relatively short-term. To kick the thing, you only need to get through about 7-12 days of symptoms without drinking any caffeine. During that period, your brain will naturally decrease the number of adenosine receptors on each cell, responding to the sudden lack of caffeine ingestion. If you can make it that long without a cup of joe or a spot of tea, the levels of adenosine receptors in your brain reset to their baseline levels, and your addiction will be broken.
Self-perceived social status predicts hippocampal function and stress hormones
A mother’s perceived social status predicts her child’s brain development and stress indicators, finds a study at Boston Children’s Hospital. While previous studies going back to the 1950s have linked objective…
Feynman Liang is one of Coursera’s ‘top 50’ students by number of courses he has completed. To date, Feynman has taken 34 courses! We have asked him to share tips for the Coursera community on how to tackle taking multiple courses on Coursera
At first, I was skeptical about taking classes…
Here is an essay version of my class notes from Class 1 of CS183: Startup. Errors and omissions are my own. Credit for good stuff is Peter’s entirely.
CS183: Startup—Notes Essay—The Challenge of the Future
Purpose and Preamble
We might describe our world as having retail sanity, but wholesale madness. Details are well understood; the big picture remains unclear. A fundamental challenge—in business as in life—is to integrate the micro and macro such that all things make sense.
Humanities majors may well learn a great deal about the world. But they don’t really learn career skills through their studies. Engineering majors, conversely, learn in great technical detail. But they might not learn why, how, or where they should apply their skills in the workforce. The best students, workers, and thinkers will integrate these questions into a cohesive narrative. This course aims to facilitate that process.
I. The History of Technology
For most of recent human history—from the invention of the steam engine in the late 17th century through about the late 1960’s or so— technological progress has been tremendous, perhaps even relentless. In most prior human societies, people made money by taking it from others. The industrial revolution wrought a paradigm shift in which people make money through trade, not plunder.
The importance of this shift is hard to overstate. Perhaps 100 billion people have ever lived on earth. Most of them lived in essentially stagnant societies; success involved claiming value, not creating it. So the massive technological acceleration of the past few hundred years is truly incredible.
The zenith of optimism about the future of technology might have been the 1960’s. People believed in the future. They thought about the future. Many were supremely confident that the next 50 years would be a half-century of unprecedented technological progress.
But with the exception of the computer industry, it wasn’t. Per capita incomes are still rising, but that rate is starkly decelerating. Median wages have been stagnant since 1973. People find themselves in an alarming Alice-in-Wonderland-style scenario in which they must run harder and harder—that is, work longer hours—just to stay in the same place. This deceleration is complex, and wage data alone don’t explain it. But they do support the general sense that the rapid progress of the last 200 years is slowing all too quickly.
以下のサイトに、「Image Classifier Demo 」と銘打った、Deep Learning ベースの画像認識器のデモアプリが公開されているので紹介します。
Indre Viskontas Daniel Dennett - Tools for Thinking
Just like Google Drive, Google+ Photos uses some amazing image recognition technology to make photos searchable, even if they don’t have captions or useful filenames. “This is powered by computer vision and machine learning technology, which uses the visual content of an image to generate searchable tags for photos combined with other sources like text tags and EXIF metadata to enable search across thousands of concepts like a flower, food, car, jet ski, or turtle,” explains Google.
Google acquired DNNresearch, a start-up created by Professor Geoffrey Hinton and two of his graduate students at the University of Toronto. They built “a system which used deep learning and convolutional neural networks and easily beat out more traditional approaches in the ImageNet computer vision competition designed to test image understanding.” Google built and trained similar large-scale models and found that this approach doubles the average precision, compared to other object recognition methods. “We took cutting edge research straight out of an academic research lab and launched it, in just a little over six months,” says Chuck Rosenberg, from the Google Image Search Team.