via here
Wednesday, February 2, 2011
Moral Self-Licensing
"How do individuals face the ethical uncertainties of social life? When under the threat that their next action might be (or appear to be) morally dubious, individuals can derive confidence from their past moral behavior, such that an impeccable track record increases their propensity to engage in otherwise suspect actions. Such moral self-licensing (Monin & Miller, 2001) occurs when past moral behavior makes people more likely to do potentially immoral things without worrying about feeling or appearing immoral. We argue that moral self-licensing occurs because good deeds make people feel secure in their moral self-regard. For example, when people are confident that their past behavior demonstrates compassion, generosity, or a lack of prejudice, they are more likely to act in morally dubious ways without fear of feeling heartless, selfish, or bigoted."
-More Here
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Exercise = Hippocampus Size = Memory Formation
"A new study shows that one year of moderate physical exercise can increase the size of the brain's hippocampus in older adults, leading to an improvement in spatial memory.
The project—conducted by researchers at the University of Pittsburgh, University of Illinois, Rice University, and Ohio State University—is considered the first study of its kind focusing on older adults who are already experiencing atrophy of the hippocampus, the brain structure involved in all forms of memory formation. The study, funded through the National Institute on Aging, appears in the Jan. 31 Proceedings of the National Academy of Sciences (PNAS).
The right hippocampus expanded in the older folks who exercised and shrank in the older folks who did not exercise. If you sit idly your capacity to form memories will decay."
-More Here
The project—conducted by researchers at the University of Pittsburgh, University of Illinois, Rice University, and Ohio State University—is considered the first study of its kind focusing on older adults who are already experiencing atrophy of the hippocampus, the brain structure involved in all forms of memory formation. The study, funded through the National Institute on Aging, appears in the Jan. 31 Proceedings of the National Academy of Sciences (PNAS).
The right hippocampus expanded in the older folks who exercised and shrank in the older folks who did not exercise. If you sit idly your capacity to form memories will decay."
-More Here
Egypt & Tunisia - Where is China?
Conspicuously missing in "action" (for obvious reasons) is the supposed future super-power China. For all its shortcomings, world will need US of A (for obvious reasons) even in a Post-American World. The CPC and PLA chief's are probably honing their skills on how to stop a revolution or how to expedite a peaceful transition to democracy (yeah, right!!).
In the mean time, here in US of A shoveling the snow is probably our "top" priority; world's largest democracy is too busy with the upcoming world-cup; life indeed does go on as usual on this planet.
"The hypocrisy of western liberals is breathtaking: they publicly supported democracy, and now, when the people revolt against the tyrants on behalf of secular freedom and justice, not on behalf of religion, they are all deeply concerned. Why concern, why not joy that freedom is given a chance? Today, more than ever, Mao Zedong's old motto is pertinent: "There is great chaos under heaven – the situation is excellent."
-More Here
In the mean time, here in US of A shoveling the snow is probably our "top" priority; world's largest democracy is too busy with the upcoming world-cup; life indeed does go on as usual on this planet.
"The hypocrisy of western liberals is breathtaking: they publicly supported democracy, and now, when the people revolt against the tyrants on behalf of secular freedom and justice, not on behalf of religion, they are all deeply concerned. Why concern, why not joy that freedom is given a chance? Today, more than ever, Mao Zedong's old motto is pertinent: "There is great chaos under heaven – the situation is excellent."
-More Here
Labrador Retriever Sniffs Out Bowel Cancer Patients
"The latest diagnostic tool for oncology comes on four paws and is defined by its very effective nose. In a small study, Japanese researchers found that a dog could detect cases of colorectal cancer by sniffing patients’ breath or stool samples. Previous experiments have shown that dogs can sniff out cases of skin, lung, bladder, and breast cancers; researchers think the tumors give off chemical signals that the dog can detect in bodily substances.
The cancer expert in this case was an eight-year-old black Labrador named Marine who was trained to search for disease traces at the St. Sugar Cancer Sniffing Dog Training Center in Chiba, Japan. She must have been a good student. The research, published in the journal Gut, showed that she had a high success rate:
The Labrador retriever was at least 95 percent as accurate as colonoscopy when smelling breath samples, and 98 percent correct with stool samples, according to the study…. The dog’s sense of smell was especially effective in early-stage cancer, and could discern polyps from malignancies, which colonoscopy can’t.
Lead researcher Hideto Sonoda says it would be impractical to use dogs for routine bowel cancer screenings, but adds that further research into dogs’ diagnostic ability could lead to the development of an electronic nose.
-More Here
Quote of the Day
"The day is not far off when the economic problem will take the back seat where it belongs, and the arena of the heart and the head will be occupied or reoccupied, by our real problems - the problems of life and of human relations, of creation and behavior and religion. Then, man will be faced with his permanent problem - how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him, to live wisely, and agreeably and well."
-John Maynard Keynes
-John Maynard Keynes
Tuesday, February 1, 2011
What I've been Reading
The Call of the Wild by Jack London. How stupid of me to have expected civility under wild justice. I had to quit half way through this book .. couldn't stomach the savagery. Yeah, life was (and is) brutal in the wild.. but I hope most of us have moved past that.
I, Algorithm - Why The Algorithm Might Soon Be The Only Game in Town
This probably is the future of Information Technology (and yeah, IT jobs as well) - Here:
Back in the 1960s, AI systems started to show great promise for replicating key aspects of the human mind. Scientists began by using mathematical logic to both represent knowledge about the real world and to reason about it, but it soon turned out to be an AI straightjacket. While logic was capable of being productive in ways similar to the human mind, it was inherently unsuited for dealing with uncertainty.
Yet after spending so long shrouded in a self-inflicted winter of discontent, the much-maligned field of AI is in bloom again. And Domingos is not the only one with fresh confidence in it. Researchers hoping to detect illness in babies, translate spoken words into text and even sniff out rogue nuclear explosions are proving that sophisticated computer systems can exhibit the nascent abilities which sparked interest in AI in the first place: the ability to reason like humans, even in a noisy and chaotic world.
Lying close to the heart of AI's revival is a technique called probabilistic programming, which combines the logical underpinnings of the old AI with the power of statistics and probability. "It's a natural unification of two of the most powerful theories that have been developed to understand the world and reason about it," says Stuart Russell, a pioneer of modern AI at the University of California, Berkeley. This powerful combination is finally starting to disperse the fog of the long AI winter. "It's definitely spring," says cognitive scientist Josh Tenenbaum at the Massachusetts Institute of Technology.
The first glimmer of spring came with the arrival of neural networks in the late 1980s. The idea was stunning in its simplicity. Developments in neuroscience had led to simple models of neurons. Coupled with advances in algorithms, this let researchers build artificial neural networks(ANNs) that could learn, ostensibly like a real brain. Invigorated computer scientists began to dream of ANNs with billions or trillions of neurons. Yet it soon became clear that our models of neurons were too simplistic and researchers couldn't tell which of the neuron's properties were important, let alone model them.
Neural networks, however, helped lay some of the foundations for a new AI. Some researchers working on ANNs eventually realised that these networks could be thought of as representing the world in terms of statistics and probability. Rather than talking about synapses and spikes, they spoke of parameterisation and random variables. "It now sounded like a big probabilistic model instead of a big brain," says Tenenbaum.
The key is a Bayesian network, a model made of various random variables, each with a probability distribution that depends on every other variable. Tweak the value of one, and you alter the probability distribution of all the others. Given the value of one or more variables, the Bayesian network allows you to infer the probability distribution of other variables - in other words, their likely values. Say these variables represent symptoms, diseases and test results. Given test results (a viral infection) and symptoms (fever and cough), one can assign probabilities to the likely underlying cause (flu, very likely; pneumonia, unlikely).
By the mid-1990s, researchers including Russell began to develop algorithms for Bayesian networks that could utilise and learn from existing data. In much the same way as human learning builds strongly on prior understanding, these new algorithms could learn much more complex and accurate models from much less data. This was a huge step up from ANNs, which did not allow for prior knowledge; they could only learn from scratch for each new problem.
Back in the 1960s, AI systems started to show great promise for replicating key aspects of the human mind. Scientists began by using mathematical logic to both represent knowledge about the real world and to reason about it, but it soon turned out to be an AI straightjacket. While logic was capable of being productive in ways similar to the human mind, it was inherently unsuited for dealing with uncertainty.
Yet after spending so long shrouded in a self-inflicted winter of discontent, the much-maligned field of AI is in bloom again. And Domingos is not the only one with fresh confidence in it. Researchers hoping to detect illness in babies, translate spoken words into text and even sniff out rogue nuclear explosions are proving that sophisticated computer systems can exhibit the nascent abilities which sparked interest in AI in the first place: the ability to reason like humans, even in a noisy and chaotic world.
Lying close to the heart of AI's revival is a technique called probabilistic programming, which combines the logical underpinnings of the old AI with the power of statistics and probability. "It's a natural unification of two of the most powerful theories that have been developed to understand the world and reason about it," says Stuart Russell, a pioneer of modern AI at the University of California, Berkeley. This powerful combination is finally starting to disperse the fog of the long AI winter. "It's definitely spring," says cognitive scientist Josh Tenenbaum at the Massachusetts Institute of Technology.
The first glimmer of spring came with the arrival of neural networks in the late 1980s. The idea was stunning in its simplicity. Developments in neuroscience had led to simple models of neurons. Coupled with advances in algorithms, this let researchers build artificial neural networks(ANNs) that could learn, ostensibly like a real brain. Invigorated computer scientists began to dream of ANNs with billions or trillions of neurons. Yet it soon became clear that our models of neurons were too simplistic and researchers couldn't tell which of the neuron's properties were important, let alone model them.
Neural networks, however, helped lay some of the foundations for a new AI. Some researchers working on ANNs eventually realised that these networks could be thought of as representing the world in terms of statistics and probability. Rather than talking about synapses and spikes, they spoke of parameterisation and random variables. "It now sounded like a big probabilistic model instead of a big brain," says Tenenbaum.
The key is a Bayesian network, a model made of various random variables, each with a probability distribution that depends on every other variable. Tweak the value of one, and you alter the probability distribution of all the others. Given the value of one or more variables, the Bayesian network allows you to infer the probability distribution of other variables - in other words, their likely values. Say these variables represent symptoms, diseases and test results. Given test results (a viral infection) and symptoms (fever and cough), one can assign probabilities to the likely underlying cause (flu, very likely; pneumonia, unlikely).
By the mid-1990s, researchers including Russell began to develop algorithms for Bayesian networks that could utilise and learn from existing data. In much the same way as human learning builds strongly on prior understanding, these new algorithms could learn much more complex and accurate models from much less data. This was a huge step up from ANNs, which did not allow for prior knowledge; they could only learn from scratch for each new problem.
Quote of the Day
“An abuser doesn’t change because he feels guilty or gets sober or finds God. He doesn’t change after seeing the fear in his children’s eyes or feeling them drift away from him. It doesn’t suddenly dawn on him that his partner deserves better treatment. Because of his self-focus, combined with the many rewards he gets from controlling you, an abuser changes only when he feels he has to, so the most important element in creating a context for change in an abuser is placing him in a situation where he has no other choice.
How do we stop the abusers who perpetrate a perpetual-growth economy? Seeing oiled pelicans and burned sea turtles won’t move them to stop. Nor will hundred-degree days in Moscow. We can’t stop them by making them feel guilty. We can’t stop them by appealing to them to do the right thing. The only way to stop them is to make it so they have no other choice."
- More Here
How do we stop the abusers who perpetrate a perpetual-growth economy? Seeing oiled pelicans and burned sea turtles won’t move them to stop. Nor will hundred-degree days in Moscow. We can’t stop them by making them feel guilty. We can’t stop them by appealing to them to do the right thing. The only way to stop them is to make it so they have no other choice."
- More Here
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