内容：Alice Ge 编辑：Alvin Wei
Wechat ID: NativeStudy / Weibo: http://weibo.com/u/3476904471
Part I: Speaker
Aversion to Broccoli May Have Genetic Roots
By Christopher Intagliata on November 12, 2019
Source: Scientific American
https://www.scientificamerican.com/podcast/episode/aversion-to-broccoli-may-have-genetic-roots/
[Rephrase 1, 03:06]
Part II: Speed
A 100-hour MRI scan captured the most detailed look yet at a whole human brain
BY Laura Sanders | JULY 8, 2019 AT 10:00 AM
[Time 2]
Over 100 hours of scanning has yielded a 3-D picture of the whole human brain that’s more detailed than ever before. The new view, enabled by a powerful MRI, has the resolution potentially to spot objects that are smaller than 0.1 millimeters wide.
“We haven’t seen an entire brain like this,” says electrical engineer Priti Balchandani of the Icahn School of Medicine at Mount Sinai in New York City, who was not involved in the study. “It’s definitely unprecedented.”
The scan shows brain structures such as the amygdala in vivid detail, a picture that might lead to a deeper understanding of how subtle changes in anatomy could relate to disorders such as post-traumatic stress disorder.
To get this new look, researchers at Massachusetts General Hospital in Boston and elsewhere studied a brain from a 58-year-old woman who died of viral pneumonia. Her donated brain, presumed to be healthy, was preserved and stored for nearly three years.
Before the scan began, researchers built a custom spheroid case of urethane that held the brain still and allowed interfering air bubbles to escape. Sturdily encased, the brain then went into a powerful MRI machine called a 7 Tesla, or 7T, and stayed there for almost five days of scanning.
[207 words]
[Time 3]
The strength of the 7T, the length of the scanning time and the fact that the brain was perfectly still led to the high-resolution images, which are described May 31 at bioRxiv.org. Associated videos of the brain, as well as the underlying dataset, are publicly available.
Researchers can’t get the same kind of resolution on brains of living people. For starters, people couldn’t tolerate a 100-hour scan. And even tiny movements, such as those that come from breathing and blood flow, would blur the images.
But pushing the technology further in postmortem samples “gives us an idea of what’s possible,” Balchandani says. The U.S. Food and Drug Administration approved the first 7T scanner for clinical imaging in 2017, and large medical centers are increasingly using them to diagnose and study illnesses.
These detailed brain images could hold clues for researchers trying to pinpoint hard-to-see brain abnormalities involved in disorders such as comas and psychiatric conditions such as depression. The images “have the potential to advance understanding of human brain anatomy in health and disease,” the authors write.
[177 words]
Source: Science News
https://www.sciencenews.org/article/mri-scan-most-detailed-look-yet-whole-human-brain
A will to survive might take AI to the next level
By Tom Siegfried | NOVEMBER 10, 2019 AT 8:00 AM
[Time 4]
Fiction is full of robots with feelings.
Like that emotional kid David, played by Haley Joel Osment, in the movie A.I. Or WALL•E, who obviously had feelings for EVE-uh. Robby the Robot sounded pretty emotional whenever warning Will Robinson of danger. Not to mention all those emotional train-wreck, wackadoodle robots on Westworld.
But in real life robots have no more feelings than a rock submerged in novocaine.
There might be a way, though, to give robots feelings, say neuroscientists Kingson Man and Antonio Damasio. Simply build the robot with the ability to sense peril to its own existence. It would then have to develop feelings to guide the behaviors needed to ensure its own survival.
“Today’s robots lack feelings,” Man and Damasio write in a new paper (subscription required) in Nature Machine Intelligence. “They are not designed to represent the internal state of their operations in a way that would permit them to experience that state in a mental space.”
So Man and Damasio propose a strategy for imbuing machines (such as robots or humanlike androids) with the “artificial equivalent of feeling.” At its core, this proposal calls for machines designed to observe the biological principle of homeostasis. That’s the idea that life must regulate itself to remain within a narrow range of suitable conditions — like keeping temperature and chemical balances within the limits of viability. An intelligent machine’s awareness of analogous features of its internal state would amount to the robotic version of feelings.
Such feelings would not only motivate self-preserving behavior, Man and Damasio believe, but also inspire artificial intelligence to more closely emulate the real thing.
[271 words]
[Time 5]
Typical “intelligent” machines are designed to perform a specific task, like diagnosing diseases, driving a car, playing Go or winning at Jeopardy! But intelligence in one arena isn’t the same as the more general humanlike intelligence that can be deployed to cope with all sorts of situations, even those never before encountered. Researchers have long sought the secret recipe for making robots smart in a more general way.
In Man and Damasio’s view, feelings are the missing ingredient.
Feelings arise from the need to survive. When humans maintain a robot in a viable state (wires all connected, right amount of electric current, comfy temperature), the robot has no need to worry about its own self-preservation. So it has no need for feelings — signals that something is in need of repair.
Feelings motivate living things to seek optimum states for survival, helping to ensure that behaviors maintain the necessary homeostatic balance. An intelligent machine with a sense of its own vulnerability should similarly act in a way that would minimize threats to its existence.
To perceive such threats, though, a robot must be designed to understand its own internal state.
Man and Damasio, of the University of Southern California, say the prospects for building machines with feelings have been enhanced by recent developments in two key research fields: soft robotics and deep learning. Progress in soft robotics could provide the raw materials for machines with feelings. Deep learning methods could enable the sophisticated computation needed to translate those feelings into existence-sustaining behaviors.
[251 words]
[Time 6]Deep learning is a modern descendant of the old idea of artificial neural networks — sets of connected computing elements that mimic the nerve cells at work in a living brain. Inputs into the neural network modify the strengths of the links between the artificial neurons, enabling the network to detect patterns in the inputs.
Deep learning requires multiple neural network layers. Patterns in one layer exposed to external input are passed on to the next layer and then on to the next, enabling the machine to discern patterns in the patterns. Deep learning can classify those patterns into categories, identifying objects (like cats) or determining whether a CT scan reveals signs of cancer or some other malady.
An intelligent robot, of course, would need to identify lots of features in its environment, while also keeping track of its own internal condition. By representing environmental states computationally, a deep learning machine could merge different inputs into a coherent assessment of its situation. Such a smart machine, Man and Damasio note, could “bridge across sensory modalities” — learning, for instance, how lip movements (visual modality) correspond to vocal sounds (auditory modality).
Similarly, that robot could relate external situations to its internal conditions — its feelings, if it had any. Linking external and internal conditions “provides a crucial piece of the puzzle of how to intertwine a system’s internal homeostatic states with its external perceptions and behavior,” Man and Damasio note. [236 words]
[The Rest]Ability to sense internal states wouldn’t matter much, though, unless the viability of those states is vulnerable to assaults from the environment. Robots made of metal do not worry about mosquito bites, paper cuts or indigestion. But if made from proper soft materials embedded with electronic sensors, a robot could detect such dangers — say, a cut through its “skin” threatening its innards — and engage a program to repair the injury.
A robot capable of perceiving existential risks might learn to devise novel methods for its protection, instead of relying on preprogrammed solutions.
“Rather than having to hard-code a robot for every eventuality or equip it with a limited set of behavioral policies, a robot concerned with its own survival might creatively solve the challenges that it encounters,” Man and Damasio suspect. “Basic goals and values would be organically discovered, rather than being extrinsically designed.”
Devising novel self-protection capabilities might also lead to enhanced thinking skills. Man and Damasio believe advanced human thought may have developed in that way: Maintaining viable internal states (homeostasis) required the evolution of better brain power. “We regard high-level cognition as an outgrowth of resources that originated to solve the ancient biological problem of homeostasis,” Man and Damasio write.
Protecting its own existence might therefore be just the motivation a robot needs to eventually emulate human general intelligence. That motivation is reminiscent of Isaac Asimov’s famous laws of robotics: Robots must protect humans, robots must obey humans, robots must protect themselves. In Asimov’s fiction, self-protection was subordinate to the first two laws. In real-life future robots, then, some precautions might be needed to protect people from self-protecting robots.
“Stories about robots often end poorly for their human creators,” Man and Damasio acknowledge. But would a supersmart robot (with feelings) really pose Terminator-type dangers? “We suggest not,” they say, “provided, for example, that in addition to having access to its own feelings, it would be able to know about the feelings of others — that is, if it would be endowed with empathy.”
And so Man and Damasio suggest their own rules for robots: 1. Feel good. 2. Feel empathy.
“Assuming a robot already capable of genuine feeling, an obligatory link between its feelings and those of others would result in its ethical and sociable behavior,” the neuroscientists contend.
That might just seem a bit optimistic. But if it’s possible, maybe there’s hope for a better future. If scientists do succeed in instilling empathy in robots, maybe that would suggest a way for doing it in humans, too. [423 words]
Source: Science News
https://www.sciencenews.org/article/will-to-survive-might-take-artificial-intelligence-next-level
Part III: Obstacle
Stalled weather patterns will get bigger due to climate change
November 13, 2019 | Rice University
[Paraphrase 7]
Climate change will increase the size of stalled high-pressure weather systems called "blocking events" that have already produced some of the 21st century's deadliest heat waves, according to a Rice University study.
Atmospheric blocking events are middle-latitude, high-pressure systems that stay in place for days or even weeks. Depending upon when and where they develop, blocking events can cause droughts or downpours and heat waves or cold spells. Blocking events caused deadly heat waves in France in 2003 and in Russia in 2010.
Using data from two sets of comprehensive climate model simulations, Rice fluid dynamicists Ebrahim Nabizadeh and Pedram Hassanzadeh, and colleagues found that the area of blocking events in the northern hemisphere will increase by as much as 17% due to anthropogenic climate change. The study, which is available online from Geophysical Research Letters, was co-authored by Da Yang of Lawrence Berkeley National Laboratory and the University of California, Davis, and Elizabeth Barnes of Colorado State University.
Hassanzadeh, an assistant professor of mechanical engineering and of Earth, environmental and planetary sciences, uses computational, mathematical and statistical models to study atmospheric flows related to a broad range of problems from extreme weather events to wind energy. He said researchers have increasingly been interested in learning how climate change might affect blocking events, but most studies have focused on whether blocking events will become more frequent as the atmosphere warms because of greenhouse gas emissions.
"Studies in the past have looked at whether you get more or less blocking events with climate change," he said. "The question nobody had asked is whether the size of these events will change or not. And the size is very important because the blocking events are more impactful when they are larger. For example, if the high-pressure system becomes bigger, you are going to get bigger heat waves that affect more people, and you are likely going to get stronger heat waves."
Nabizadeh, a mechanical engineering graduate student in Rice's Brown School of Engineering, set out to answer the question two years ago. Using a hierarchical modeling approach, he began with experiments on a model of atmospheric turbulence that's far simpler than the real atmosphere.
The simple model, which captures the fundamental dynamics of blocking events, allowed Nabizadeh to do a great deal of exploration. Making slight changes in one parameter or another, he ran thousands of simulations. Then the data was analyzed using a powerful dimensional analysis technique called the Buckingham-Pi theorem, which is often used in designing large and complex engineering systems that involve fluid flows.
The goal was finding a scaling law, a mathematical formula that described the size of a blocking event using variables that climate scientists already study and understand. Nabizadeh started with scaling laws that have been developed to predict the size of day-to-day weather patterns, but he found that none of the variables were predictive for blocking events.
His persistence eventually paid off with a simple formula that relates the area of blocking events to the width, latitude and strength of the jet stream, all of which are well-studied and measured.
"I gave a talk about this recently, and one of the people came up after and said, 'This is magical, that these powers add up and suddenly you get the right answer.' But it took a lot of work by Ebrahim to get this elegantly simple result," he said.
At a one point, Nabizadeh had analyzed the data from many simulations and produced a comparison that included page upon page of figures, and Hassanzadeh said the scaling law discovery was encouraged by an unlikely agency: the Texas Department of Motor Vehicles (DMV).
"Ebrahim went to the DMV one weekend, and I went to the DMV the week after, and at the DMV you have to sit and you don't have anything to do," he said. "So after staring at these numbers for hours, we realized this is the right scaling."
They also compared the simple-model results with the output of increasingly complex models of the Earth's weather and climate. Nabizadeh said the scaling law predicted changes in the size of future winter blocking events in comprehensive climate model simulations with remarkable accuracy.
"It performs better for winter events than summer events for reasons we don't yet understand," Nabizadeh said. "Our results suggest future studies should focus on better understanding summer blocks and also how larger blocking events might affect the size, magnitude and persistence of extreme-weather events like heat waves."
Source: Science Daily
https://www.sciencedaily.com/releases/2019/11/191113075107.htm |
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发表于 2019-11-16 23:37:36
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