Deep Black: The Dreams of Data
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The U. PLUS a quarter of Latino households. This RacialWealthDivide is deeply systemic—but there are solutions: www. Today the median white family has 41x more wealth than the median Black family and 22x more wealth than the median Latino family. This RacialWealthDivide is a direct result of policy discrimination: www. The median Black family in the U. Learn more about the systemic reality of the RacialWealthDivide: www. Domenica Ghanem domenica ips-dc. Jessicah Pierre jessicah ips-dc.
Dreams Deferred. Introduction: January 15, , the release date of this report, would have been the 90th birthday of Dr. For this reason, our report focuses on the racial dimensions of wealth inequality in America Wealth is a critical measure of financial security because it buffers families from the ups and downs of income changes and economic cycles and allows households to take advantage of socio-economic opportunities.
Download Full Report. Black family wealth is on track to reach zero wealth by The Forbes richest Americans own more wealth than the entire Black population plus a quarter of the Latino population combined. That is 44 million times more wealth than the median Black family, and 24 million times more wealth than a Latino family.
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Social Media Kit. Wealth at the Middle, Top, and Bottom. The Racial Wealth Divide Today Going beyond the economic indicator of unemployment and looking more holistically at where the country is in terms of racial economic parity reveals a deep, pervasive and ongoing racial economic division.
That means despite three decades of economic growth and leaps in productivity, the typical U. This reduction was not experienced evenly across racial groups. Brain waves become even slower. REM sleep first occurs about 90 minutes after falling asleep. Your eyes move rapidly from side to side behind closed eyelids. Mixed frequency brain wave activity becomes closer to that seen in wakefulness. Your breathing becomes faster and irregular, and your heart rate and blood pressure increase to near waking levels.
Your arm and leg muscles become temporarily paralyzed, which prevents you from acting out your dreams. As you age, you sleep less of your time in REM sleep.
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Two internal biological mechanisms —circadian rhythm and homeostasis—work together to regulate when you are awake and sleep. Circadian rhythms direct a wide variety of functions from daily fluctuations in wakefulness to body temperature, metabolism, and the release of hormones. They control your timing of sleep and cause you to be sleepy at night and your tendency to wake in the morning without an alarm.
Circadian rhythms synchronize with environmental cues light, temperature about the actual time of day, but they continue even in the absence of cues. Sleep-wake homeostasis keeps track of your need for sleep. The homeostatic sleep drive reminds the body to sleep after a certain time and regulates sleep intensity. This sleep drive gets stronger every hour you are awake and causes you to sleep longer and more deeply after a period of sleep deprivation.
Factors that influence your sleep-wake needs include medical conditions, medications, stress, sleep environment, and what you eat and drink. Perhaps the greatest influence is the exposure to light. Specialized cells in the retinas of your eyes process light and tell the brain whether it is day or night and can advance or delay our sleep-wake cycle. Exposure to light can make it difficult to fall asleep and return to sleep when awakened. Night shift workers often have trouble falling asleep when they go to bed, and also have trouble staying awake at work because their natural circadian rhythm and sleep-wake cycle is disrupted.
In the case of jet lag, circadian rhythms become out of sync with the time of day when people fly to a different time zone, creating a mismatch between their internal clock and the actual clock. Your need for sleep and your sleep patterns change as you age, but this varies significantly across individuals of the same age. Babies initially sleep as much as 16 to 18 hours per day, which may boost growth and development especially of the brain. School-age children and teens on average need about 9. Most adults need hours of sleep a night, but after age 60, nighttime sleep tends to be shorter, lighter, and interrupted by multiple awakenings.
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Elderly people are also more likely to take medications that interfere with sleep. In general, people are getting less sleep than they need due to longer work hours and the availability of round-the-clock entertainment and other activities. Many people feel they can "catch up" on missed sleep during the weekend but, depending on how sleep-deprived they are, sleeping longer on the weekends may not be adequate. Everyone dreams.
You spend about 2 hours each night dreaming but may not remember most of your dreams. Events from the day often invade your thoughts during sleep, and people suffering from stress or anxiety are more likely to have frightening dreams. Dreams can be experienced in all stages of sleep but usually are most vivid in REM sleep. Some people dream in color, while others only recall dreams in black and white. Clusters of sleep-promoting neurons in many parts of the brain become more active as we get ready for bed.
GABA is associated with sleep, muscle relaxation, and sedation. Norepinephrine and orexin also called hypocretin keep some parts of the brain active while we are awake. Other neurotransmitters that shape sleep and wakefulness include acetylcholine, histamine, adrenaline, cortisol, and serotonin.
Genes may play a significant role in how much sleep we need. In , researchers at Google modified a deep-learning-based image recognition algorithm so that instead of spotting objects in photos, it would generate or modify them. By effectively running the algorithm in reverse, they could discover the features the program uses to recognize, say, a bird or building. The resulting images , produced by a project known as Deep Dream, showed grotesque, alien-like animals emerging from clouds and plants, and hallucinatory pagodas blooming across forests and mountain ranges.
But the images also hinted at how different deep learning is from human perception, in that it might make something out of an artifact that we would know to ignore. Google researchers noted that when its algorithm generated images of a dumbbell, it also generated a human arm holding it. The machine had concluded that an arm was part of the thing. Further progress has been made using ideas borrowed from neuroscience and cognitive science. A team led by Jeff Clune, an assistant professor at the University of Wyoming, has employed the AI equivalent of optical illusions to test deep neural networks.
His tool targets any neuron in the middle of the network and searches for the image that activates it the most. It is the interplay of calculations inside a deep neural network that is crucial to higher-level pattern recognition and complex decision-making, but those calculations are a quagmire of mathematical functions and variables.
In the office next to Jaakkola is Regina Barzilay, an MIT professor who is determined to apply machine learning to medicine. She was diagnosed with breast cancer a couple of years ago, at age The diagnosis was shocking in itself, but Barzilay was also dismayed that cutting-edge statistical and machine-learning methods were not being used to help with oncological research or to guide patient treatment. She says AI has huge potential to revolutionize medicine, but realizing that potential will mean going beyond just medical records. After she finished cancer treatment last year, Barzilay and her students began working with doctors at Massachusetts General Hospital to develop a system capable of mining pathology reports to identify patients with specific clinical characteristics that researchers might want to study.
However, Barzilay understood that the system would need to explain its reasoning. So, together with Jaakkola and a student, she added a step: the system extracts and highlights snippets of text that are representative of a pattern it has discovered.
Report: Dreams Deferred - Institute for Policy Studies
Barzilay and her students are also developing a deep-learning algorithm capable of finding early signs of breast cancer in mammogram images, and they aim to give this system some ability to explain its reasoning, too. The U. Here more than anywhere else, even more than in medicine, there is little room for algorithmic mystery, and the Department of Defense has identified explainability as a key stumbling block. A silver-haired veteran of the agency who previously oversaw the DARPA project that eventually led to the creation of Siri, Gunning says automation is creeping into countless areas of the military.
Intelligence analysts are testing machine learning as a way of identifying patterns in vast amounts of surveillance data.