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      "body": "Hi! I am a robot. I just upvoted you! I found similar content that readers might be interested in:\nhttps://www.sciencedaily.com/releases/2018/11/181105132939.htm",
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      "body": "![181105132939_1_540x360.jpg](https://cdn.steemitimages.com/DQmZ2rcoCn6QQpkiX6CjiWMvgxiR4oibhCH1GPanCdHx2gA/181105132939_1_540x360.jpg)\nScreens are now a fixture of modern childhood. And as young people spend an increasing amount of time on electronic devices, the effects of these digital activities has become a prevalent concern among parents, caregivers, and policy-makers. Research indicating that between 50% to 90% of school-age children might not be getting enough sleep has prompted calls that technology use may be to blame. However, the new research findings from the Oxford Internet Institute at the University of Oxford, has shown that screen time has very little practical effect on children's sleep.\n\nThe study was conducted using data from the United States' 2016 National Survey of Children's Health. Parents from across the country completed self-report surveys on themselves, their children and household.\n\n\"The findings suggest that the relationship between sleep and screen use in children is extremely modest,\" says Professor Andrew Przybylski, author of the study published in the Journal of Pediatrics. \"Every hour of screen time was related to 3 to 8 fewer minutes of sleep a night.\"\n\nIn practical terms, while the correlation between screen time and sleep in children exists, it might be too small to make a significant difference to a child's sleep. For example, when you compare the average nightly sleep of a tech-abstaining teenager (at 8 hours, 51 minutes) with a teenager who devotes 8 hours a day to screens (at 8 hours, 21 minutes), the difference is overall inconsequential. Other known factors, such as early starts to the school day, have a larger effect on childhood sleep.\n\n\"This suggests we need to look at other variables when it comes to children and their sleep,\" says Przybylski. Analysis in the study indicated that variables within the family and household were significantly associated with both screen use and sleep outcomes. \"Focusing on bedtime routines and regular patterns of sleep, such as consistent wake-up times, are much more effective strategies for helping young people sleep than thinking screens themselves play a significant role.\"\n\nThe aim of this study was to provide parents and practitioners with a realistic foundation for looking at screen versus the impact of other interventions on sleep. \"While a relationship between screens and sleep is there, we need to look at research from the lens of what is practically significant,\" says Przybylski. \"Because the effects of screens are so modest, it is possible that many studies with smaller sample sizes could be false positives -- results that support an effect that in reality does not exist.\"\n\n\"The next step from here is research on the precise mechanisms that link digital screens to sleep. Though technologies and tools relating to so-called 'blue light' have been implicated in sleep problems, it is not clear whether play a significant causal role,\" says Przybylski. \"Screens are here to stay, so transparent, reproducible, and robust research is needed to figure out how tech effects us and how we best intervene to limit its negative effects.\"",
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      "body": "In the basement of MIT's Building 3, a robot is carefully contemplating its next move. It gently pokes at a tower of blocks, looking for the best block to extract without toppling the tower, in a solitary, slow-moving, yet surprisingly agile game of Jenga.\n![190131101016_1_540x360.jpg](https://cdn.steemitimages.com/DQmceiGyVT4BEg8qiSSEt78h5LFdafxmUH78LNsBjMninXc/190131101016_1_540x360.jpg)\nThe robot, developed by MIT engineers, is equipped with a soft-pronged gripper, a force-sensing wrist cuff, and an external camera, all of which it uses to see and feel the tower and its individual blocks.\n\nAs the robot carefully pushes against a block, a computer takes in visual and tactile feedback from its camera and cuff, and compares these measurements to moves that the robot previously made. It also considers the outcomes of those moves -- specifically, whether a block, in a certain configuration and pushed with a certain amount of force, was successfully extracted or not. In real-time, the robot then \"learns\" whether to keep pushing or move to a new block, in order to keep the tower from falling.\n\nDetails of the Jenga-playing robot are published in the journal Science Robotics. Alberto Rodriguez, the Walter Henry Gale Career Development Assistant Professor in the Department of Mechanical Engineering at MIT, says the robot demonstrates something that's been tricky to attain in previous systems: the ability to quickly learn the best way to carry out a task, not just from visual cues, as it is commonly studied today, but also from tactile, physical interactions.\n\n\"Unlike in more purely cognitive tasks or games such as chess or Go, playing the game of Jenga also requires mastery of physical skills such as probing, pushing, pulling, placing, and aligning pieces. It requires interactive perception and manipulation, where you have to go and touch the tower to learn how and when to move blocks,\" Rodriguez says. \"This is very difficult to simulate, so the robot has to learn in the real world, by interacting with the real Jenga tower. The key challenge is to learn from a relatively small number of experiments by exploiting common sense about objects and physics.\"\n\nHe says the tactile learning system the researchers have developed can be used in applications beyond Jenga, especially in tasks that need careful physical interaction, including separating recyclable objects from landfill trash and assembling consumer products.\n\n\"In a cellphone assembly line, in almost every single step, the feeling of a snap-fit, or a threaded screw, is coming from force and touch rather than vision,\" Rodriguez says. \"Learning models for those actions is prime real-estate for this kind of technology.\"\n\nThe paper's lead author is MIT graduate student Nima Fazeli. The team also includes Miquel Oller, Jiajun Wu, Zheng Wu, and Joshua Tenenbaum, professor of brain and cognitive sciences at MIT.\n\nPush and pull\n\nIn the game of Jenga -- Swahili for \"build\" -- 54 rectangular blocks are stacked in 18 layers of three blocks each, with the blocks in each layer oriented perpendicular to the blocks below. The aim of the game is to carefully extract a block and place it at the top of the tower, thus building a new level, without toppling the entire structure.\n\nTo program a robot to play Jenga, traditional machine-learning schemes might require capturing everything that could possibly happen between a block, the robot, and the tower -- an expensive computational task requiring data from thousands if not tens of thousands of block-extraction attempts.\n\nInstead, Rodriguez and his colleagues looked for a more data-efficient way for a robot to learn to play Jenga, inspired by human cognition and the way we ourselves might approach the game.\n\nThe team customized an industry-standard ABB IRB 120 robotic arm, then set up a Jenga tower within the robot's reach, and began a training period in which the robot first chose a random block and a location on the block against which to push. It then exerted a small amount of force in an attempt to push the block out of the tower.\n\nFor each block attempt, a computer recorded the associated visual and force measurements, and labeled whether each attempt was a success.\n\nRather than carry out tens of thousands of such attempts (which would involve reconstructing the tower almost as many times), the robot trained on just about 300, with attempts of similar measurements and outcomes grouped in clusters representing certain block behaviors. For instance, one cluster of might represent attempts on a block that was hard to move, versus one that was easier to move, or that toppled the tower when moved. For each data cluster, the robot developed a simple model to predict a block's behavior given its current visual and tactile measurements.\n\nFazeli says this clustering technique dramatically increases the efficiency with which the robot can learn to play the game, and is inspired by the natural way in which humans cluster similar behavior: \"The robot builds clusters and then learns models for each of these clusters, instead of learning a model that captures absolutely everything that could happen.\"\n\nStacking up\n\nThe researchers tested their approach against other state-of-the-art machine learning algorithms, in a computer simulation of the game using the simulator MuJoCo. The lessons learned in the simulator informed the researchers of the way the robot would learn in the real world.\n\n\"We provide to these algorithms the same information our system gets, to see how they learn to play Jenga at a similar level,\" Oller says. \"Compared with our approach, these algorithms need to explore orders of magnitude more towers to learn the game.\"\n\nCurious as to how their machine-learning approach stacks up against actual human players, the team carried out a few informal trials with several volunteers.\n\n\"We saw how many blocks a human was able to extract before the tower fell, and the difference was not that much,\" Oller says.\n\nBut there is still a way to go if the researchers want to competitively pit their robot against a human player. In addition to physical interactions, Jenga requires strategy, such as extracting just the right block that will make it difficult for an opponent to pull out the next block without toppling the tower.\n\nFor now, the team is less interested in developing a robotic Jenga champion, and more focused on applying the robot's new skills to other application domains.\n\n\"There are many tasks that we do with our hands where the feeling of doing it 'the right way' comes in the language of forces and tactile cues,\" Rodriguez says. \"For tasks like these, a similar approach to ours could figure it out.\"\n\nThis research was supported, in part, by the National Science Foundation through the National Robotics Initiative.",
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