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Video games seem to be a unique type of digital activity. Empirically, the cognitive benefits of video games have support from multiple observational and experimental studies23,24,25. Their benefits to intelligence and school performance make intuitive sense and are aligned with theories of active learning and the power of deliberate practice26,27. There is also a parallel line of evidence from the literature on cognitive training intervention apps28,29, which can be considered a special (lab developed) category of video games and seem to challenge some of the same cognitive processes. Though, like for other digital activities, there are contradictory findings for video games, some with no effects30,31 and negative effects32,33.

The contradictions among studies on screen time and cognition are likely due to limitations of cross-sectional designs, relatively small sample sizes, and, most critically, failures to control for genetic predispositions and socio-economic context10. Although studies account for some confounding effects, very few have accounted for socioeconomic status and none have accounted for genetic effects. This matters because intelligence, educational attainment, and other cognitive abilities are all highly heritable9,34. If these genetic predispositions are not accounted for, they will confound the potential impact of screen time on the intelligence of children. For example, children with a certain genetic background might be more prone to watch TV and, independently, have learning issues. Their genetic background might also modify the impact over time of watching TV. Genetic differences are a major confounder in many psychological and social phenomena35,36, but until recently this has been hard to account for because single genetic variants have very small effects. Socioeconomic status (SES) could also be a strong moderator of screen time in children37. For example, children in lower SES might be in a less functional home environment that makes them more prone to watch TV as an escape strategy, and, independently, the less functional home environment creates learning issues. Although SES is commonly assumed to represent a purely environmental factor, half of the effect of SES on educational achievement is probably genetically mediated38,39—which emphasizes the need for genetically informed studies on screen time.

Here, we estimated the impact of different types of screen time on the change in the intelligence of children in a large, longitudinal sample, while accounting for the critical confounding influences of genetic and socioeconomic backgrounds. In specific, we had a strong expectation that time spent playing video games would have a positive effect on intelligence, and were interested in contrasting it against other screen time types. Our sample came from the ABCD study (http://abcdstudy.org) and consisted of 9,855 participants aged 9–10 years old at baseline and 5,169 of these followed up two years later.

Cosmologist, noted author, Astronomer Royal and recipient of the 2015 Nierenberg Prize for Science in the Public Interest Lord Martin Rees delivers a thought-provoking and insightful perspective on the challenges humanity faces in the future beyond 2050. [3/2016] [Show ID: 30476]

Frontiers of Knowledge.
(https://www.uctv.tv/frontiers-of-knowledge)

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Science and technology continue to change our lives. University of California scientists are tackling the important questions like climate change, evolution, oceanography, neuroscience and the potential of stem cells.

UCTV is the broadcast and online media platform of the University of California, featuring programming from its ten campuses, three national labs and affiliated research institutions. UCTV explores a broad spectrum of subjects for a general audience, including science, health and medicine, public affairs, humanities, arts and music, business, education, and agriculture. Launched in January 2000, UCTV embraces the core missions of the University of California — teaching, research, and public service – by providing quality, in-depth television far beyond the campus borders to inquisitive viewers around the world.
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How to use causal influence diagrams to recognize the hidden incentives that shape an AI agent’s behavior.


There is rightfully a lot of concern about the fairness and safety of advanced Machine Learning systems. To attack the root of the problem, researchers can analyze the incentives posed by a learning algorithm using causal influence diagrams (CIDs). Among others, DeepMind Safety Research has written about their research on CIDs, and I have written before about how they can be used to avoid reward tampering. However, while there is some writing on the types of incentives that can be found using CIDs, I haven’t seen a succinct write up of the graphical criteria used to identify such incentives. To fill this gap, this post will summarize the incentive concepts and their corresponding graphical criteria, which were originally defined in the paper Agent Incentives: A Causal Perspective.

A causal influence diagram is a directed acyclic graph where different types of nodes represent different elements of an optimization problem. Decision nodes represent values that an agent can influence, utility nodes represent the optimization objective, and structural nodes (also called change nodes) represent the remaining variables such as the state. The arrows show how the nodes are causally related with dotted arrows indicating the information that an agent uses to make a decision. Below is the CID of a Markov Decision Process, with decision nodes in blue and utility nodes in yellow:

The first model is trying to predict a high school student’s grades in order to evaluate their university application. The model uses the student’s high school and gender as input and outputs the predicted GPA. In the CID below we see that predicted grade is a decision node. As we train our model for accurate predictions, accuracy is the utility node. The remaining, structural nodes show how relevant facts about the world relate to each other. The arrows from gender and high school to predicted grade show that those are inputs to the model. For our example we assume that a student’s gender doesn’t affect their grade and so there is no arrow between them. On the other hand, a student’s high school is assumed to affect their education, which in turn affects their grade, which of course affects accuracy. The example assumes that a student’s race influences the high school they go to. Note that only high school and gender are known to the model.

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It started with JPL agreeing to land something on Mars – cheaply – and do it in a radically different way. This is how the era NASA called “Faster, Better, Cheaper” began. The documentary film “The Pathfinders” tells the story of a small group of engineers at NASA’s Jet Propulsion Laboratory who did not heed warnings that the audacious challenge of landing on Mars with airbags would likely not be a career-enhancing move.

From relying on a parachute that could not be tested in a way to match the Martian atmosphere to receiving the late addition of an unwanted rover that wouldn’t have looked out of place in a toy store, the Mars Pathfinder mission was a doubter’s dream, taken on by a mostly young group of engineers and scientists guided by a grizzled manager known for his maverick ways.

“The Pathfinders” retraces the journey of this daring mission to Mars that captured the imagination of people around the world with its dramatic landing and its tiny rover – the first wheels ever to roll on Mars.

Documentary length: 60 minutes.

The Universe is a vast place, filled with more galaxies than we’ve ever been able to count, even in just the portion we’ve been able to observe. Some 40 years ago, Carl Sagan taught the world that there were hundreds of billions of stars in the Milky Way alone, and perhaps as many as 100 billion galaxies within the observable Universe. Although he never said it in his famous television series, Cosmos, the phrase “billions and billions” has become synonymous with his name, and also with the number of stars we think of as being inherent to each galaxy, as well as the number of galaxies contained within the visible Universe.

But when it comes to the number of galaxies that are actually out there, we’ve learned a number of important facts that have led us to revise that number upwards, and not just by a little bit. Our most detailed observations of the distant Universe, from the Hubble eXtreme Deep Field, gave us an estimate of 170 billion galaxies. A theoretical calculation from a few years ago — the first to account for galaxies too small, faint, and distant to be seen — put the estimate far higher: at 2 trillion. But even that estimate is too low. There ought to be at least 6 trillion, and perhaps more like 20 trillion, galaxies, if we’re ever able to count them all. Here’s how we got there.

Artificial intelligence; it’s everywhere! Our homes, our cars, our schools and work. So where, if ever, does it stop? And how close to ourselves can our devices reasonably get? For this video, Unveiled uncovers plans to use human brain implants to improve the performance of our brains! What do you think? Are neural implants a good thing, or a bad thing?

This is Unveiled, giving you incredible answers to extraordinary questions!

Find more amazing videos for your curiosity here:
What If Humanity Was A Type III Civilisation? — https://www.youtube.com/watch?v=jcx_nKWZ4Uw.
Why the Microverse Might Be a Reality — https://www.youtube.com/watch?v=BF6n-bjYr7Y

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