Lifeboat Foundation

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 studies^23,24,25. Their benefits to intelligence and school performance make intuitive sense and are aligned with theories of active learning and the power of deliberate practice^26,27. There is also a parallel line of evidence from the literature on cognitive training intervention apps^28,29, 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 effects^30,31 and negative effects^32,33.

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 heritable^9,34. 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 phenomena^35,36, 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 mediated^38,39—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.

“China claims to have developed an AI that can read the minds of Communist Party members to determine how receptive they are to ‘thought education’ in since-deleted article”.

AI developed by experts at Hefei Comprehensive National Science Centre is reportedly intended to check loyalty of Communist Party members.

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)

Continue reading “Looking Beyond 2050 — On Earth and in Space with Lord Martin Rees” »

ON THE PANEL…

Alys Denby, Deputy Editor, CapX
Mark Johnson, Legal and Policy Officer, Big Brother Watch.
Christopher Snowdon, Head of Lifestyle Economics, IEA
Victoria Hewson, Head of Regulatory Affairs, IEA

Continue reading “Live With(out) Littlewood | Alys Denby, Mark Johnson, Christopher Snowdon + more! | Ep.66” »

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:

Continue reading “Spotting Unfair or Unsafe AI using Graphical Criteria” »

A new generation of scientists is growing up with CRISPR technology. Here’s how some high school students learn to edit genes.

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Continue reading “Bill Faloon presents a classic Age Reversal Update” »

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.

Continue reading “JPL & the Space Age: The Pathfinders” »

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.