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In today’s business world, machine-learning algorithms are increasingly being applied to decision-making processes, which affects employment, education, and access to credit. But firms usually keep algorithms secret, citing concerns over gaming by users that can harm the predictive power of algorithms. Amid growing calls to require firms to make their algorithms transparent, a new study developed an analytical model to compare the profit of firms with and without such transparency. The study concluded that there are benefits but also risks in algorithmic transparency.

Conducted by researchers at Carnegie Mellon University (CMU) and the University of Michigan, the study appears in Management Science.

“As managers face calls to boost , our findings can help them make decisions to benefit their firms,” says Param Vir Singh, Professor of Business Technologies and Marketing at CMU’s Tepper School of Business, who coauthored the study.

This artificial intelligence software can acutely analyze facial expressions and brain waves to monitor if subjects were attentive to thought and political education by using a combination of polygraphs and facial scans. It can provide real data for organizers of ideological and political education, so they can keep improving their methods of education and enrich content. It can judge how party members have accepted thought and political education.

The Smart Political Education Bar analyses user’s brain waves and deploys facial recognition to discern the level of acceptance for ideological and political education. Making it possible to ascertain the levels of concentration, recognition, and mastery of ideological and political education so as to better understand its effectiveness.

President Xi, secretary of the Communist Party and leader of the nation of 1.4 billion, has demanded absolute loyalty to the party and has previously declared that thought and political education is an essential part of the government’s doctrine. They are using this technology to treat all party members as potential anti-CCP agents. The use of these techniques on officials demonstrates the sorry state of affairs within party ranks.

Circa 2021


Finding and fixing bugs in code is a time-consuming, and often frustrating, part of everyday work for software developers. Can deep learning address this problem and help developers deliver better software, faster? In a new paper, Self-Supervised Bug Detection and Repair, presented at the 2021 Conference on Neural Information Processing Systems (NeurIPS 2021), we show a promising deep learning model, which we call BugLab can be taught to detect and fix bugs, without using labelled data, through a “hide and seek” game.

To find and fix bugs in code requires not only reasoning over the code’s structure but also understanding ambiguous natural language hints that software developers leave in code comments, variable names, and more. For example, the code snippet below fixes a bug in an open-source project in GitHub.

Here the developer’s intent is clear through the natural language comment as well as the high-level structure of the code. However, a bug slipped through, and the wrong comparison operator was used. Our deep learning model was able to correctly identify this bug and alert the developer.

Molecular machines that kill infectious bacteria have been taught to see their mission in a new light.

New nanoscale drills have been developed that are effective at killing bacteria. These novel molecular machines are activated by visible light and can punch holes through the cell membranes of bacteria in just two minutes. As bacteria have no natural defenses against this mechanism, it could be a useful strategy to treat antibiotic-resistant bacteria.

The latest iteration of nanoscale drills developed at Rice University are activated by visible light rather than ultraviolet (UV), as in earlier versions. These have also proven effective at killing bacteria through tests on real infections.

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)

Explore More Science & Technology on UCTV
(https://www.uctv.tv/science)
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.
(https://www.uctv.tv)

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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

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