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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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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.