# Four short links: 11 January 2019

Storage Orchestration, Trolls and Media, Language Bias, and AI Attitudes

January 11, 2019
1. Rookstorage orchestration for Kubernetes.
2. Why We Can’t Have Nice Things (MIT Press) — Trolls’ actions are born of and fueled by culturally sanctioned impulses—which are just as damaging as the trolls’ most disruptive behaviors. […] For trolls, exploitation is a leisure activity; for media, it’s a business strategy. (via Greg J. Smith)
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4. Language Bias in Accident InvestigationThe SAIG [Forest Service’s Serious Accident Investigation Guide] influences investigators to apply linear, hindsight-biased, “cause and effect” reasoning toward human actors in the event. The guide’s use of agentive descriptions, binary opposition, and the active verb voice creates a seemingly exclusive causal attribution toward humans. Objective analysis was found to be impossible, using the SAIG’s language and report structure. This stands in contrast to the agency’s goal of accident prevention. nota bene, post-mortem facilitators. (via John Allspaw)
5. Artificial Intelligence: American Attitudes and TrendsThis report is based on findings from a nationally representative survey conducted by the Center for the Governance of AI, housed at the Future of Humanity Institute, University of Oxford, using the survey firm YouGov. The survey was conducted between June 6 and 14, 2018, with a total of 2,000 American adults (18+) completing the survey. Findings include Demographic characteristics account for substantial variation in support for developing high-level machine intelligence. There is substantially more support for developing high-level machine intelligence by those with larger reported household incomes, such as those earning over $100,000 annually (47%) than those earning less than$30,000 (24%); by those with computer science or programming experience (45%) than those without (23%); by men (39%) than women (25%). These differences are not easily explained away by other characteristics (they are robust to our multiple regression). (via Miles Brundage)
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