In 2022, after a series of upgrades, CERN's Large Hadron Collider (LHC) is expected to turn back on for a final run and, once again, furiously smash particles together in search of new clues about the fundamental structure of our universe. When the LHC turns on, it will be operating at its highest energies yet: the collider will crash together protons every 25 nanoseconds, leading to the production of hundreds of particles passing through the detector. To help with the deluge of data, scientists are turning to new artificial intelligence (AI) technologies. We met with Jennifer Ngadiuba, a Robert A. Millikan Postdoctoral Scholar Research Associate in Physics at Caltech, over Zoom to learn more about these developments. Ngadiuba is also supported by the U.S. Department of Energy through Fermilab's Machine Intelligence group.

It might be known as the Red Planet, but according to observations from NASA - Mars'glows green' at night due to chemical reactions in its upper atmosphere. The eerie phenomenon was captured by NASA's'MAVEN' orbiter but astronauts are unlikely to see it as it is only visible as ultraviolet light - naked to the human eye. The discovery could help create a more detailed picture of Martian weather - which will help the first crewed missions to Mars expected to leave sometime in the 2030s. The first crewed mission to Mars will need better forecasts than are currently available to avoid wild winds and storms that can last weeks, the authors explained. Each evening the upper atmosphere softly flickers in ultraviolet light as the sun sets and temperatures fall to minus -79.6 degrees Fahrenheit and below.

In the 20 years of the "war on terror" Australia has led from the front in expanding powers for law enforcement and ramping up surveillance at the expense of public rights and freedoms. Among the seemingly endless barrage of national security legislation and surveillance that creeps into every aspect of our personal lives, more and more of our public spaces have been smothered by surveillance cameras and facial recognition technology. Corporations large and small, towns and cities, federal and state government departments and agencies have deployed these systems, snooping on us all wherever we go without any of us getting a say. State and federal law enforcement officers are accessing these technologies without any oversight. As anti-police protests spread around the world, tools and processes that exacerbate racist bias – and the wasteful spending and abuses of power that comes with it –within law enforcement and judicial systems have fallen under renewed scrutiny. Once again, Australia is lagging behind the debate.

The coronavirus pandemic has been a boon for the test proctoring industry. About half a dozen companies in the US claim their software can accurately detect and prevent cheating in online tests. Examity, HonorLock, Proctorio, ProctorU, Respondus and others have rapidly grown since colleges and universities switched to remote classes. While there's no official tally, it's reasonable to say that millions of algorithmically proctored tests are happening every month around the world. Proctorio told the New York Times in May that business had increased by 900% during the first few months of the pandemic, to the point where the company proctored 2.5 million tests worldwide in April alone. I'm a university librarian and I've seen the impacts of these systems up close.

Japan will ban the flying of drones over 15 U.S. military facilities next month without advance permission as a measure against potential terrorist acts, the Defense Ministry said Friday. The 15 sites include Yokota Air Base in western Tokyo and the U.S. Marines' Camp Schwab on Okinawa. The restrictions, based on a law enacted last year, will be implemented on Sept. 6 following a notice period but have triggered concerns from media organizations over the potential disruption to news-gathering activities. Drone pilots will be barred from flying within 300 meters of the boundary of the designated sites, including the Henoko coastal district in Nago, Okinawa, near Camp Schwab where landfill work is underway to replace the U.S. Marine Corps Air Station Futenma despite local opposition. Police and the Self-Defense Forces are permitted to seize or destroy drones if they are flown near the designated zones without permission, and violators face up to a year in prison or a maximum fine of ¥500,000 ($4,700).

"Britons on way to France risk quarantine" is the front page headline in the Times, as it reports that Whitehall officials have placed the country on a list of destinations to be closely monitored. A senior aviation source is quoted saying France is "bubbling" with cases and that travellers should only book trips which can be re-arranged at 24 hours' notice. The Daily Telegraph also reports the close monitoring of France as cases there overtake the numbers for Portugal, which has reduced its infection rate. The paper says about 450,000 Britons are currently holidaying in France, a scale which would make any new restrictions a logistical nightmare. The Guardian leads with an exclusive warning from doctors' leaders that shutting down non-Covid NHS services to deal with any second wave will leave thousands of patients unacceptably "stranded", risking more deaths.

"Artificial Intelligence In Military Market is growing at a High CAGR during the forecast period 2020-2026.

Tax policy analysis is a well-developed field with a robust body of research and extensive modeling infrastructure across think tanks and government agencies. Because tax policy affects everyone, and especially wealthy people, it gets both a lot of attention and research funding (notably from individual foundations like those of Peter G. Peterson and Koch brothers). In addition to empirical studies, organizations like the Urban-Brookings Tax Policy Center and the Joint Committee on Taxation produce microsimulations of tax policy to comprehensively model thousands of levers of policymaking. However, because it is difficult to guess how people will react to changing public policy scenarios, these models are limited in how much they account for individual behavioral factors. Although it is far from certain, artificial intelligence (AI) might be able to help address this notable deficiency in tax policy, and recent work has highlighted this possibility.

The U.K.'s Home Office, which oversees immigration and law enforcement, among other responsibilities, announced that it would suspend use of the automated decision-making system, referred to as the Streaming Tool, which the agency used to determine whether visa applicants represented a high-, medium-, or low-risk to the nation. The decision came as a result of a legal complaint filed with the British High Court by the nonprofit Joint Council for the Welfare of Immigrants (JCWI) and Foxglove Legal, a technology justice advocacy group.

Data cleaning is naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered, corrupted, and joined to yield incomplete, dirty, and denormalized datasets. Based on this view, we present PClean, a unified generative modeling architecture for cleaning and normalizing dirty data in diverse domains. Given an unclean dataset and a probabilistic program encoding relevant domain knowledge, PClean learns a structured representation of the data as a relational database of interrelated objects, and uses this latent structure to impute missing values, identify duplicates, detect errors, and propose corrections in the original data table. PClean makes three modeling and inference contributions: (i) a domain-general non-parametric generative model of relational data, for inferring latent objects and their network of latent connections; (ii) a domain-specific probabilistic programming language, for encoding domain knowledge specific to each dataset being cleaned; and (iii) a domain-general inference engine that adapts to each PClean program by constructing data-driven proposals used in sequential Monte Carlo and particle Gibbs. We show empirically that short (< 50-line) PClean programs deliver higher accuracy than state-of-the-art data cleaning systems based on machine learning and weighted logic; that PClean's inference algorithm is faster than generic particle Gibbs inference for probabilistic programs; and that PClean scales to large real-world datasets with millions of rows.