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Artificial intelligence Tool Used To Catch People Lying To The Police

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British scientists have created a new computer program that can spot if someone has lied to police about being robbed.

The groundbreaking software analyses the wording of a victim’s statement in order to identify tell tale signs of fake reports.

Spanish police, who have been using the tool, found it was successful in more than 80 per cent of cases helping them to identify 64 false reports in just one week.

Developed by experts at Cardiff University, VeriPol, uses a combination of automatic text analysis and artificial intelligence to recognize when somebody has been lying or exaggerating to the police.

Thousands of false reports are submitted to the police each year with many perpetrators hoping to receive inflated insurance payouts or claims for crimes that never happened in the first place.

Using algorithms the machine is able to carefully analyze various features in the text, such as adjectives, acronyms, verbs, nouns, punctuation marks and numbers.

Experts claim a false statement is more likely to contain certain traits and giveaway signs, that can be spotted using artificial intelligence.

It is believed that false statements are more likely to be shorter than genuine ones and focus on the details of the stolen property rather than the incident itself.

The Shut down

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More than 400,000 federal employees are working without pay, trash is overflowing in our National Parks, and the presidents of labor unions—one of which is suing President Trump—have said that requiring workers to punch in without pay is “nothing short of inhumane.”

There were still faint glimmers of civilization left in a divided, deadlocked Washington: the 19 Smithsonian Institution museums and galleries along the National Mall remained opened to the public for free due to unused “prior-year funds”; and the National Gallery of Art remained open as well. Even without a paycheck, government employees could check out the Apollo 11 command module at the National Air and Space Museum, the contemporary art in the permanent collection of the Hirshhorn Museum and Sculpture Garden, Chuck Berry’s sparkling Cadillac Eldorado at the National Museum of African American History and Culture, or Barack and Michelle Obama’s new portraits at the National Portrait Gallery.

The Library of Congress and the U.S. Botanic Garden—and the Capitol Visitor Center and Capitol Building, ironically—are operating as normal, since they were funded by the 2019 Legislative Branch Appropriations Bill.

List of federal shutdowns

1980

On May 1, 1980, the Federal Trade Commission (FTC) was shut down for one day after Congress failed to pass an appropriations bill for the agency

1981, 1984, and 1986

On November 23, 1981, 241,000 federal employees were furloughed for one day.The shutdown occurred because President Ronald Reagan vetoed a spending bill that contained a smaller set of spending cuts than he had proposed. The shutdown was estimated to cost taxpayers $80–90 million in back pay and other expenses Not all government departments shut down during the funding gap.

On October 4, 1984, 500,000 federal employees were furloughed for one afternoon.This shutdown occurred due to the inclusion of a water projects package and a civil rights measure that Reagan opposed. The bill was passed the following day after Congress removed these programs, and also included a compromise on funding of the Nicaraguan Contras.

On October 17, 1986, 500,000 federal employees were furloughed for one afternoon over a wide range of issues. The cost was estimated at $62 million in lost work.

1990

The 1990 shutdown occurred over Columbus Day weekend, from Saturday, October 6 through Monday, October 8. The shutdown stemmed from the fact that a deficit reduction package negotiated by President George H. W. Bush contained tax increases, despite his campaign promise of “read my lips: no new taxes”,leading to a revolt led by then House Minority Whip Newt Gingrich that defeated the initial appropriations package

1995–1996

The two shutdowns of 1995 and 1995–96 were the result of conflicts between Democratic President Bill Clinton and the Republican Congress over funding for Medicare, education, the environment, and public health in the 1996 federal budget. The government shut down after Clinton vetoed the spending bill the Republican Party-controlled Congress sent him. Government workers were furloughed and non-essential services suspended during November 14–19, 1995 (for 5 days), and from December 16, 1995, to January 6, 1996 (for 22 days), in total 27 days.

2013

Letter from President Barack Obama to US Government employees affected by the shutdown in 2013

The 2013 shutdown lasted 16 days, beginning on Tuesday, October 1, 2013. During the shutdown, approximately 800,000 federal employees were furloughed for 16 days, while another 1.3 million were required to report to work without known payment dates

January 2018

The first shutdown of 2018 began at midnight EST on Saturday, January 20. On January 19, a bill failed to pass the Senate 50–49 with the majority of Democrats voting “no”.Five Republicans voted “no” and five Democrats voted “yes” in the Republican majority senate (60 votes were required for passage). Senate Democrats insisted that the issue of immigration, specifically the funding of DACA, be addressed in the budget.

February 2018

A related funding gap occurred during the first 9 hours of Friday, February 9, 2018 EST. The funding gap was widely referred to in media reports as a second shutdown, although no workers were furloughed and government services were not disrupted because the funding gap occurred overnight and was resolved close to the beginning of the workday.

December 2018–January 2019

The third shutdown of 2018 began at midnight EST on Saturday, December 22 with a House-passed continuing resolution to fund the United States Government awaiting a full floor vote in the Senate. The point of contention was the inclusion of $5.7 billion in funding for a border wall that was a core Trump campaign promise.Under pressure from vocal members of his political base such as Ann Coulter and Rush Limbaugh for failing to secure the funding, Trump claimed ownership of the shutdown while in a televised meeting with Democratic leaders Nancy Pelosi and Chuck Schumer.This shutdown is ongoing as of January 2019.

Roughly 380,000 federal workers were placed on unpaid leave, while some 420,000 “essential” personnel were required to work without pay, including tens of thousands of workers in federal law enforcement and national security positions, such as FBI, Border Patrol, Secret Service and Transportation Security Administration agents. Hundreds of TSA agents at major airports called in sick during the second week of the shutdown, reportedly in protest or to pick up income elsewhere. The Washington Post reported on 4 January 2019 that the Trump administration had not anticipated the shutdown would be prolonged and were now grasping the consequences of an extended shutdown, including sharp reductions in SNAP payments and delays of $140 billion in tax refunds

People Stealing Video Ring Door Bells

A victim of the recorded theft, Ring doorbell was stolen about 4 a.m. After taking the Ring doorbell, the thief proceeded to steal Ring devices from other homes in her Congress Park neighborhood.

The theft only took about 15 seconds. “He knew that he was going to be on film and he didn’t care,” the victim says..

The Denver police have received 20 stolen doorbell reports this year, according to The Denver Channel.

Owners should report thefts to Ring no later than six months after the police report concerning the theft. Ring will replace the stolen device with the same model. You can find out more on Ring’s website here.

Facial Recognition Tech & Abuse

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Major tech firms have been pressured to develop strong policies regarding facial recognition. Microsoft has helped lead the way on that front, promising to put in place stricter policies, calling for greater regulation and asking fellow companies to follow suit.

Google SVP Kent Walker affirmed the company’s commitment not to sell facial recognition APIs. The executive cites concerns over how the technology could be abused.

Microsoft President Brad Smith called for governments to pay close attention at how facial detection technology is being implemented across the globe. The executive is calling out fellow technology purveyors to help address myriad issues around the technology before it becomes too pervasive.

Across the world, this technology is being used right now as ways to control their populations and China is investing billions of dollars to upgrade.

Artificial Intelligence & Jail Time

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 ProPublica found that algorithms tend to reinforce racial bias in law enforcement data. Algorithmic assessments tend to falsely flag black defendants as future criminals at almost twice the rate as white defendants. What is more, the judges who relied on these risk-assessments typically did not understand how the scores were computed.

This is problematic, because machine learning models are only as reliable as the data they’re trained on. If the underlying data is biased in any form, there is a risk that structural inequalities and unfair biases are not just replicated, but also amplified. So, AI engineers must be especially wary of their blind spots and implicit assumptions; it is not just the choice of machine learning techniques that matters, but also all the small decisions about finding, organizing and labeling training data for AI models.

In order to guard against unfair bias, all subjects should have an equal chance of being represented in the data. Sometimes this means that underrepresented populations need to be thoughtfully added to any training datasets.

Black Girl Whose White Classmates Put Rope Around Her Neck Awarded Only $68,000

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The family of a Black girl who claims her white classmates violently put a rope around her neck has been awarded $68,000. A jury in Austin, Texas has ordered the private school where she attended to pay damages for their failure to properly and immediately address the apparent bullying the girl had long experienced.

 

According to the lawsuit, the incident happened when the girl was standing near a swing that was hanging from a tree which has a separate rope used to pull it higher. K.P. claimed thrhttps://thehill.com/homenews/state-watch/414506-school-ordered-to-pay-68000-to-black-girl-after-classmates-allegedlyee of her classmates, who often bullied her, used the separate rope to pull around her neck and violently jerked her to the ground.

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The girl, who is now 15 years old, is being home-schooled.

Robotics & Racism

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A 2016 investigation by ProPublica found that an algorithm used in the U.S. to influence prison sentencing, was racially biased, predicting that black defendants pose a higher risk of repeating offences than they actually do.

While in office, U.S. Attorney General Eric Holder voiced concerns about these technologies to the U.S. Sentencing Commission, and making sure the use of aggregate data analysis won’t have unintended consequences.

According to experts, users should not assume that there will be algorithmic fairness and lack of bias in AI programming, especially when these algorithms are trained from human-created datasets.

Because AI algorithms are also designed to perceive patterns in human decision making, they can pick up the implicit biases of their creators.

The criminal justice system is not the only realm in which the implementation of these algorithms have backfired, creating tension between government agencies, technology companies, and directly affected citizens.

Twitter’s attempt at using artificial intelligence to engage with millennials in the U.S. in 2016 went awry after Tay, their verified Twitter chatbot, began spewing anti-semitic and racist comments at users.

Experts agree that A lack of laws exclusively designed to protect against discrimination in relation to big-data and machine learning is a problem. Researchers and computer scientists now face the challenge of creating cutting-edge technology that refrains from relying on decades-old trends of institutional biases and discrimination.

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