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AIs that learn from photos become sexist

Daily Mail - Science & tech

Image recognition AIs that have been trained by some of the most-used research-photo collections are developing sexist biases, according to a new study. University of Virginia computer science professor Vicente Ordóñez and colleagues tested two of the largest collections of photos and data used to train these types of AIs (including one supported by Facebook and Microsoft) and discovered that sexism was rampant. He began the research after noticing a disturbing pattern of sexism in the guesses made by the image recognition software he was building. 'It would see a picture of a kitchen and more often than not associate it with women, not men,' Ordóñez told Wired, adding it also linked women with images of shopping, washing, and even kitchen objects like forks. The AI was also associating men with stereotypically masculine activities like sports, hunting, and coaching, as well as objects sch as sporting equipment.


Spanish police corner, gun down Barcelona van attacker

The Japan Times

SUBIRATS, SPAIN – Spanish police on Monday shot dead an Islamist militant who killed 13 people with a van in Barcelona last week, ending a five-day manhunt for the perpetrator of Spain's deadliest attack in over a decade. Police said they tracked 22-year-old Younes Abouyaaqoub to a rural area near Barcelona and shot him after he held up what looked like an explosives belt and shouted "Allahu Akbar" (God is Greatest). A bomb squad then used a robot to approach his body. Abouyaaqoub had been on the run since Thursday evening, after he drove at high speed into throngs of strollers along Barcelona's most famous avenue, Las Ramblas. After fleeing the scene, he hijacked a car and fatally stabbed its driver.


Machines Learn a Biased View of Women

WIRED

Last fall, University of Virginia computer-science professor Vicente Ordóñez noticed a pattern in some of the guesses made by image-recognition software he was building. "It would see a picture of a kitchen and more often than not associate it with women, not men," he says. That got Ordóñez wondering whether he and other researchers were unconsciously injecting biases into their software. So he teamed up with colleagues to test two large collections of labeled photos used to "train" image-recognition software. Two prominent research-image collections--including one supported by Microsoft and Facebook--display a predictable gender bias in their depiction of activities such as cooking and sports.


Tech leaders warn against robotic weapons

Daily Mail - Science & tech

Killer robots should be urgently banned before a wave of weapons of mass destruction gets out of control, industry leaders say. Robotics and artificial intelligence experts have signed of an open letter demanding the UN prohibit the use of such weapons internationally. Among the 116 signatories are Tesla founder Elon Musk and Mustafa Suleyman, head of applied AI at Google's Deep Mind. The weapons, including lethal microdrone swarms, are on the edge of development with the potential to create global instability, they warn. Killer robots should be urgently banned before a wave of weapons of mass destruction gets out of control, industry leaders say.


Two-year-olds should learn to code, says computing pioneer

The Guardian

Children as young as two should be introduced to the basics of coding, according to one of Britain's most eminent computing pioneers. Dame Stephanie Shirley, whose company was one of the first to sell software in the 1960s, said that engaging very young children – in particular girls – could ignite a passion for puzzles and problem-solving long before the "male geek" stereotype took hold. "I don't think you can start too early," she said, adding that evidence suggested that the best time to introduce children to simple coding activities was between the ages of two and seven years. "Most successful later coders start between five and six," she added. "In a sense, those years are the best for learning anything … and means that programming [hasn't] become set in your mind as geeky or nerdy."


A new machine learning app for reporting on hate in America

#artificialintelligence

This led ProPublica -- with the support of the Google News Lab -- to form Documenting Hate earlier this year, a collaborative reporting project that aims to create a national database for hate crimes by collecting and categorizing news stories related to hate crime attacks and abuses from across the country. Now, with ProPublica, we are launching a new machine learning tool to help journalists covering hate news leverage this data in their reporting. The Documenting Hate News Index -- built by the Google News Lab, data visualization studio Pitch Interactive and ProPublica -- takes a raw feed of Google News articles from the past six months and uses the Google Cloud Natural Language API to create a visual tool to help reporters find news happening across the country. The feed is generated from news articles that cover events suggestive of hate crime, bias or abuse -- such as anti-semitic graffiti or local court reports about incidents.


Google uses machine learning to help journalists track hate

#artificialintelligence

"The feed is generated from news articles that cover events suggestive of hate crime, bias or abuse -- such as anti-semitic graffiti or local court reports about incidents," Google writes. "We are monitoring it to look our for errant stories that slip in, i.e. searches for phrases that just include the word'hate' -- it hasn't happened yet, but we will be paying close attention." The web app is available as of today and Google says that it'll keep tweaking it over the next few months as use-case data starts rolling in.


Deep Learning is not the AI future

#artificialintelligence

Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. Remaining 99% is what's used in practice for most tasks.


In new tactic, smugglers use drone to fly meth over Mexican border into San Diego, officials say

Los Angeles Times

The buzz of a motor overhead at nearly 11:30 p.m. was the tip-off. A remote control-operated drone flew over the border fence from Mexico, heading for San Ysidro while a Border Patrol agent listened and watched. He radioed ahead to other agents to be on the lookout for the small aircraft. Ten minutes later, federal authorities had what they say is their first confirmed San Diego case of drug smuggling by drone. Late on the night of Aug. 8, agents arrested a man carrying a bag full of heroin -- more than 13 pounds valued at an estimated $46,000.


Coursera co-founder, Andrew Ng, sets out to raise $150M for AI Fund

#artificialintelligence

Andrew Ng, one of the founders of Coursera, has set out to raise a $150 million fund – dubbed AI Fund – in order to invest in artificial intelligence startups. The news comes just a few months after he announced his own startup, deeplearning.ai. The fund's existence was revealed because of a filing with the US Securities and Exchange Commission (SEC). The document filed with the SEC was filed under Andrew Ng's name on 14 August. At the end of June, we reported that Ng had left the Chinese company, Baidu, where he was in charge of the AI team to form his new startup, deeplearning.ai.