It'll be close, but it looks like women will be allowed to drive in Saudi Arabia with some time to spare before the automobile industry converts entirely to self-driving cars. A royal decree announced Tuesday that women would finally be allowed behind the wheel, heralding a preposterously overdue end to the most high-profile and infamous of the repressive kingdom's restrictions on women. Even a woman in prison requires a male guardian to agree to her release, according to the monitoring group Human Rights Watch, which described the guardianship system as the most significant impediment to women's rights in Saudi Arabia -- and even a barrier to the government's own plans to improve the economy. The abolition of the male guardianship system should be the next announcement we hear from the Saudi government.
The real danger is in something called confirmation bias: when you come up with an answer first and then begin the process of only looking for information that supports that conclusion. Take the following example: if the number of women seeking truck driving jobs is less than men, on a job-seeking website, a pattern emerges. That pattern can be interpreted in many ways, but in truth it only means one specific factual thing: there are less women on that website looking for truck driver jobs than men. If you tell an AI to find evidence that triangles are good at being circles it probably will, that doesn't make it science.
Stanford researcher Dr Michal Kosinski went viral last week after publishing research (pictured) suggesting AI can tell whether someone is straight or gay based on photos. Stanford researcher Dr Michal Kosinki claims he is working on AI software that can identify political beliefs, with preliminary results proving positive. Dr Kosinki claims he is now working on AI software that can identify political beliefs, with preliminary results proving positive. Dr Kosinki claims he is now working on AI software that can identify political beliefs, with preliminary results proving positive.
He said the deep learning algorithms which drive AI software are "not transparent", making it difficult to to redress the problem. Currently approximately 9 per cent of the engineering workforce in the UK is female, with women making up only 20 per cent of those taking A Level physics. "We have a problem," Professor Sharkey told Today. Professor Sharkey said researchers at Boston University had demonstrated the inherent bias in AI algorithms by training a machine to analyse text collected from Google News.
Although people of both genders struggle with age discrimination, research has shown women begin to experience age discrimination in hiring practices before they reach 50, whereas men don't experience it until several years later. Just as technology is causing barriers inside the workplace for older employees, online applications and search engines could be hurting older workers looking for jobs. Many applications have required fields asking for date of birth and high school graduation, something many older employees choose to leave off their resumes. Furthermore, McCann said, some search engines allow people to filter their search based on high school graduation date, thereby allowing employers and employees to screen people and positions out of the running.
Robots are picking up sexist and racist biases based on information used to program them predominantly coming from one homogenous group of people, suggests a new study from Princeton University and the U.K.'s University of Bath. But robots based on artificial intelligence (AI) and machine learning learn from historic human data and this data usually contain biases," Caliskan tells The Current's Anna Maria Tremonti. With the federal government recently announcing a $125 million investment in Canada's AI industry, Duhaime says now is the time to make sure funding goes towards pushing women forward in this field. "There is an understanding in the research community that we have to be careful and we have to have a plan with respect to ethical correctness of AI systems," she tells Tremonti.
I am trapped at a dull dinner following a dull talk: part of a series of dinners and talks that grad students organise, unpaid (though at considerable expense to themselves--experience! The pop culture idea of Kirk, Captain of the Enterprise for the first Star Trek series (ST:TOS) and the original run of films, has become almost synonymous with Zapp Brannigan from Futurama. The article "Captain Kirk's 8 Most Impressive Love Conquests" gives us such bon mots as these: For three glorious seasons, Star Trek's Captain James T. Kirk boldly seduced and explored women no Earth-man had been with before. Kirk's storied history of womanising seemingly consists of his having seriously dated a fairly small number of clever women in Uni.
Open up the photo app on your phone and search "dog," and all the pictures you have of dogs will come up. This was no easy feat. Your phone knows what a dog "looks" like. This and other modern-day marvels are the result of machine learning. These are programs that comb through millions of pieces of data and start making correlations and predictions about the world.
We live in a world that's increasingly being shaped by complex algorithms and interactive artificial intelligence assistants who help us plot out our days and get from point A to point B. According to a new Princeton study, though, the engineers responsible for teaching these AI programs things about humans are also teaching them how to be racist, sexist assholes. The study, published in today's edition of Science magazine by Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, focuses on machine learning, the process by which AI programs begin to think by making associations based on patterns observed in mass quantities of data. In a completely neutral vacuum, this would mean that AI would learn to provide responses based solely on objective, data-driven facts. But because the data sets fed to the AI are selected and influenced by humans, there's a degree to which certain biases become a part of the AI's diet. To demonstrate this, Caliskan and her team created a modified version of an Implicit Association Test, an exercise that tasks participants to quickly associate concrete ideas like people of color and women with abstract concepts like goodness and evil.
When Microsoft released an artificially intelligent chatbot named Tay on Twitter last March, things took a predictably disastrous turn. Within 24 hours, the bot was spewing racist, neo-Nazi rants, much of which it picked up by incorporating the language of Twitter users who interacted with it. Unfortunately, new research finds that Twitter trolls aren't the only way that AI devices can learn racist language. In fact, any artificial intelligence that learns from human language is likely to come away biased in the same ways that humans are, according to the scientists. The researchers experimented with a widely used machine-learning system called the Global Vectors for Word Representation (GloVe) and found that every sort of human bias they tested showed up in the artificial system.