Here comes the nice thing about modern NLP techniques like word vectors and neural nets. So we trained our word embeddings on a large coreference annotated dataset without supplying any information regarding word gender. On the right side, after training, our word vectors shows feminine and masculine nouns nicely separated along the principal components of the vectors even though we didn't supply any information regarding word gender (gender has become a direction of main variance of our trained word vectors). Obviously the quality of our trained embeddings depends a lot on the training dataset.
It's used to train algorithms for things like sentiment analysis, predictive typing, automatic proofreading, and so on. In an analysis of 150 years of British periodicals, researchers were able to accurately detect changes in society: When electricity replaced steam; when trains replaced horses; epidemics; wars; and so on. Here's a chart of 50 jobs analyzed in the study, showing how strongly that job is associated with the female gender, and what the actual gender representation is: It makes technically good predictions that are morally bad. But at the same time, this gender bias is actually an accurate representation of the data; analyzing it over time, we could even predict a trendline of changes.
"This is the age of The Hunger Games; of the Star Wars movies being fronted by a female lead; of Wonder Woman utterly demolishing its box office rivals. "In 2017 there shouldn't be anything major about a TV series changing from a male lead to a female one. O'Hara also points out that leading women are selling big at the box office - and film companies aren't there to address gender equality, they're there to make a profit. Even for supposed male films, the first Guardians of the Galaxy movie, a superhero and a sci-fi film, (the audience) was something like 47% female.
The last few years have seen a growing range of technologies deployed to assist humanitarian efforts, whether it's peacekeeping drones, crowdsourcing, or image analytics. The paper uses AI to predict the gender of pre-paid mobile phone users with a high degree of accuracy. Rescue teams already use mobile phone data to help track those in need of assistance, but this new approach aims to go even further by helping to identify their gender, and therefore identify vulnerable groups such as women and children. Whilst mobile phones are almost ubiquitous, in the developing world, many are pre-paid, meaning that data often lacks key demographic identifiers.
Opinions expressed by Forbes Contributors are their own. Homogenous thinking in the AI industry has implications far beyond the racial makeup of PhD programs and AI conference attendees. "In the United States, price discrimination is illegal if based on race, religion, nationality, or gender," her report states, but the enforcement of the law is challenging in online commerce where differential pricing is wrapped up in opaque algorithms. The organization is on track to dramatically increase the participation of black researchers at notable AI conferences.
As such, there has been a surge of academic and commercial interest in predicting values for gender, age, race, location, interests, personality, and more, given some portion of the information available in data about individuals, including social profiles, customer records, and more. Such programs are informed by research in natural language processing, computer vision, psychology and related fields, and they can be used for positive, negative, and mixed ends. His main research interests include categorial grammars, parsing, semi-supervised learning for NLP, reference resolution and text geolocation. He has long been active in the creation and promotion of open source software for natural language processing: he is one of the co-creators of the Apache OpenNLP Toolkit and OpenCCG, and he has contributed to many others, including ScalaNLP, Junto, and TextGrounder.
Attendees were polled for their views on four big questions surrounding an industry that seems to be changing the world byte by byte. Besides closing the gap on the west, Ulrich added that China's tech innovations are also being "diffused" to other parts of the world. It seems technology has not totally eliminated people's regard for the college degree. The industry's sexism problem has been widely discussed, but do tech companies need gender quotas to balance gender representation?
Growing concerns about how artificial intelligence (AI) makes decisions has inspired U.S. researchers to make computers explain their "thinking." "In fact, it can get much worse where if the AI agents are part of a loop where they're making decisions, even the future data, the biases get reinforced," he added. Researchers hope that, by seeing the thought process of the computers, they can make sure AI doesn't pick up any gender or racial biases that humans have. But Singh says understanding the decision process is critical for future use, particularly in cases where AI is making decisions, like approving loan applications, for example.
Taking place alongside the Deep Learning Summer School, this full day program seeks to create a discussion about creating work environments that meet the needs of women in research and science. Maluuba Research Manager Layla El Asri took part in a panel sharing her personal perspective as a female in research. said Layla El Asri, Research Manager at Maluuba. To continue the momentum of support for women in AI, come say hi to Maluuba's Community Manager, Jessica, who will be giving opening remarks at McGill's AI For Social Good Summer Lab Closing, today at the Desjadins Labs.
Nobel prize laureate Sir Christopher Pissarides's comments at a conference in Norway attracted fierce criticism. The gender and accent of Apple's voice assistant across iPhone, iPad, Mac and other Apple devices has historically been dependent on regional settings. "The comments made do reflect consistent results that people make social judgements about computer speech outputs, and those seem to relate to gender stereotypes that exist in the wider world," Dr Kate Hone, a computer science academic at Brunel University, told the BBC. Out of the 15 male and 17 female participants interviewed, the participants mainly preferred male voices because they found the voices to be more reassuring.