Press Release – BOULDER, CO – September 6, 2019 – YWCA Boulder County, one of the first two YWCA branches in the country to be awarded a grant by Google to increase black and Latina students' access to computer science and artificial intelligence education, has announced a Google JAM session series to kick off the organization's STEM E3 program. Three JAM sessions will be offered from 1– 6 p.m. at Google with the first session scheduled for September 16, 2019 (followed by sessions on October 14, 2019 and November 11, 2019). The program will provide an opportunity for young women of color between the ages of 9 to 14 to be introduced to a STEM E3 (Education, Employment and Entrepreneurship) program, which works with young women and girls in science, technology, engineering, and math (STEM). YWCA STEM E3 curriculum helps young women and girls of color build the confidence and skills required for future careers in computer science and artificial intelligence. "We're thrilled to have been chosen as one of two YWCAs in the country to launch the pilot STEM E3 program," said Debbie Pope, CEO of YWCA Boulder County.

British privacy activist Ed Bridges is set to appeal a landmark ruling that endorses the "sinister" use of facial recognition technology by the police to hunt for suspects. In what is believed to be the world's first case of its kind, Bridges told the High Court in Wales that the local police breached his rights by scanning his face without consent. "This sinister technology undermines our privacy and I will continue to fight against its unlawful use to ensure our rights are protected and we are free from disproportionate government surveillance," Bridges said in a statement. But judges said the police's use of facial recognition technology was lawful and legally justified. Civil rights group Liberty, which represented 36-year-old Bridges, said it would appeal the "disappointing" decision, while police chiefs said they understood the fears of the public.

Research forecasts that by 2025, machines will perform more current work tasks than humans. Murat Sonmez, member of the managing board, and Head of the Centre for the WEF Fourth Industrial Revolution Network, expands on the role humans might play. The Fourth Industrial Revolution (4IR) is at the center of the current economic frontier. In reality, is Africa prepared for such changes? Moving quickly and being agile are key principles of success in the 4IR.

An internal project to rewrite how Apple's Siri voice assistant handles "sensitive topics" such as feminism and the #MeToo movement advised developers to respond in one of three ways: "don't engage", "deflect" and finally "inform". The project saw Siri's responses explicitly rewritten to ensure that the service would say it was in favour of "equality", but never say the word feminism – even when asked direct questions about the topic. Last updated in June 2018, the guidelines are part of a large tranche of internal documents leaked to the Guardian by a former Siri "grader", one of thousands of contracted workers who were employed to check the voice assistant's responses for accuracy until Apple ended the programme last month in response to privacy concerns raised by the Guardian. In explaining why the service should deflect questions about feminism, Apple's guidelines explain that "Siri should be guarded when dealing with potentially controversial content". When questions are directed at Siri, "they can be deflected … however, care must be taken here to be neutral".

In one of the first lawsuits to address the use of live facial recognition technology by governments, a British court ruled on Wednesday that police use of the systems is acceptable and does not violate privacy and human rights. The case has been closely watched by law enforcement agencies, privacy groups and government officials because there is little legal precedent concerning the use of cameras in public spaces that scan people's faces in real time and attempt to identify them from photo databases of criminal suspects. While the technology has advanced quickly, with many companies building systems that can be used by police departments, laws and regulations have been slower to develop. The High Court dismissed the case brought by Ed Bridges, a resident of Cardiff, Wales, who said his rights were violated by the use of facial recognition by the South Wales Police. Mr. Bridges claimed that he had been recorded without permission on at least two occasions -- once while shopping and again while attending a political rally.

"We use cookies to improve your website experience. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. By continuing to use the website, you consent to our use of cookies." As the first wave of AI patents are now emerging globally, litigation is not far behind. When it was released in 1968, Stanley Kubrik's 2001: A Space Odyssey predicted that in 2001 artificial intelligence (AI) would be part of everyday life and humankind would be battling against rogue machines.

When I first saw Terminator 2: Judgment Day in grade school, August 29, 1997 seemed so far away. When I first heard Billy Joel's "Miami 2017" as a child, 2017 seemed so far away. Now, 1997 and 2017 have come and gone. Thankfully, Skynet has not become self-aware, but technology is way beyond where I thought it would be by 2019. For example, in my wildest dreams, I never would have imagined that we could carry a phone, music player, virtual note pad, calculator, compass, camera, and more in our pockets.

For this research, computer scientists at the University of Southern California (USC) and the University of California, Los Angeles, probed two state-of-the-art natural language systems: OpenAI's small GPT-2 model, which sports 124 million parameters, and Google's recurrent neural network [PDF] – referred to as LM_1B in the Cali academics' paper [PDF] – that was trained using the 1 Billion Word Language Benchmark. Machine-learning code, it seems, picks up all of its prejudices from its human creators: the software ends up with sexist, racist, and homophobic tendencies by learning from books, articles, and webpages subtly, or not so subtly, laced with our social and cultural biases. Multiple experiments have demonstrated that trained language models assume doctors are male, and are more likely to associate positive terms with Western names popular in Europe and America than African-American names, for instance. "Despite the fact that biases in language models are well-known, there is a lack of systematic evaluation metrics for quantifying and analyzing such biases in language generation," Emily Sheng, first author of the study and a PhD student at the USC, told The Register. And so, to evaluate the output of GPT-2 and LM_1B in a systematic way, the researchers trained two separate text classifiers, one to measure bias, and the other to measure sentiment.

There is a small number of case studies of automatic land cover classification on the coastal area. Here, I test extraction of seagrass beds, sandy area, oyster farming rafts at Mangoku-ura Lagoon, Miyagi, Japan by comparing manual tracing, simple image segmentation, and image transformation using deep learning. The result was used to extract the changes before and after the earthquake and tsunami. The output resolution was best in the image transformation method, which showed more than 69% accuracy for vegetation classification by an assessment using random points on independent test data. The distribution of oyster farming rafts was detected by the segmentation model. Assessment of the change before and after the earthquake by the manual tracing and image transformation result revealed increase of sand area and decrease of the vegetation. By the segmentation model only the decrease of the oyster farming was detected. These results demonstrate the potential to extract the spatial pattern of these elements after an earthquake and tsunami. Index Terms: Great East Japan Earthquake of 2011, Land use land cover (LULC), Zosteracea seagrass, cultured oyster, deep learning, Mangoku Bay

We discuss training techniques, objectives and metrics toward mass personalization of deep learning models. In machine learning, personalization refers to the fact that every trained model should be targeted towards an individual by optimizing one or several performance metrics and often obeying additional constraints. We investigate three methods for personalized training of neural networks. They constitute three forms of curriculum learning. The methods are partially inspired by the "shaping" concept from psychology. Interestingly, we discover that extensive exposure to a limited set of training data in terms of class diversity \emph{early} in the training can lead to an irreversible reduction of the capability of a network to learn from more diverse training data. This is in close alignment with existing theories in human development. In contrast, training on a small data set covering all classes \emph{early} in the training can lead to better performance.