Editor's Note: Get caught up in minutes with our speedy summary of today's must-read news stories and expert opinions that moved the precious metals and financial markets. The Australian government is setting up two mining research centres in partnership with universities and commercial supporters, according to an announcement made on Tuesday. The centers will be based in Sydney and Adelaide. The Australian government said research activity at the University of Sydney "...will focus on data analytics related to the long-term impact of resource use on Australia's economy, society and environment. It will help develop the necessary data science skills for Australia's resource industries to make the best possible evidence-based decisions when using our natural resources." The University of Adelaide will focus on advanced sensors and data analytics.

Data sets for fairness relevant tasks can lack examples or be biased according to a specific label in a sensitive attribute. We demonstrate the usefulness of weight based meta-learning approaches in such situations. For models that can be trained through gradient descent, we demonstrate that there are some parameter configurations that allow models to be optimized from a few number of gradient steps and with minimal data which are both fair and accurate. To learn such weight sets, we adapt the popular MAML algorithm to Fair-MAML by the inclusion of a fairness regularization term. In practice, Fair-MAML allows practitioners to train fair machine learning models from only a few examples when data from related tasks is available. We empirically exhibit the value of this technique by comparing to relevant baselines.

The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? W e perform a case study using French-English news translation task and separate test sets based on their original languages. W e show that forward translation delivers superior gains in terms of BLEU on sentences that were originally in the source language, complementing previous studies which show large improvements with back-translation on sentences that were originally in the target language. To better understand when and why forward and back-translation are effective, we study the role of domains, translationese, and noise. While translationese effects are well known to influence MT evaluation, we also find evidence that news data from different languages shows subtle domain differences, which is another explanation for varying performance on different portions of the test set. W e perform additional low-resource experiments which demonstrate that forward translation is more sensitive to the quality of the initial translation system than back-translation, and tends to perform worse in low-resource settings.

Automatic question generation aims at the generation of questions from a context, with the corresponding answers being sub-spans of the given passage. Whereas, most of the methods mostly rely on heuristic rules to generate questions, more recently also neural network approaches have been proposed. In this work, we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions. To this end, we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT -2 (Radford et al., 2019) model with Transformer encoder BERT (De-vlin et al., 2018) for the downstream task for question answering. The model is trained in an end-to-end fashion, where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question. Our result of neural question generation from text on the SQuAD 1.1 dataset (Rajpurkar et al., 2016) suggests that our method can produce semantically correct and diverse questions. Additionally, we assessed the performance of our proposed method for the downstream task of question answering. The analysis shows that our proposed generation & answering collaboration framework relatively improves both tasks and is particularly powerful in the semi-supervised setup. The results further suggest a robust and comparably lean pipeline facilitating question generation in the small-data regime.

WASHINGTON (Reuters) - U.S. government funding in artificial intelligence has fallen short and the country needs to invest in research, train an AI-ready workforce and apply the technology to national security missions, a government-commissioned panel led by Google's former CEO said in an interim report on Monday. The National Security Commission on Artificial Intelligence (NSCAI), created by Congress last year, raised concerns about the progress China has made in this area. It also said the U.S. government still faces enormous work before it can transition AI from "a promising technological novelty into a mature technology integrated into core national security missions." The commission thinks an allied effort on AI in the realm of national security is important, Robert Work, vice chairman of the NSCAI and a former deputy secretary of defense, told reporters. The NSCAI has spoken with Japan, Canada, the United Kingdom, Australia and the European Union, Work said. China is investing more than the United States in AI, said the report, which referred to the Asian nation more than 50 times.

WASHINGTON, Nov 4 (Reuters) - U.S. government funding in artificial intelligence has fallen short and the country needs to invest in research, train an AI-ready workforce and apply the technology to national security missions, an independent government-commissioned panel said in an interim report on Monday. The National Security Commission on Artificial Intelligence (NSCAI) said it believes the U.S. government still confronts enormous work before it can transition AI from "a promising technological novelty into a mature technology integrated into core national security missions." The commission thinks an allied effort on AI in the realm of national security is important, Robert Work, vice chairman of the NSCAI and a former deputy secretary of defense, told reporters. The NSCAI has spoken with Japan, Canada, the United Kingdom, Australia and the European Union, Work said. China is investing more than the United States in artificial intelligence, said the report, which referred to the Asian nation more than 50 times. "China takes advantage of the openness of U.S. society in numerous ways - some legal, some not - to transfer AI know-how," the report said.

In the last few years facial recognition has been gradually introduced across a range of different technologies. Some of these are relatively modest and useful; thanks to facial recognition software you can open you smartphone just by looking at it, and log into your PC without a password. You can even use your face to get cash out of an ATM, and increasingly it's becoming a standard part of your journey through the airport now. And facial recognition is still getting smarter. Increasingly it's not just faces that can be recognised, but emotional states too, if only with limited success right now.

If somebody hacked communications to grid-connected devices and interrupted a demand response (DR) event, peak demand might not be cut, capacity prices could spike and that somebody could make a lot of money. Because of the fast-rising number of grid-connected devices in DR programs like smart thermostats and water heaters and the even faster-rising number of smart phones and other Internet technologies through which customers communicate with DR programs, market manipulations like that are possible, cybersecurity experts from the Electric Power Research Institute (EPRI) told the Demand Response World Forum October 17. It is one of many potential intrusions of communications between utilities and customers with grid connected devices and distributed energy resources (DER), they said. To counter these threats, data analytics experts are using the laws of physics and unprecedented masses of data to find cybersecurity breaches. And their work is leading to machine learning (ML) and artificial intelligence (AI) algorithms which, though only just beginning to find actual deployment, are expected to soon advance the ability to identify patterns to the intrusions and raise the level of protection for critical power systems.

A few steps remain for China's plans for a military expansion into Taiwan with the incorporation of an AI Digital Brain that can connect to the 5G network to power drones, machines, robotics, surveillance systems, and total control of the Taiwanese people. Through IP theft, forced tech transfers, espionage, social engineering, open-source sharing, collaborations, investments, and mergers, China's government has put their tentacles into every country, and every domain and sphere that incorporates trade, and human existence. China's moves against Taiwan and the West links Chip Makers (TSMC) for AI guided Weapons that interconnect Huawei, Baidu, Megvii Face, Sensetime, and numerous tech companies. These tech companies interlink with social engineering of U.S democratic politicians, the penetration of KMT by Chinese and Taiwanese implants, with the laying of the massive groundwork to control Taiwan with AI automated drones, robotics & smart cities on the 5G network. This interconnection includes soft power initiative's at a global level to social engineer reporters, media, politicians, corporate tech leaders and the Chinese citizens to imprint a mental impression that China's 70 year track record of concentration camps, one party system, torture, rape, and Orwellian surveillance, will not transfer over to worldwide Chinese subjugation of humanity. The social engineering also makes the implication, "Chinese government stands by its contracts and words", yet they are breaking their agreement by invading Hong Kong and embedding soldiers within the Hong Kong populous, including Hong Kong police.

WASHINGTON (Reuters) - U.S. government funding in artificial intelligence has fallen short and the country needs to invest in research, train an AI-ready workforce and apply the technology to national security missions, a government-commissioned panel led by Google's former CEO said in an interim report on Monday. The National Security Commission on Artificial Intelligence (NSCAI), created by Congress last year, raised concerns about the progress China has made in this area. It also said the U.S. government still faces enormous work before it can transition AI from "a promising technological novelty into a mature technology integrated into core national security missions." The commission thinks an allied effort on AI in the realm of national security is important, Robert Work, vice chairman of the NSCAI and a former deputy secretary of defense, told reporters. The NSCAI has spoken with Japan, Canada, the United Kingdom, Australia and the European Union, Work said. China is investing more than the United States in AI, said the report, which referred to the Asian nation more than 50 times.