Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes. These artefacts are exploited by neural networks even when only considering the hypothesis and ignoring the premise, leading to unwanted biases. Previous work proposed tackling this problem via adversarial training, but this leads to learned sentence representations that still suffer from the same biases. As a solution, we propose using an ensemble of adversaries during the training, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data. We show that using an ensemble of adversaries can prevent the bias from being relearned after the model training is completed, further improving how well the model generalises to different NLI datasets. In particular, these models outperformed previous approaches when tested on 12 different NLI datasets not used in the model training. Finally, the optimal number of adversarial classifiers depends on the dimensionality of the sentence representations, with larger dimensional representations benefiting when trained with a greater number of adversaries.

It's time for AI startups to step up and use their formidable technology expertise in AI to help get ... [ ] more Americans back to work now. Bottom Line: A.I.'s ability to predict and recommend job matches will help get more Americans back to work, helping to reduce the 16.8 million unemployed today. One in ten Americans is out of work today based latest U.S. Department of Labor data. They're primarily from the travel and hospitality, food services, and retail trade and manufacturing industries, with many other affected sectors. McKinsey & Company's recent article, A new AI-powered network, is helping workers displaced by the coronavirus crisis provides context around the scope of challenges involved in closing the unemployment gap.

An epic number of citizens are video-conferencing to work in these lockdown times. But as they trade in a gas-burning commute for digital connectivity, their personal energy use for each two hours of video is greater than the share of fuel they would have consumed on a four-mile train ride. Add to this, millions of students'driving' to class on the internet instead of walking. Meanwhile in other corners of the digital universe, scientists furiously deploy algorithms to accelerate research. Yet, the pattern-learning phase for a single artificial intelligence application can consume more compute energy than 10,000 cars do in a day.

Better internet access, high transmission rates, low delay and broad bandwidth would deliver military advances, such as gathering intelligence, visualising combat operations and delivering precise logistical support. The article added: "Based on the 6G network, the commander could make the right decisions quickly after the control-and-command network mined, learned and analysed vast data from the ground," The China National Defence News report said that battle units could get highly specific and instantaneous information on troop locations and equipment, allowing the military to make tailored logistic plans. China officially started researching the 6G telecoms technology in early November, according to a Ministry of Science and Technology notice. The ministry announced that it had two teams overseeing 6G research.

Citing a range of evidence obtained from official but largely ignored organizations, decisions and documents in recent years, Webb thoughtfully describes a frightening view of the techno tyranny that is almost upon us and for which the latest moves are being rapidly implemented under the guise of combating COVID-19. Involving an unsavory alliance of the'intelligence' community, the Pentagon and Silicon Valley, COVID-19 is being used as cover to remove economic and social'obstacles' (including so-called'legacy systems' with which we are all familiar) to implementing the so-called fourth industrial revolution – 'a revolution characterized by discontinuous technological development in areas like artificial intelligence (AI), big data, fifth-generation telecommunications networking (5G), nanotechnology and biotechnology, robotics, the Internet of Things (IoT), and quantum computing' – to achieve everything from a cashless society and AI-driven technologies (particularly for mass surveillance and law enforcement) to driverless cars and'telemedicine'.

Artificial intelligence is here to save us from coronavirus. It spots new outbreaks, identifies people with fevers, diagnoses cases, prioritizes the patients most in need, reads the scientific literature, and is on its way to creating a cure. Alex Engler is a David M. Rubenstein Fellow at the Brookings Institution and an adjunct professor and affiliated scholar at Georgetown University's McCourt School of Public Policy. As the world confronts the outbreak of coronavirus, many have lauded AI as our omniscient secret weapon. Although corporate press releases and some media coverage sing its praises, AI will play only a marginal role in our fight against Covid-19.

If one was to explain ecommerce personalization in a simple manner, this could be it. When you walk into a physical brick and mortar store, what really impresses you? The ways in which the store engages you and treats you like you are their most special customer, isn't it? If you get what you are looking for, easily and quickly, without searching for it, then certainly the shopping experience is a breeze. If the owner is able to understand your preferences and shows you products according to your likes and thus, helps you save on time and effort, wouldn't you like to visit the store again and again?

In a digital world, how do we build empathy into the algorithms that run our lives? Join us for a special virtual briefing hosted by Greg Williams, Editor of WIRED with Rana el Kaliouby, co-founder and CEO of Affectiva, and author of'Girl Decoded' as they discuss how to bring emotional intelligence to technology during a global health emergency, such as the COVID-19 outbreak in 2020. Even before the coronavirus pandemic began earlier this year, the world had already moved to an increasingly digital setting. Emails, texts and social media interactions were fast-becoming the norm over physical meetings. However, words convey only a fraction of the true meaning behind a message, and as a result, we are missing key physical, non-verbal cues that give us a more complete understanding of each other.

"The species of the world are connected with each other. I think that's actually one of the lessons, the real reminders, if you will, of the year 2020, as we all live every day with the issues obviously created by COVID-19," observed Microsoft President Brad Smith, during a video launch Wednesday for the company's latest sustainability push -- an ambitious initiative to support global biodiversity. Smith's remarks, of course, refer to the links tracing the novel coronavirus back to animal origins, a phenomenon that has become far more common as humankind encroaches on forests, wetlands and other habitats. During the modern era, Smith noted, wetlands that purify and store water for thousands of species have been reduced by 87 percent while coral reefs have declined by 50 percent -- data points explored in the United Nations' first global assessment (PDF) of ecosystems and biodiversity published in 2019. An estimated one-quarter of species are threatened with extinction, which could amplify the collapse of other habitats.

Summary: Using 3D imaging and artificial intelligence, researchers discovered the shortest distance between two points on the curved surface of the face predicted, with 89% accuracy, which patients had sleep apnea. Facial features analyzed from 3D photographs could predict the likelihood of having obstructive sleep apnea, according to a study published in the April issue of the Journal of Clinical Sleep Medicine. Using 3D photography, the study found that geodesic measurements -- the shortest distance between two points on a curved surface -- predicted with 89 percent accuracy which patients had sleep apnea. Using traditional 2D linear measurements alone, the algorithm's accuracy was 86 percent. "This application of the technique used predetermined landmarks on the face and neck," said principle investigator Peter Eastwood, who holds a doctorate in respiratory and sleep physiology and is the director of the Centre for Sleep Science at the University of Western Australia (UWA).