In order to address the increasing demands of real-world applications, the research for knowledge-intensive NLP (KI-NLP) should advance by capturing the challenges of a truly open-domain environment: web scale knowledge, lack of structure, inconsistent quality, and noise. To this end, we propose a new setup for evaluating existing KI-NLP tasks in which we generalize the background corpus to a universal web snapshot. We repurpose KILT, a standard KI-NLP benchmark initially developed for Wikipedia, and ask systems to use a subset of CCNet - the Sphere corpus - as a knowledge source. In contrast to Wikipedia, Sphere is orders of magnitude larger and better reflects the full diversity of knowledge on the Internet. We find that despite potential gaps of coverage, challenges of scale, lack of structure and lower quality, retrieval from Sphere enables a state-of-the-art retrieve-and-read system to match and even outperform Wikipedia-based models on several KILT tasks - even if we aggressively filter content that looks like Wikipedia. We also observe that while a single dense passage index over Wikipedia can outperform a sparse BM25 version, on Sphere this is not yet possible. To facilitate further research into this area, and minimise the community's reliance on proprietary black box search engines, we will share our indices, evaluation metrics and infrastructure.

We present a new neighbor sampling method on temporal graphs. In a temporal graph, predicting different nodes' time-varying properties can require the receptive neighborhood of various temporal scales. In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time. Learning how to sample neighbors is non-trivial, since the neighbor indices in time order are discrete and not differentiable. To address this challenge, we transform neighbor indices from discrete values to continuous ones by interpolating the neighbors' messages. TNS can be flexibly incorporated into popular temporal graph networks to improve their effectiveness without increasing their time complexity. TNS can be trained in an end-to-end manner. It needs no extra supervision and is automatically and implicitly guided to sample the neighbors that are most beneficial for prediction. Empirical results on multiple standard datasets show that TNS yields significant gains on edge prediction and node classification.

"COMMON SENSE" is the Dark Matter of Artificial Intelligence. In the present era of Artificial Intelligence, Deep Learning, advanced quantum computing, we humans are literally surrounded by machines, everywhere, everyday. Many critics point to Artificial Intelligence as the main threat to humankind; while on the other hand, the supporters of AI claim that humans can never be replaced by machines, and would only ever compliment our abilities. Over the past decade, Artificial Intelligence has undoubtedly emerged as one of the technological successes and with the amount of research and investment going into this domain, it is nowhere near an end. AI has impacted our lives greatly, with so many services and products relying on it that it is irrevocably connected with our everyday world.

Voice-enabled technologies like Siri have gone from a novelty to a routine way to interact with technology in the past decade. In the coming years, our devices will only get chattier as the market for voice-enabled apps, technologies and services continues to expand. But the growth of voice-enabled technology is not universal. For much of the world, technology remains frustratingly silent. "Speech is a natural way for people to interact with devices, but we haven't realized the full potential of that yet because so much of the world is shut out from these technologies," said Mark Mazumder, a Ph.D. student at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Graduate School of Arts and Sciences.

It may be smart, but it's not that clever. Artificial intelligence is nothing without human input. The algorithms that drive AI rely on the expertise of programmers and it's still no more than a tool – albeit a powerful one – that scientists and engineers can use to solve problems. Yet this is not to say that AI isn't the fastest-growing deep technology in the world, with the potential to transform people's lives and boost nations' economies. Facilitating AI innovation has even become a priority for the UK government, as laid out in the National AI Strategy it published in September.

Artificial intelligence (AI) technology can generate plausible, entertaining, and scientifically interesting titles for potential research articles, finds a study in the Christmas issue of The BMJ. A study of The BMJ's most popular Christmas research articles--which combine evidence based science with light hearted or quirky themes--finds that AI generated titles were as attractive to readers but that, as in other areas of medicine, performance was enhanced by human input. As such, the researchers say AI could have a role in generating hypotheses or directions for future research. AI is already used to help doctors diagnose conditions, based on the idea that computer systems can learn from data and identify patterns. But can AI be used to generate worthwhile hypotheses for medical research?

The United States on Thursday unleashed a volley of actions to censure China's treatment of the Uyghur minority, with lawmakers voting to curb trade and new sanctions slapped on the world's top consumer drone maker. The United States has been ramping up pressure on China amid a crop of disputes, with President Joe Biden's administration a day earlier targeting producers of painkillers that have contributed to America's addiction crisis. The U.S. Senate unanimously voted to make the United States the first country to ban virtually all imports from China's northwestern Xinjiang region over concerns of the prevalence of forced labor. "We know it's happening at an alarming, horrific rate with the genocide that we now witness being carried out," said Senator Marco Rubio, a driver behind the act, which already passed the House of Representatives and which the White House says Biden will sign. After prolonged negotiations to secure its passage, Rubio lifted objections and the Senate confirmed veteran diplomat Nicholas Burns as ambassador to China.

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.

Artificial intelligence (AI) is beginning to make an impact on manufacturing. Data from predictive maintenance is moving into useful analytics. The manufacturing supply chain is getting optimized. AI is helping manufacturers to improve uptime, increase yield, and reduce downtime. Recently we've seen machine learning bring significant benefits to manufacturing.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.S. military downed a drone deemed to have hostile intent Tuesday that was headed toward a base in southeast Syria that houses 200 American troops, a senior defense official told Fox News' Jennifer Griffin. Two unmanned aerial systems were spotted entering the At Tanf Garrison Deconfliction Zone located along the Iraq and Jordan-Syria border. One of the two drones traveled deeper into the zone and was shot down after "demonstrating hostile intent," Capt.