The U.S. Department of Energy's (DOE) Office of Fossil Energy (FE) will award up to $1.5 million to winning innovators in a prize challenge to support FE's SMART (Science-informed Machine Learning to Accelerate Real Time Decisions in the Subsurface) initiative. Click here to watch a short video about the SMART Visualization Platform Prize Challenge and learn how to register to take part in this unique software development contest. SMART leverages the expertise of seven national laboratories, as well as industry partners, universities, unconventional field laboratories and carbon storage regional initiatives to realize breakthroughs in understanding the subsurface environment through machine learning. A thorough understanding of the subsurface is necessary to reduce risks and increase the efficiency of enhanced and unconventional oil and natural gas recovery, geothermal energy technologies, geological carbon storage and other operations. Currently, approaches to analyze subsurface data are extremely rigorous, require expert training and are time-consuming and costly.

Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries. The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries. These transformations are inspired by an error analysis of state-of-the-art summarization model outputs. Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset. We also find that transferring from artificial error correction to downstream settings is still very challenging.

The road ahead for artificial intelligence (AI) depended on and would be driven by human intentions, Prime Minister Narendra Modi said here today. He was speaking after dedicating the Wadhwani Institute for Artificial Intelligence in suburban Kalina to the nation. "It is our intention that will determine outcomes of AI," Modi said. "With every technological revolution, the scalability of technology has increased manifold. This has given humans increasingly more power," he said.

The most striking aspect to any longtime "Animal Crossing" player was the train set room in the basement, which featured different colored versions of several different train models. This meant the designer likely utilized the "time travel" exploit to force the game to sell them multiple versions of the same train. This kind of work could last hours, if not days. "Sorry, I can't vote for an Animal Crossing time traveler," said one chat message in KindaFunny's Twitch stream. "Legalize time traveling," said another user, snarkypuppers.

While the U.S. has lacked central organizing of its AI, it has an advantage in its flexible tech industry, said Nand Mulchandani, the acting director of the U.S. Department of Defense Joint Artificial Intelligence Center. Mulchandani is skeptical of China's efforts at "civil-military fusion," saying that governments are rarely able to direct early stage technology development. Tensions over how to accelerate AI are driven by the prospect of a tech cold war between the U.S. and China, amid improving Chinese innovation and access to both capital and top foreign researchers. "They've learned by studying our playbook," said Elsa B. Kania of the Center for a New American Security. "Many commentators in Washington and Beijing have accepted the fact that we are in a new type of Cold War," said Ulrik Vestergaard Knudsen, deputy secretary general of Organization for Economic Cooperation and Development (OECD), which is leading efforts to develop global AI cooperation.

The 2020 election is said to be'the most important election in our lifetime' and Google wants to help the 150 million Americans projected to vote get to the polls. The tech giant has rolled out new tools to Search and Maps that shows exact locations for in person voting and ballot drop boxes. When users look up information for'early voting' or'ballot drop boxes near me,' they will be shown details for specific places, along with hours of operation. The tech giant also announced its virtual assistant will'soon' be able to provide citizens with information - with just saying, 'Hey Google, where can I vote?' Google has rolled out new tools to Search and Maps that shows exact locations for in person voting and ballot drop boxes. Yunhan Xu, Google product manager, shared in the announcement: 'This year, searches for'how to vote' in the U.S. are higher than ever before.

Artificial intelligence, or AI, has long been the object of excitement and fear. In July, the Financial Times Future Forum think-tank convened a panel of experts to discuss the realities of AI -- what it can and cannot do, and what it may mean for the future. Entitled "The Impact of Artificial Intelligence on Business and Society", the event, hosted by John Thornhill, the innovation editor of the FT, featured Kriti Sharma, founder of AI for Good UK, Michael Wooldridge, professor of computer sciences at Oxford university, and Vivienne Ming, co-founder of Socos Labs. For the purposes of the discussion, AI was defined as "any machine that does things a brain can do". Intelligent machines under that definition still have many limitations: we are a long way from the sophisticated cyborgs depicted in the Terminator films. Such machines are not yet self-aware and they cannot understand context, especially in language. Operationally, too, they are limited by the historical data from which they learn, and restricted to functioning within set parameters. Rose Luckin, professor at University College London Knowledge Lab and author of Machine Learning and Human Intelligence, points out that AlphaGo, the computer that beat a professional (human) player of Go, the board game, cannot diagnose cancer or drive a car.

What would totalitarian governments of the past have looked like if they were never defeated? The Nazis operated with 20th Century technology and it still took a world war to stop them. How much more powerful – and permanent – could the Nazis have been if they had beat the US to the atomic bomb? Controlling the most advanced technology of the time could have solidified Nazi power and changed the course of history. When we think of existential risks, events like nuclear war or asteroid impacts often come to mind.

The Joint Artificial Intelligence Center (JAIC) is the Department of Defense's lead organization for accelerating the adoption of artificial intelligence (AI) across the services. And it's a critical role, as top leaders believe AI will eventually impact every warfighting domain, even every mission, the DOD undertakes. With NPS faculty and students currently teaching and researching varied AI concepts and applications, and translating them into future naval capabilities, the university is deeply embedded in advancing the technology and the DOD's AI workforce. With this role in mind, NPS hosted two of the JAIC's most senior leaders, retired Air Force Lt. Gen. John N.T. "Jack" Shanahan, the inaugural and former Director, and Nand Mulchandani, the current Chief Technology Officer, to speak to students, faculty and staff about their experiences organizing efforts to develop artificial intelligence (AI) projects on a DOD scale during NPS' latest virtual Secretary of the Navy Guest Lecture (SGL), held Oct. 13. Shanahan and Mulchandani are the latest high-profile leaders to participate in the virtual SGL series, following the likes of retired Adm. Mike Mullen, Army Gen. Keith B. Alexander and retired Navy Vice Adm. Jan E. Tighe, and retired Adm. William McRaven.

The findings come from a cross-sectional study, published in BMJ Open, of the comments submitted to the US Food and Drug Administration (FDA) 'Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)--Discussion Paper and Request for Feedback'. Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care, continually incorporating insights from the vast amount of data generated every day during the delivery of health care. Many such devices must have regulatory approval or clearance before being available for clinical practice, and in the US that regulation falls to the FDA. The suitability of traditional medical device regulatory pathways for AI/ML have been called into question because the nature of the technology means it is continually evolving and adapting to improve performance. Under the current framework it would mean that as devices evolved they would require further review and approval, which could be time consuming and may affect patient safety and interests.