The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.

As a former Silicon Valley intellectual property attorney for more than 20 years, the potential of disruptive technology has long been of special interest to me. Artificial intelligence (AI) promises to be one of the most important innovations that powers many disruptive ventures and brings exciting changes to our legal system. AI is already influencing the way we work, travel, shop, and play. From autonomous vehicles to improved medical diagnostics to voice assistants, AI is increasingly at the forefront of innovation. As a continuation of the United States Patent and Trademark Office's (USPTO) policy leadership in the field of AI, the USPTO convened a conference on Artificial Intelligence: Intellectual Property Policy Considerations on January 31 this year.

The funds will go towards building out Litterati's taxonomy which currently applies a COMB framework - Category soda / Object can / Material aluminum / Brand Coke. This system is the foundation for a smarter tagging system, where machine learning is applied to the images, presenting a list of tags with increasing precision. By building a more granular database, Litterati can derive more specific insights about the types of litter seen in various communities.

The effort to curtail the impact of influenza just got a shot in the arm with a research award from a division of the U.S. Department of Health & Human Services …

The Dutch data protection agency has asked Microsoft's lead privacy regulator in Europe to investigate ongoing concerns it has attached to how Windows 10 gathers user data. Back in 2017 the privacy watchdog found Microsoft's platform to be in breach of local privacy laws on account of how it collects telemetry metadata. After some back and forth with the regulator, Microsoft made changes to how the software operates in April last year -- and it was in the course of testing those changes that the Dutch agency found fresh reasons for concern, discovering what it calls in a press release "new, potentially unlawful, instances of personal data processing". Since the agency's investigation of Windows 10 started a new privacy framework is being enforced in Europe -- the General Data Protection Regulation (GDPR) -- which means Microsoft's lead EU privacy regulator is the Irish Data Protection Commission (DPC), where its regional HQ is based. This is why the Dutch agency has referred its latest concerns to Ireland.

Pentagon and industry developers are now testing a new series of hypersonic weapons prototypes as part of a large-scale effort to fast-track the weapons to service. The U.S. acceleration of the weapons, which includes air flights, ground-firing, wind-tunnels, simulation and various kinds of prototyping, is widely discussed as much needed response to Russian and Chinese progress in the area of hypersonics. Flight tests, demonstrations, ground testing and advanced air-vehicle configuration prototyping are all providing data for an Air Force, DARPA and Raytheon hypersonic weapons program called Hypersonic Air-Breathing Weapons Concept, or HAWC. DARPA statements on the program, citing program manager Andrew Knoedler, identify key areas of developmental emphasis to include "hydrocarbon scramjet-powered propulsion to enable sustained hypersonic cruise." DARPA information, mirrored by Raytheon weapons developers, explains that "sustaining" speeds at 5-times the speed of sound is a technical characteristic to hypersonic weapons...and the HAWC in particular.

Billionaire Jack Ma said Thursday envisions a bright new future for the chronically overworked: a 12-hour work week. According to Ma, a Chinese business magnate who helms mega-retailer, Alibaba, meteoric advances in artificial intelligence and reforms in education could mean many workers will eventually work only a quarter of their current weekly hours. Theoretically, that would mean working as little as three days per week for four hours each day, he said inconversation with Tesla CEO, Elon Musk, at an AI conference in China. Alibaba chief, Jack Ma (pictured above) thinks that with the proper advances in AI and the education system that workers could soon enjoy a 12-hour work week. 'For the next 10, 20 years, every human being, country, government should focus on reforming the education system, making sure our kids can find a job, a job that only requires three days a week, four hours a day,' Ma said at the World Artificial Intelligence Conference in Shanghai Thursday according to Bloomberg.

In the context of technology, and particularly AI, trust and how it is built is what matters. I would argue that building trust requires four key ingredients: benevolence, competency, consistency and integrity. But, even in the early days of AI, these behaviours have become sporadic and unreliable in the implementation of some systems. This year, for instance, it emerged that a big AI education vendor had inserted social psychological interventions into one of its commercial learning programs to test how thousands of students would respond, unbeknown to them, their parents or their teachers. What is made is a reflection of the people who make it.

MONTGOMERY, ALABAMA – The Air Force is touting a new digital plan that it thinks will unfetter it from an industrialized way of thinking, open itself to faster networks, better weapons systems and make the service a more attractive employer. That seems like a lot coming from a plan that is only four pages long (including the title page), but Air Force Secretary Matt Donovan told Federal News Network in an exclusive interview after the Air Force Information Technology & Cyberpower Education and Training Event in Alabama that the strategy is a roadmap to shift the service's culture and how it operates. "We've been very successful so far with the American military and the individual weapons platforms," Donovan said. "The problem is these exquisite capabilities are largely unconnected from each other. We need to get to a point where we have a family of systems and everything is connected for command and control. We are ultimately trying to get to a point where any sensor will provide information to any shooter."

Newswise -- According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."