Nations sell goods and services to each other because this exchange is generally mutually beneficial. It's easy to understand that Iceland should not be growing its own oranges, given its climate. Instead, Iceland should buy oranges from Spain, which can grow them more cheaply, and sell Spaniards fish, which are abundant in its waters. That's why the explosion in free trade since the first bilateral deal was penned between Britain and France in the mid-1800s has generated unprecedented wealth and prosperity for the vast majority of the world's population. Hundreds of trade agreements later, the United States and several other countries established an international rules-based trading system after World War II. But now the U.S., which has played an integral role in bolstering this system, is actively trying to subvert it.

Following the backlash over its Project Maven plans to develop AI for the US military, Google has since withdrawn and published its ethical principles. Project Maven was Google's collaboration with the US Department of Defense. In March, leaks indicated that Google supplied AI technology to the Pentagon to help analyse drone footage. The following month, over 4,000 employees signed a petition demanding that Google's management cease work on Project Maven and promise to never again "build warfare technology." In April 2018, Google's infamous'Don't be evil' motto was removed from the code of conduct's preface -- but retained in its last sentence.

Robotics and artificial intelligence (AI) have fed two kinds of dreams, the first of the hopeful pleasant sort, and the other a nightmare. The first tells of great abundance, convenience and wealth. The other warns of job loss and widespread unemployment, among both workers and the managerial class. Both have some validity, but only up to a point. AI, like most technological advances before it, will offer society great advances in prosperity and productivity, though they will emerge at a slower pace than the enthusiasts predict. It will also take some jobs, but contrary to much commentary on the subject, it will not lead to mass unemployment.

Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.

The increasing volume of scientific publications and grant proposals has generated an unprecedentedly high workload to scientific communities. Consequently, review quality has been decreasing and review outcomes have become less correlated with the real merits of the papers and proposals. A novel distributed peer review (DPR) approach has recently been proposed to address these issues. The new approach assigns principal investigators (PIs) who submitted proposals (or papers) to the same program as reviewers. Each PI reviews and ranks a small number (such as seven) of other PIs' proposals. The individual rankings are then used to estimate a global ranking of all proposals using the Modified Borda Count (MBC). In this study, we perform simulation studies to investigate several parameters important for the decision making when adopting this new approach. We also propose a new method called Concordance Index-based Global Ranking (CIGR) to estimate global ranking from individual rankings. An efficient simulated annealing algorithm is designed to search the optimal Concordance Index (CI). Moreover, we design a new balanced review assignment procedure, which can result in significantly better performance for both MBC and CIGR methods. We found that CIGR performs better than MBC when the review quality is relatively high. As review quality and review difficulty are tightly correlated, we constructed a boundary in the space of review quality vs review difficulty that separates the CIGR-superior and MBC-superior regions. Finally, we propose a multi-stage DPR strategy based on CIGR, which has the potential to substantially improve the overall review performance while reducing the review workload.

Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.

The first batch of flamethrowers sold by Elon Musk's tunnel construction business The Boring Company have been handed out to customers - with some people traveling thousands of miles to pick one up. The Tesla entrepreneur had suggested the idea of selling a flamethrower at the end of 2017, with the project aiming to raise $10m for The Boring Company, which was founded with the intention of building a network of tunnels to help reduce traffic congestion across the US. Mr Musk claimed that the company had sold 20,000 of the $500 in four days in during January this year, with the first flamethrowers handed out at Boring's Hawthorne, California offices over the weekend. The event took place in a car park adjacent to another of Mr Musk's companies - SpaceX - with the tech billionaire announcing on Twitter that the first 1,000 flamethrowers were bring picked up. Mr Musk has called the item "Not-a-flamethrower" to get around any legal issues of shipping items called flamethrowers, but some customers could not wait to get it into their hands.

The day is coming, experts tell us, when artificial intelligence and robotics will massively disrupt the labor market. Autonomous vehicles will put 3.5 …

The FDA has approved a new artificial intelligence tool called OsteoDetect that helps doctors diagnose wrist fractures. The tool is a computer-aided detection and diagnosis software application that uses AI algorithms to help healthcare providers determine if a wrist fracture is present at a faster rate than traditional diagnostic technologies. The FDA has increasingly approved new technologies that offer novel ways to diagnose and support healthcare providers. The new OsteoDetect approval is the latest example of the FDA's increased acceptance of new technologies, this one specifically targeted at diagnostics. The software works by using AI to analyze 2D x-ray images of the patient's wrist.

The portable robot's name is Mabu, and at the recommendation of his health-care provider, she now lives with Mr. Byrd to help monitor his irregular heartbeat. She checks in on him two or three times a day to make sure he weighs himself, takes his medication and exercises regularly--and relays information back to his health team. "She's my little blue-eyed girlfriend," he says. "She keeps me on my toes." With the senior-citizen population expected to nearly double to 88 million by 2050 and some nursing programs stretched thin, researchers and elder-care centers are exploring the potential of digital companions, in robot or chatbot form, to help the elderly.