Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.

Two Artificial Intelligence-driven Internet paradigms may emerge in the near future. One will be based on logic, smart enterprises and human merit while the other may morph into an Orwellian control tool. Even former Google CEO Eric Schmidt has foreseen a bifurcation of the Internet by 2028 and China's eventual triumph in the AI race by 2030. In the meantime, the US seems more interested in deflecting the smart questions of today than in building the smart factories of tomorrow. Nothing embodies this better than the recent attempt by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) to create an AI-based filter to "stamp out fake-news outlets before the stories spread too widely."

The rise of automation has led experts and workers to debate whether robots will steal jobs, augment employees, free them up to do more complex tasks, or some combination thereof. While potential benefits of the artificial intelligence (AI) revolution include more productive work and employees, this technology could also lead to upheaval in the job market, according to a recent report from SmartAsset. And certain states will feel the impact more than others, the report found. The report examined data from the US Bureau of Labor Statistics and Oxford University, comparing the jobs most likely to be impacted by automation to the number of workers holding those occupations in each state, to determine the vulnerability of each state's working population. The American South faces the most trouble, the report found, with several of those states making the top 10.

A typical driving test considers basic skills: Can you parallel park? Do you merge safely? Do you know to yield to pedestrians? No such government exam is required for cars driven by a computer. The idea has been dismissed by federal officials who oppose regulation and industry leaders who say they need freedom from rules to innovate. But a new study by the Rand Corp., funded by Uber's autonomous vehicle division and released Thursday, tries to map out what independent tests of driverless safety might look like and how they might be implemented.

The robot takeover of farm labor inched forward in a big way this week as researchers unveiled a prototype of a machine that's able to perform a task once possible only with the human hand. A team of engineers at the University of Cambridge have developed a robot that can maneuver so deftly that it's able to pick up a lettuce head, find its outer layer of leaves, and delicately remove them. The machine is able to perform this task using three tools: a camera to size-up an individual lettuce head and find its stem, an arm to roll and position the lettuce for tearing, and another arm with a suction device on the end for removing leaves. "When you harvest a lettuce, the outer leaves of the lettuce need to be removed before the lettuce is ready to go into retail," said Luca Scrimeca, one of the researchers, in a statement. "The leaves are soft, they tear easily, and the shape of the lettuce is never a given." The technology was developed by the university's Machine Intelligence Laboratory, and it's currently able to perform full leaf removal about 50% of the time and in about 27 seconds.

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Use of medical data, also known as electronic health records, in research helps develop and advance medical science. However, protecting patient confidentiality and identity while using medical data for analysis is crucial. Medical data can be in the form of tabular structures (i.e. tables), free-form narratives, and images. This study focuses on medical data in the free form longitudinal text. De-identification of electronic health records provides the opportunity to use such data for research without it affecting patient privacy, and avoids the need for individual patient consent. In recent years there is increasing interest in developing an accurate, robust and adaptable automatic de-identification system for electronic health records. This is mainly due to the dilemma between the availability of an abundance of health data, and the inability to use such data in research due to legal and ethical restrictions. De-identification tracks in competitions such as the 2014 i2b2 UTHealth and the 2016 CEGS N-GRID shared tasks have provided a great platform to advance this area. The primary reasons for this include the open source nature of the dataset and the fact that raw psychiatric data were used for 2016 competitions. This study focuses on noticeable trend changes in the techniques used in the development of automatic de-identification for longitudinal clinical narratives. More specifically, the shift from using conditional random fields (CRF) based systems only or rules (regular expressions, dictionary or combinations) based systems only, to hybrid models (combining CRF and rules), and more recently to deep learning based systems. We review the literature and results that arose from the 2014 and the 2016 competitions and discuss the outcomes of these systems. We also provide a list of research questions that emerged from this survey.

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90\% and 98.01\% on the legal small and large test dataset.

Future warfare will likely be conducted by armies of robots and humans enhanced by gene editing and drugs, according to a new Ministry of Defence report. As the world becomes more volatile due to increased threats from terrorism and climate change, "new areas of conflict" will also open up, including space and cyberspace, it is thought. In an analysis developed with experts from around the world, the potential challenges facing the UK are laid out. The document, entitled The Future Starts Today, also warns of an increasing risk from nuclear and chemical weapons as technology rapidly advances. 'Killer robots' ban blocked by US and Russia at UN meeting'Killer robots' ban blocked by US and Russia at UN meeting "This report makes clear that we are living in a world that is becoming rapidly more dangerous, with intensifying challenges from state aggressors who flout the rules, terrorists who want to harm our way of life and the technological race with our adversaries," said defence secretary Gavin Williamson.

In the months following the failed Apollo 13 mission, investigators discovered that a seemingly benign event two years earlier was the root cause of this near national disaster. Engineers handling one of two oxygen tanks built for the service module accidentally let one slip and fall. I once dropped my iPhone from my seat at a hockey game and watched helplessly as it fell 15 feet toward the cement floor. Miraculously, it landed at just the right angle and survived. In a fateful moment years before launch, at the North American Aviation plant in Downey, California, a simple slip of just two inches created enough structural damage to set in motion a series of failures that nearly killed three astronauts.