Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in particular, the strategy used to select the seed set, or the set of documents an attorney reviews first to establish an initial model. Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well. Our research demonstrated that the seed set selection strategy can have a significant impact on the precision of a predictive model. Enabling attorneys with the results of this study will allow them to initiate the most effective predictive modeling process to comb through the terabytes of data typically present in modern litigation. This study used documents from four actual legal cases to evaluate eight different seed set selection strategies. Attorneys can use the results contained within this paper to enhance their approach to predictive coding.

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.

Adversary scenarios in driving, where the other vehicles may make mistakes or have a competing or malicious intent, have to be studied not only for our safety but also for addressing the concerns from public in order to push the technology forward. Classical planning solutions for adversary driving do not exist so far, especially when the vehicles do not communicate their intent. Given recent success in solving hard problems in artificial intelligence (AI), it is worth studying the potential of reinforcement learning for safety driving in adversary settings. In most recent reinforcement learning applications, there is a deep neural networks that maps an input state to an optimal policy over primitive actions. However, learning a policy over primitive actions is very difficult and inefficient. On the other hand, the knowledge already learned in classical planning methods should be inherited and reused. In order to take advantage of reinforcement learning good at exploring the action space for safety and classical planning skill models good at handling most driving scenarios, we propose to learn a policy over an action space of primitive actions augmented with classical planning methods. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the classical planning methods that our agent queries as skills.

We present a machine-readable movement writing for sleight-of-hand moves with cards -- a "Labanotation of card magic." This scheme of movement writing contains 440 categories of motion, and appears to taxonomize all card sleights that have appeared in over 1500 publications. The movement writing is axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally as an Ontology of Card Sleights, a computational ontology that extends the Basic Formal Ontology and the Information Artifact Ontology. The Ontology of Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web Ontology Language. While ontologies have historically been used to classify at a less granular level, the algorithmic nature of card tricks allows us to transcribe a performer's actions step by step. We conclude by discussing design criteria we have used to ensure the ontology can be accessed and modified with a simple click-and-drag interface. This may allow database searches and performance transcriptions by users with card magic knowledge, but no ontology background.

Early diagnosis of acute coronary artery occlusion based on electrocardiogram (ECG) findings is essential for prompt delivery of primary percutaneous coronary intervention. Current ST elevation (STE) criteria are specific but insensitive. Consequently, it is likely that many patients are missing out on potentially life-saving treatment. Experts combining non-specific ECG changes with STE detect ischaemia with higher sensitivity, but at the cost of specificity. We show that a deep learning model can detect ischaemia caused by acute coronary artery occlusion with a better balance of sensitivity and specificity than STE criteria, existing computerised analysers or expert cardiologists.

The esports industry is expected to eclipse $1 billion soon, yet the U.S. amateur system is almost entirely unorganized. Super League Gaming is hoping to fill that void, with a little league for esports that welcomes gamers as young as 6. (Dec. Player Xmithie (pronounced "ex-myth-ie"), for example, practices Tuesday through Friday with games and tournaments on the weekend during the regular season and playoffs. Days start around 9:30 a.m., team meetings are around 10 a.m. with practices and scrimmages from 11 a.m. until 9 p.m. His day typically ends around midnight. This is the lifestyle of a professional athlete.

Tomorrow, Germany begins auctioning frequencies to build 5G mobile networks. It is both a highly technical event and the center of a geopolitical storm. Like much of Europe, Germany is squeezed between its economic ties to China and its longtime alliance with the U.S. NPR's Joanna Kakissis reports from Berlin. COMPUTER-GENERATED VOICE: To keep your estimated arrival time... JOANNA KAKISSIS, BYLINE: 5G will not just allow you to download movies in seconds on your smartphone. Since it's supposed to be up to 1,000 times faster than current mobile speeds, it can handle communication for self-driving cars, for example.

In America, 2018 was supposed to be a very big year for self-driving cars. Uber quietly prepped to launch a robo-taxi service. Waymo said riders would be able to catch a driverless ride by year's end. General Motors' Cruise said it would start testing in New York City, the country's traffic chaos capital. Congress was poised to pass legislation that would set broad outlines for federal regulation of the tech.

Shelley Chang was working as a business analyst for a computer company in 2010 when she met Jason Ho through some mutual friends. Ho was tall and slender with a sly smile, and they hit it off right away. A computer programmer, Ho ran his own company from San Francisco. He also loved to travel. Less than a month after they met, Ho surprised Chang by buying a plane ticket to meet her in Taiwan, where she'd temporarily relocated.

As helicopter flights go, this one was especially boring. We took off, hovered for a bit, and maneuvered around the airport. We flew to a spot about 10 miles away, did some turns and gentle banks, then came back and landed. I've been on more exciting ferris wheels, with views more inspiring than those of rural Connecticut. Still, the flight was impressive for at least one reason: The pilot controlling the 12,000-pound Sikorsky S-76 had never before operated a helicopter.