Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Mattea Roach, a tutor from Toronto, Canada, had won $560,983 over the course of her winning streak. This image released by Sony Pictures Television shows Mattea Roach, a 23-year-old Canadian contestant on the game show "Jeopardy!" Heading into the final round of Friday's match, Roach was leading with $19,200 and wagered $3,001 on the Final Jeopardy question.

Want to improve this question? Update the question so it focuses on one problem only by editing this post. I have a project where I want to give recommendations of products based on answers to autogenerated questions. I have texts that explain for every product, in which cases they make sense for a client to buy (this project is about insurance policies). Based on these I want to generate multiple choice questions.

Knowledge base question answering (KBQA) aims to answer a natural language question over a knowledge base (KB) as its knowledge source. A knowledge base (KB) is a structured database that contains a collection of facts in the form subject, relation, object, where each fact can have properties attached called qualifiers. For example, the sentence "Barack Obama got married to Michelle Obama on 3 October 1992 at Trinity United Church" can be represented by the tuple Barack Obama, Spouse, Michelle Obama, with the qualifiers start time 3 October 1992 and place of marriage Trinity United Church . Popular knowledge bases are DBpedia and WikiData. Early works on KBQA focused on simple question answering, where there's only a single fact involved.

Communication is a natural part of our everyday lives. People interact using voice and text, forming sentences to express what they desire. And yet, most of the search and discovery patterns out there rely on menu items and filter facets. Building on our mission at Booking.com: "Making it easier for everyone to experience the world", the ML & AI Product teams based in Tel Aviv decided to challenge the conventional search patterns by allowing the most natural way for everyone to communicate: using their voice. This is the story of how we built a native in-app voice assistant at Booking.com, and as far as I know, the first voice search available today by a global online travel company.

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.

Subsequent time your energy stays on throughout a extreme climate occasion, you could have a machine studying mannequin to thank. Researchers on the MIT-IBM Watson AI Lab are utilizing synthetic intelligence to resolve energy grid failures. The supervisor of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine studying mannequin that works to investigate knowledge collected from tons of of 1000's of sensors situated throughout the U.S. energy grid. The sensors, parts of what's referred to as synchrophasor expertise, compile huge quantities of real-time knowledge associated to electrical present and voltage in an effort to monitor the well being of the grid and find anomalies that would trigger outages. Synchrophasor evaluation requires intensive computational sources as a result of dimension and real-time nature of the info streams the sensors produce.

Next time your power stays on during a severe weather event, you may have a machine learning model to thank. Researchers at the MIT-IBM Watson AI Lab are using artificial intelligence to solve power grid failures. The manager of the MIT-IBM Watson AI Lab, Jie Chen, and his colleagues have developed a machine learning model that works to analyze data collected from hundreds of thousands of sensors located across the U.S. power grid. The sensors, components of what is known as synchrophasor technology, compile vast amounts of real-time data related to electric current and voltage in order to monitor the health of the grid and locate anomalies that could cause outages. Synchrophasor analysis requires intensive computational resources due to the size and real-time nature of the data streams the sensors produce.

We consider entailment problems involving powerful constraint languages such as frontier-guarded existential rules in which we impose additional semantic restrictions on a set of distinguished relations. We consider restricting a relation to be transitive, restricting a relation to be the transitive closure of another relation, and restricting a relation to be a linear order. We give some natural variants of guardedness that allow inference to be decidable in each case, and isolate the complexity of the corresponding decision problems. Finally we show that slight changes in these conditions lead to undecidability.

We investigate practical algorithms for inconsistency-tolerant query answering over prioritized knowledge bases, which consist of a logical theory, a set of facts, and a priority relation between conflicting facts. We consider three well-known semantics (AR, IAR and brave) based upon two notions of optimal repairs (Pareto and completion). Deciding whether a query answer holds under these semantics is (co)NP-complete in data complexity for a large class of logical theories, and SAT-based procedures have been devised for repair-based semantics when there is no priority relation, or the relation has a special structure. The present paper introduces the first SAT encodings for Pareto- and completion-optimal repairs w.r.t. general priority relations and proposes several ways of employing existing and new encodings to compute answers under (optimal) repair-based semantics, by exploiting different reasoning modes of SAT solvers. The comprehensive experimental evaluation of our implementation compares both (i) the impact of adopting semantics based on different kinds of repairs, and (ii) the relative performances of alternative procedures for the same semantics.

Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG has focused on exploring graph-based networks to equip the traditional Sequence-to-sequence framework with reasoning ability. However, these models do not take full advantage of the constraint between questions and answers. Furthermore, studies on multi-hop question answering (QA) suggest that Transformers can replace the graph structure for multi-hop reasoning. Therefore, in this work, we propose a novel framework, QA4QG, a QA-augmented BART-based framework for MQG. It augments the standard BART model with an additional multi-hop QA module to further constrain the generated question. Our results on the HotpotQA dataset show that QA4QG outperforms all state-of-the-art models, with an increase of 8 BLEU-4 and 8 ROUGE points compared to the best results previously reported. Our work suggests the advantage of introducing pre-trained language models and QA module for the MQG task.