We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution. PAC semantics, introduced by Valiant, is one rigorous, general proposal for learning to reason in formal languages: although weaker than classical entailment, it allows for a powerful model theoretic framework for answering queries while requiring minimal assumptions about the form of the distribution in question. To date, however, the most significant limitation of that approach, and more generally most machine learning approaches with robustness guarantees, is that the logical language is ultimately essentially propositional, with finitely many atoms. Indeed, the theoretical findings on the learning of relational theories in such generality have been resoundingly negative. This is despite the fact that first-order logic is widely argued to be most appropriate for representing human knowledge. In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. Our results exploit symmetries exhibited by constants in the language, and generalize the notion of implicit learnability to show how queries can be computed against (implicitly) learned first-order background knowledge.

President Trump's last-minute decision to pull back from a retaliatory strike on Iran underscored the absence of appealing options available to him as Tehran races toward its next big challenge to the United States: building up and further enriching its stockpile of nuclear fuel. Two weeks of flare-ups over the attacks on oil tankers and the downing of an American surveillance drone, administration officials said, have overshadowed a larger, more complex and fast-intensifying showdown over containing Iran's nuclear program. In meetings in the White House Situation Room in recent days, Secretary of State Mike Pompeo contended that the potential for Iran to move closer to being able to build a nuclear weapon was the primary threat from Tehran, one participant said, a position echoed by Mr. Trump on Twitter on Friday. Left unsaid was that Iran's moves to bolster its nuclear fuel program stemmed in substantial part from the president's decision last year to pull out of the 2015 international accord, while insisting that Tehran abide by the strict limits that agreement imposed on its nuclear activities. Mr. Trump has long asserted that the deal would eventually let Iran restart its nuclear program and did too little to curb its support for terrorism.

Tuesday of this past week was quite fortuitous: In my Data Science Cohort at Lambda School, we are working a predictive maintenance competition on Kaggle regarding Water pumps in Tanzania. And, I went to a Data Science networking event at a defense contractor who spoke of the importance of Predictive Maintenance Solutions -- in their case, they were predicting the failure rates of parts of the F35 Joint Strike Fighter. According to IoT world, The Predictive Maintenance report forecasts a compound annual growth rate for Predictive Maintenance of 39% between 2016–2022, with annual technology spending reaching US$10.96 This has a large positive impact on Data Science and Machine Learning if the industry can keep up with the needs of predictive maintenance problems. What is predictive maintenance and why is it so important to different domains?

Healthcare Cognitive Computing market report is based on present industry situations, market demands, business strategies utilized by prominent players involved in this market along with their growth synopsis. This report has been segmented into types, applications and regions. The report also comprises major drivers boosting this market. Healthcare Cognitive Computing market worth about XX million USD in 2018 and it is expected to reach YY million USD in 2026 with a CAGR of AA% during the forecast period. Cognitive computing (CC) describes technology platforms that are based on the scientific disciplines of artificial intelligence and signal processing.

You may not be using machine learning, often referred to as artificial intelligence, for business applications yet, but there is little doubt you have read or heard about how it could or should be used. The issue is not that there are not legitimate business uses for machine learning (ML) options, the challenge is knowing which types of ML may work best for your business needs and finding the right provider or recruiting the right people to implement it. Initially understanding machine learning is hard, but with a few big concepts under your belt, it becomes easy. It then gets complicated again, but by then you will be ready to deal with generative adversarial networks! This is the most basic version about the content and should get you ready to listen to our special A Word On Artificial Intelligence podcast hosted by Primedia Broadcasting Head of Digital Allan Kent.

Aggregated data is commonplace in areas such as epidemiology and demography. For example, census data for a population is usually given as averages defined over time periods or spatial resolutions (city, region or countries). In this paper, we present a novel multi-task learning model based on Gaussian processes for joint learning of variables that have been aggregated at different input scales. Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task. We are then able to compute the cross-covariance between the different tasks either analytically or numerically. We also allow each task to have a potentially different likelihood model and provide a variational lower bound that can be optimised in a stochastic fashion making our model suitable for larger datasets. We show examples of the model in a synthetic example, a fertility dataset and an air pollution prediction application.

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such approaches fail to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a probabilistic topic model in order to infer topic distributions of sentences. As each topic defines a semantic connection among a group of sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate a polynomial time algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarization systems is included in the paper. Our obtained results show the superiority of our method in terms of content coverage of the summaries.

In 2016, the Johannesburg team at IBM Research discovered that the process of reporting cancer data to the government, which used it to inform national health policies, took four years after diagnosis in hospitals. In the US, the equivalent data collection and analysis takes only two years. The additional lag turned out to be due in part to the unstructured nature of the hospitals' pathology reports. Human experts were reading each case and classifying it into one of 42 different cancer types, but the free-form text on the reports made this very time-consuming. So the researchers went to work on a machine-learning model that could label the reports automatically.

While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.

TEHRAN - Iran's Revolutionary Guard shot down a U.S. surveillance drone Thursday in the Strait of Hormuz, marking the first time the Islamic Republic directly attacked the American military amid tensions over Tehran's unraveling nuclear deal with world powers. The two countries disputed the circumstances leading up to an Iranian surface-to-air missile bringing down the U.S. Navy RQ-4A Global Hawk, an unmanned aircraft with a wingspan larger than a Boeing 737 jetliner and costing over $100 million. Iran said the drone "violated" its territorial airspace, while the U.S. called the missile fire "an unprovoked attack" in international airspace over the narrow mouth of the Persian Gulf and President Donald Trump tweeted that "Iran made a very big mistake!" Trump later appeared to play down the incident, telling reporters in the Oval Office that he had a feeling that "a general or somebody" being "loose and stupid" made a mistake in shooting down the drone. The incident immediately heightened the crisis already gripping the wider region, which is rooted in Trump withdrawing the U.S. a year ago from Iran's 2015 nuclear deal and imposing crippling new sanctions on Tehran.