A study appearing today in the Proceedings of the National Academy of Sciences has brought answers. In work that combines forensic science with psychology and computer vision research, a team of scientists from the National Institute of Standards and Technology (NIST) and three universities has tested the accuracy of professional face identifiers, providing at least one revelation that surprised even the researchers: Trained human beings perform best with a computer as a partner, not another person. "This is the first study to measure face identification accuracy for professional forensic facial examiners, working under circumstances that apply in real-world casework," said NIST electronic engineer P. Jonathon Phillips. "Our deeper goal was to find better ways to increase the accuracy of forensic facial comparisons." The team's effort began in response to a 2009 report by the National Research Council, "Strengthening Forensic Science in the United States: A Path Forward," which underscored the need to measure the accuracy of forensic examiner decisions.

Zero is an extremely hard concept to understand. Quantities of things--whether they are bundles of fruit, communities of people, or blocks of wood for construction--are vital to our livelihood. But nothing, as far as the brain is concerned, is far different than something. Humans have had a hard time coming to terms with this concept. But our ability to grasp zero as a distinct numerical value is a vital part of modern mathematics, engineering, and technology.

Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...

An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST$^2$ method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the $\mathcal{F}_1$ score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST$^2$ is also analytically and numerically shown.

This document gives a knowledge-oriented analysis of about 20 interesting Recognizing Textual Entailment (RTE) examples, drawn from the 2005 RTE5 competition test set. The analysis ignores shallow statistical matching techniques between T and H, and rather asks: What would it take to reasonably infer that T implies H? What world knowledge would be needed for this task? Although such knowledge-intensive techniques have not had much success in RTE evaluations, ultimately an intelligent system should be expected to know and deploy this kind of world knowledge required to perform this kind of reasoning. The selected examples are typically ones which our RTE system (called BLUE) got wrong and ones which require world knowledge to answer. In particular, the analysis covers cases where there was near-perfect lexical overlap between T and H, yet the entailment was NO, i.e., examples that most likely all current RTE systems will have got wrong. A nice example is #341 (page 26), that requires inferring from "a river floods" that "a river overflows its banks". Seems it should be easy, right? Enjoy!

Kaggle is an AirBnB for Data Scientists – this is where they spend their nights and weekends. It's a crowd-sourced platform to attract, nurture, train and challenge data scientists from all around the world to solve data science and predictive analytics problems through machine learning. It has over 536,000 active members from 194 countries and it receives close to 150,000 submissions per month. Started from Melbourne, Australia Kaggle moved to Silicon Valley in 2011, raised some 11 million dollars from the likes of Hal Varian (Chief Economist at Google), Max Levchin (Paypal), Index and Khosla Ventures and then ultimately been acquired by the Google in March of 2017. Kaggle is the number one stop for data science enthusiasts all around the world who compete for prizes and boost their Kaggle rankings.

Artificial intelligence is the beating heart at the center of delivery robots, autonomous cars, and, as it turns out, ocean ecology trackers. In a blog post on Friday, Global Fishing Watch, a platform founded by Google, Skytruth, and Oceana that monitors fishing activity around the globe, announced the addition of two new data layers to increase "transparency" and "awareness" around overfishing. One of the new layers tracks transshipment, a method whereby one fishing vessel offloads its catch to another, refrigerated ship at sea. It's often used to combine illicitly caught fish with legal seafood, and it usually takes place in international waters, making it difficult for authorities to track. Working from a database of 300,000 ships across 12 categories, Global Fishing Watch trained machine learning algorithms to identify when a fishing vessel is docked alongside a refrigerator vessel, and to determine the likelihood that transshipment is taking place.

The football World Cup is almost upon us. Many millions will watch the tournament unfold on TV screens around the globe. But what if you could enjoy a mini virtual reconstruction of each match on your dining table instead? To create such an experience, Konstantinos Rematas and colleagues at the University of Washington trained a machine learning algorithm to convert 2D YouTube clips into 3D reconstructions. They began by gathering footage from the football videogame FIFA. "I had to play FIFA 2017 for, I don't know, … To continue reading this premium article, subscribe for unlimited access.

More and more organizations are finding ways to use artificial intelligence to power their digital transformation efforts. This ebook looks at the potential benefits and risks of AI technologies, as well as their impact on business, culture, the economy, and the employment landscape. From the ebook: The actual impact of artificial intelligence (AI) on the world's economy and jobs will likely be somewhere between the utopian and dystopian futures that it is often discussed in terms of, according to a report from the Economist Intelligence Unit. The report, commissioned by Google, examined how AI will impact certain industries in the US, the UK, Australia, Japan, and Asia as a whole. The findings are based on econometric modelling, desk research, and interviews with academic and industry experts.

Whether you're aware of it or not, artificial intelligence (AI) has a ubiquitous presence in our lives today – think the personalised playlists on Spotify or the'Recommended for you' lists on Netflix, both of which use AI to curate a selection tailored just for you. Now its presence is being felt in the area of document management, with AI and cognitive computing set to revolutionise the ways in which we store, archive, process and extract information. Here are 5 ways AI is transforming document management systems . Automatic classification and processing - While OCR (optical character recognition) technology allows for text recognition, AI takes this a step further by being able to "read" the information on that document, classify it correctly and automate workflows based on that classification – all at a fraction of the speed a human could. While the system is initially guided by a set of rules, its identification and processing capabilities continue to improve using machine learning, meaning it is able to learn from repeated exposure to documents, as well as from the actions taken by employees upon those documents.