Plenty of companies are taking advantage of machine learning to tag and search visual content. Pinterest lets you find visually-similar images in order to track down that recipe or jacket you're looking for, and Pornhub is using machine learning to automatically identify porn stars in videos. Stock image company Shutterstock, though, has developed one of the more novel implementations of this sort of technology: using machine learning to identify the layout of images. The new tool, launched today, is currently available on the company's test site, Shutterstock Labs. You can search for various elements (in the case of the link above, wine and cheese) and then move icons about to specify where you want them to appear in the image.

While tech giants Google, Microsoft and Apple are integrating AI across their user interfaces, such as personalizing search and marketing, small businesses that lack the economic muscle are benefitting from AI-powered forecasting that is driving marketing operations and personalization. Case in point – Boxx.ai that rolled out its AI-powered product AIDA, aimed at marketers to help personalize their customer touch, thereby improving engagement, increasing transactions and reducing attrition. Another Bangalore-based startup Artifacia has an AI-powered visual discovery platform that is revolutionizing visual search for e-commerce companies.

On the morning of September 11, 2001, at 8:46 an airliner carrying 10,000 gallons of fuel crashed into the north tower of the World Trade Center in lower Manhattan. A few minutes later, at 9:03 a second plane hit the south tower. Both structures collapsed in less than 90 minutes. On the same morning, at 9:37 a third airliner slammed into the Pentagon and at 10:03 a fourth plane crashed in a field in Pennsylvania, its target never reached due to the heroic actions of passengers with knowledge of the previous attacks. The human death toll from these events amounted to nearly 2700 (9/11 Commission, 2004). Nineteen young Arab men, implementing the plans of Islamic extremists in Afghanistan, committed these acts of terrorism. Some had been in the United States for over a year and blended into the population. While four had training as pilots, the rest were not well educated and spoke English poorly. In small groups, they were able to carry knives, box cutters, Mace, or pepper spray onto the hijacked jetliners and convert them into deadly weapons (9/11 Commission, 2004). How were they organized and financed? How did the authorities fail to anticipate and prevent this tragedy? Those events highlight the inability of law enforcement and the intelligence community to effectively share information. The 9/11 Commission Report found that the United States, while having access to vast amounts of data and information, is ill equipped to process the data that it has.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs.

"This book appears in the Information Science and Statistics Series commissioned by the publishers.

In the emerging advancement in the branch of autonomous robotics, the ability of a robot to efficiently localize and construct maps of its surrounding is crucial. This paper deals with utilizing thermal-infrared cameras, as opposed to conventional cameras as the primary sensor to capture images of the robot's surroundings. For localization, the images need to be further processed before feeding them to a navigational system. The main motivation of this paper was to develop an edge detection methodology capable of utilizing the low-SNR poor output from such a thermal camera and effectively detect smooth edges of the surrounding environment. The enhanced edge detector proposed in this paper takes the raw image from the thermal sensor, denoises the images, applies Canny edge detection followed by CSS method. The edges are ranked to remove any noise and only edges of the highest rank are kept. Then, the broken edges are linked by computing edge metrics and a smooth edge of the surrounding is displayed in a binary image. Several comparisons are also made in the paper between the proposed technique and the existing techniques.

Using AI and deep learning to create a quick report on the statistics and quotes used should be relatively simple, scanning through complex and variable data sources to discover patterns that show whether information is correct or not. These tools could even check that images accompanying the article show the correct picture and context. A recent article on Breitbart, for instance, could see them end up in court after they used an image of Lukas Podolski, a German soccer player who has appeared for his country 130 times. The image of Podolski and another man appeared under the headline'Spanish police crack gang moving migrants on jet skis', but a 10 second Google image search would have shown that this was actually an image of Podolski on a Jet Ski trip during the Rio 2016 World Cup. A simple AI system would have picked this up almost instantly through image recognition and allowed them to avoid the embarrassment and potential law suit that Podolski is reportedly considering against them.

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that could be exploited by strategies such as score normalization. Indeed, it has been demonstrated over and over experimentally, that various ad-hoc score normalization recipes do work. We present a first attempt at using probability theory to design a generative score-space normalization model which gives similar improvements to ZT-norm on the text-dependent RSR 2015 database.

YARA is a powerful pattern-matching tool for binary analysis. Unlike a simple hash-based signature, YARA rules can classify entire families of malware according to common patterns. As YARA sees more widespread use within the security community, we wanted to find a way to leverage YARA rules to scan for malicious files across our entire organization. Other security tools support YARA rule integration, but we could not find a private, low-cost, scalable, batteries-included solution that was easy to deploy and maintain. For example, VirusTotal supports YARA rule matching against file submissions, but it is a public service and not designed for analyzing internal files and documents with varying levels of confidentiality and sensitivity.

Pattern-recognition algorithms can transform horses into zebras; winter scenes can become summer; artificial intelligence algorithms can generate art; robot radiologists can analyze your X-rays with remarkable precision. We have reached the point where pattern-recognition algorithms and artificial intelligence (A.I.) are more accurate than humans at the visual diagnosis and observation of X-rays, stained breast cancer slides and other medical signs involving general correlations between normal and abnormal health patterns. Before we run off and fire all the doctors, let's better understand the A.I. landscape and the technology's broad capabilities. A.I. won't replace doctors -- it will help to empower them and extend their reach, improving patient outcomes. The challenge with artificial intelligence is that no single and agreed-upon definition exists.