When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow. Pattern classification and machine learning are very hot topics and used in almost every modern application: Optical Character Recognition (OCR) in the post office, spam filtering in our email clients, barcode scanners in the supermarket … the list is endless. In this article, I want to give a quick overview about the main concepts of a typical supervised learning task as a primer for future articles and implementations of various learning algorithms and applications. Predictive modeling is the general concept of building a model that is capable of making predictions. Typically, such a model includes a machine learning algorithm that learns certain properties from a training dataset in order to make those predictions.

Image recognition, determining all the objects within a photo, is something Facebook's AI does with relative ease. The company's approach to machine learning is called deep learning, a popular route to AI also followed by Google and others. Deep learning employs algorithms to recognize patterns, learn from those patterns and complete sophisticated tasks. For Facebook, it could be tagging friends. For Google, it may be creating a program that plays the game Go well enough to beat human champions.

When MIT Computer Science and Artificial Intelligence Lab researchers showed videos of a drumstick hitting and brushing through various objects, subjects were fooled into believing that the sounds they heard actually came from the objects and materials on screen. Instead, a computer programmed to analyze the video and apply the correct sounds from its own library of samples chose the audio clips for all the videos. And the subjects were none the wiser. The team's work is described in a new paper released Monday and being presented next week at the Computer Vision and Pattern Recognition conference in Las Vegas. To be clear, there really isn't any such thing as an Auditory Turing test.

When MIT Computer Science and Artificial Intelligence Lab researchers showed videos of a drumstick hitting and brushing through various objects, subjects were fooled into believing that the sounds they heard actually came from the objects and materials on screen. Instead, a computer programmed to analyze the video and apply the correct sounds from its own library of samples chose the audio clips for all the videos. And the subjects were none the wiser. The team's work is described in a new paper released Monday and being presented next week at the Computer Vision and Pattern Recognition conference in Las Vegas. To be clear, there really isn't any such thing as an Auditory Turing test.

In this paper, we describe a case study in a big metropolis, in which from data collected by digital sensors, we tried to understand mobility patterns of persons using buses and how this can generate knowledge to suggest interventions that are applied incrementally into the transportation network in use. We have first estimated an Origin-Destination matrix of buses users from datasets about the ticket validation and GPS positioning of buses. Then we represent the supply of buses with their routes through bus stops as a complex network, which allowed us to understand the bottlenecks of the current scenario and, in particular, applying community discovery techniques, to identify clusters that the service supply infrastructure has. Finally, from the superimposing of the flow of people represented in the OriginDestination matrix in the supply network, we exemplify how micro-interventions can be prospected by means of an example of the introduction of express routes.

I read a few articles claiming that the windshield was possibly hit by a bullet or rock, shortly before the deadly derailment. The FBI experts say that a broken windshield will exhibit different patterns, depending on whether it was hit by an impact (rock) or because of the crash.

Terrapattern is a visual search engine that, from the first moment you use it, you wonder: Why didn't Google come up with this 10 years ago? Click on a feature on the map -- a baseball diamond, a marina, a roundabout -- and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. And how did a handful of digital artists and developers create it -- and for under 35,000?

In a series of papers posted online in recent weeks, mathematicians have solved a problem about the pattern-matching card game Set that predates the game itself. The solution, whose simplicity has stunned mathematicians, is already leading to advances in other combinatorics problems. Invented in 1974, Set has a simple goal: to find special triples called "sets" within a deck of 81 cards. Each card displays a different design with four attributes--color (which can be red, purple or green), shape (oval, diamond or squiggle), shading (solid, striped or outlined) and number (one, two or three copies of the shape). In typical play, 12 cards are placed face-up and the players search for a set: three cards whose designs, for each attribute, are either all the same or all different.

A simple Google image search highlighted on Twitter has been said to highlight the pervasiveness of racial bias and media profiling. "Three black teenagers" was a trending search on Google on Thursday after a US high school student pointed out the stark difference in results for "three black teenagers" and "three white teenagers". Kabir Alli of Virginia posted a clip to Twitter of himself carrying out a straightforward search of "three black teenagers", which overwhelmingly turns up prisoners' mugshots. He and others erupt in laughter when the result for "three white teenagers" show stock photos of smiling, wholesome-looking young people. The tweet has been retweeted by more than 60,100 users and favourited nearly 55,500 times since it was posted on Tuesday – but Alli's video was later reposted by World Star Hip Hop, an entertainment website with an enormous following on social media.

On June 6, 2016, Riverside County District Attorney, Michael Hestrin, announced that seven people were indicted for insurance fraud and conspiracy for one of the largest workers' compensation (healthcare) fraud investigations in the County. In two separate but related grand jury indictments, a result of a joint investigation by the Riverside County District Attorney's Office and the California Department of Insurance, 98 million was fraudulently billed, resulting in 12.4 million being paid by 18 insurance companies allegedly defrauded in this scheme. But that was no easy feat until smartC was introduced as one of the tools to help the prosecution. Once the data was accessible, the software was able to quickly analyze the content and ultimately help the district attorney file charges against those individuals who filed false workers compensation claims. Working closely with Infinilytics, a team of claims professionals, data scientists, and law enforcement professionals with a background in insurance investigations and special investigation unit (SIU) protocols, the DA successfully won their case after evidence was presented to the grand jury over a six-week period.