In statistics, ordinal regression (also called "ordinal classification") is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. It can be considered an intermediate problem in between (metric) regression and classification.[1] Ordinal regression turns up often in the social sciences, for example in the modeling of human levels of preference (on a scale from, say, 1–5 for "very poor" through "excellent"), as well as in information retrieval. In machine learning, ordinal regression may also be called ranking learning.[2][a] Ordinal regression can be performed using a generalized linear model (GLM) that fits both a coefficient vector and a set of thresholds to a dataset.

Facebook CEO, Mark Zuckerberg says that within five to 10 years, artificial intelligence could advance to the point where computers can see, hear and understand language better than people. Zuckerberg stated this yesterday during the company's earnings call for the first quarter of 2016. Zuckerberg has already been focussing on AI through his company which already has research groups dedicated to advancing the company's capabilities in artificial intelligence, machine learning, computer vision and natural language processing and speech. Earlier this month, for example, it introduced an iOS feature called "automatic alternative text" that uses object recognition technology to provide spoken descriptions of Facebook photos to people who are visually impaired. Facebook has also unveiled new bot and chatbot technology as part of its Messenger Platform.

NEW ORLEANS--The problem with robots on the battlefield today, according to Marine Corps Colonel Jim "Jinx" Jenkins, is that they still have to be driven by humans. That's why the Marine Corps and the Department of Defense are researching ways for robots to act more like teammates on the battlefield than just another piece of hardware. Jenkins, who serves as director of science and technology at the Marine Corps' Warfighting Lab at Quantico, Virginia, said in a presentation at the Association for Unmanned Systems International's XPONENTIAL conference that while robots such as those used for explosive ordnance disposal and other roles on the battlefield take soldiers and Marines out of some dangerous situations, they take their operators out of the fight. "A marine is driving, so we haven't improved our manpower situation, and sometimes it costs more manpower," he noted, since operators have to pay such close attention to what they're doing with the robot that they need someone watching their back. "We need to move toward autonomy" for robots and other uncrewed systems, he said.

DATAVERSITY Education, LLC announced the agenda and opened registration for the company's newest online conference, Smart Data Online (SDO). The event will be held online at smartdataweek.com on July 13th, 2016 from 8:00 am to 2:20 pm Pacific Time. Registration is free and attendees will receive access to the on demand recordings, slides, and materials following the event. SDO is the newest event to be added to DATAVERSITY's educational programs, and is designed to provide guidance on executing and implementing a successful data strategy using new technologies in the fields of machine learning, cognitive computing, and artificial intelligence. Throughout the day on July 13th there will be six, 40-minute presentations, each followed by a 10 minute "Q & A" discussion with the presenter(s).

Watch our founder and CEO, Parsa Ghaffari (@parsaghaffari) and Kevin Koidl (@koidl), a Research Fellow at Trinity College Dublin's Department of Computer Science and the ADAPT research centre, talk Computer intelligence at a recent talk they gave at Science Gallery, Dublin. This interactive discussion, takes you from general AI, right through to the modern day applications of narrow AI paying, particular attention to a real life example, the Bigfoot App, which was created as part of the Lifelogging exhibition, currently running at Science Gallery, Dublin.

In this post I will explain the Support Vector Machines method. First, what is a Support Vector? It's the margins of an Hyperplane (linear or not) that divides two or more groups, whose margin (distance between two groups) must be maximized: The Hyperplane borders are H1 and H2 and the distance to be maximized is d1 and d2. First, let's do a regular Linear Regression with black dots (X1) and white dots (X2). You can easily see that the estimated Y (red line) barely touches some of the blue dots (real Y).

For all of Amazon's grand plans regarding delivery drones, it still needs to figure out concepts we take for granted with traditional courier methods. Namely, figuring out how to drop off your latest order without destroying anything (including the UAV itself) during transit and landing. That's where advanced computer vision comes in from Jeff Bezos' new team of Austria-based engineers, according to The Verge. The group invented methods for reconstructing geometry from images and contextually recognizing environmental objects, giving the drones the ability to differentiate between, say, a swimming pool and your back patio. Both are flat surfaces, but one won't leave your PlayStation VR headset waterlogged after drop-off.

If you're also single and looking for love, then Maple Match – a new matchmaking website that promises to "Make dating great again" – may be the catch-all solution you've been waiting for. According to its website, Maple Match aims to make it "easy for Americans to find the ideal Canadian partner to save them from the unfathomable horror of a Trump presidency." "After more than 35,000 hits and more than 4,500 signups in just four days, we are confident that Maple Match will fulfill a clear need in the dating space," site founder and CEO Joe Goldman told Tech Times. He added that the site aims to be operational "as soon as possible." Even Canadians – who are known for being unflinchingly polite – have been vocal about their dislike for Donald Trump, though perhaps that dislike is more out of concern than anything else.

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process. They developed a framework that uses uncertainties to iteratively guide the next experiments to be performed in search of a shape-memory alloy with very low thermal hysteresis (or dissipation).

I frequently ask young people, particularly undergraduates, what they plan to do with their future. The responses are typically vague and void of direction. Most responses involve waiting for someone else to provide the guidance. You do not have to wait. You can get started today.