Germany's Interior Minister says he wants to introduce facial recognition software at train stations and airports to help identify suspects following two attacks in the country last month. In a report published on Sunday in the German newspaper Bild am Sonntag, Thomas de Maiziere said internet software was able to determine whether persons shown in photographs were celebrities or politicians. "I would like to use this kind of facial recognition technology in video cameras at airports and train stations. Then, if a suspect appears and is recognised, it will show up in the system," he told the paper. Germany's Thomas de Maiziere takes aim at face veils He said a similar system was already being tested for unattended luggage, which the camera reports after a certain number of minutes.

XGBoost is a popular implementation of Gradient Boosting because of its speed and performance. Internally, XGBoost models represent all problems as a regression predictive modeling problem that only takes numerical values as input. If your data is in a different form, it must be prepared into the expected format. In this post you will discover how to prepare your data for using with gradient boosting with the XGBoost library in Python. Data Preparation for Gradient Boosting with XGBoost in Python Photo by Ed Dunens, some rights reserved.

"Speaker: Ben Hamner This tutorial will offer an introduction machine learning and how to apply it to a Kaggle competition. We will cover methodologies that have worked well across a diverse set of problems, and then work on a current Kaggle competition together using iPython notebook and scikit-learn. We will cover concepts including feature extraction, feature selection, model evaluation, and data visualization.

Introducing the world's first beer brewed by artificial intelligence We've created a premium beer that uses complex machine learning algorithms to improve itself from your feedback. After you've tried one of our four bottled conditioned beers, you can tell our A.I. what you think of it, via our online feedback system. This data is then used by our algorithm to brew the next batch. Because our A.I. is constantly reacting to user feedback, we can brew beer that matches what you want, more quickly than anyone else can. That means we get more data and you get a better, fresher beer.

An anonymous reader quotes a report from Gizmag: One of the most hotly contested bits of real estate today is one you can't see. As we move into an increasingly wireless-connected world, staking out a piece of the crowded electromagnetic spectrum becomes more important. DARPA is hoping to help solve this issue with its latest Grand Challenge, which calls for the use of machine-learning technologies to enable devices to share bandwidth. The Spectrum Collaboration Challenge (SC2) is based on the idea that wireless devices would work better if they cooperated with one another rather than fought for bandwidth. Since not all devices are active at all times, the agency says, it should be possible through the use of artificial intelligence machine-learning algorithms to allow them to figure out how to share the spectrum with a minimum of conflict.

Jibun Bank customers to take advantage of Alpaca's AI and Database technologies

There has been much written about the 80/20 rule and how it can be applied to different facets of our lives. At work, 20% of your time should be spent on passion projects. At home, ensure 20% of your time is spent doing the things that make you feel fulfilled. But what if we lived in a world where we could flip that equation, and instead spend 80% of our time on our passion, our creativity, and the things that drive our happiness? The advent of artificial intelligence, for all its undeniable promise, has brought about a lot of fear.

Artificial intelligence (AI) is slowly making a shift from the research and development stage to touching our lives wherever we go. With so many applications to choose from, it can get confusing as to what exactly artificial intelligence will be able to do over the next five to ten years. Here's a glimpse of the different areas in your life where AI can make a major difference. First of all, who are the companies at the forefront of this? The list is long, but you know these companies: IBM, Apple, Amazon, Facebook, Microsoft and Google are currently the biggest players in the artificial intelligence space.

Jibun Bank customers to take advantage of Alpaca's AI and Database technologies

Today's push behind machine learning and artificial intelligence is so powerful that it has led to some pretty high expectations for performance and deliverables. When it comes to business value and ROI, can it really live up to the claims? Evaluating the value of a full machine learning project requires moving past the hype and managing the realities of this evolving technology, one example at the time. We'll look at the realities of a pure machine learning approach through the lens of a typical enterprise use case. If we're to believe the past couple of years' worth of marketing hype, machine learning is a magic box, supported by an evolved approach, strengthened by the latest technology and most importantly, able to effortlessly produce results.