Lush combines three languages in one: a very simple to use, loosely-typed interpreted language, a strongly-typed compiled language with the same syntax, and the C language, which can be freely mixed with the other languages within a single source file, and even within a single function. Lush has a library of over 14,000 functions and classes, some of which are simple interfaces to popular libraries: vector/matrix/tensor algebra, linear algebra (LAPACK, BLAS), numerical function (GSL), 2D and 3D graphics (X, SDL, OpenGL, OpenRM, PostScipt), image processing, computer vision (OpenCV), machine learning (gblearning, Torch), regular expressions, audio processing (ALSA), and video grabbing (Video4linux). If you do research and development in signal processing, image processing, machine learning, computer vision, bio-informatics, data mining, statistics, or artificial intelligence, and feel limited by Matlab and other existing tools, Lush is for you. If you want a simple environment to experiment with graphics, video, and sound, Lush is for you. Lush is Free Software (GPL) and runs under GNU/Linux, Solaris, and Irix.

There are few things hotter in tech right now than artificial intelligence. You'll hear people with titles like "chief experience officer" and "thinkfluence concierge" talk about "neural networks" and "machine learning" and "natural language processing." The idea is, you can talk to your computer as if it were a person. Eventually, the idea goes, you can hold full conversations with an AI chat bot, asking it to answer complex questions and undertake complicated tasks. But nearly two decades ago, our current era of AI overload started with two simple sentences.

Cybersecurity companies are increasingly looking to artificial intelligence tech to improve defense systems and create the next generation of cyber protection. These trends are driving demand for automated cybersecurity, i.e. AI-driven software that can use machine learning and other technologies to differentiate benign or harmful activity on a system or network. We used CB Insights data to understand when artificial intelligence began to be linked to cybersecurity, and we identify 13 companies to watch at the intersection of AI and security. To inform our analysis in the charts below, we used the Trends tool on the CB Insights Platform, which analyzes millions of media articles to track technology trends.

Ford reported a sharp drop in October sales in the U.S. Unit sales declined 12% as passenger car sales slumped. Ford postponed its release on Tuesday due to fire delays at its headquarters in Michigan. Ford is working on a new technology that uses an array of sensors that monitors activity going on behind a car that is backing up and stops it if the driver doesn't notice a pedestrian or another car. SAN FRANCISCO -- Ford Motor is working on a suite of new driver-assist safety features for its production cars that stop short of offering full autonomy. Among the technologies being developed at the automaker's Research and Innovation Center in Aachen, Germany, include camera- and laser-enabled systems that can take over the steering wheel in an emergency to avoid high-speed collisions, as well as mapping-triggered dash alerts that warn drivers they're traveling down a one-way road.

This post is by Max Kaznady, Data Scientist, Miguel Fierro, Data Scientist, Richin Jain, Solution Architect, T. J. Hazen, Principal Data Scientist Manager, and Tao Wu, Principal Data Scientist Manager, all at Microsoft. Today's businesses collect vast volumes of images, video, text and other types of data – data which can provide tremendous business value if efficiently processed at scale and using sophisticated machine learning algorithms. Example applications include real-time labeling and monitoring of sentiment in tweets, itemization of equipment and materials at construction sites through video surveillance, and real-time fraud detection in the financial domain, to name a few. In a previous blog post, we described how to set up DNNs in the cloud using a high performance GPU VM and MXNet. In this sequel, we outline a pipeline process for training and scoring with DNNs in a large-scale production environment.

This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond). Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. This is a summary (with links) to an article series that's intended to be a comprehensive, in-depth guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques in the field.

Behavioral biometrics that uses machine learning is behind new features being added to IBM's Trusteer Pinpoint Detect platform, which financial institutions use to head off crooks who may have stolen the username and password of legitimate account holders. The new feature looks for anomalies between legitimate users' normal mouse gestures and those of the current user, and over time refines the accuracy of its analysis, says Brooke Satti Charles, Financial Crime Prevention Strategist for IBM Security. That analysis creates a risk score that banks can use to decide whether an ongoing transaction is fraudulent and trigger an alert. The institutions have to decide what to do about the alerts, but they could cut off the transaction or require further ID before the customer is allowed to continue, she says. The platform already checks the geolocation and IP address of customers as they login in order to detect fraudulent use, and behavior biometrics is an enhancement.

What has changed in Machine Learning in the past 25 years? You may not care about this question. You may even not realize that Machine Learning as a technical and scientific field is older than 25 years. But I do care about this question. I care because I got a PhD in Machine Learning in 1990.

The Global Credit Card industry is rapidly changing and the participants are increasingly facing new challenges with exploding volumes, regulatory pressures and new entrants competing for the market share. The industry has responded to these challenges by looking at avenues to cut costs, increase efficiencies and provide better, safer products and services to attract new and retain existing customers. To help our customers address this challenge, Hortonworks and Capgemini are collaborating to create a suite of Credit Card Analytics solutions designed to enhance decision making by leveraging all of the data available including customer data, transactions, third party data, open data, government data, location data, social data, etc. The first solution in this suite of solutions is focused on Credit Card fraud. Fraudulent behaviours evolve and so must the solutions that are used to detect them.

Luke Dormehl is a tech writer and journalist, he recently had his latest book published, Thinking Machines: The Inside Story of Artificial Intelligence. In light of all the scary stories of how AI will disrupt the world of work and life as we know it, we asked Luke to put a few things into perspective and ease our worries... The classic, sci-fi dystopia often begins with self-aware computers taking the world by force. Today, that view looks more and more like the Hollywood fantasy that it is. The real imminent threat of automation is what it's going to do to employment.