This is an R implementation of Leo Breiman's and Adele Cutler's Random Forest algorithms for classification and regression, with optimizations for performance and for handling of data sets that are too large to be processed in memory. Forests can be built in parallel at two levels. First, trees can be grown in parallel on a single machine using foreach. Second, multiple forests can be built in parallel on multiple machines, then merged into one. For large data sets, disk-based big.matrix's may be used for storing data and intermediate computations, to prevent excessive virtual memory swapping by the operating system.

Succeeding in today's fast-paced business economy requires companies to harness data quickly and at scale. As the volume, velocity, and variety of data increase, it's becoming necessary to use machine learning and artificial intelligence (AI) to sift through all the incoming information, make sense of it, and accurately predict future business direction. It takes the right expertise, the right tools, and the right data to achieve the promise of machine learning. Even with all of those factors in place, it's still easy to get it wrong. "Machine learning gives us a very powerful set of techniques for making predictions, but it can also lead to disastrous results if you don't understand what your machine learning algorithm is doing," said Spencer Greenberg, a mathematician and founder of decision-making website ClearerThinking.org, in an interview.

Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip -- which belongs to the family of high-performance Xeon Phi processors -- gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.

At Harvard Business School (HBS), MBA students are pondering a future when robots rule the road. The pioneers of the driverless car movement -- such as Google and Tesla -- are mapping the MBAs a future in which artificial intelligence and robotics will likely impact the entire job market and global economy. David Yoffie, professor of international business administration at HBS, believes such disruptive technologies are now an "essential" part of the b-school landscape. "What I'm trying to teach students is: What can these technologies deliver? And what are the challenges and opportunities for a company that does AI?" he says. David's offered his MBAs two cases on artificial intelligence (or AI) and deep learning, and reckons that many of his colleagues at HBS are bringing robots into the curriculum too: "It's a capability that MBAs need to know about," he says.

David Cronenberg's 1999 feature film EXISTENZ with Jennifer Jason Leigh and Jude Law unfolds in multiple layers of reality. It takes place in a future where games are made from biological materials powered by peoples' bodies. The game consoles have fleshy appendages and buttons, which require stroking. They attach to the body via an umbilical cord. Dr. Duncan Buell is a computer scientist at the University of South Carolina who has loved science fiction since he was a child.

Man vs. Machine: 10 years ago, I would never have guessed that I would be writing about this topic with such serious concern. Yet, some people are predicting that machine learning technology will produce a jobless future for certain professions, including actuaries. And with news headlines like "Google's AlphaGo AI beats Lee Sedol again to win Go series 4-1" and "Meet Ross, the IBM Watson-Powered Lawyer"; you have to wonder what the future holds for the actuarial profession and just how much computers can take over the human role within actuarial departments. With the tremendous advancements in machine learning, many financial institutions are already making extensive use of these technologies to do all types of (traditional and new) actuarial work including competitor rating reconstruction and intelligent claims handling. Let's take a moment to see what people are thinking about when it comes to the impact of machine learning on the development of their actuarial teams: Yes, give it time, machine learning will take over!

There is so much data today that no one can possibly process it all. While a significant amount of companies have data that can reveal customer satisfaction and attrition, many don't know how to use or even find it. There is hope from a field called machine learning, and the next big wave in this field is all about democratizing the technology from a few to many. Open source tools are reshaping the potential of data management with machine learning. Learn now about ways in-memory compute engines can unify developers, data scientists and data engineers in a user-friendly format.

Machine intelligence capable of learning complex procedural behavior, inducing (latent) programs, and reasoning with these programs is a key to solving artificial intelligence. The problems of learning procedural behavior and program induction have been studied from different perspectives in many computer science fields such as program synthesis [1], probabilistic programming [2], inductive logic programming [3], reinforcement learning [4], and recently in deep learning. However, despite the common goal, there seems to be little communication and collaboration between the different fields focused on this problem. Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. This has led to the development of neural networks with differentiable data structures such as Neural Turing Machines [5], Memory Networks [6], Neural Stacks [7, 8], and Hierarchical Attentive Memory [11], among others. Simultaneously, neural program induction models like Neural Program-Interpreters [9] and the Neural Programmer [10] have created much excitement in the field, promising induction of algorithmic behavior, and enabling inclusion of programming languages in the processes of execution and induction, while remaining trainable end-to-end. Trainable program induction models have the potential to make a substantial impact on many problems involving long-term memory, reasoning, and procedural execution, such as question answering, dialog, and robotics. The aim of the NAMPI workshop is to bring together researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, inductive programming and reinforcement learning, to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines. Through this workshop we look to identify common challenges, exchange ideas and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.

Tasks that cannot be substituted by automation are generally complemented by it. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. There have been periodic warnings in the last two centuries that automation and new technology would wipe out large numbers of middle-class jobs. In the early 19th century, for instance, a group of English textile artisans, known as Luddites, famously protested the automation of textile production by seeking to destroy some of the machines. A century later, concern rose again over "The Automation Jobless," as they were called in the title of a Time magazine story of February 24, 1961.

IDF16 Intel is working on a powerful Xeon Phi processor for servers and workstations that is "optimized" for artificial-intelligence software – and it's codenamed Knights Mill. Chipzilla's data center group boss Diane Bryant flashed up this slide during this morning's Intel Developer Forum keynote in San Francisco: The chip is geared towards deep-learning applications, and is expected to be available in 2017, we're told. It will use RAM stacked into the top of its die, feature many, many cores, and have a focus on high-performance floating-point calculations – all of which should help it perform the operations necessary for high-throughput machine learning. Crucially, the Mill is not an accelerator or coprocessor: it can run x86 code and can boot and run operating systems and apps without the need of a host CPU. This sets it apart from rival chips, like Nvidia's GPUs, which need a host processor to direct them.