Rackspace sells for 4.3 billion to Apollo Global Cloud Integration Market (2016-2021) - Key Players are IBM, MuleSoft & NEC Corporation ... Cloud player Rackspace goes private in 4.3B deal Rackspace jumps after Apollo Global buys it in 4.3 billion deal

Video Friday is your weekly selection of awesome robotics videos, collected by your thick-necked Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply.

Canadian financial services firm Manulife Financial Corp.'s plan to bring machine-learning to the financial services industry continues apace, with a Boston-based startup now being added to the artificial intelligence (AI)-based project the company announced earlier this month. In addition to Silicon Valley-based Nervana Systems, Manulife's Toronto-based Lab of Forward Thinking (LOFT) will be collaborating with deep learning platform developer Indico Data Solutions in its quest to develop a new application that could help portfolio managers analyze the high volume of online information, financial news, emails, and documents they receive when researching investments. In an Aug. 26 statement, Manulife executive vice president and CIO Greg Framke said the partnership would accelerate his company's efforts to improve its analysts, portfolio managers, and researchers' decision-making capabilities. In the same statement, Indico CEO and co-founder Slater Victoroff said that despite Google and Facebook's embrace of deep learning, which involves using multiple algorithms to build a model for high-level data analysis, the majority of enterprises have barely scratched the surface of what they can do with the technology, and that he was pleased by Manulife's initiative. Building on the company's existing project, Manulife's LOFT team – a division that researches ways the company can incorporate technology and unconventional business processes into its products and services – will now be using Indico's platform to develop an AI- and deep learning-based tool that will analyze unstructured financial data, including news articles, analyst reports, and company releases, and present advisors and other professionals with appropriate recommendations.

Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs.

We all know that hiring managers can be racist when choosing the "right" (read: white) candidate for a job, but what about computers? If you have a name like Ebony or Jamal at the top of your resume, new research suggests that some algorithms will make less "pleasant" associations with your moniker than if you are named Emily or Matt. Machines are increasingly being used to make all kinds of important decisions, like who gets what kind of health insurance coverage, or which convicts are most likely to reoffend. The idea here is that computers, unlike people, can't be racist, but we're increasingly learning that they do in fact take after their makers. As just one example, ProPublica reported in May that an algorithm used by officials in Florida systematically rated white offenders as being lower risk of committing a future crime than blacks.

Data Natives Speaker Dr. Stefan Kühn is Lead Data Scientist at codecentric. Together with his team he is developing robust, fast and intelligent algorithms and is applying modern machine learning methods for analyzing large datasets. Before codecentric he was a researcher in the Scientific Computing Group at the Max Planck Institute for Mathematics in the Sciences Leipzig with a focus on Tensor Approximation and Higher-Order Singular Value Decomposition. He earned a Diploma in Applied Mathematics, with a focus on Mathematical Optimization, at the University of Hamburg. He went on to get PhD in Applied Mathematics with a focus on Numerics, Tensor Approximation and Higher-Order Singular Value Decomposition, at the Max Planck Insitute for Mathematics in the Sciences Leipzig in 2012. High-dimensional data is very hard to analyze and understand.

Summary: We show in illustrations how the machine learning'training' process happens in Tensorflow, and tie them back to the Tensorflow code. This paves the way for discussing'training' variations, namely stochastic/mini-batch/batch, and adaptive learning rate gradient descent. The'training' variation code snippets presented serve to reinforce the understanding of the role of Tensorflow placeholders. In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/). In machine learning (ML) literature, we come across the term'training' very often, let us literally look at what that means in TF.

If you're fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes were balanced, meaning there were approximately the same number of examples of each class. Instructors usually employ cleaned up datasets so as to concentrate on teaching specific algorithms or techniques without getting distracted by other issues. Usually you're shown examples like the figure below in two dimensions, with points representing examples and different colors (or shapes) of the points representing the class: The goal of a classification algorithm is to attempt to learn a separator (classifier) that can distinguish the two. But when you start looking at real, uncleaned data one of the first things you notice is that it's a lot noisier and imbalanced.

Manulife's Lab of Forward Thinking is partnering with indico data solutions, a Boston-based company specializing in deep Learning, to better analyze unstructured financial data, the insurer announced today. Indico's platform will enable the Canadian insurer to evaluate data from news articles and analyst reports and recommend investment decisions to portfolio managers. Deciphering natural language and extracting insights is one of indico's platform's core strengths, according to the companies. "Indico will help us accelerate our use of deep learning to improve the decision-making capabilities of our analysts, portfolio managers and researchers," said Greg Framke, executive vice president and chief information officer, Manulife, in a statement. "By introducing new capabilities that we know will add to our user experience and overall impact, we will improve the customer experience."

A few weeks ago Lydia Dishman wrote a fantastic piece characterizing the top tech companies as defined by the "quality" of their talent using the Paysa CompanyRank algorithm and how these companies change in rank over time. CompanyRank is an Expectation-Maximization algorithm applied over space and time to quantifying the network-wide flux of tech workers to/fro/retained at each technical company at each month in time…a distant cousin to Google's PageRank algorithm. Another example of emergent structure learned from perceived independent chaos of a large, sparsely-connected complex system. Figure 1 depicts the Paysa CompanyRank time-series of Uber, Facebook, Google and Zynga over time. The top slot, e.g., most talent dense company gets rank of #1 and decays down to an arbitrary low rank (high number)…think 1 is better than 2 is better than 3 and so forth.