Kepware Technologies, a software development business focused on industrial connectivity, has donated 10,000 to Acton Elementary School, in Acton, Maine, as part of its third annual school grant contest. With its headquarters in Portland, Kepware is committed to the advancement of Science, Technology, Engineering, and Mathematics (STEM) education and workforce development in Maine and abroad. The donation will enable Acton Elementary School students to hone mechanical and computer skills essential for building and programming autonomous robots. "The solution to Maine's shortage of engineering talent starts with education," said Tony Paine, Platform President, Kepware. "Local programs and institutions aimed at nurturing STEM curiosity can help encourage Maine's youth to explore rewarding careers in science and engineering. We're honored to be able to assist in these efforts by bringing information and technology into the lives of students."

Deep learning, a new and growing area of machine learning, is widely regarded as an important step forward on the path toward true artificial intelligence. Tech firms such as Facebook and Google, as well as companies like Bloomberg (which focuses on financial technology and information), are already beginning to incorporate the technology into their product development. And while the exact definition of deep learning is still somewhat fluid, opportunities are growing for experts who can apply the technology to everything from speech recognition to bioinformatics, drug discovery and equities trading. The global analytics firm SAS says deep learning trains computers to perform human-like tasks such as recognizing speech, identifying images or making predictions. "Deep learning can break patterns into sub-components and build models very accurately," Wayne Thompson, the chief data scientist of SAS Data Science Technologies in Cary, N.C., explained.

Danaher/Institute for Ethics and Emerging TechnologiesAlgorithms are everywhere, and in most ways they make our lives better. In the simplest terms, algorithms are procedures or formulas aimed at solving problems. Implemented on computers, they sift through big databases to reveal compatible lovers, products that please, faster commutes, news of interest, stocks to buy, and answers to queries. Dud dates or boring book recommendations are no big deal. But John Danaher, a lecturer in the law school at the National University of Ireland, warns that algorithmic decision-making takes on a very different character when it guides government monitoring and enforcement efforts.

Lauded as the world's first AI lawyer the story of "ROSS" began with a divorce When Jimoh Ovbiagele was 10 years old, his parents decided to separate. His mother started seeking out divorce lawyers, but was quickly halted by the astronomical hourly rates. "As a single mother with two very young kids, she couldn't pay for even a couple hours of this this divorce lawyer's time," says Ovbiagele. Years later, law seemed like a natural path for Ovbiagele – a way to help ensure others would not have to go through what his mother did--but while the University of Texas computer science major considered applying to law school in 2011, he was turned off by the amount of time he'd be expected to devote to research, rather than the practice, in the profession. The seed, however, had been planted.

Machine learning is the science of discovering statistical dependencies in data, and the use of those dependencies to perform predictions. During the last decade, machine learning has made spectacular progress, surpassing human performance in complex tasks such as object recognition, car driving, and computer gaming. However, the central role of prediction in machine learning avoids progress towards general-purpose artificial intelligence. As one way forward, we argue that causal inference is a fundamental component of human intelligence, yet ignored by learning algorithms. Causal inference is the problem of uncovering the cause-effect relationships between the variables of a data generating system. Causal structures provide understanding about how these systems behave under changing, unseen environments. In turn, knowledge about these causal dynamics allows to answer "what if" questions, describing the potential responses of the system under hypothetical manipulations and interventions. Thus, understanding cause and effect is one step from machine learning towards machine reasoning and machine intelligence. But, currently available causal inference algorithms operate in specific regimes, and rely on assumptions that are difficult to verify in practice. This thesis advances the art of causal inference in three different ways. First, we develop a framework for the study of statistical dependence based on copulas and random features. Second, we build on this framework to interpret the problem of causal inference as the task of distribution classification, yielding a family of novel causal inference algorithms. Third, we discover causal structures in convolutional neural network features using our algorithms. The algorithms presented in this thesis are scalable, exhibit strong theoretical guarantees, and achieve state-of-the-art performance in a variety of real-world benchmarks.

The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a measure of data similarity and was demonstrated effective in machine learning tasks. In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM kernel to linear kernel. We call this approach as ``GMM-GCWS''. In the machine learning literature, there is a popular algorithm which we call ``RBF-RFF''. That is, one can use the ``random Fourier features'' (RFF) to convert the ``radial basis function'' (RBF) kernel to linear kernel. It was empirically shown in (Li, 2016) that RBF-RFF typically requires substantially more samples than GMM-GCWS in order to achieve comparable accuracies. The Nystrom method is a general tool for computing nonlinear kernels, which again converts nonlinear kernels into linear kernels. We apply the Nystrom method for approximating the GMM kernel, a strategy which we name as ``GMM-NYS''. In this study, our extensive experiments on a set of fairly large datasets confirm that GMM-NYS is also a strong competitor of RBF-RFF.

High-dimensional big data appears in many research fields such as image recognition, biology and collaborative filtering. Often, the exploration of such data by classic algorithms is encountered with difficulties due to `curse of dimensionality' phenomenon. Therefore, dimensionality reduction methods are applied to the data prior to its analysis. Many of these methods are based on principal components analysis, which is statistically driven, namely they map the data into a low-dimension subspace that preserves significant statistical properties of the high-dimensional data. As a consequence, such methods do not directly address the geometry of the data, reflected by the mutual distances between multidimensional data point. Thus, operations such as classification, anomaly detection or other machine learning tasks may be affected. This work provides a dictionary-based framework for geometrically driven data analysis that includes dimensionality reduction, out-of-sample extension and anomaly detection. It embeds high-dimensional data in a low-dimensional subspace. This embedding preserves the original high-dimensional geometry of the data up to a user-defined distortion rate. In addition, it identifies a subset of landmark data points that constitute a dictionary for the analyzed dataset. The dictionary enables to have a natural extension of the low-dimensional embedding to out-of-sample data points, which gives rise to a distortion-based criterion for anomaly detection. The suggested method is demonstrated on synthetic and real-world datasets and achieves good results for classification, anomaly detection and out-of-sample tasks.

Vivek Wadhwa has been named to the Carnegie Mellon University College of Engineering faculty as a distinguished fellow on its Silicon Valley campus, the Pittsburgh, Pa.-based university recently announced. In his role, Wadhwa will be teaching classes in exponential technologies, technology convergence and industry disruption, and the new rules of innovation. He will also be researching technologies and helping members of the Pittsburgh faculty connect with the Silicon Valley. "CMU is doing some of the most advanced research in areas such as robotics, artificial intelligence, Internet of Things, autonomous cars and almost every field of engineering and bioengineering," Wadhwa said in an emailed statement. "This will provide me direct access to the amazing faculty and enable me to help them make a much greater impact on the world."

Priyadharshini writes on Project Management, IT, Six Sigma, & e-Learning. With a penchant for writing and a passion for professional education & development, she is adept at penning educative articles. She was previously associated with Oxford University Press and Pearson Education, India.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.