This type of approach is extremely versatile, and allows manufacturing teams to visualize large design projects through miniature scale models, design and create small runs of custom parts and equipment for customers and prototype new products. As 3D printing speeds increase, Gartner predicts the 3D printing industry will be a $4.6 billion market by 2019. Until now, the primary application for 3D printing in discrete industries has been prototyping new parts and equipment, but there's significant room for expansion, especially in the efficient fabrication of spare parts. Most discrete manufacturers are already producing spare parts, but few have adopted tactical 3D printing as an update to their process. The lead time currently required to create many spare parts can be both long and expensive, so the only way to ensure these parts are available to the customer in a timely fashion is to create and store them in advance.

We need to focus on early child education, higher education, alternatives to traditional education, on workers whose jobs might be impacted by any form of automation and how they should be prepared for the next set of jobs that will become available,

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature.

This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture. This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture.

While document review used to be tedious work for lawyers, Kirk says they can now sift through gigabytes of data within days with the help of artificial intelligence. While document review used to be tedious work for lawyers, Kirk says they can now sift through gigabytes of data within days with the help of artificial intelligence. This is part of an occasional series: Is My Job Safe? These stories look at jobs that might be at risk because of technology and automation. Shannon Capone Kirk's first job as a young lawyer in the late '90s was "document review."

A report by Human Rights Watch and the Harvard Law School International Human Rights Clinic calls for humans to remain in control over all weapons systems at a time of rapid technological advances. It says that requiring humans to remain in control of critical functions during combat, including the selection of targets, saves lives and ensures that fighters comply with international law. 'Machines have long served as instruments of war, but historically humans have directed how they are used,' said Bonnie Docherty, senior arms division researcher at Human Rights Watch, in a statement. 'Now there is a real threat that humans would relinquish their control and delegate life-and-death decisions to machines.' Some have argued in favour of robots on the battlefield, saying their use could save lives.

Automatic differentiation (AD) is an essential primitive for machine learning programming systems. Tangent is a new library that performs AD using source code transformation (SCT) in Python. It takes numeric functions written in a syntactic subset of Python and NumPy as input, and generates new Python functions which calculate a derivative. This approach to automatic differentiation is different from existing packages popular in machine learning, such as TensorFlow and Autograd. Advantages are that Tangent generates gradient code in Python which is readable by the user, easy to understand and debug, and has no runtime overhead. Tangent also introduces abstractions for easily injecting logic into the generated gradient code, further improving usability.

Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has for instance been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.

We introduce a new sub-linear space data structure---the Weight-Median Sketch---that captures the most heavily weighted features in linear classifiers trained over data streams. This enables memory-limited execution of several statistical analyses over streams, including online feature selection, streaming data explanation, relative deltoid detection, and streaming estimation of pointwise mutual information. In contrast with related sketches that capture the most commonly occurring features (or items) in a data stream, the Weight-Median Sketch captures the features that are most discriminative of one stream (or class) compared to another. The Weight-Median sketch adopts the core data structure used in the Count-Sketch, but, instead of sketching counts, it captures sketched gradient updates to the model parameters. We provide a theoretical analysis of this approach that establishes recovery guarantees in the online learning setting, and demonstrate substantial empirical improvements in accuracy-memory trade-offs over alternatives, including count-based sketches and feature hashing.

The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs has focussed on the state estimation problem, i.e., computing a posterior of the latent state given the model. However, the key challenge in GPSSMs has not been satisfactorily addressed yet: system identification, i.e., learning the model. To address this challenge, we impose a structured Gaussian variational posterior distribution over the latent states, which is parameterised by a recognition model in the form of a bi-directional recurrent neural network. Inference with this structure allows us to recover a posterior smoothed over sequences of data. We provide a practical algorithm for efficiently computing a lower bound on the marginal likelihood using the reparameterisation trick. This further allows for the use of arbitrary kernels within the GPSSM. We demonstrate that the learnt GPSSM can efficiently generate plausible future trajectories of the identified system after only observing a small number of episodes from the true system.