A filmmaker has captured heartbreaking images of albatrosses killed by plastic on a remote Pacific island. Decaying birds are shown with their stomachs full of plastic waste while young chicks are pictured surroundeded by debris. One picture shows a young bird with its neck folded into its back while seemingly still living in the nest where it was born. The snaps shine a light on the plastic pollution that has killed tens of thousands of birds on the island, which sits 2,000 miles (3,200km) from the nearest continent. American photographer and filmmaker Chris Jordan said he hopes the shocking pictures force people to emotionally engage with the problems plastic is causing on a global scale.

Video: Who should be liable for robot misbehavior? In early 2017, President Barack Obama's top economic and science advisers wrote a report entitled, Artificial Intelligence, Automation, and the Economy. The report is a cleared-eyed collation of current data and predictions pertaining to the accelerating pace of adoption of automation and AI technologies starting from around 2010. The full report is worth a look, but the top line findings are probably familiar. The analysts found that the day when robots will replace all workers is still remote, and any timeline for that happening is speculative at best.

A team of British engineers are building lifelike robots that can dance, talk in several languages and even scare London pub-goers. Engineering Arts is developing the automatons in a sleepy Cornish seaside town. Photos taken at the firm's factory reveal the inner-workings of how the company combines prosthetics, robotics and artistry. A team of British engineers are building lifelike robots that can dance, talk in several languages and even scare London pub-goers. This robot, described as'indistinguishable from humans', was created as part of a stunt to promote TV Series Westworld Founded in 2004, the company operating from an industrial unit in Penryn, near Falmouth, is a world leader in life sized commercially available humanoid robots.

Fintech firms in the Middle East are increasing, luring investors and pushing companies to digitize and innovate their operations. This insurance company has officially launched in the UAE after securing about $18 million in Series A funding. The investment came from its corporate founders, who are the shareholders behind Barents, an A-rated international Reinsurance group, and Choueiri Group, a leading media, and marketing group of the region, according to MENAbytes, a platform covering tech and startups from MENA. READ: Ready to leave: UAE expats driven away by high living expenses? Between all the players in insuretech, Aqeed thinks it's one of a kind.

Artificial intelligence startup Avaamo has raised $14.2 million (about Rs 95 crore) in a series-A round of funding led by Intel Capital. Ericsson Ventures, Mahindra Partners, Wipro Ventures and WI Harper also participated in the round. The Los Altos, California and Bengaluru-based company, which specialises in conversational interfaces to solve specific enterprise problems, plans to use the funding to expand its sales and marketing. With this fundraising, the company has, so far, raised $23.5 million. "We felt we should get our ecosystem to work with us," said Avaamo CEO Ram Menon.

We study an interacting particle system in $\mathbf{R}^d$ motivated by Stein variational gradient descent [Q. Liu and D. Wang, NIPS 2016], a deterministic algorithm for sampling from a given probability density with unknown normalization. We prove that in the large particle limit the empirical measure converges to a solution of a non-local and nonlinear PDE. We also prove global well-posedness and uniqueness of the solution to the limiting PDE. Finally, we prove that the solution to the PDE converges to the unique invariant solution in large time limit.

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.

In this paper, we propose a novel approach based on cost-sensitive ensemble weighted extreme learning machine; we call this approach AE1-WELM. We apply this approach to text classification. AE1-WELM is an algorithm including balanced and imbalanced multiclassification for text classification. Weighted ELM assigning the different weights to the different samples improves the classification accuracy to a certain extent, but weighted ELM considers the differences between samples in the different categories only and ignores the differences between samples within the same categories. We measure the importance of the documents by the sample information entropy, and generate cost-sensitive matrix and factor based on the document importance, then embed the cost-sensitive weighted ELM into the AdaBoost.M1 framework seamlessly. Vector space model(VSM) text representation produces the high dimensions and sparse features which increase the burden of ELM. To overcome this problem, we develop a text classification framework combining the word vector and AE1-WELM. The experimental results show that our method provides an accurate, reliable and effective solution for text classification.

In traditional ELM and its improved versions suffer from the problems of outliers or noises due to overfitting and imbalance due to distribution. We propose a novel hybrid adaptive fuzzy ELM(HA-FELM), which introduces a fuzzy membership function to the traditional ELM method to deal with the above problems. We define the fuzzy membership function not only basing on the distance between each sample and the center of the class but also the density among samples which based on the quantum harmonic oscillator model. The proposed fuzzy membership function overcomes the shortcoming of the traditional fuzzy membership function and could make itself adjusted according to the specific distribution of different samples adaptively. Experiments show the proposed HA-FELM can produce better performance than SVM, ELM, and RELM in text classification.