Sir Tim Berners-Lee, the man credited with inventing the world wide web, has given a series of interviews in which he has criticised how the internet has developed, condemned how advertising has evolved and warned of the risks that global connectivity poses to users' privacy. In an interview with The Guardian, Sir Tim said that the Trump administration's decision to allow internet service providers to sign away their customers' privacy and sell users' browsing habits is "disgusting" and "appalling". The problem with the internet, he said, is that it can be "ridiculously revealing". "You have the right to go to a doctor in privacy where it's just between you and the doctor. And similarly, you have to be able to go to the web."

The present wave of automation, driven by artificial intelligence (AI) – the development of computer systems able to perform tasks normally requiring human intelligence – is creating a gap between current legislation and new laws necessary for an emerging workplace reality, states a report published today by the International Bar Association Global Employment Institute (IBA GEI). Gerlind Wisskirchen, IBA GEI Vice Chair for Multinationals and coordinator of the report, commented: 'Certainly, technological revolution is not new, but in past times it has been gradual. What is new about the present revolution is the alacrity with which change is occurring, and the broadness of impact being brought about by AI and robotics. Jobs at all levels in society presently undertaken by humans are at risk of being reassigned to robots or AI, and the legislation once in place to protect the rights of human workers may be no longer fit for purpose, in some cases.' She added:'The AI phenomenon is on an exponential curve, while legislation is doing its best on an incremental basis.

Lecture 1 introduces the concept of Natural Language Processing (NLP) and the problems NLP faces today. The concept of representing words as numeric vectors is then introduced, and popular approaches to designing word vectors are discussed. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/

Developing training programmes for a large group of sales or technical or services personnel is a challenging task as the programme is meant for a diverse group, and has to be engaging and meaningful for the participants. The programmes are mostly delivered at multiple locations, they have to be updated from time to time and at times, also require to be culturally sensitive to remain relevant as well as contemporary. Effective assessment strategy is also important to ensure the programmes meet the stated business objectives. In the digital era, there is a plethora of content available on the internet. A lot of it is free of cost via options such as MOOCs, Course Era, You Tube and others.

A recent survey compiled by MIT Technology Review and Google Cloud suggests that machine learning (ML) is being adopted by businesses at a rapid pace. According to data collected, 60 percent of respondents indicated they have already implemented ML strategies, with almost a third attesting they were at the "mature stage" of those efforts. The survey, which was conducted in 2016, gathered responses from 375 businesses of all sizes. Companies ranged from one-person shops to those with more than 3,000 employees. About half of the companies surveyed employed less than 50 people.

Fast-learning bees die sooner than their slower working co-workers, research has found. These fast-learning bees collected fewer resources for the colony than their less intelligent counterparts overall. The researchers suggest that this may be because the energy that intelligence requires takes away from limited resources, leaving smart bees with less energy to look for food than their less-intelligent co-workers. Dr Nigel Raine, a co-author of the study and professor in the school of environmental sciences at the University of Guelph, Canada, said: 'Ultimately, the results revealed that fast-learning bumblebees collected fewer resources for the colony over their foraging career. 'These findings provide the first evidence of a learning-associated cost in the wild.' 'Our results are surprising because we typically associate enhanced learning performance and cognitive ability with improved fitness, because it is considered beneficial to the survival of an individual or group,' said co-author Dr. Lisa Evans, with Plant and Food Research in New Zealand.

Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms. We designed several experiments, with the candidate algorithms being the same network structure with different model checkpoints within a single training process, networks with same structure but trained multiple times stochastically, and networks with different structure. In addition, we further studied the over-confidence phenomenon of the neural networks, as well as its impact on the ensemble methods. Across all of our experiments, the Super Learner achieved best performance among all the ensemble methods in this study.

Single-core computational performance relentlessly improved for decades, but recently that progress has begun to slow [7]. As a result, alternative computational architectures have sprung up including multi-core processors [8], graphic processing units [9], neuromorphic computing [10], and application-specific integrated circuits to name a few. Here we explore the use of another new architecture: quantum annealing [11]. In particular, we utilize the form of quantum annealing realized with D-Wave hardware [1, 3]. We focus on a machine learning problem based on matrix factorizations, and describe an algorithm for computing these matrix factorizations that leverages D-Wave hardware. We apply the algorithm to learn features in a set of facial images. There is an ongoing back-and-forth regarding whether or not D-Wave's hardware provides performance benefits over classical single-core computing [12, 13, 14, 15].

Comment on Biologically inspired protection of deep networks from adversarial attacks Wieland Brendel 1,3 and Matthias Bethge 1,2,3,4 1 Werner Reichardt Center for Integrative Neuroscience, University of T ubingen, Germany 2 Max Planck Institute for Biological Cybernetics, T ubingen, Germany 3 Bernstein Center for Computational Neuroscience, T ubingen, Germany 4 Institute for Theoretical Physics, University of T ubingen, Germany Dated: October 29, 2017 A recent paper [1] suggests that Deep Neural Networks can be protected from gradient-based adversarial perturbations by driving the network activations into a highly saturated regime. Here we analyse such saturated networks and show that the attacks fail due to numerical limitations in the gradient computations. A simple stabilisation of the gradient estimates enables successful and efficient attacks. Thus, it has yet to be shown that the robustness observed in [1] is not simply due to numerical limitations. Evaluating the robustness of neural networks is difficult.

Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge. In particular, one may infer from them relations between scientific concepts, such as synonyms and hyponyms. Artificial neural networks have been recently explored for relation extraction. In this work, we continue this line of work and present a system based on a convolutional neural network to extract relations. Our model ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C).