"Artificial intelligence" can be defined as the theory and development of computer systems able to perform tasks that normally require human intervention. Artificial intelligence (AI) is being used in new products and services across numerous industries and for a variety of policy-related purposes, raising questions about the resulting legal implications, including its effect on individual privacy. Aspects of AI related to privacy concerns are the ability of systems to make decisions and to learn by adjusting their code in response to inputs received over time, using large volumes of data. Following the European Commission's declaration on AI in April 2018, its High-Level Expert Group on Artificial Intelligence (AI HLEG) published Draft Ethics Guidelines for Trustworthy AI in December 2018. A consultation process regarding this working document concluded on February 1, 2019, and a revised draft of the document based on the comments that were received is expected to be delivered to the European Commission in April 2019.

Microsoft has said it turned down a request from law enforcement in California to use its facial recognition technology in police body cameras and cars, reports Reuters. Speaking at an event at Stanford University, Microsoft president Brad Smith said the company was concerned that the technology would disproportionately affect women and minorities. Past research has shown that because facial recognition technology is trained primarily on white and male faces, it has higher error rates for other individuals. "Anytime they pulled anyone over, they wanted to run a face scan," said Smith of the unnamed law enforcement agency. "We said this technology is not your answer."

Go player Lee Sedol (R) during the third game of the Google DeepMind Challenge Match against Google-developed supercomputer AlphaGo. Leading Australian artificial intelligence scientist Professor Toby Walsh is warning that we are "sleepwalking" into an AI future in which billions of machines and computers will be able to think. Professor Walsh, from the University of New South Wales, is calling for a national discussion about whether society needs to adopt clear boundaries and guidelines around how AI is developed and how it's used in our lives. In his book It's Alive: Artificial Intelligence From The Logic Piano to Killer Robots, he has highlighted key questions in a series of predictions that describe how our future could be far better or far worse because of AI. Here's how he thinks society might change by 2050 thanks to artificial intelligence.

Almost 20 years ago, the medical and scientific communities were overjoyed. With the Human Genome Project finished, there was an air of inevitability that the causes of some of the most common and destructive diseases would soon be pinpointed and eradicated. It'd be simple: one gene, one problem, one solution. We even heard Francis Collins, at the time, say "over the longer term, perhaps in another 15 or 20 years, you will see a complete transformation in therapeutic medicine." Unfortunately, it was never going to be that easy.

As advances in signature recognition have reached a new plateau of performance at around 2% error rate, it is interesting to investigate alternative approaches. The approach detailed in this paper looks at using Variational Auto-Encoders (VAEs) to learn a latent space representation of genuine signatures. This is then used to pass unlabelled signatures such that only the genuine ones will successfully be reconstructed by the VAE. This latent space representation and the reconstruction loss is subsequently used by random forest and kNN classifiers for prediction. Subsequently, VAE disentanglement and the possibility of posterior collapse are ascertained and analysed. The final results suggest that while this method performs less well than existing alternatives, further work may allow this to be used as part of an ensemble for future models.

Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we aim to optimize the sentence encoder with respect to the new corpus while maintaining its accuracy on the old corpora. To address this problem, we propose to initialize sentence encoders with the help of corpus-independent features, and then sequentially update sentence encoders using Boolean operations of conceptor matrices to learn corpus-dependent features. We evaluate our approach on semantic textual similarity tasks and show that our proposed sentence encoder can continually learn features from new corpora while retaining its competence on previously encountered corpora.

How to model and encode the semantics of human-written text and select the type of neural network to process it are not settled issues in sentiment analysis. Accuracy and transferability are critical issues in machine learning in general. These properties are closely related to the loss estimates for the trained model. I present a computationally-efficient and accurate feedforward neural network for sentiment prediction capable of maintaining low losses. When coupled with an effective semantics model of the text, it provides highly accurate models with low losses. Experimental results on representative benchmark datasets and comparisons to other methods show the advantages of the new approach.

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

Using large mini-batches when training generative adversarial networks (GANs) has been recently shown to significantly improve the quality of the generated samples. This can be seen as a simple but computationally expensive way of reducing the noise of the gradient estimates. In this paper, we investigate the effect of the noise in this context and show that it can prevent the convergence of standard stochastic game optimization methods, while their respective batch version converges. To address this issue, we propose a variance-reduced version of the stochastic extragradient algorithm (SVRE). We show experimentally that it performs similarly to a batch method, while being computationally cheaper, and show its theoretical convergence, improving upon the best rates proposed in the literature. Experiments on several datasets show that SVRE improves over baselines. Notably, SVRE is the first optimization method for GANs to our knowledge that can produce near state-of-the-art results without using adaptive step-size such as Adam.

Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus becomes important to early detect them. Many methods have been introduced to detect rumours using the content or the social context of news. However, most existing methods ignore or do not explore effectively the propagation pattern of news in social media, including the sequence of interactions of social media users with news across time. In this work, we propose a novel method for rumour detection based on deep learning. Our method leverages the propagation process of the news by learning the users' representation and the temporal interrelation of users' responses. Experiments conducted on Twitter and Weibo datasets demonstrate the state-of-the-art performance of the proposed method.