This list of readings about journalism and AI is based on research for the Polis report on AI and Journalism published in November 2018. We will update this list and welcome suggestions for further readings to: c.h.beckett@lse.ac.uk What is machine learning and why should I care? AI is going to save journalism – here's how Is AI and journalism a good mix? First in the world: Yle's smart news assistant Voitto ensures that you don't miss the news you want to read Can science writing be automated?

Car manufacturers know: There's a huge amount of interest in AI-driven cars. Many people would love to automate the task of driving, because they find it tedious or at times impossible. A competent AI driver would have lightning-fast reflexes, would never weave or drift in its lane, and would never drive aggressively. An AI driver would never get tired and could take the wheel for endless hours while we humans nap or party. While AI does need huge volumes of data to program and guide it, that shouldn't be a problem.

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. WikiGenderBias has sentences specifically curated to analyze gender bias in relation extraction systems. We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE. We also analyze how name anonymization, hard debiasing for word embeddings, and counterfactual data augmentation affect gender bias in predictions and performance.

Take all the help you can get. If parallel computing has a central tenet, that might be it. Some of the crazy-complex computations asked of today's hardware are so demanding that the compute burden must be borne by multiple processors, effectively "parallelizing" whatever task is being performed. Perhaps the most notable push toward parallelism happened around 2006, when tech hardware powerhouse Nvidia approached Wen-mei Hwu, a professor of electrical and computer engineering at the University of Illinois-Urbana Champaign. Nvidia was designing graphics processing units (GPUs) -- which, thanks to large numbers of threads and cores, had far higher memory bandwidth than the traditional central processing unit (CPUs) -- as a way to process huge numbers of pixels.

Are the Liberal Arts Degrees Useless?

As Vietnam moves from low-tech manufacturing to a service-oriented economy, the country's information technology (IT) market is increasingly gaining traction, giving competition to IT firms in China and India. This has partly been encouraged by the growth of Vietnam as a regional market for domestic enterprises and global technology vendors. As Vietnam adopts Industry 4.0 across all industries, investors should consider the benefits of locating their IT business to the country. Most of the industry is foreign-invested, with multinationals funding projects to build electronic components in the country. At least 86 percent of total IT revenues in 2017 were derived from hardware.

Despite the growing use of artificial-intelligence tools on a global basis, there is no universal code of ethics to govern its use. This is a key question the technology industry is beginning to wrestle with as the use of AI generates results both positive and negative. The technology has already been used for positive outcomes in a number of areas, including improving Australia's beaches, delivering reliable weather forecasts, and detecting human disease more accurately. There is also the other side of the coin, where AI has come under fire for injecting racial bias into criminal sentencing decisions and reinforcing gender discrimination. AI-powered facial recognition tools have been subjected to especially harsh criticism by privacy and human rights organizations.

Perhaps the simplest type of multilingual transfer learning is instance-based transfer learning, in which data from the target language and the auxiliary languages are pooled, and a single model is learned from the pooled data. It is not immediately obvious when instance-based transfer learning will improve performance in this multilingual setting: for instance, a plausible conjecture is this kind of transfer learning would help only if the auxiliary languages were very similar to the target. Here we show that at large scale, this method is surprisingly effective, leading to positive transfer on all of 35 target languages we tested. We analyze this improvement and argue that the most natural explanation, namely direct vocabulary overlap between languages, only partially explains the performance gains: in fact, we demonstrate target-language improvement can occur after adding data from an auxiliary language with no vocabulary in common with the target. This surprising result is due to the effect of transitive vocabulary overlaps between pairs of auxiliary and target languages.

Classical stochastic models, such as the Erd os-R enyi, Barabasi-Albert, and the stochastic block model generate graphs based on a predefined set of parameters, such as the probability of edge formation within and between communities [1]. In contrast, modern approaches to graph generation based on deep learning, including NetGAN [2], GraphGAN [3], and GraphRNN [4], are flexible enough to learn multiple different properties of an input graph simultaneously. The graphs generated by these architectures may be used for downstream learning tasks such as data augmentation [5], recommendation [6], and link prediction [7]. Many real-world networks consist of entities with complex mutual interrelations. Such networks cannot be modeled effectively as graphs with simple pairwise relations, despite the fact that pairwise relations provide a wealth of information for learning. Studying higher-order relationships in a graph is fundamental for our understanding of the network behavior and function. Higher-order relationships are usually termed hyperedges (collections of more than two nodes) [8, 9] or network motifs (recurrent node connectivity patterns that are statistically significant compared to some ground truth random graph model) [10]. These higher-order structures are the actual building blocks of complex networks, as they capture fundamental functional properties.

We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs.