The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.

Abstract--Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior. Today's robots are most commonly found in manufacturing Human receivers assessed on-the-go handovers to be better than stop-and-deliver in most subjective metrics.

World Input-Output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (part of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and Matrix Completion (MC) with a LASSO-like nuclear norm penalty, to impute missing entries of a partially unknown I/O matrix. Through simulations based on synthetic matrices we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input-Output Database (WIOD) tables - which are an example of industry-by-industry I/O tables - is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.

Marco Iansiti and Karin Lakhani write in their book, Competing in the Age of AI, "the learning algorithms at the heart of new digital systems can be misused to tailor, optimize and amplify inaccurate and harmful information from targeting and shaping misleading ads to creating highly realistic fake social personas that are used to extract personal information from users." The question is what should CIOs and other data leaders do to protect enterprises and their key stakeholders? CIO Anthony McMahon of Target State Consulting suggests in a recent #CIOChat on Twitter that ethical and privacy issues are not unique to artificial intelligence (AI). "Every decision on how to use data, either in platform or offline, has an ethical consideration," he said. Other data leaders claim there are all sorts of unique ethical and privacy issues to AI. Privacy is particularly important when a piece of data becomes connected in a new and novel way.

Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence. The context encoding is undertaken by contextual parameters, trained on document-level data. In this work, we discuss the difficulty of training these parameters effectively, due to the sparsity of the words in need of context (i.e., the training signal), and their relevant context. We propose to pre-train the contextual parameters over split sentence pairs, which makes an efficient use of the available data for two reasons. Firstly, it increases the contextual training signal by breaking intra-sentential syntactic relations, and thus pushing the model to search the context for disambiguating clues more frequently. Secondly, it eases the retrieval of relevant context, since context segments become shorter. We propose four different splitting methods, and evaluate our approach with BLEU and contrastive test sets. Results show that it consistently improves learning of contextual parameters, both in low and high resource settings.

Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, IteraTeR is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations. Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.

False assumptions about sex and gender are deeply embedded in the medical system, including that they are binary, static, and concordant. Machine learning researchers must understand the nature of these assumptions in order to avoid perpetuating them. In this perspectives piece, we identify three common mistakes that researchers make when dealing with sex/gender data: "sex confusion", the failure to identity what sex in a dataset does or doesn't mean; "sex obsession", the belief that sex, specifically sex assigned at birth, is the relevant variable for most applications; and "sex/gender slippage", the conflation of sex and gender even in contexts where only one or the other is known. We then discuss how these pitfalls show up in machine learning studies based on electronic health record data, which is commonly used for everything from retrospective analysis of patient outcomes to the development of algorithms to predict risk and administer care. Finally, we offer a series of recommendations about how machine learning researchers can produce both research and algorithms that more carefully engage with questions of sex/gender, better serving all patients, including transgender people.

Ukraine is using Clearview AI's facial recognition software to uncover Russian assailants and identify Ukrainians who've sadly lost their lives in the conflict. The company's chief executive, Hoan Ton-That, told Reuters that Ukraine's defence ministry began using the software on Saturday. Clearview AI's facial recognition system is controversial but indisputably powerful--using billions of images scraped from the web to identify just about anyone. Ton-That says that Clearview has more than two billion images from Russian social media service VKontakte alone. Reuters says that Ton-That sent a letter to Ukrainian authorities offering Clearview AI's assistance.

Ukraine is now using Clearview AI's facial recognition technology for purposes such as identifying Russian soldiers, its CEO claimed. Hoan Ton-That told Reuters the company offered Ukraine's defense ministry free access to its system following the invasion by Russia. According to the report, Clearview suggested Ukraine could use the tech to reunite refugees with family members, fight misinformation, assess at checkpoints whether someone is a person of interest and to identify dead bodies. The company hasn't offered its technology to Russia. Engadget has contacted the defense ministry for comment.

Warwick Business School's Distance Learning MBA started 36 years ago as a postal course -- a mode of delivery that must seem positively quaint to any students born in that inaugural year of 1986. Today's learners access the course via a bespoke online platform which, Warwick says, enables them to "engage in lectures in real time . . . As online MBA providers vie to attract students, all are becoming more inventive in the way they deliver content. Before long, technologies such as virtual reality and artificial intelligence may make current courses look as outdated as an envelope of study materials thudding on to a doormat. Investment has been accelerated by the coronavirus pandemic, which forced business schools to teach even conventional MBA students remotely. Find out which schools are in our ranking of Online MBA degrees. Take a look at our analysis and methodology. Also, read the rest of our coverage at www.ft.com/online-learning. Warwick's technology now includes green-screen video studios that allow presenters to be superimposed on different backgrounds. "We take some content from a member of faculty that's a flat information-sharing process," says Dot Powell, the school's director of teaching and learning enhancement. "Around that, we'll design activities, interactive features and encourage the students to engage with the content and with each other.