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.

March 13 (Reuters) - Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive told Reuters, after the U.S. startup offered to uncover Russian assailants, combat misinformation and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under U.S. presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation." Ukraine's Ministry of Defense did not reply to requests for comment.

Ukraine's defense ministry on Saturday began using Clearview AI's facial recognition technology, the company's chief executive told Reuters, after the U.S. startup offered to uncover Russian assailants, combat misinformation and identify the dead. Ukraine is receiving free access to Clearview AI's powerful search engine for faces, letting authorities potentially vet people of interest at checkpoints, among other uses, added Lee Wolosky, an adviser to Clearview and former diplomat under U.S. Presidents Barack Obama and Joe Biden. The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Clearview said it had not offered the technology to Russia, which calls its actions in Ukraine a "special operation." Ukraine's Ministry of Defense did not reply to requests for comment.

Training neural networks takes a lot of time, even with the fastest and costliest accelerators on the market. It's maybe no surprise then that a number of startups are looking at how to speed up the process at the software level and remove some of the current bottlenecks in the training process. For Strong Compute, a Sydney, Australia-based startup that was recently accepted into Y Combinator's Winter '22 class, it's all about removing these inefficiencies in the training process. By doing so, the team argues that it can speed up the training process by 100x or more. "PyTorch is beautiful and so is TensorFlow. These toolkits are amazing, but the simplicity they have -- and the ease of implementation they have -- comes at the cost of things being inefficient under the hood," said Strong Compute CEO and founder Ben Sand, who previously co-founded AR company Meta (before Facebook used that name).

We propose VALSE (Vision And Language Structured Evaluation), a novel benchmark designed for testing general-purpose pretrained vision and language (V&L) models for their visio-linguistic grounding capabilities on specific linguistic phenomena. VALSE offers a suite of six tests covering various linguistic constructs. Solving these requires models to ground linguistic phenomena in the visual modality, allowing more fine-grained evaluations than hitherto possible. We build VALSE using methods that support the construction of valid foils, and report results from evaluating five widely-used V&L models. Our experiments suggest that current models have considerable difficulty addressing most phenomena. Hence, we expect VALSE to serve as an important benchmark to measure future progress of pretrained V&L models from a linguistic perspective, complementing the canonical task-centred V&L evaluations.

Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called "microplastics" resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time and low-light object detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets, by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency. Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and another dataset for marine debris detection. Finally, we investigated how the detector performance is affected by low-light conditions and compared two low-light underwater image enhancement strategies both in terms of accuracy and latency. Source code and dataset are publicly available.