Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts is often only available within full-text event descriptions from newspapers and other reports. Extracting numbers from text is challenging: numbers have different formats and may require numeric reasoning. This renders purely string matching-based approaches insufficient. As a consequence, fine-grained counts of injured, displaced, or abused victims beyond fatalities are often not extracted and remain unseen. We cast victim count extraction as a question answering (QA) task with a regression or classification objective. We compare regex, dependency parsing, semantic role labeling-based approaches, and advanced text-to-text models. Beyond model accuracy, we analyze extraction reliability and robustness which are key for this sensitive task. In particular, we discuss model calibration and investigate few-shot and out-of-distribution performance. Ultimately, we make a comprehensive recommendation on which model to select for different desiderata and data domains. Our work is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact.

Parsing spoken dialogue presents challenges that parsing text does not, including a lack of clear sentence boundaries. We know from previous work that prosody helps in parsing single sentences (Tran et al. 2018), but we want to show the effect of prosody on parsing speech that isn't segmented into sentences. In experiments on the English Switchboard corpus, we find prosody helps our model both with parsing and with accurately identifying sentence boundaries. However, we find that the best-performing parser is not necessarily the parser that produces the best sentence segmentation performance. We suggest that the best parses instead come from modelling sentence boundaries jointly with other constituent boundaries.

When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism. Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint. We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrained deep ImageNet classifiers that are available in popular repositories. We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training. Moreover, we find a subset of ViT models that outperform any other models in terms of uncertainty estimation performance. For example, we discovered an unprecedented 99% top-1 selective accuracy on ImageNet at 47% coverage (and 95% top-1 accuracy at 80%) for a ViT model, whereas a competing EfficientNet-V2-XL cannot obtain these accuracy constraints at any level of coverage. Our companion paper, also published in ICLR 2023 (A framework for benchmarking class-out-of-distribution detection and its application to ImageNet), examines the performance of these classifiers in a class-out-of-distribution setting.

Deep learning based image enhancement models have largely improved the readability of fundus images in order to decrease the uncertainty of clinical observations and the risk of misdiagnosis. However, due to the difficulty of acquiring paired real fundus images at different qualities, most existing methods have to adopt synthetic image pairs as training data. The domain shift between the synthetic and the real images inevitably hinders the generalization of such models on clinical data. In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. The student network uses synthetic pairs for supervised enhancement, and regularizes the enhancement model to reduce domain-shift by enforcing teacher-student prediction consistency on the real fundus images without relying on enhanced ground-truth. Moreover, we also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network. Our MAGE-Net utilizes multi-stage enhancement module and retinal structure preservation module to progressively integrate the multi-scale features and simultaneously preserve the retinal structures for better fundus image quality enhancement. Comprehensive experiments on both real and synthetic datasets demonstrate that our framework outperforms the baseline approaches. Moreover, our method also benefits the downstream clinical tasks.

Sydney, Australia Xinhua A global study released on Wednesday has found that three out of five people, about 61 percent, are wary about trusting artificial intelligence (AI) systems, reporting either ambivalence or an unwillingness to trust. Researchers from the University of Queensland and KPMG Australia surveyed more than 17,000 people from 17 countries across the world, such as Australia, China, France and the United States, which were regarded by the study as leaders in AI activity and readiness within their region. According to the study, only 39 percent said they are willing to trust AI systems, while a third of people worldwide reported high acceptance. Though about 85 percent believed the use of AI will result in a wide range of benefits, including improved efficiency, innovation, reduced costs and better use of resources, 73 percent of the respondents also voiced concern over potential risks from AI use. Among the nine listed risks, cybersecurity is the dominant concern raised by 84 percent of people, followed by manipulation or harmful use of AI and job loss due to automation.

In a Brooklyn lab stuffed with 3D printers and a makeshift pickleball court, employees at a brain interface startup called Synchron are working on technology designed to transform daily life for people with paralysis. The Synchron Switch is implanted through the blood vessels to allow people with no or very limited physical mobility to operate technology such as cursors and smart home devices using their mind. So far, the nascent technology has been used on three patients in the U.S. and four in Australia. "I've seen moments between patient and partner, or patient and spouse, where it's incredibly joyful and empowering to have regained an ability to be a little bit more independent than before," Synchron CEO Tom Oxley told CNBC in an interview. "It helps them engage in ways that we take for granted."

Five days after Russia launched its full-scale invasion of Ukraine, a year ago this week, US-based facial recognition company Clearview AI offered the Ukrainian government free access to its technology, suggesting that it could be used to reunite families, identify Russian operatives, and fight misinformation. Soon afterward, the Ukraine government revealed it was using the technology to scan the faces of dead Russian soldiers to identify their bodies and notify their families. By December 2022, Mykhailo Fedorov, Ukraine's vice prime minister and minister of digital transformation, was tweeting a picture of himself with Clearview AI's CEO Hoan Ton-That, thanking the company for its support. Accounting for the dead and letting families know the fate of their relatives is a human rights imperative written into international treaties, protocols, and laws like the Geneva Conventions and the International Committee of the Red Cross' (ICRC) Guiding Principles for Dignified Management of the Dead. It is also tied to much deeper obligations.

AI in pharma refers to the use of automated algorithms to jobs that normally need human intelligence. Large datasets including disease patterns can be successfully identified by AI solutions in pharma, and they can also assist in understanding which medication formulations would be most effective for treating certain ailments. The artificial intelligence (AI) in the pharma market covered in the report is segmented by technology into context-aware processing, natural language processing, querying method, and deep learning; by drug type into the small molecule, large molecules; by application into diagnosis, clinical trial research, drug discovery, research and development, epidemic prediction. The artificial intelligence (AI) in pharma market research report is one of a series of new reports from The Business Research Company that provides artificial intelligence (AI) in pharma market statistics, including artificial intelligence (AI) in pharma industry global market size, regional shares, competitors with artificial intelligence (AI) in pharma market share, detailed artificial intelligence (AI) in pharma market segments, market trends and opportunities, and any further data you may need to thrive in the artificial intelligence (AI) in pharma industry. This artificial intelligence (AI) in pharma market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Without urgent investment in building a sovereign capability in locally developed artificial intelligence tools, Australia risks ceding control of its strategic systems and technology to foreign Interests, a group of eminent scientists have warned. An open letter from 14 of the nation's leading experts on AI and robotics published on Wednesday called for the urgent development of national research and education strategies to build locally-owned and operated AI capacity. The so-called Kingston AI Group, which includes professors from eight universities, says Australia must build a domestic pipeline of top AI talent in order to reduce the significant risks and maximise the potential economic and security benefits of this rapidly emerging technology. "A failure to deliver the AI workforce Australia needs will harm our future economic growth, shrink our economic complexity, and weaken our sovereign control in key industry sectors," the authors of the paper say. "If done strategically, a major investment would result in Australia becoming one of the leading countries in AI."

The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to propose the approximate spectral clustering (ASC). However, most of ASC methods assumed that the number of clusters $k$ was known. In practice, manual setting of $k$ could be subjective or time consuming. The proposed algorithm has two relevance metrics for estimating $k$ in two vital steps of ASC. One for selecting the eigenvectors spanning the embedding space, and the other to discover the number of clusters in that space. The algorithm used a growing neural gas (GNG) approximation, GNG is superior in preserving input data topology. The experimental setup demonstrates the efficiency of the proposed algorithm and its ability to compete with similar methods where $k$ was set manually.