Leading researchers discussed which requirements AI algorithms must meet to fight bias in healthcare during the'Artificial Intelligence and Implications for Health Equity: Will AI Improve Equity or Increase Disparities?' session which was held on 1 December. The speakers were: Ziad Obermeyer, associate professor of health policy and management at the Berkeley School of Public Health, CA; Luke Oakden-Rayner, director of medical imaging research at the Royal Adelaide Hospital, Australia; Constance Lehman, professor of radiology at Harvard Medical School, director of breast imaging, and co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital; and Regina Barzilay, professor in the department of electrical engineering and computer science and member of the Computer Science and AI Lab at the Massachusetts Institute of Technology. The discussion was moderated by Judy Wawira Gichoya, assistant professor in the Department of Radiology at Emory University School of Medicine, Atlanta. Artificial intelligence (AI) may unintentionally intensify inequities that already exist in modern healthcare and understanding those biases may help defeat them. Social determinants partly cause poor healthcare outcomes and it is crucial to raise awareness about inequity in access to healthcare, as Prof Sam Shah, founder and director of Faculty of Future Health in London, explained in a keynote during the HIMSS & Health 2.0 European Digital event.

In line with Microsoft's recent announcements in Chile, Brad Smith, President of Microsoft, met with an ESO delegation, headed by its Director General, Xavier Barcons, to sign a new step of their agreement that addresses to optimize and enhance the science made from ESO Paranal Observatory telescopes through Artificial Intelligence (AI). Thanks to this initiative, ESO and Microsoft will work in three areas of great interest for the operations of the Paranal Observatory. The first project is Turbulence Nowcasting, which makes real-time weather and atmospheric predictions to determine whether weather conditions are suitable for different observations. The second project is Anomaly Detection in calibration images taken with ESO s scientific instruments. The visual inspection of the images is replaced by the automatic inspection through Machine Learning algorithms.

Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE.

The ubiquity of smart voice assistants has made conversational shopping commonplace. This is especially true for low consideration segments like grocery. A central problem in conversational grocery is the automatic generation of short product titles that can be read out fast during a conversation. Several supervised models have been proposed in the literature that leverage manually labeled datasets and additional product features to generate short titles automatically. However, obtaining large amounts of labeled data is expensive and most grocery item pages are not as feature-rich as other categories. To address this problem we propose a pre-training based solution that makes use of unlabeled data to learn contextual product representations which can then be fine-tuned to obtain better title compression even in a low resource setting. We use a self-attentive BiLSTM encoder network with a time distributed softmax layer for the title compression task. We overcome the vocabulary mismatch problem by using a hybrid embedding layer that combines pre-trained word embeddings with trainable character level convolutions. We pre-train this network as a discriminator on a replaced-token detection task over a large number of unlabeled grocery product titles. Finally, we fine tune this network, without any modifications, with a small labeled dataset for the title compression task. Experiments on Walmart's online grocery catalog show our model achieves performance comparable to state-of-the-art models like BERT and XLNet. When fine tuned on all of the available training data our model attains an F1 score of 0.8558 which lags the best performing model, BERT-Base, by 2.78% and XLNet by 0.28% only, while using 55 times lesser parameters than both. Further, when allowed to fine tune on 5% of the training data only, our model outperforms BERT-Base by 24.3% in F1 score.

Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is hard to capture and 2) randomly sample training examples from the enormous space would be highly data-inefficient. Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d, compositional, and zero-shot. To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64,331 questions, GrailQA, and provide evaluation settings for all three levels of generalization. In addition, we propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.

Many graph-based machine learning models are known to be vulnerable to adversarial attacks, where even limited perturbations on input data can result in dramatic performance deterioration. Most existing works focus on moderate settings in which the attacker is either aware of the model structure and parameters (white-box), or able to send queries to fetch model information. In this paper, we propose a query-free black-box adversarial attack on graphs, in which the attacker has no knowledge of the target model and no query access to the model. With the mere observation of the graph topology, the proposed attack strategy flips a limited number of links to mislead the graph models. We prove that the impact of the flipped links on the target model can be quantified by spectral changes, and thus be approximated using the eigenvalue perturbation theory. Accordingly, we model the proposed attack strategy as an optimization problem, and adopt a greedy algorithm to select the links to be flipped. Due to its simplicity and scalability, the proposed model is not only generic in various graph-based models, but can be easily extended when different knowledge levels are accessible as well. Extensive experiments demonstrate the effectiveness and efficiency of the proposed model on various downstream tasks, as well as several different graph-based learning models.

"As a TPA [third-party administrator], our primary obligation to the insurance industry is to serve our clients," she said. "Technology is playing a key role here, in terms of employees and external stakeholders being able to stay connected and function cohesively despite being geographically separated, and this is something we continue to invest in. "Over the past couple of years, we have really seen how technological advancements have been improving the claims journey for our customers. Insurers are embracing next-generation capabilities such as artificial intelligence, advanced analytics and automation to understand the needs of the customer." Read more: Are cloud-based technologies and AI the future for insurance? Williams' emphasis on delivering a positive customer experience is reflected by the broader culture at GB. The company was recently presented with an Excellence award at this year's Insurance Business Australia Awards, and it took "great pride in receiving this industry recognition," according to Williams, who ascribed GB's success during COVID-19 to its status as a value-driven organisation. "Values are the foundation of our workplace culture at GB, and in times of crisis being guided by them is really important to us," she noted. "We serve as an extension of our clients' team, brand and reputation, so understanding their values as well and what they view as strengths ensures that we deliver a service that reflects their expectations." "As a TPA, it's our mission to support and deliver exceptional value to our stakeholders during this challenging time.

New drone footage shows Christchurch Cathedral that's been untouched since the 2011 earthquake.

Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variables and Earth's surface at such local scales remains unresolved by current physical models. Large Earth observation datasets now enable us to create machine learning models capable of translating coarse weather information into high-resolution Earth surface forecasts. Here, we define high-resolution Earth surface forecasting as video prediction of satellite imagery conditional on mesoscale weather forecasts. Video prediction has been tackled with deep learning models. Developing such models requires analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset containing target spatio-temporal Sentinel 2 satellite imagery at 20 m resolution, matched with high-resolution topography and mesoscale (1.28 km) weather variables. With over 32000 samples it is suitable for training deep neural networks. Comparing multiple Earth surface forecasts is not trivial. Hence, we define the EarthNetScore, a novel ranking criterion for models forecasting Earth surface reflectance. For model intercomparison we frame EarthNet2021 as a challenge with four tracks based on different test sets. These allow evaluation of model validity and robustness as well as model applicability to extreme events and the complete annual vegetation cycle. In addition to forecasting directly observable weather impacts through satellite-derived vegetation indices, capable Earth surface models will enable downstream applications such as crop yield prediction, forest health assessments, coastline management, or biodiversity monitoring. Find data, code, and how to participate at www.earthnet.tech .

We propose TabTransformer, a novel deep tabular data modeling architecture for supervised and semi-supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Through extensive experiments on fifteen publicly available datasets, we show that the TabTransformer outperforms the state-of-the-art deep learning methods for tabular data by at least 1.0% on mean AUC, and matches the performance of tree-based ensemble models. Furthermore, we demonstrate that the contextual embeddings learned from TabTransformer are highly robust against both missing and noisy data features, and provide better interpretability. Lastly, for the semi-supervised setting we develop an unsupervised pre-training procedure to learn data-driven contextual embeddings, resulting in an average 2.1% AUC lift over the state-of-the-art methods.