You've probably never wondered what a knight made of spaghetti would look like, but here's the answer anyway--courtesy of a clever new artificial intelligence program from OpenAI, a company in San Francisco. The program, DALL-E, released earlier this month, can concoct images of all sorts of weird things that don't exist, like avocado armchairs, robot giraffes, or radishes wearing tutus. OpenAI generated several images, including the spaghetti knight, at WIRED's request. DALL-E is a version of GPT-3, an AI model trained on text scraped from the web that's capable of producing surprisingly coherent text. DALL-E was fed images and accompanying descriptions; in response, it can generate a decent mashup image.

Solar power installations are becoming common in residential and commercial areas, largely due to their decreasing costs. However, the power system is vulnerable to some anomalies such as rainstorm or hurricane, which cost greatly to restoration. As a result, detecting and predicting abnormal events from the spatialtemporal series plays a vital role in the solar system, aiming to capture the variety of intrinsic reasons for the anomalies. For example, the rainstorm and drought would bring out different types and patterns of anomalies. In many cases, the abnormal event will also start at one location and then propagate to its neighbors with a time delay, leading to spatial-temporal correlation among anomalies. Thus it is crucial to make observations at multiple locations, which correspondingly form the spatial-temporal series. In this paper, we address non-stationarity and strong spatial-temporal correlation through the following contributions: - Strong spatial-temporal correlation: We present a spatial-temporal Bernoulli process (also extended to categorical observations), which is proposed by [19]. The model can flexibly capture the spatial-temporal correlations and interactions without assuming time-decaying influence. It can also efficiently make predictions for any location at any future time for timely ramp event detection.

Thus, social media platforms are often held responsible for framing the views and opinions of a large number of people (Duggan et al., 2017). However, this freedom to voice our opinion has been challenged by the increase in the use of hate speech (Mondal et al., 2017). The anonymity of the internet grants people the power to completely change the context of a discussion and suppress a person's personal opinion (Sticca and Perren, 2013). These hateful posts and comments not only affect the society at a micro scale but also at a global level by influencing people's views regarding important global events like elections, and protests (Duggan et al., 2017). Given the volume of online communication happening on various social media platforms and the need for more fruitful communication, there is a growing need to automate the detection of hate speech. For the scope of this paper we adopt the definition of hate speech and offensive speech as defined in the Mandl et al. (2019) as "insulting, hurtful, derogatory, or obscene content directed from one person to another person" (quoted from (Mandl et al., 2019)). In order to automate hate speech detection the Natural Language Processing (NLP) community has made significant progress which has been accelerated by organization of numerous shared tasks aimed at identifying hate speech (Mandl et al., 2019; Kumar et al., 2020, 2018).

We introduce MADGRAD, a novel optimization method in the family of AdaGrad adaptive gradient methods. MADGRAD shows excellent performance on deep learning optimization problems from multiple fields, including classification and image-to-image tasks in vision, and recurrent and bidirectionally-masked models in natural language processing. For each of these tasks, MADGRAD matches or outperforms both SGD and ADAM in test set performance, even on problems for which adaptive methods normally perform poorly.

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose this second iteration of the MineRL Competition. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.

Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explicitly learn this process with deep neural networks. We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. The proposed model naturally adapts to image structures and can effectively improve the denoised results. Furthermore, we develop a spatio-temporal pixel aggregation network for video denoising to efficiently sample pixels across the spatio-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes. In addition, we introduce a new regularization term for effectively training the proposed video denoising model. We present extensive analysis of the proposed method and demonstrate that our model performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.

Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and trained with local supervision. We experimentally show that simply training local modules with E2E loss tends to collapse task-relevant information at early layers, and hence hurts the performance of the full model. To avoid this issue, we propose an information propagation (InfoPro) loss, which encourages local modules to preserve as much useful information as possible, while progressively discard task-irrelevant information. As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm. In fact, we show that the proposed method boils down to minimizing the combination of a reconstruction loss and a normal cross-entropy/contrastive term. Extensive empirical results on five datasets (i.e., CIFAR, SVHN, STL-10, ImageNet and Cityscapes) validate that InfoPro is capable of achieving competitive performance with less than 40% memory footprint compared to E2E training, while allowing using training data with higher-resolution or larger batch sizes under the same GPU memory constraint. Our method also enables training local modules asynchronously for potential training acceleration. Code is available at: https://github.com/blackfeather-wang/InfoPro-Pytorch.

Disagreements are pervasive in human communication. In this paper we investigate what makes disagreement constructive. To this end, we construct WikiDisputes, a corpus of 7 425 Wikipedia Talk page conversations that contain content disputes, and define the task of predicting whether disagreements will be escalated to mediation by a moderator. We evaluate feature-based models with linguistic markers from previous work, and demonstrate that their performance is improved by using features that capture changes in linguistic markers throughout the conversations, as opposed to averaged values. We develop a variety of neural models and show that taking into account the structure of the conversation improves predictive accuracy, exceeding that of feature-based models. We assess our best neural model in terms of both predictive accuracy and uncertainty by evaluating its behaviour when it is only exposed to the beginning of the conversation, finding that model accuracy improves and uncertainty reduces as models are exposed to more information.

Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.

With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One key enabler is the dynamic prediction of time-to-event outcomes. Conventional explanatory statistical approach lacks the capability of making precise individual level prediction, while the data adaptive binary predictors does not provide nominal survival curves for biologically plausible survival analysis. The purpose of this article is to elucidate that the knowledge of explanatory survival analysis can significantly enhance the current black-box data adaptive prediction models. We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data and testify that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.