AI has enormous promise for improving and enriching our lives. However, serious concerns exist about its use, intrusion, and abuse. The Google AI arm revealed a variety of artificial intelligence projects it was working on, including one focused on preventing blindness. At its annual developer conference, Google unveiled 12 new AI project categories, some of which could lead to improved healthcare, others that could be used for creative purposes, and others that might be fun to play with. Google's new wildfire tracking feature is now available in the United States, Canada, Mexico, and some parts of Australia.

Google Play has been committed to giving users safe and convenient ways to pay for their favorite apps and games – while helping developers of all sizes transact with millions of users globally and build successful businesses on the platform. In line with this commitment, Google announced that it is introducing UPI Autopay as a payment option for subscription-based purchases on Google Play in India. Introduced under UPI 2.0 by NPCI, UPI Autopay helps customers make recurring payments using any UPI application that supports the feature. Saurabh Agarwal, Head of Google Play Retail & Payments Activation – India, Vietnam, Australia & New Zealand said, "We are always looking at adding popular and effective forms of payment around the world to ensure people can pay for apps and in-app content conveniently. With the introduction of UPI Autopay on the platform, we aim to extend the convenience of UPI to subscription-based purchases, helping many more people access helpful and delightful services – while enabling local developers to grow their subscription-based businesses on Google Play."

Artificial intelligence has revealed that prehistoric footprints thought to be made by a vicious dinosaur predator were in fact from a timid herbivore. In an international collaboration, University of Queensland paleontologist Dr. Anthony Romilio used AI pattern recognition to re-analyze footprints from the Dinosaur Stampede National Monument, south-west of Winton in Central Queensland. "Large dinosaur footprints were first discovered back in the 1970s at a track site called the Dinosaur Stampede National Monument, and for many years they were believed to be left by a predatory dinosaur, like Australovenator, with legs nearly two meters long," said Dr. Romilio. "The mysterious tracks were thought to be left during the mid-Cretaceous Period, around 93 million years ago. "But working out what dino species made the footprints exactly--especially from tens of millions of years ago--can be a pretty difficult and confusing business.

Distinguishing between tridactyl (three-toed) dinosaur tracks of the herbivorous ornithischians and the predominantly carnivorous theropods is a complex and long-standing problem [1–9]. Broadly, ornithischian tracks are expected to be wider and more symmetric than theropod tracks, with digit impression III less projecting beyond digit impressions II and IV, and with digit impressions being broader, more splayed apart, and terminating in blunt hoof marks instead of sharp claw marks. However, any of these characteristics can be found in both groups, and which are the most important depends on the particular track type in question. Moratalla et al. [1] presented a quantitative approach to discriminate these groups, albeit limited to larger theropod and ornithopod tracks. Limitations of this approach include the small sample size, issues with the measurement scheme and omission of relevant shape characteristics [1,5,6]; despite this, the method has found wide application [3,9–12]. To overcome the limitations of previous statistical approaches, and to remove as much subjectivity as possible, we trained and then employed an artificial neural network to categorize outlines of tridactyl dinosaur footprints as theropod or ornithischian. Artificial neural networks are a type of nonlinear model that can learn from data, and a principal component of machine learning and artificial intelligence. Inspired by the structure of the human brain, such neural networks comprise interconnected nodes (or neurons), with each connection represented by a number (weight).

Deep learning has achieved great success in learning features from massive remote sensing images (RSIs). To better understand the connection between feature learning paradigms (e.g., unsupervised feature learning (USFL), supervised feature learning (SFL), and self-supervised feature learning (SSFL)), this paper analyzes and compares them from the perspective of feature learning signals, and gives a unified feature learning framework. Under this unified framework, we analyze the advantages of SSFL over the other two learning paradigms in RSIs understanding tasks and give a comprehensive review of the existing SSFL work in RS, including the pre-training dataset, self-supervised feature learning signals, and the evaluation methods. We further analyze the effect of SSFL signals and pre-training data on the learned features to provide insights for improving the RSI feature learning. Finally, we briefly discuss some open problems and possible research directions.

Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through the long list of these articles faster and document the crucial takeaways from each of these articles? In this work, we propose CCS Explorer, an end-to-end system for relevance prediction of sentences, extractive summarization, and patient, outcome, and intervention entity detection from CCS. CCS Explorer is packaged in a web-based graphical user interface where the user can provide any disease name. CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end. For each task, CCS Explorer fine-tunes pre-trained language representation models based on transformers with additional layers. The models are evaluated using two publicly available datasets. CCS Explorer obtains a recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus, CCS Explorer can reliably extract relevant information to summarize articles, saving time by $\sim \text{660}\times$.

Quantification, variously called "supervised prevalence estimation" or "learning to quantify", is the supervised learning task of generating predictors of the relative frequencies (a.k.a. "prevalence values") of the classes of interest in unlabelled data samples. While many quantification methods have been proposed in the past for binary problems and, to a lesser extent, single-label multiclass problems, the multi-label setting (i.e., the scenario in which the classes of interest are not mutually exclusive) remains by and large unexplored. A straightforward solution to the multi-label quantification problem could simply consist of recasting the problem as a set of independent binary quantification problems. Such a solution is simple but na\"ive, since the independence assumption upon which it rests is, in most cases, not satisfied. In these cases, knowing the relative frequency of one class could be of help in determining the prevalence of other related classes. We propose the first truly multi-label quantification methods, i.e., methods for inferring estimators of class prevalence values that strive to leverage the stochastic dependencies among the classes of interest in order to predict their relative frequencies more accurately. We show empirical evidence that natively multi-label solutions outperform the na\"ive approaches by a large margin. The code to reproduce all our experiments is available online.

Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only deal with the KG entities seen in the training set and cannot perform well in predicting links concerning novel entities in the test set. Similar problem exists in temporal knowledge graphs (TKGs), and no previous temporal knowledge graph completion (TKGC) method is developed for modeling newly-emerged entities. Compared to KGs, TKGs require temporal reasoning techniques for modeling, which naturally increases the difficulty in dealing with novel, yet unseen entities. In this work, we focus on the inductive learning of unseen entities' representations on TKGs. We propose a few-shot out-of-graph (OOG) link prediction task for TKGs, where we predict the missing entities from the links concerning unseen entities by employing a meta-learning framework and utilizing the meta-information provided by only few edges associated with each unseen entity. We construct three new datasets for TKG few-shot OOG link prediction, and we propose a model that mines the concept-aware information among entities. Experimental results show that our model achieves superior performance on all three datasets and our concept-aware modeling component demonstrates a strong effect.

We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level. In this paper, we present some new insights in MAB and formulate the problem in the penalization framework, where rigorous penalized regret can be well defined and more sophisticated regret analysis is possible. Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including asymptotic fairness, nearly optimal regret, better tradeoff between reward and fairness. Both gap-dependent and gap-independent regret bounds have been established. Multiple insightful comments are given to illustrate the soundness of our theoretical analysis. Numerous experimental results corroborate the theory and show the superiority of our method over other existing methods.

Question answering over knowledge bases (KBQA) has become a popular approach to help users extract information from knowledge bases. Although several systems exist, choosing one suitable for a particular application scenario is difficult. In this article, we provide a comparative study of six representative KBQA systems on eight benchmark datasets. In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle. On top of that, we propose an advanced mapping algorithm to aid existing models in achieving superior results. Moreover, we also develop a multilingual corpus COVID-KGQA, which encourages COVID-19 research and multilingualism for the diversity of future AI. Finally, we discuss the key findings and their implications as well as performance guidelines and some future improvements. Our source code is available at \url{https://github.com/tamlhp/kbqa}.