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A young man exiled to a reeducation camp for the "digitally unsafe" learns to keep his face blank, as cameras everywhere read expressions, and signs of anger and resistance are quickly punished. The elderly victim of an attack feels empty after winning justice from a "panel of metal judges" in a future courtroom beyond human biases. An online karate class is taught by artificial intelligence and robots, but over the decades, even as the sport thrives, much of its crucial human element is forgotten. These tales of AI and its effects on future life -- and many more, from points around the world -- are gathered in "Telling Stories: On Culturally Responsive Artificial Intelligence," presented by the University of Washington Tech Policy Lab. The lab is an interdisciplinary collaboration of the UW Paul G. Allen School of Computer Science & Engineering, Information School and School of Law, to "enhance technology policy through research, education and thoughtful leadership."

This paper aims to formulate the problem of estimating the optimal baseline values for the Shapley value in game theory. The Shapley value measures the attribution of each input variable of a complex model, which is computed as the marginal benefit from the presence of this variable w.r.t.its absence under different contexts. To this end, people usually set the input variable to its baseline value to represent the absence of this variable (i.e.the no-signal state of this variable). Previous studies usually determine the baseline values in an empirical manner, which hurts the trustworthiness of the Shapley value. In this paper, we revisit the feature representation of a deep model from the perspective of game theory, and define the multi-variate interaction patterns of input variables to define the no-signal state of an input variable. Based on the multi-variate interaction, we learn the optimal baseline value of each input variable. Experimental results have demonstrated the effectiveness of our method.

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When Covid-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson Covid-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For Covid-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

Matrix completion has attracted attention in many fields, including statistics, applied mathematics, and electrical engineering. Most of the works focus on the independent sampling models under which the observed entries are sampled independently. Motivated by applications in the integration of multiple Electronic Health Record (EHR) datasets, we propose the method Block-wise missing Embedding Learning Transformer (BELT) to treat rowwise/column-wise missingness. Specifically, BELT can recover block-wise missing matrices efficiently when every pair of matrices has an overlap. Our idea is to exploit the orthogonal Procrustes problem to align the eigenspace of the two sub-matrices using their overlap, then complete the missing blocks by the inner product of the two low-rank components. Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption. Simulation studies show that the method performs well under a variety of configurations. In the real data analysis, the method is applied to two tasks: (i) the integrating of several point-wise mutual information matrices built by English EHR and Chinese medical text data, and (ii) the machine translation between English and Chinese medical concepts. Our method shows an advantage over existing methods.

Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work has used now-outdated deep learning architectures to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate five alternative deep learning models which exploit developments in the field from the last five years, largely the introduction of residual neural networks, which are intended to allow the model to use semantic information in the memorability estimation process. These new models were tested against the prior state of the art with a combined dataset built to optimize both within-category and across-category predictions. Our findings suggest that the key prior memorability network had overstated its generalizability and was overfit on its training set. Our new models outperform this prior model, leading us to conclude that Residual Networks outperform simpler convolutional neural networks in memorability regression. We make our new state-of-the-art model readily available to the research community, allowing memory researchers to make predictions about memorability on a wider range of images.

Charles Darwin's The Descent of Man was published in 1871. Ever since, it has been the foundation stone of human evolutionary studies. Richerson et al. reviewed how modern studies of human biological and cultural evolution reflect the ideas in Darwin's work. They emphasize how cooperation, social learning, and cumulative culture in the ancestors of modern humans were key to our evolution and were enhanced during the environmental upheavals of the Pleistocene. The evolutionary perspective has come to permeate not just human biology but also the social sciences, vindicating Darwin's insights. Science , aba3776, this issue p. [eaba3776][1] ### BACKGROUND Charles Darwin’s The Descent of Man , published on 24 February 1871, laid the grounds for scientific studies into human origins and evolution. We look at the advances in our understanding of these processes through the lenses of modern speciation theory. Applying this theory to specific cases requires one to identify and understand the nature of (i) the ancestor and various preexisting adaptations and traits that it possessed that allowed or simplified the speciation process, (ii) evolutionary forces responsible for major differences between the emergent species and its close relatives, and (iii) the most salient adaptations characteristic of the new species and its evolutionary history (such as genetic, morphological, behavioral, spatial, and temporal). ### ADVANCES Modern research shows that we share many developmental, physiological, morphological, cognitive, and psychological characteristics as well as about 96% of our DNA with the anthropoid apes. We now know that since our last common ancestor with the other apes 6 million to 8 million years ago, human evolution followed the path common for other species with diversification into closely related species and some subsequent hybridization between them. Since Darwin, a long series of unbridgeable gaps have been proposed between humans and other animals. They focused on tool-making, cultural learning and imitation, empathy, prosociality and cooperation, planning and foresight, episodic memory, metacognition, and theory of mind. However, new insights from neurobiology, genetics, primatology, and behavioral biology only reinforce Darwin’s view that most differences between humans and higher animals are “of degree and not of kind.” What makes us different is that our ancestors evolved greatly enhanced abilities for (and reliance on) cooperation, social learning, and cumulative culture—traits emphasized already by Darwin. Cooperation allowed for environmental risk buffering, cost reduction, and the access to new resources and benefits through the “economy of scale.” Learning and cumulative culture allowed for the accumulation and rapid spread of beneficial innovations between individuals and groups. The enhanced abilities to learn from and cooperate with others became a universal tool, removing the need to evolve specific biological organs for specific environmental challenges. These human traits likely evolved as a response to increasing high-frequency climate changes on the millennial and submillennial scales during the Pleistocene. Once the abilities for cumulative culture and extended cooperation were in place, a suite of subsequent evolutionary changes became possible and likely unavoidable. In particular, human social systems evolved to support mothers through the recruitment of males and nonreproductive females. The most distinctive feature of our species, language, appeared arguably driven by selection for simplifying cooperation. Reliance on social learning and conformity led to the emergence of new factors constraining and driving human behavior, such as morality, social norms, and social institutions. These forces often act against the immediate biological or material interests of individuals, promoting instead the interests of the society as a whole or of its powerful segments. Continuous engagement in cooperation has led to the evolution of strong coalitionary psychology, which can bring us together whenever we perceive that our identity group faces outside threats. Coalitionary psychology also has an undesirable byproduct: often negative or even hostile reaction to others who differ from us in their looks, behaviors, beliefs, caste, or class. ### OUTLOOK Our society faces challenges, including climate change; various types of inequality; economic crises; political, cultural, and religious conflicts; and pandemics. Similar challenges have repeatedly arisen and were dealt with in the past with varying success. What makes the current situation different is not only the scale of societal threats but also that modern science can provide guidance on how to respond to them. Adequately answering these challenges requires understanding humans’ social behavior and the roles of cooperation, social learning, and culture for human decision-making. Evolutionary perspective is already helping to synthesize the contributions of social sciences, including anthropology, psychology, economics, political science, and history. The impact of Descent on the social sciences and on the development and implementation of different policies by practitioners and policymakers to improve our society will only grow. ![Figure][2] Depictions of organic evolution versus cultural evolution. (Left) Organic evolution and (right) cultural evolution, as depicted in Alfred L. Kroeber’s 1923 textbook Anthropology: Cultural Patterns and Processes . Biological inheritance is rigid from parents to offspring in eukaryotes, and species mostly do not exchange genes. Culture is potentially acquired from anyone in a person’s social network, and ideas spread rather readily from culture to culture. IMAGE: N. CARY/ SCIENCE Charles Darwin’s The Descent of Man , published 150 years ago, laid the grounds for scientific studies into human origins and evolution. Three of his insights have been reinforced by modern science. The first is that we share many characteristics (genetic, developmental, physiological, morphological, cognitive, and psychological) with our closest relatives, the anthropoid apes. The second is that humans have a talent for high-level cooperation reinforced by morality and social norms. The third is that we have greatly expanded the social learning capacity that we see already in other primates. Darwin’s emphasis on the role of culture deserves special attention because during an increasingly unstable Pleistocene environment, cultural accumulation allowed changes in life history; increased cognition; and the appearance of language, social norms, and institutions. [1]: /lookup/doi/10.1126/science.aba3776 [2]: pending:yes

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When COVID-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson-Janssen COVID-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For COVID-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

Interactive news recommendation has been launched and attracted much attention recently. In this scenario, user's behavior evolves from single click behavior to multiple behaviors including like, comment, share etc. However, most of the existing methods still use single click behavior as the unique criterion of judging user's preferences. Further, although heterogeneous graphs have been applied in different areas, a proper way to construct a heterogeneous graph for interactive news data with an appropriate learning mechanism on it is still desired. To address the above concerns, we propose a graph-based behavior-aware network, which simultaneously considers six different types of behaviors as well as user's demand on the news diversity. We have three main steps. First, we build an interaction behavior graph for multi-level and multi-category data. Second, we apply DeepWalk on the behavior graph to obtain entity semantics, then build a graph-based convolutional neural network called G-CNN to learn news representations, and an attention-based LSTM to learn behavior sequence representations. Third, we introduce core and coritivity features for the behavior graph, which measure the concentration degree of user's interests. These features affect the trade-off between accuracy and diversity of our personalized recommendation system. Taking these features into account, our system finally achieves recommending news to different users at their different levels of concentration degrees.

Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference resolution and ellipses by query rewrite. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. Given an ongoing dialogue between a user and a dialogue assistant, for the user query, our joint learning model first predicts coreference links between the query and the dialogue context, and then generates a self-contained rewritten user query. To evaluate our model, we annotate a dialogue based coreference resolution dataset, MuDoCo, with rewritten queries. Results show that the performance of query rewrite can be substantially boosted (+2.3% F1) with the aid of coreference modeling. Furthermore, our joint model outperforms the state-of-the-art coreference resolution model (+2% F1) on this dataset.