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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."

Extreme E, the pioneering electric off-road racing series, is teaming up with WSC Sports, the global leader in artificial intelligence (AI)-driven sports video technology ahead of its second X Prix from 29-30 May 2021 at Lac Rose in Dakar, Senegal. Extreme E will have access to WSC Sports' cloud-based live-clipping platform Clipro, allowing the series to create and publish near-live and post-race highlights in a matter of seconds, as well as WSC Sports' innovative Graphics Engine, which automatically adds stunning visuals to videos to help brand and monetise content. During the series Extreme E and WSC Sports will work closely to apply WSC Sports' state-of-the-art AI technology to automatically generate real time highlights for this new sport. WSC Sports has already adapted its technology to support car racing working in 2020 with its partner NASCAR to automatically produce real-time race highlights. WSC Sports will also assist Extreme E in distributing race highlights to all its media partners, as well as drivers' social channels thanks to WSC Sports' partnership with Socialie.

Egocentric visual context detection can support intelligence augmentation applications. We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection. PAL has a wearable device with a camera, heart-rate sensor, on-device deep learning, and audio input/output. PAL also has a mobile/web application for personalized context labeling. We used on-device deep learning models for generic object and face detection, low-shot custom face and context recognition (e.g., activities like brushing teeth), and custom context clustering (e.g., indoor locations). The models had over 80\% accuracy in in-the-wild contexts (~1000 images) and we tested PAL for intelligence augmentation applications like behavior change. We have made PAL is open-source to further support intelligence augmentation using personalized and privacy-preserving egocentric visual contexts.

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."

Netflix might be planning to expand into the $150 billion video game industry, according to a media report. The popular streaming company is'excited to do more with interactive entertainment' beyond its popular offerings'from series to documentaries, film, local language originals and reality TV', a spokesperson told DailyMail.com. 'Members also enjoy engaging more directly with stories they love - through interactive shows like Bandersnatch and You v. Wild, or games based on Stranger Things, La Casa de Papel and To All the Boys. So we're excited to do more with interactive entertainment.' The Information, which first broke the news, reports that the Los Gatos, California-based company has approached veteran executives in the industry to lead its efforts.

With the release of the much-anticipated National Security Commission on Artificial Intelligence report, the U.S. must confront an inconvenient truth: America, in the words of co-chairmen Eric Schmidt and Bob Work, "is not prepared to defend or compete in the AI era." Schmidt, the former chief executive of Google, and Work, former deputy secretary of defense, are as deeply versed in this subject as anyone in government or the private sector. Americans should treat this threat as a looming tower. What is the state of play and where does the U.S. go from here? First, let's address the most obvious and concerning opponent in the AI field: China.

Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are generated, what do they mean and what relations exist with the raw inputs. Then users cannot determine whether the predictions provided by DNNs are reliable, and not trust the predictions providing by such "black box" models. Therefore, in this paper, we propose a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms. The proposed model realizes explainable enterprise credit ratings. Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.

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."