The United States has the highest maternal mortality rate of all high-income countries. Compared to women in Canada and France, women in the United States are twice as likely to die from childbirth complications. This crisis is especially pronounced in ethnic and racial minority populations: Black and Native American women in the United States are much more likely to perish from pregnancy-related complications than their white counterparts and are more likely to suffer severe maternal morbidity due to postpartum hemorrhage, hypertensive disorders, and sepsis. The impact of the Covid-19 pandemic on these groups is not yet known, but given the way it has exacerbated racial inequities nationally and globally, it is expected to have made the situation worse. However, data from the U.S. Centers for Disease Control and Prevention (CDC) suggests that approximately 60% of maternal deaths are preventable. Not only would this strategy improve outcomes, it would also significantly reduce medical costs.

I am constantly looking to learn new things and use that to push the limits of my imagination - I have known for a while that #walmart has a patent on #robot bees - Walmart Now let's take a step further. What if we could bring back animals that have long been extinct with this... What resources have we lost due to animals going extinct #earth #extinction #machinelearning Selective breeding does violate animal rights. So, what resources could we gain by selective breeding of new species as robots instead of animals Citizens' Climate Lobby

Orbit Logic has been awarded a Phase I Small Business Technology Transfer (STTR) contract by NASA to develop its proof of concept for Lunar Fault Learning Agent for Prediction, Protection and Early Response (Lunar FLAPPER) – an onboard software solution that would automate routine tasks and perform rapid and intelligent responses to degradation or failures of spacecraft systems, resulting in improved mission results and safer crew environments. The solution could be relevant to systems such as NASA's planned Lunar Gateway, a multi-purpose outpost orbiting the Moon, which will be uninhabited for periods of up to nine months. When the spacecraft is not occupied by crew, robust autonomy would significantly ease (or even eliminate) mission control operator workload and safely maintain systems until astronauts return; Lunar FLAPPER is being developed in collaboration with the University of Maryland, College Park (UMD). Traditionally, spacecraft have implemented hard-coded, rules-based fault trees and sometimes require operators to be in-the-loop. These types of approaches are rigid and are not adaptive to evolving conditions or emerging fault types.

A new framework for neural networks' processing enables artificial intelligence to better judge objects and potential threats in hostile environments. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and university partners from the Internet of Battlefield Things Collaborative Research Alliance, or IoBT CRA, developed a method for neural networks to be more confident in their understanding of battlefield environments. To achieve this, researchers reviewed frameworks to represent uncertainty, categorized sources of uncertainty in military information-networks' common operating environment, and most importantly created solutions to manage uncertainty within systems. The researchers developed insights from the uncertainty management approaches into a workflow that maximizes effectiveness in accomplishing mission goals despite the presence of uncertainty in data inputs. Through this process, they teach neural networks when to say, "I am sure," and be right about it.

The Department of Health and Human Services is developing an artificial intelligence playbook to help teams overcome common obstacles and challenges that come with implementing AI technologies. HHS Chief AI Officer Oki Mek discussed the playbook and how it plays into his overall priority of making AI a collaboratively cultivated technology at the agency during a NextGov event July 29. He said that one of the elements that he hopes to include in the playbook is to help with barriers to data acquisition, which he added is especially difficult within HHS. "Having a playbook could really help in terms of, what are the obstacles that you will encounter when you go on this AI, machine learning journey, because the two biggest obstacles are really the data acquisition, getting the data, especially with Health and Human Services because health records and data are very heavily regulated, so data acquisition will be tough," Mek said. "We could help provide some guidance and some lessons learned, some best practices around that." Mek added that the playbook could also provide some guidance around cleaning data, since cleaning and processing data is a big component of getting it ready for AI usage. The playbook will also provide definitions around AI, which Mek argued is a broad term that can have different meanings and applications.

Mind mapping is a creative thinking tool that has been in use for centuries. In the third century BC, Porphyry of Tyros used the tool to organize the works of Aristotle, one of the greatest thinkers ever. These tools are still popular and widely used by companies and individuals across the world. Microsoft Chairman Bill Gates and former Vice President Al Gore are said to be fans of online mind mapping tools. A mind map is a collection of ideas that have been put into the format of a visual diagram.

This report from the Montreal AI Ethics Institute covers the most salient progress in research and reporting over the second quarter of 2021 in the field of AI ethics with a special emphasis on "Environment and AI", "Creativity and AI", and "Geopolitics and AI." The report also features an exclusive piece titled "Critical Race Quantum Computer" that applies ideas from quantum physics to explain the complexities of human characteristics and how they can and should shape our interactions with each other. The report also features special contributions on the subject of pedagogy in AI ethics, sociology and AI ethics, and organizational challenges to implementing AI ethics in practice. Given MAIEI's mission to highlight scholars from around the world working on AI ethics issues, the report also features two spotlights sharing the work of scholars operating in Singapore and Mexico helping to shape policy measures as they relate to the responsible use of technology. The report also has an extensive section covering the gamut of issues when it comes to the societal impacts of AI covering areas of bias, privacy, transparency, accountability, fairness, interpretability, disinformation, policymaking, law, regulations, and moral philosophy.

Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model, especially, emerging deep neural network model, relies on a large volume of training data and high-powered computational resources. The need for a vast volume of available data raises serious privacy concerns because of the risk of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data. Furthermore, a trained ML model may also be vulnerable to adversarial attacks such as membership/property inference attacks and model inversion attacks. Hence, well-designed privacy-preserving ML (PPML) solutions are crucial and have attracted increasing research interest from academia and industry. More and more efforts of PPML are proposed via integrating privacy-preserving techniques into ML algorithms, fusing privacy-preserving approaches into ML pipeline, or designing various privacy-preserving architectures for existing ML systems. In particular, existing PPML arts cross-cut ML, system, security, and privacy; hence, there is a critical need to understand state-of-art studies, related challenges, and a roadmap for future research. This paper systematically reviews and summarizes existing privacy-preserving approaches and proposes a PGU model to guide evaluation for various PPML solutions through elaborately decomposing their privacy-preserving functionalities. The PGU model is designed as the triad of Phase, Guarantee, and technical Utility. Furthermore, we also discuss the unique characteristics and challenges of PPML and outline possible directions of future work that benefit a wide range of research communities among ML, distributed systems, security, and privacy areas.

Teamwork is cooperative, participative and power sharing. In science of science, few studies have looked at the impact of team collaboration from the perspective of team power and hierarchy. This research examines in depth the relationships between team power and team success in the field of Computer Science (CS) using the DBLP dataset. Team power and hierarchy are measured using academic age and team success is quantified by citation. By analyzing 4,106,995 CS teams, we find that high power teams with flat structure have the best performance. On the contrary, low-power teams with hierarchical structure is a facilitator of team performance. These results are consistent across different time periods and team sizes.

Political stance detection has become an important task due to the increasingly polarized political ideologies. Most existing works focus on identifying perspectives in news articles or social media posts, while social entities, such as individuals and organizations, produce these texts and actually take stances. In this paper, we propose the novel task of entity stance prediction, which aims to predict entities' stances given their social and political context. Specifically, we retrieve facts from Wikipedia about social entities regarding contemporary U.S. politics. We then annotate social entities' stances towards political ideologies with the help of domain experts. After defining the task of entity stance prediction, we propose a graph-based solution, which constructs a heterogeneous information network from collected facts and adopts gated relational graph convolutional networks for representation learning. Our model is then trained with a combination of supervised, self-supervised and unsupervised loss functions, which are motivated by multiple social and political phenomenons. We conduct extensive experiments to compare our method with existing text and graph analysis baselines. Our model achieves highest stance detection accuracy and yields inspiring insights regarding social entity stances. We further conduct ablation study and parameter analysis to study the mechanism and effectiveness of our proposed approach.