Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can. Anthropomorphism is attributing human traits, emotions, or intentions to non-human entities. It is considered to be an innate tendency of human psychology. Anthropomorphism is so endemic that when anthropomorphic AGI robots come along, we will take them for granted. Although there is a cultural acceptance to anthropomorphic objects, anthropomorphic AGI differs in one critical manner: that the interaction is two-way.

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The manufacturing industry includes a broad range of businesses varying in size, from small manufacturers producing limited quantities of items to multi-national organisations involved in the large-scale production of manufactured goods. This industry encompasses manufacturers of food and beverages to textiles, chemical manufacturing and even heavy machinery. As an industry, manufacturing contributes around $100 billion to the Australian GDP annually (ABS, 2020). Thus, an industry with such varying degrees of complexity, breadth and scale is one that seeks to maximise the broad benefits of cloud technologies. Regardless of the end product, commonly faced challenges experienced within the manufacturing industry include innovation, risk management, operational efficiency and cost optimisation, remaining sustainable and time to market.

Soft robotics has the potential to revolutionize robotic locomotion, in particular, soft robotic swimmers offer a minimally invasive and adaptive solution to explore and preserve our oceans. Unfortunately, current soft robotic swimmers are vastly inferior to evolved biological swimmers, especially in terms of controllability, efficiency, maneuverability, and longevity. Additionally, the tedious iterative fabrication and empirical testing required to design soft robots has hindered their optimization. In this work, we tackle this challenge by providing an efficient and straightforward pipeline for designing and fabricating soft robotic swimmers equipped with electrostatic actuation. We streamline the process to allow for rapid additive manufacturing, and show how a differentiable simulation can be used to match a simplified model to the real deformation of a robotic swimmer. We perform several experiments with the fabricated swimmer by varying the voltage and actuation frequency of the swimmer's antagonistic muscles. We show how the voltage and frequency vary the locomotion speed of the swimmer while moving in liquid oil and observe a clear optimum in forward swimming speed. The differentiable simulation model we propose has various downstream applications, such as control and shape optimization of the swimmer; optimization results can be directly mapped back to the real robot through our sim-to-real matching.

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.

Surveillance of health care–associated infection (HAI) is the foundation of infection control and one of the first steps in infection prevention. Traditionally, however, surveillance is performed by infection control professionals (ICPs) who manually review patients' records, searching for defined criteria. Such an approach leaves room for subjective interpretation, resulting in low interrater reliability. Moreover, depending on the surveillance method used -- for instance, a search based on antimicrobial results -- it may have low sensitivity. In Brazil, leaders at Tacchini Hospital and Qualis, a startup that offers infection control advisory and antimicrobial stewardship, have developed a machine-learning–algorithm robot that has been demonstrated to be a reliable tool for identifying patients with HAIs using a semiautomated method.

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "Using AI to Empower Collaborative Team Workflows: Two Implementations for Advance Care Planning and Care Escalation" compares AI implementations for improving the rate of advanced care planning and earlier prediction of clinical deterioration. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, first author Ron C. Li, MD, describes the care escalation intervention and key takeaways from the case study. "Our work starts with a foundational premise: that we need to change how we think about AI in health care," says Li. Instead of starting with a machine learning model and then deciding how to deploy it, Li says that health care should start with a problem and think about AI not as the solution, but as a capability that enables a broader set of solutions. AI will not replace humans in health care, but empower them.

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "AI Insurance: How Liability Insurance Can Drive the Responsible Adoption of Artificial Intelligence in Health Care" explores how AI liability insurance can mitigate predictable risks and uncertainties to health care AI adoption. The big challenge for health care delivery is overcoming institutional mismatch, according to Stern. "The technologies that have the greatest potential to transform health care delivery --this includes, but is not limited, to AI -- would be unrecognizable to the 20th-century architects of our regulatory and health care delivery institutions," says Stern. "And this problem is getting worse. The pace of innovation that we see today coupled with our rapidly transforming analytical and technological capabilities is increasingly mismatched to our existing institutions."

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support" explores how health systems decide which AI products to use. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, senior author Christina Silcox, PhD, shares best practices for choosing health care AI tools. Potential for AI in the health space is enormous, from population health to individual health, health system administration, and biomedical innovation. Silcox and fellow researchers at the Duke-Margolis Center for Health Policy focused on how health systems choose which specific population and individual health tools to use.

A roundtable discussion on artificial intelligence initiatives to improve public health, with the Director of Solution and Experience for Digital Care Transformation at Mass General Brigham, Vice Chair for Clinical Affairs at Geisinger Health, Director of the Surgical Informatics Lab at Harvard Medical School, and Assistant Public Health Officer for the County of Santa Clara Public Health Department.