Why are patients not finding their doctors online?: Wendy Sue Swanson at TEDxNijmegen 2013.

A journalist who covers wildfires responds to Premee Mohamed's "All That Burns Unseen." In "All That Burns Unseen," set in a dystopian but not-too-distant future, we finally get the drone sidekick we didn't know we needed. Premee Mohamed's heroine, Vaughn Collins, is a government worker gone rogue as a wildfire burns. Along the way, she rescues a dazed, glitchy fire extinguisher drone. When a funnel of flames heads for Vaughn's truck, threatening everything, her new friend dives into the blaze and sprays.

CSET Research Fellows apply their varied experience and expertise to challenging policy questions at the intersection of national security and emerging technology. Comfortable working with empirical data and evidence to support policy recommendations, Research Fellows are expected to lead research projects, brief policymakers, participate in public events, and manage and mentor CSET's Research Analysts, Research Assistants, and student affiliates. They are also expected to work closely with CSET's data scientists to conduct empirical analyses. Generally, Research Fellows are less than 10 years out of graduate school (MA, PhD or JD) programs and are encouraged to enter public service at the conclusion of their fellowship. Please note that each position has separate, detailed position descriptions, requirements and application instructions.

With direct funding plus prize cash that reached into the hundreds of thousands, DARPA inspired worldwide collaborations amongst prime educational establishments in addition to business. A sequence of three preliminary circuit occasions would give groups expertise with every atmosphere. In the course of the Tunnel Circuit occasion, which happened in August 2019 within the Nationwide Institute for Occupational Security and Well being's experimental coal mine, on the outskirts of Pittsburgh, many groups misplaced communication with their robots after the primary bend within the tunnel. Six months later, on the City Circuit occasion, held at an unfinished nuclear energy station in Satsop, Wash., groups beefed up their communications with every part from an easy tethered Ethernet cable to battery-powered mesh community nodes that robots would drop like breadcrumbs as they went alongside, ideally simply earlier than they handed out of communication vary. The Cave Circuit, scheduled for the autumn of 2020, was canceled on account of COVID-19. By the point groups reached the SubT Remaining Occasion within the Louisville Mega Cavern, the main target was on autonomy slightly than communications.

Palantir Technologies announced that it will expand its work with U.S Army Research Laboratory to implement data and (AI)/ (ML) capabilities.

An NHS trust in Liverpool is partnering with Tata Consultancy Services (TCS) to develop an AI chatbot to help tackle patient wait times. Brits have become used to long NHS wait times for many years. There are many strong views on what NHS reforms are needed, but one thing everyone can agree on is that the current trajectory is unsustainable. Modern technologies will be vital in delivering the improvements that will help both NHS staff and patients. The Walton Centre NHS Foundation Trust has announced a partnership with TCS to develop digital solutions that increase the productivity of specialists, reduce waiting times for patients, and improve the overall experience.

Incorporating artificial intelligence has been a key goal for agencies across the executive branch for quite some time. But now, Congress is considering jumping on the bandwagon as well. Lawmakers on the House Select Committee on the Modernization of Congress are interested in exploring just what AI might be able to help them accomplish. Joe Mariani, a research manager for the Deloitte Center for Government Insights, told the committee during a July 28 hearing about... Incorporating artificial intelligence has been a key goal for agencies across the executive branch for quite some time. But now, Congress is considering jumping on the bandwagon as well.

The composition of a macroscopic plasticity model often requires the following steps. First, there are observations of causality relations deduced by modelers to hypothesize mechanisms that lead to the plastic flow. These causality relations along with constraints inferred from physics and universally accepted principles lead to mathematical equations. For instance, the family of Gurson models employs the observation of void growth to employ the yield surface (Gurson, 1977). Crystal plasticity models relate the plastic flow with slip systems to predict the anisotropic responses of single crystals (Rice, 1971; Uchic et al., 2004; Clayton, 2010; Ma and Sun, 2020; Ma et al., 2021). Granular plasticity models propose theories that relate the fabric of force chains and porosity to the onset of plastic yielding and the resultant plastic flow (Cowin, 1985; Kuhn et al., 2015; Wang and Sun, 2018; Sun et al., 2022). Finally, the mathematical equations are then either used directly in engineering analysis and designs (e.g. the Mohr-Coulomb envelope) or are incorporated into a boundary value problem in which the approximation solution can be obtained from a partial differential equation solver that provides incremental updates of stress-strain relations. However, a subtle but significant limitation of this paradigm is that it imposes the burdens on modelers of being able to describe the mechanisms verbally via terminologies or atomic facts (cf.

To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73\% of F1) on distantly supervised RE benchmark datasets.

Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.