Artificial intelligence (AI) isn't just transforming the world -- it's helping protect and preserve the future of the Amazon River. Rapid hydropower expansion has radically altered the Amazon River. When the natural flow of a river is altered, there are often serious, cascading changes. Now, AI and other computer science tools can help reduce these adverse and devastating effects on the environment, according to new research published in Science. FIU researcher Elizabeth Anderson was a part of a collaborative team of scientists from across the United States, Europe and South America who examined how cutting-edge technology can inform more sustainable and strategic planning.

It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

In 2018, while the Argentine Congress was hotly debating whether to decriminalize abortion, the Ministry of Early Childhood in the northern province of Salta and the American tech giant Microsoft presented an algorithmic system to predict teenage pregnancy. They called it the Technology Platform for Social Intervention. Diego Jemio is a journalist, educator, and podcaster. He currently writes for the Clarín newspaper (Buenos Aires), Vértice (Mexico), and other media. He is the creator of the podcast Epistolar.

In 2020, IDRC and the Swedish International Development Cooperation Agency (Sida) launched the Artificial Intelligence for Development in Africa (IAPD Africa) program. This program aims to support the AI community and policymakers in developing responsible, ethical, and equitable AI that meets the continent's challenges, under the leadership of Africa. IDRC, the International Development Research Centre, was established in Canada in 1970 with a mission "to initiate, encourage, support and conduct research into the problems of the developing regions of the world and into the application of scientific, technical and other knowledge for the economic and social advancement of those regions . IDRC sees climate change and inequality, combined with the HIV/AIDS pandemic, as major obstacles to achieving the UN's sustainable development goals, and it is these challenges that it helps to address. While the center is headquartered in Ottawa, Canada, its five regional offices are located in India, Jordan, Kenya, Senegal, and Uruguay to be as close as possible to the researchers and projects it funds.

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state of affairs, this work proposes efficient subgraph-level training via resource-aware graph partitioning (SUGAR). SUGAR first partitions the initial graph into a set of disjoint subgraphs and then performs local training at the subgraph-level. We provide a theoretical analysis and conduct extensive experiments on five graph benchmarks to verify its efficacy in practice. Our results show that SUGAR can achieve up to 33 times runtime speedup and 3.8 times memory reduction on large-scale graphs. We believe SUGAR opens a new research direction towards developing GNN methods that are resource-efficient, hence suitable for IoT deployment.

Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variables (e.g., cardiac contractility and output and vascular resistance) that have a strong effect on the patients diagnosis and treatment. We do not have any information about this specific patient, but, extensive offline information is available about previous patients, that may only be partially related to the present patient (a case of dataset shift). This information constitutes our prior knowledge, and is both partial and approximate. The basic question is how to best use this prior knowledge, combined with online patient data, to assist in diagnosing the current patient most effectively. Our proposed approach consists of three stages: (i) Use the abundant offline data in order to create both a non-causal and a causal estimator for the relevant unmeasured physiological variables. (ii) Based on the non-causal estimator constructed, and a set of measurements from a new group of patients, we construct a causal filter that provides higher accuracy in the prediction of the hidden physiological variables for this new set of patients. (iii) For any new patient arriving in the ICU, we use the constructed filter in order to predict relevant internal variables. Overall, this strategy allows us to make use of the abundantly available offline data in order to enhance causal estimation for newly arriving patients. We demonstrate the effectiveness of this methodology on a (non-medical) real-world task, in situations where the offline data is only partially related to the new observations. We provide a mathematical analysis of the merits of the approach in a linear setting of Kalman filtering and smoothing, demonstrating its utility.

Abstract-- Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle.

The United States military just inched one step closer to bringing autonomous helicopters to the battlefield. Like most strange feats of advanced military technology, this one comes from The Pentagon's Defense Advanced Research Projects Agency, better known simply as "DARPA." On Tuesday, DARPA said a UH-60A Black Hawk helicopter outfitted with its experimental Aircrew Labor In-Cockpit Automation System (ALIAS) system safely completed a test flight without anyone in the chopper. The 30-minute test flight occurred over the weekend above a U.S. Army installation at Fort Campbell, Kentucky. DARPA describes its Aircrew Labor In-Cockpit Automation System (ALIAS) as a "tailorable, drop-in, removable kit," meant to add sophisticated automation to pre-built aircraft at a fraction of the cost of upgrading individual models with new, advanced avionics and software.

Given the segment's solid growth prospects, we think it could be wise to bet on quantum computing stocks Microsoft (MSFT), Alphabet (GOOGL), International Business Machines (IBM), and Hitachi (HTHIY). Given quantum computing's growth prospects, tech giants are investing significantly in the space to grab market share. Technology giant MSFT's broad product portfolio includes personal computers, tablets, gaming and entertainment consoles, and related accessories. On Jan. 25, 2022, Satya Nadella, chairman and CEO, MSFT, said, "As tech as a percentage of global GDP continues to increase, we are innovating and investing across diverse and growing markets, with a common underlying technology stack and an operating model that reinforces a common strategy, culture, and a sense of purpose." Mountain View, Calif.-based GOOGL provides online advertising services in the United States, Europe, the Middle East, Africa, Asia-Pacific, Canada, and Latin America.

According to recent surveys, 83% of Americans would like a remote 4-day work week. People are willing to work longer hours for more flexibility. It is estimated that 23% of the US workforce plan to quit their jobs in 2022 and 70% in Q1. This creates an immediate need for a global workforce that leverages people, processes, and technology – the gig workforce is an inevitable component of the future economy. BraynCX CEO and industry thought leader, Tariq Alinur, believes that video conferencing, Human assisted bots, and artificial intelligence (AI) technology will help to facilitate this change.