Master of Science in Geoinformatics at TERI SAS is a two years interdisciplinary program for students who want to develop expertise in and applying geospatial technologies to solve world's most pressing real-world challenges in environmental, social and economic domains. Geoinformatics is a rapidly evolving field that brings meaningful insights to solve real world problems by bringing together technologies and tools required for acquisition, exploration, visualization, analysis and integration of various spatial data. There are several components of Geoinformatics that include cartographic geovisualization, GIS, Remote sensing, photogrammetry, spatial statistics, geostatistics, multivariate statistics and other advanced tools and techniques. The core strength of the programme lies in its innovative curriculum that imbues present and future professionals on development and the use of cutting-edge geospatial technologies to emulate real-life problems. Over the period of two years, students gain sound knowledge in the scientific principles behind computational and analytical foundation of Geoinformatics as well as its applications in domains such as conservation biology, urban planning, meteorology and natural resource management through hands-on exercises, training programmes, 8 weeks summer internship, independent study and a semester long major project.

This image taken by NASA's Hubble Space Telescope shows a spiral galaxy (bottom left) in front of a large galaxy cluster. New research leveraged an artificial tool to estimate the masses of galaxy clusters more accurately. Astronomers at the Institute for Advanced Study and the Flatiron Institute, along with their collaborators, have utilized artificial intelligence to improve the method of calculating the mass of massive clusters of galaxies. The AI revealed that by incorporating a simple term into an existing equation, researchers can now achieve much more accurate mass estimates than before. The newly enhanced calculations will allow scientists to determine the basic characteristics of the universe with greater precision, according to a report by the astrophysicists, which was published in the Proceedings of the National Academy of Sciences.

Astrophysicists at the Institute for Advanced Study, the Flatiron Institute and their colleagues have leveraged artificial intelligence to uncover a better way to estimate the mass of colossal clusters of galaxies. The AI discovered that by just adding a simple term to an existing equation, scientists can produce far better mass estimates than they previously had. The improved estimates will enable scientists to calculate the fundamental properties of the universe more accurately, the astrophysicists reported in the Proceedings of the National Academy of Sciences. "It's such a simple thing; that's the beauty of this," says study co-author Francisco Villaescusa-Navarro, a research scientist at the Flatiron Institute's Center for Computational Astrophysics (CCA) in New York City. "Even though it's so simple, nobody before found this term. People have been working on this for decades, and still they were not able to find this."

This launch event for the Royal Society volume New Approaches to 3D Vision explores how AI, animals, & humans see & navigate the 3D world. In Artificial Intelligence (AI), 3D vision is enabling autonomous cars & robots to freely navigate the world & helping AI to solve fundamental scientific questions like protein folding. In animals, brain recordings from freely moving animals are enabling us to understand how animals process & navigate through space. In humans, virtual reality, augmented reality, & 3D cinema are all having a transformative effect on our 3D visual experience. In turn, these innovations are revolutionizing our understanding of 3D vision & navigation.

In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment, and you will build projects in NumPy and PyTorch. You will learn from experts in the field, and gain exclusive insights from working professionals. For anyone interested in building expertise with this transformational technology, this Nanodegree program is an ideal point-of-entry. In this program, you'll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence.

Boffins from UC Berkeley, MIT, and the Institute for Advanced Study in the United States have devised techniques to implant undetectable backdoors in machine learning (ML) models. Their work suggests ML models developed by third parties fundamentally cannot be trusted. In a paper that's currently being reviewed – "Planting Undetectable Backdoors in Machine Learning Models" – Shafi Goldwasser, Michael Kim, Vinod Vaikuntanathan, and Or Zamir explain how a malicious individual creating a machine learning classifier – an algorithm that classifies data into categories (eg "spam" or "not spam") – can subvert the classifier in a way that's not evident. "On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation," the paper explains. "Importantly, without the appropriate'backdoor key,' the mechanism is hidden and cannot be detected by any computationally-bounded observer."

DIGIMAN aims at contributing to successfully shaping workforce transformation by developing a post-graduation for the future T-shaped professionals with a combination of high-tech skills (cyber-physical systems and IoT, analytics and machine learning, robotics and Artificial Intelligence, lean 4.0) and general skills across multiple domains (creativity, innovation, entrepreneurship, etc.). DIGIMAN trains professionals that will contribute to a wider implementation of advanced manufacturing technologies with the provision of the right skills. DIGIMAN targets engineers and managers with professional experience and offers to upgrade their knowledge with new expertise on industry 4.0 and improve their qualifications. For more information visit our webpage or send us email to digiman.eitm@fe.up.pt DIGIMAN is organised in 2 semesters, with 5 courses in each semester, and will be delivered using the EIT Manufacturing Skills.move platform.

IMAGE: As a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence, Regenstrief Institute Research Scientist Suranga Kasthurirathne, PhD, is studying the role of operationalizing artificial intelligence (AI) within... view more INDIANAPOLIS -- Regenstrief research scientist and Indiana University School of Medicine faculty member Suranga Kasthurirathne, PhD, has been selected as a fellow of the 4th Intercontinental Academia (ICA): Intelligence and Artificial Intelligence. He and the other outstanding early and midcareer researchers chosen as fellows will work together on cross-disciplinary projects while being mentored by some of the most renowned scientists from around the world, including Nobel Prize winners. Through its fellowship program, the ICA seeks to create a global network of future research leaders. Each fellow proposes a project. Dr. Kasthurirathne's focuses on the role of operationalizing artificial intelligence (AI) within learning health systems.

An international team of researchers has produced a map of the dark matter within the local universe, using a model to infer its location due to its gravitational influence on galaxies (black dots). These density maps -- each a cross section in different dimensions -- reproduce known, prominent features of the universe (red) and also reveal smaller filamentary features (yellow) that act as hidden bridges between galaxies. A new map of dark matter in the local universe reveals several previously undiscovered filamentary structures connecting galaxies. The map, developed using machine learning by an international team including a Penn State astrophysicist, could enable studies about the nature of dark matter as well as about the history and future of our local universe. Dark matter is an elusive substance that makes up 80% of the universe.

As the name implies, this course takes a more applied perspective than Andrew Ng's machine learning lecture at Stanford. You will see more code than mathematics. Concepts and algorithms are using the popular Python libraries scikit-learn and Keras.