Scientists from around the world have unveiled the first image of a supermassive black hole.
The black hole image was recorded using the Event Horizon Telescope (EHT) — a planet-scale network of eight radio telescopes around the world. The image reveals the black hole at the center of Messier 87 (M87), a massive galaxy in the nearby Virgo galaxy cluster.
Black holes are extraordinary cosmic objects with enormous masses but extremely compact sizes. The presence of these objects affects their environment in extreme ways, warping spacetime and superheating any surrounding material.
Creating the EHT was a formidable challenge which required upgrading
and connecting a worldwide network of eight pre-existing telescopes
deployed at a variety of challenging high-altitude sites. These
locations included volcanoes in Hawai`i and Mexico, mountains in Arizona
and the Spanish Sierra Nevada, the Chilean Atacama Desert, and
The EHT observations use a technique called very-long-baseline interferometry (VLBI) which synchronises telescope facilities around the world and exploits the rotation of our planet to form one huge, Earth-size telescope observing at a wavelength of 1.3mm. VLBI allows the EHT to achieve an angular resolution of 20 micro-arcseconds — enough to read a newspaper in New York from a café in Paris.
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ACM, the Association for Computing Machinery, named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of the 2018 ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
Bengio is Professor at the University of Montreal and Scientific Director at Mila, Quebec’s Artificial Intelligence Institute; Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of The Vector Institute, and University Professor Emeritus at the University of Toronto; and LeCun is Professor at New York University and VP and Chief AI Scientist, Facebook.
Working independently and together, Hinton, LeCun and Bengio developed conceptual foundations for the field, identified surprising phenomena through experiments, and contributed engineering advances that demonstrated the practical advantages of deep neural networks. In recent years, deep learning methods have been responsible for astonishing breakthroughs in computer vision, speech recognition, natural language processing, and robotics—among other applications.