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Artificial Intelligence Platform Detects Power Grid Flaws And Wildfire Dangers Better And Faster Than Humans

StartX startup Buzz Solutions out of Stanford, California just introduced its AI solution to help utilities quickly spot powerline and grid faults so repairs can be made before wildfires start.

Their unique platform uses AI and machine vision technology to analyze millions of images of powerlines and towers from drones, helicopters, and aircraft to find dangerous faults and flaws as well as overgrown vegetation, in and around the grid infrastructure to help utilities identify problem areas and repair them before a fire starts.

This system can do the analysis at half the cost and in a fraction of the time compared to humans, hours to days not months to years.

New Argonne supercomputer, built for next-gen AI, will be most powerful in U.S.

“‘Aurora will enable us to explore new frontiers in artificial intelligence and machine learning,’ said Narayanan ‘Bobby’ Kasthuri, assistant professor of neurobiology at the University of Chicago and researcher at Argonne. ‘This will be the first time scientists have had a machine powerful enough to match the kind of computations the brain can do.’”

Super computer Aurora will help map the human brain at “quintillion—or one billion billion—calculations per second, 50 times quicker than today’s most powerful supercomputers.”

Note: the article discusses implications beyond neuroscience.


Argonne, DOE and Intel announce exascale computer built for next-generation AI and machine learning.

This AI Creates Dogs From Cats…And More!

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers

Their instrumentation of this paper is available here:
https://app.wandb.ai/stacey/stargan/reports/Cute-Animals-and…zoxNzcwODQ

📝 The paper “StarGAN v2: Diverse Image Synthesis for Multiple Domains” is available here:
- Paper: https://arxiv.org/abs/1912.01865
- Code: https://github.com/clovaai/stargan-v2
- Youtube Video: https://youtu.be/0EVh5Ki4dIY

The paper with the latent space material synthesis is available here:

Gaussian Material Synthesis – ACM Transactions on Graphics (SIGGRAPH 2018) – Károly Zsolnai-Fehér, Peter Wonka, Michael Wimmer

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Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Daniel Hasegan, Eric Haddad, Eric Martel, Gordon Child, Javier Bustamante, Lorin Atzberger, Lukas Biewald, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh.
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Károly Zsolnai-Fehér’s links:

Content-based features predict social media influence operations

Researchers have developed an automated machine learning system they say can detect social media posts involved in coordinated political influence campaigns—such as Russia’s alleged efforts to sway the results of the 2016 elections in the United States—regardless of platform and based only on the content of the posts.

Read more in Science Advances.


We study how easy it is to distinguish influence operations from organic social media activity by assessing the performance of a platform-agnostic machine learning approach. Our method uses public activity to detect content that is part of coordinated influence operations based on human-interpretable features derived solely from content. We test this method on publicly available Twitter data on Chinese, Russian, and Venezuelan troll activity targeting the United States, as well as the Reddit dataset of Russian influence efforts. To assess how well content-based features distinguish these influence operations from random samples of general and political American users, we train and test classifiers on a monthly basis for each campaign across five prediction tasks. Content-based features perform well across period, country, platform, and prediction task. Industrialized production of influence campaign content leaves a distinctive signal in user-generated content that allows tracking of campaigns from month to month and across different accounts.

The same features that make social media useful to activists—low barriers to entry, scalability, easy division of labor, and freedom to produce media targeted at any given country from almost anywhere in the world (1, 2)—also render it vulnerable to industrialized manipulation campaigns by well-resourced actors, including domestic and foreign governments. We define coordinated influence operation as (i) coordinated campaigns by one organization, party, or state to affect one or more specific aspects of politics in domestic or another state, and (ii) through social media, by (iii) producing content designed to appear indigenous to the target audience or state. South Korea conducted the first documented coordinated influence operation on social media in 2012. Since then, what some term political astroturfing has spread widely [on this phenomenon in U.S. domestic politics, see ].

NASA: Virtual Guest Mars 2020 Perseverance

We’re going back to Mars, and we’d like you to be our virtual guest on the trip. On July 30, NASA will launch the Mars 2020 Perseverance rover on a seven-month journey to the Red Planet. After landing in Jezero Crater, the robotic astrobiologist and scientist will search for signs that microbes might have lived on Mars long ago, collect soil samples to be returned to Earth on a future mission and pave the way for human exploration beyond the Moon. Perseverance will be accompanied by a helicopter called Ingenuity, the first attempt at powered flight on another world.

Because of the coronavirus pandemic and in the interest of health and safety, NASA can’t invite you to Florida to watch the launch personally. However, there are many ways you can participate virtually: