Kyiv will lose nearly two-thirds of its deposits if the Kremlin is successful in annexing Ukrainian territory.
At least $12.4 trillion worth of Ukraine’s essential natural resources, including energy and mineral deposits, are now under Russian control.
“The Kremlin is robbing Ukraine” of its natural resources, the backbone of it’s economy, according to an analysis by SecDev posted by Washington Post on August 10.
Facebook (now Meta) popularized the Silicon Valley ethos with the saying “Move fast and break things”. This approach might have worked when disrupting the social media business, but it’s causing all sorts of problems for them as well as other major AI players. Breaking things and moving fast might be the reason why so many AI projects are failing. According to an MIT study, over 85% of AI projects fail to deliver their stated objectives, and 70% of data science projects never make it to fruition. Clearly moving fast and breaking things doesn’t work if you’re not getting closer to success.
There’s a difference between Iterating to Success and Breaking Things.
The oft-cited Silicon Valley ethos of “Move fast and break things” isn’t working that well for AI.
Have you heard about the biggest rocket launch in human history? It’s getting almost zero press coverage, but before breakfast on Monday, August 29, 2022, NASA’s Space Launch System (SLS) rocket will make its maiden voyage.
It’s now on the launchpad in Florida.
Florida prepares to witness its biggest rocket launch ever as NASA sends its “mega moon rocket” on a 42-day mission.
Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.
The researchers, from the University of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimize battery degradation and charging times.
The team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.
Summary: A new language-switching experiment revealed traditional categorization of brain areas may not be sufficient. Researchers set their sights on the caudal inferior parietal cortex to better understand functional categorization in the brain.
Source: Leiden University.
Based on the results of a language-switching experiment, Ph.D. candidate Fatemeh (Simeen) Tabassi Mofrad MA and Professor Niels Schiller have discovered that the traditional categorization of brain areas is not sufficient.
A team of researchers at the Max Planck Institute for Intelligent Systems, working with a pair of colleagues from the Harbin Institute of Technology, has developed a tiny actuated gearbox that can be used to give very tiny robots more power. In their paper published in the journal Science Robotics, the group describes how their gearbox works and the power improvements observed in several types of tiny robots.
Over the past several years, scientists have been working toward the development of tiny robots that can be injected into the human body to carry out medical procedures. The hope is that such robots can be sent to find and destroy cancerous tumors, for example. Such tiny robots are too small to carry their own power plant; thus, they must be manipulated using an external magnetic field. Unfortunately, as the robots grow ever tinier, their power diminishes as they have too little mass. In this new effort, the researchers have found a way to increase the power of the tiny robots using a tiny gearbox that helps them become stronger.
Three-dimensional computing-in-memory circuits based on vertical resistive random-access memory and complementary metal–oxide–semiconductor technologies can be used to create efficient hardware for artificial neural networks.
A team of researchers at Google’s Deep Mind London project, has taught animated players how to play a realistic version of soccer on a computer screen. In their paper published in the journal Science Robotics, the group describes teaching the animated players to play as solo players and also in teams.
For several years, robot engineers have been working diligently to create robots capable of playing soccer. Such work has resulted in competition between various groups to see who can devise the best robot players. And that has led to the creation of RoboCup, which has several leagues, both in the real world and simulated. In this new effort, the researchers applied a new degree of artificial intelligence programming and learning networks to teach simulated robots how to play soccer without ever giving them the rules.
Using a single memory atom in a cavity, a deterministic protocol is implemented to efficiently grow Greenberger–Horne–Zeilinger and linear cluster states by means of single-photon emissions.
