Lifeboat Foundation

How a single cell slime mold makes smart decisions without a central nervous system. Having a memory of past events enables us to make smarter decisions about the future. Researchers at the Max-Planck Institute for Dynamics and Self-Organization (MPI-DS) and the Technical University of Munich (TUM) have now identified how the slime mold Physarum polycephalum saves memories – although it has no nervous system.

Over the past few decades, researchers have developed deep neural network-based models that can complete a broad range of tasks. Some of these techniques are specifically designed to process and generate coherent texts in multiple languages, translate texts, answer questions about a text and create summaries of news articles or other online content.

Robots that could take on basic healthcare tasks to support the work of doctors and nurses may be the way of the future. Who knows, maybe a medical robot can prescribe your medicine someday? That’s the idea behind 3D structural-sensing robots being developed and tested at Simon Fraser University by Woo Soo Kim, associate professor in the School of Mechatronic Systems Engineering.

We’ve all met people so smart and informed that we don’t understand what they’re talking about. The investment advisor discussing derivatives, the physician elaborating about B cells and T cells, the auto mechanic talking about today’s computerized engines—we trust their decisions, even though we do not completely grasp the meaning of their words.

A research team from Skoltech and FBK (Italy) has presented a methodology to derive 4D building models using historical maps and machine learning. The implemented method relies on geometric, neighborhood, and categorical attributes in order to predict building heights. The method is useful for understanding urban phenomena and changes that contributed to defining our cities’ actual shape. The results were published in Applied Sciences.

In Finland, stormy weather can happen at any time of year. This is an issue because Finland is heavily forested, and falling trees can knock out power lines and disable transformers, causing power blackouts for hundreds of thousands of people a year. Researchers at Aalto University and the Finnish Meteorological Institute (FMI) are using artificial intelligence and machine learning to try and predict when these weather-inflicted blackouts happen. Their new method can now predict these storms days in advance, allowing electricity companies to prepare their repair crews before the storm has even happened.

Meet the Creator and Director of the Radical Remission Docuseries! 👋
For the past fifteen years, Kelly Turner, Ph.D. has conducted research in 10 countries and analyzed over 1500 cases of radical remission. She slowly began interviewing these radical remission survivors—one by one—until their stories filled the pages of her notebook…enough to fill a book, in fact.
Her book Radical Remission went on to be a New York Times bestseller, and now, it’s a docuseries, too—brought to you by Hay House Productions and directed by Kelly Turner herself.
The inspiring Radical Remission Docuseries starts on March 16th — but you can register here now!

👉 https://hhafftrk.com?a=8372&c=8169&p=r&utm_campaign=aff_8372_social_opt-in

Researchers at the University of California, Berkeley, have found a new way to harness properties of light waves that can radically increase the amount of data they carry. They demonstrated the emission of discrete twisting laser beams from antennas made up of concentric rings roughly equal to the diameter of a human hair, small enough to be placed on computer chips.

The total amount of data generated worldwide is expected to reach 175 zettabytes (1 ZB equals 1 billion terabytes) by 2025. If 175 ZB were stored on Blu-ray disks, the stack would be 23 times the distance to the moon. There is an urgent need to develop storage technologies that can accommodate this enormous amount of data.