Lifeboat Foundation

I think we should. If it is corrupt or makes mistakes, it will at least be correctable.

LONDON — A study has found that most Europeans would like to see some of their members of parliament replaced by algorithms.

Researchers at IE University’s Center for the Governance of Change asked 2769 people from 11 countries worldwide how they would feel about reducing the number of national parliamentarians in their country and giving those seats to an AI that would have access to their data.

Continue reading “More than half of Europeans want to replace lawmakers with AI, study says” »

Researchers from Zurich have developed a compact, energy-efficient device made from artificial neurons that is capable of decoding brainwaves. The chip uses data recorded from the brainwaves of epilepsy patients to identify which regions of the brain cause epileptic seizures. This opens up new perspectives for treatment.

Current neural network algorithms produce impressive results that help solve an incredible number of problems. However, the electronic devices used to run these algorithms still require too much processing power. These artificial intelligence (AI) systems simply cannot compete with an actual brain when it comes to processing sensory information or interactions with the environment in real time.

Transmission electron microscopy (TEM) is a technique that involves beaming electrons through a specimen to form an image. This enables the generation of significantly higher resolution than traditional optical microscopes. While the latter devices are typically limited to around 1000x magnification due to the resolving power of visible light, TEM can provide zoom capabilities that are orders of magnitude greater – surpassing even a scanning electron microscope (SEM).

In recent years, TEM instruments have begun to reach extraordinary levels of detail. Spatial resolutions are now edging into the realm of individual atoms, measuring less than 0.0000005 millimetres (mm).

However, TEM is prone to lens aberrations and multiple scattering, limiting its use to samples thin enough to let electrons pass through. The process is technically challenging and requires additional tools to perform. In 2018, researchers at Cornell University offered a potential solution. They built a high-powered detector combined with a new algorithm-driven process called ptychography. This achieved a new record for microscopic resolution, tripling the previous state-of-the-art.

Researchers created an algorithm to identify similar cell types from species – including fish, mice, flatworms and sponges – that have diverged for hundreds of millions of years, which could help fill in gaps in our understanding of evolution.

Cells are the building blocks of life, present in every living organism. But how similar do you think your cells are to a mouse? A fish? A worm?

Comparing cell types in different species across the tree of life can help biologists understand how cell types arose and how they have adapted to the functional needs of different life forms. This has been of increasing interest to evolutionary biologists in recent years because new technology now allows sequencing and identifying all cells throughout whole organisms. “There’s essentially a wave in the scientific community to classify all types of cells in a wide variety of different organisms,” explained Bo Wang, an assistant professor of bioengineering at Stanford University.

Summary: A new algorithm that uses data from memory tests and blood samples is able to accurately predict an individual’s risk for developing Alzheimer’s disease.

Source: Lund University.

Researchers at Lund University in Sweden have developed an algorithm that combines data from a simple blood test and brief memory tests, to predict with great accuracy who will develop Alzheimer’s disease in the future.

Google and national hospital chain HCA will work to develop algorithms to help improve operating efficiency, monitor patients and guide doctors’ decisions.

Cosmic rays are high-energy atomic particles continually bombarding Earth’s surface at nearly the speed of light. Our planet’s magnetic field shields the surface from most of the radiation generated by these particles. Still, cosmic rays can cause electronic malfunctions and are the leading concern in planning for space missions.

Researchers know cosmic rays originate from the multitude of stars in the Milky Way, including our sun, and other galaxies. The difficulty is tracing the particles to specific sources, because the turbulence of interstellar gas, plasma, and dust causes them to scatter and rescatter in different directions.

In AIP Advances, University of Notre Dame researchers developed a simulation model to better understand these and other cosmic ray transport characteristics, with the goal of developing algorithms to enhance existing detection techniques.

Creating robots that can perform acrobatic movements such as flips or spinning jumps can be highly challenging. Typically, in fact, these robots require sophisticated hardware designs, motion planners and control algorithms.

Researchers at Massachusetts Institute of Technology (MIT) and University of Massachusetts Amherst recently designed a new humanoid robot supported by an actuator-aware kino-dynamic motion planner and a landing controller. This design, presented in a paper pre-published on arXiv, could allow the humanoid robot to perform back flips and other acrobatic movements.

“In this work, we tried to come up with realistic control algorithm to make a real humanoid robot perform acrobatic behavior such as back/front/side-flip, spinning jump, and jump over an obstacle,” Donghyun Kim, one of the researchers who developed the robot’s software and controller, told TechXplore. “To do that, we first experimentally identified the actuator performance and then represent the primary limitations in our motion planner.”

Strong AI will make excellent scientists.

Calculating the most influential scientific equations is no easy task. But these five certainly rank in the top tier.