Lifeboat Foundation

The researchers started with a sample taken from the temporal lobe of a human cerebral cortex, measuring just 1 mm³. This was stained for visual clarity, coated in resin to preserve it, and then cut into about 5300 slices each about 30 nanometers (nm) thick. These were then imaged using a scanning electron microscope, with a resolution down to 4 nm. That created 225 million two-dimensional images, which were then stitched back together into one 3D volume.

Machine learning algorithms scanned the sample to identify the different cells and structures within. After a few passes by different automated systems, human eyes “proofread” some of the cells to ensure the algorithms were correctly identifying them.

Continue reading “Google and Harvard map brain connections in unprecedented detail” »

With 2700 locations across 10000 U.S. communities, YMCA is becoming a major hub for healthy living — From vaccinations and diabetes prevention programs, to healthy aging and wellness — Siva Balu, VP/Chief Information Officer — The Y of the U.S.A.

Mr. Siva Balu is Vice President and Chief Information Officer of YMCA of the U.S. (Y-USA), where he is working to rethink and reorganize the work of the organization’s information technology strategy to meet the changing needs of Y-USA and Ys throughout the country.

Continue reading “Siva Balu — VP / Chief Information Officer — YMCA of the U.S.A. — People, Potential, & Purpose” »

Deep-learning expert weighs in on getting to AGI, assessing algorithmic intelligence, autonomous vehicles,” “Mark Ryan is a Data Science Manager at Intact Insurance and the author of the recently-released “Deep Learning with Structured Data”. He holds a Masters degree in Computer Science from the University of Toronto, and is interested in chatbots and natural language processing.

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.