Lifeboat Foundation

Candian artist Patrick Doan discusses his art, cyberpunk philosophy, and his recent trip to Vietnam.

Latent diffusion models (LDMs), a subclass of denoising diffusion models, have recently acquired prominence because they make generating images with high fidelity, diversity, and resolution possible. These models enable fine-grained control of the image production process at inference time (e.g., by utilizing text prompts) when combined with a conditioning mechanism. Large, multi-modal datasets like LAION5B, which contain billions of real image-text pairs, are frequently used to train such models. Given the proper pre-training, LDMs can be used for many downstream activities and are sometimes referred to as foundation models (FM).

LDMs can be deployed to end users more easily because their denoising process operates in a relatively low-dimensional latent space and requires only modest hardware resources. As a result of these models’ exceptional generating capabilities, high-fidelity synthetic datasets can be produced and added to conventional supervised machine learning pipelines in situations where training data is scarce. This offers a potential solution to the shortage of carefully curated, highly annotated medical imaging datasets. Such datasets require disciplined preparation and considerable work from skilled medical professionals who can decipher minor but semantically significant visual elements.

Despite the shortage of sizable, carefully maintained, publicly accessible medical imaging datasets, a text-based radiology report often thoroughly explains the pertinent medical data contained in the imaging tests. This “byproduct” of medical decision-making can be used to extract labels that can be used for downstream activities automatically. However, it still demands a more limited problem formulation than might otherwise be possible to describe in natural human language. By prompting pertinent medical terms or concepts of interest, pre-trained text conditional LDMs could be used to synthesize synthetic medical imaging data intuitively.

Have you ever wondered what makes you intelligent? How are you able to see, hear, think, read, sing, solve problems, and perform any number of intelligent tasks?

Your brain learns a model of the world, and this model recreates the structure of everything you know. Everything you do and experience is based on this model. Intelligence is the ability to create this model of the world.

Continue reading “How the Brain Works: The Thousand Brains Theory of Intelligence | Numenta” »

Mitochondria, bioenergetics, information and electric fields: implications for repair and regeneration.
Professor Michael Levin, Allen Discovery Centre, Tufts University.
Professor Wayne Frasch, Biomedicine and Biotechnology faculty group, Arizona State University.
The Guy Foundation Autumn Series 2022.

Visit our website: www.theguyfoundation.org.

The symptoms of severe obsessive-compulsive disorder, or OCD as it is more popularly known, may be reduced by half with deep brain stimulation, according to a pooled data analysis of the available data, which was recently published in the Journal of Neurology, Neurosurgery, & Psychiatry.

According to the research, two-thirds of individuals who were affected saw a significant improvement after two years.

OCD is characterized by intrusive and persistent obsessive thoughts, as well as dysfunctional and ritualized behaviors. It is estimated that up to 3% of the population is affected by it.

In this video, we’ll discuss whether or not humans should be immortal. We’ll look at the benefits and drawbacks of immortality, and weigh the pros and cons of living forever.

Does living forever sound like a desirable goal? Or is it something you’d rather not risk putting your health and wellbeing at risk? Let’s discuss in this video!

Did you hear that #physicists simulated a baby #wormhole in a lab? Well, it’s even more true that #StringTheory and #ExtraDimensions were discovered in the ’60s. Think I’m joking? I’m not. To learn what’s true/false in the wormhole story, read this first.

Researchers used deep reinforcement learning to steer atoms into a lattice shape, with a view to building new materials or nanodevices.

In a very cold vacuum chamber, single atoms of silver form a star-like lattice. The precise formation is not accidental, and it wasn’t constructed directly by human hands either. Researchers used a kind of artificial intelligence called deep reinforcement learning to steer the atoms, each a fraction of a nanometer in size, into the lattice shape. The process is similar to moving marbles around a Chinese checkers board, but with very tiny tweezers grabbing and dragging each atom into place.

The main application for deep reinforcement learning is in robotics, says postdoctoral researcher I-Ju Chen. “We’re also building robotic arms with deep learning, but for moving atoms,” she explains. “Reinforcement learning is successful in things like playing chess or video games, but we’ve applied it to solve technical problems at the nanoscale.”

It’s claimed to have been created following calls made by Chinese authorities for the independent development of cutting-edge technology.

In East China’s Fujian Province, a huge offshore wind turbine with the world’s largest per-unit capacity has come off the assembly line, according to China’s China Three Gorges Corporation (CTG). According to CTG, the 16-megawatt wind turbine boasts the world’s longest impeller diameter of 827-foot (252 meters), the lightest per megawatt weight, and a 480-foot (146-meter) hub, which is equivalent to the height of a 50-story structure.

Continue reading “Chinese companies claim they have built the world’s biggest 16 MW wind turbine” »