Induce lucid dreams on demand to unravel the mystery of consciousness.
The combination of ultrasound and machine learning models (created using EEG & fMRI data) allows us to detect when dreamers are in REM to induce and stabilize lucid dreams.
“Dinkinesh really did live up to its name; this is marvelous,” said Hal Levison, referring to the meaning of Dinkinesh in the Amharic language, “marvelous.” Levison is principal investigator for Lucy from the Boulder, Colorado, branch of the San-Antonio-based Southwest Research Institute. “When Lucy was originally selected for flight, we planned to fly by seven asteroids. With the addition of Dinkinesh, two Trojan moons, and now this satellite, we’ve turned it up to 11.”
In the weeks prior to the spacecraft’s encounter with Dinkinesh, the Lucy team had wondered if Dinkinesh might be a binary system, given how Lucy’s instruments were seeing the asteroid’s brightness changing with time. The first images from the encounter removed all doubt. Dinkinesh is a close binary. From a preliminary analysis of the first available images, the team estimates that the larger body is approximately 0.5 miles (790 m) at its widest, while the smaller is about 0.15 miles (220 m) in size.
This encounter primarily served as an in-flight test of the spacecraft, specifically focusing on testing the system that allows Lucy to autonomously track an asteroid as it flies past at 10,000 mph, referred to as the terminal tracking system.
Hussam Amrouch has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. As reported in the journal Nature, the professor at the Technical University of Munich (TUM) applies a new computational paradigm using special circuits known as ferroelectric field effect transistors (FeFETs). Within a few years, this could prove useful for generative AI, deep learning algorithms, and robotic applications.
The basic idea is simple: unlike previous chips, where only calculations were carried out on transistors, they are now the location of data storage as well. That saves time and energy.
“As a result, the performance of the chips is also boosted,” says Hussam Amrouch, a professor of AI processor design at the Technical University of Munich (TUM).
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.
Researchers from MIT
MIT is an acronym for the Massachusetts Institute of Technology. It is a prestigious private research university in Cambridge, Massachusetts that was founded in 1861. It is organized into five Schools: architecture and planning; engineering; humanities, arts, and social sciences; management; and science. MIT’s impact includes many scientific breakthroughs and technological advances. Their stated goal is to make a better world through education, research, and innovation.
Some researchers see formal specifications as a way for autonomous systems to “explain themselves” to humans. But a new study finds that we aren’t understanding.
As autonomous systems and artificial intelligence become increasingly common in daily life, new methods are emerging to help humans check that these systems are behaving as expected. One method, called formal specifications, uses mathematical formulas that can be translated into natural-language expressions. Some researchers claim that this method can be used to spell out decisions an AI will make in a way that is interpretable to humans.
Research Findings on Interpretability.
OpenAI’s first DevDay was jam-packed with updates and announcements. But according to CEO Sam Altman, we’ve yet to scratch the AI surface.
You requested a video exploring the future of medical records, and your wish is our command!
We’re aware that administrative tasks are often the bane of a physician’s work, contributing significantly to burnout. So, let’s embark on a journey together to discover how the future might unfold, and whether artificial intelligence has the potential to lighten this heavy burden.
Gates paints a detailed picture of what our world will look like when everyone has a personalized A.I. assistant.
Connectomics, the ambitious field of study that seeks to map the intricate network of animal brains, is undergoing a growth spurt. Within the span of a decade, it has journeyed from its nascent stages to a discipline that is poised to (hopefully) unlock the enigmas of cognition and the physical underpinning of neuropathologies such as in Alzheimer’s disease.
At its forefront is the use of powerful electron microscopes, which researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Samuel and Lichtman Labs of Harvard University bestowed with the analytical prowess of machine learning. Unlike traditional electron microscopy, the integrated AI serves as a “brain” that learns a specimen while acquiring the images, and intelligently focuses on the relevant pixels at nanoscale resolution similar to how animals inspect their worlds.
“SmartEM” assists connectomics in quickly examining and reconstructing the brain’s complex network of synapses and neurons with nanometer precision. Unlike traditional electron microscopy, its integrated AI opens new doors to understand the brain’s intricate architecture. “SmartEM: machine-learning guided electron microscopy” has been published on the pre-print server bioRxiv.
This was arguably the most momentous week for artificial intelligence since the launch of ChatGPT last year.
It was the biggest week in AI since the launch of ChatGPT.
