Yahoo Finance speaks with Nikola founder Trevor Milton as the electric- and hydrogen-powered truck maker debuts on the Nasdaq.
A team including researchers from the Department of Chemistry at the University of Tokyo has successfully captured video of single molecules in motion at 1,600 frames per second. This is 100 times faster than previous experiments of this nature. They accomplished this by combining a powerful electron microscope with a highly sensitive camera and advanced image processing. This method could aid many areas of nanoscale research.
When it comes to film and video, the number of images captured or displayed every second is known as the frames per second or fps. If video is captured at high fps but displayed at lower fps, the effect is a smooth slowing down of motion which allows you to perceive otherwise inaccessible details. For reference, films shown at cinemas have usually been displayed at 24 frames per second for well over 100 years. In the last decade or so, special microscopes and cameras have allowed researchers to capture atomic-scale events at about 16 fps. But a new technique has increased this to a staggering 1,600 fps.
Paleontologists at St Petersburg University created the most detailed virtual 3D-model of the endocranial cast and blood vessels of the head of an ankylosaurian.
Paleontologists from St Petersburg University have been the first to study in detail the structure of the brain and blood vessels in the skull of the ankylosaur Bissektipelta archibaldi. It was a herbivorous dinosaur somewhat similar in appearance to a modern armadillo. The first three-dimensional computer reconstruction of a dinosaur endocast made in Russia — a digital cast of its braincase — was of help to the scientists. It made it possible to find out that ankylosaurs, and Bissektipelta in particular, were capable of cooling their brains, had an extremely developed sense of smell, and heard low-frequency sounds. However, their brain was one and a half times smaller than that of modern animals of the same size.
Ankylosaurs appeared on Earth in the middle of the Jurassic — about 160 million years ago — and existed until the end of the dinosaur era, which ended 65 million years ago. These herbivorous animals were somewhat reminiscent of modern turtles or armadillos, were covered with thick armor, and sometimes even had a bony club on the tail. The researchers became interested in the uniquely-preserved remains of ankylosaurs from Uzbekistan. Although these fossils have been known for 20 years, only now have the scientists had a unique opportunity to study the specimens from the inside using cutting-edge methods.
A visualization of the electronic structure of semiconductors made with 2D materials may pave the way for quantum computers and better mobile devices.
Over the last few years, the size of deep learning models has increased at an exponential pace (famously among language models):
And in fact, this chart is out of date. As of this month, OpenAI has announced GPT-3, which is a 175 billion parameter model—or roughly ten times the height of this chart.
As models grow larger, they introduce new infrastructure challenges. For my colleagues and I building Cortex (open source model serving infrastructure), these challenges are front and center, especially as the number of users deploying large models to production increases.
Researchers suggest that an innovative handheld UV light device could effectively sanitize surfaces, killing pathogens including SARS-CoV-2.
When Plato set out to define what made a human a human, he settled on two primary characteristics: We do not have feathers, and we are bipedal (walking upright on two legs). Plato’s characterization may not encompass all of what identifies a human, but his reduction of an object to its fundamental characteristics provides an example of a technique known as principal component analysis.
Now, Caltech researchers have combined tools from machine learning and neuroscience to discover that the brain uses a mathematical system to organize visual objects according to their principal components. The work shows that the brain contains a two-dimensional map of cells representing different objects. The location of each cell in this map is determined by the principal components (or features) of its preferred objects; for example, cells that respond to round, curvy objects like faces and apples are grouped together, while cells that respond to spiky objects like helicopters or chairs form another group.
The research was conducted in the laboratory of Doris Tsao (BS ‘96), professor of biology, director of the Tianqiao and Chrissy Chen Center for Systems Neuroscience and holder of its leadership chair, and Howard Hughes Medical Institute Investigator. A paper describing the study appears in the journal Nature on June 3.
Learning quantum error correction: the image visualizes the activity of artificial neurons in the Erlangen researchers’ neural network while it is solving its task. © Max Planck Institute for the Science of Light.
Neural networks enable learning of error correction strategies for computers based on quantum physics
Quantum computers could solve complex tasks that are beyond the capabilities of conventional computers. However, the quantum states are extremely sensitive to constant interference from their environment. The plan is to combat this using active protection based on quantum error correction. Florian Marquardt, Director at the Max Planck Institute for the Science of Light, and his team have now presented a quantum error correction system that is capable of learning thanks to artificial intelligence.
Going without sleep for too long kills animals but scientists haven’t known why. Newly published work suggests that the answer lies in an unexpected part of the body.
