The Curiosity rover captured a scenic view of Mars’ mountains during its ongoing mission on the Red Planet to find signs of ancient alien life.
Nov 30 (Reuters) — Amazon.com Inc’s (AMZN.O) cloud computing unit on Tuesday introduced two new custom computing chips aimed at helping its customers beat the cost of using chips from Intel Corp (INTC.O) and Nvidia Corp (NVDA.O).
With $45.37 billion in sales in 2020, Amazon Web Services (AWS) is the world’s biggest cloud computing provider and one of the biggest buyers of data center chips, whose computing power AWS rents out to its customers. Ever since buying a startup called Annapurna Labs in 2015, AWS has worked to develop its own custom chips.
On Tuesday, the company released the third generation of its Graviton chip that is designed to compete with central processors from Intel and Advanced Micro Devices (AMD.O). The Graviton3 is 25% faster than its predecessor, and Dave Brown, vice president of Elastic Compute Cloud at Amazon, told Reuters that the company expects it to provide a better performance per dollar than Intel’s chips.
Summary: A newly developed system dubbed Opto-vTrap can temporarily trap vesicles from being released from brain cells.
Source: Institute for Basic Science.
Controlling signal transmission and reception within the brain circuits is necessary for neuroscientists to achieve a better understanding of the brain’s functions. Communication among neuron and glial cells is mediated by various neurotransmitters being released from the vesicles through exocytosis. Thus, regulating vesicular exocytosis can be a possible strategy to control and understand brain circuits.
The Metaverse Group describes itself as “the world’s first virtual real estate company”.
Decentraland space will be used to host virtual fashion shows for avatars.
Our hunter-gatherer ancestors are huddled around a campfire when they suddenly hear the nearby bushes rustling. They have two options: investigate if the movement was caused by small prey such as a rabbit, or flee, assuming there was a predator such as a saber-tooth tiger. The former could lead to a nutritious meal, while the latter could ensure survival. What call do you think our ancestors would have made?
Evolution ensured the survival of those who fled the scene on the margin of safety rather than those who made the best decision by analyzing all possible scenarios. For thousands of years, humans have made snap decisions in fight-or-flight situations. In many ways, the human race learned to survive by jumping to conclusions.
Continue reading “Are You Guilty Of These 3 Cognitive Biases In Decision Making?” »
It feels a bit like a headline ripped from the plotline of the 2013 flick “Gravity” — NASA astronauts suddenly find themselves having to worry more about the threat of space debris whipping around Earth at over 17,000 miles per hour.
Just two weeks after the current crew of the International Space Station had to take emergency shelter in the Russian Soyuz and SpaceX Crew Dragon capsules that are docked to the ISS, NASA has now postponed a planned spacewalk because of the threat.
One source of the increased threat is Russia’s recent anti-satellite missile test that created hundreds, if not thousands, of new pieces of debris in low-earth orbit. On November 15 it was reported that Russia blasted one of its own defunct satellites to smithereens, a move that drew global condemnation.
Most times when we think of deepfakes, we think of the myriad negative applications. From pornography to blackmail to politics, deepfakes are a product of machine learning. They create a lie that is so realistic that it is hard to believe it is not the real thing. In a society plagued by fake news, deepfakes have the potential to do a substantial amount of harm.
But a recent team of researchers found another use for deepfakes — to deepfake the mind. And using machine learning to simulate artificial neural data in this way may make a world of difference for those with disabilities.
For people with full body paralysis, the body can seemingly become a prison. Communicating and the simplest of tasks may appear to be an insurmountable challenge. But even if the body is frozen, the mind may be very active. Brain-computer interfaces (BCIs) offer a way for these patients to interact with the world.
Continue reading “Benefits Of ‘Deepfaking’ The Mind In Creating Brain-Computer Interfaces” »
And it uses components already commercially available.
Engineers at Stanford University have demonstrated a new, simpler design for a quantum computer that could help practical versions of the machine finally become a reality, a report from New Atlas reveals.
The new design sees a single atom entangle with a series of photons, allowing it to process and store more information, as well as run at room temperature — unlike the prototype machines being developed by the likes of Google and IBM.
Continue reading “A New, Simpler Quantum Computer Runs at Room Temperature” »
And it’s down for tests this year.
Last month, Pantuo Aviation revealed the PANTALA Concept H, a sleek-looking flying taxi concept that has strong similarities to Lilium’s ducted fan eVTOL aircraft at the same time as featuring some key design differences.
Continue reading “The PANTALA Concept H Travels at 186 MPH and Has a 155 Mile Range” »
The original 2017 transformer model was designed for natural language processing (NLP), where it achieved SOTA results. Its performance intrigued machine learning researchers, who have since successfully adapted the attention-based architecture to perception tasks in other modalities, such as the classification of images, video and audio. While transformers have shown their power and potential in these areas, achieving SOTA performance requires training a separate model for each task. Producing a single transformer model capable of processing multiple modalities and datasets and sharing its learnable parameters has thus emerged as an attractive research direction.
To this end, a team from Google Research, University of Cambridge and Alan Turing Institute has proposed PolyViT; a single transformer architecture co-trained on image, audio and video that is parameter-efficient and learns representations that generalize across multiple domains.
The PolyViT design is motivated by the idea that human perception is inherently multimodal and previous studies that have demonstrated transformers’ ability to operate on any modality that can be tokenized. PolyViT shares a single transformer encoder across different tasks and modalities, enabling up to a linear reduction in parameters with the number of tasks.
