Omuterema AkhahendaAdmin.
đ€ if it is Optimus, can I buy it with Amazon Prime.
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Alan LightAdmin.
More than just free delivery: self-delivery.
We should really come up with a new term for an autonomous robot that delivers itself this way. Probably some combination of the terms âautoâ (for âselfâ) and âbotâ.⊠See more.
Ron FriedmanJust when I thought I have enough Tesla stocks, it looks like Iâll buy more.
Denoising an image is a classical problem that researchers are trying to solve for decades. In earlier times, researchers used filters to reduce the noise in the images. They used to work fairly well for images with a reasonable level of noise. However, applying those filters would add a blur to the image. And if the image is too noisy, then the resultant image would be so blurry that most of the critical details in the image are lost.
There has to be a better way to solve this problem. As a result, I have implemented several deep learning architectures that far surpass the traditional denoising filters. In this blog, I will explain my approach step-by-step as a case study, starting from the problem formulation to implementing the state-of-the-art deep learning models, and then finally see the results.
Might there be a better way? Perhaps.
A new paper published on the preprint server arXiv describes how a type of algorithm called a âhypernetworkâ could make the training process much more efficient. The hypernetwork in the study learned the internal connections (or parameters) of a million example algorithms so it could pre-configure the parameters of new, untrained algorithms.
The AI, called GHN-2, can predict and set the parameters of an untrained neural network in a fraction of a second. And in most cases, the algorithms using GHN-2âs parameters performed as well as algorithms that had cycled through thousands of rounds of training.
Hibernating astronauts could be the best way to save mission costs, reduce the size of spacecraft by a third and keep crew healthy on their way to Mars. An ESA-led investigation suggests that human hibernation goes beyond the realm of science-fiction and may become a game-changing technique for space travel.
When packing for a return flight to the Red Planet, space engineers account for around two yearsâ worth of food and water for the crew.
âWe are talking about 30 kg per astronaut per day, and on top of that we need to consider radiation as well as mental and physiological challenges,â explains Jennifer Ngo-Anh, ESA research and payload coordinator of Human and Robotic Exploration and one of the authors of the paper that links biology to engineering.
Elon Musk has always said that Neuralink, the company he created in 2016 to build brain-computer interfaces, would do amazing things: Eventually, he says, it aims to allow humans to interact seamlessly with advanced artificial intelligence through thought alone. Along the way, it would help to cure people with spinal cord injuries and brain disorders ranging from Parkinsonâs to schizophrenia.
Now the company is approaching a key test: a human clinical trial of its brain-computer interface (BCI). In December, Musk told a conference audience that âwe hope to have this in our first humansâ in 2022. In January, the company posted a job listing for a clinical trial director, an indication that it may be on track to meet Muskâs suggested timeline.
Musk has put the startup under unrelenting pressure to meet unrealistic timelines, these former employees say. âThere was this top-down dissatisfaction with the pace of progress even though we were moving at unprecedented speeds,â says one former member of Neuralinkâs technical staff, who worked at the company in 2019. âStill Elon was not satisfied.â Multiple staffers say company policy, dictated by Musk, forbade employees from faulting outside suppliers or vendors for a delay; the person who managed that relationship had to take responsibility for missed deadlines, even those outside their control.
The machine-learning model could help scientists speed the development of new medicines.
This technique could help scientists better understand some biological processes that involve protein interactions, like DNA replication and repair; it could also speed up the process of developing new medicines.
âDeep learning is very good at capturing interactions between different proteins that are otherwise difficult for chemists or biologists to write experimentally. Some of these interactions are very complicated, and people havenât found good ways to express them. This deep-learning model can learn these types of interactions from data,â says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.
Ganeaâs co-lead author is Xinyuan Huang, a graduate student at ETH Zurich. MIT co-authors include Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in CSAIL, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering in CSAIL and a member of the Institute for Data, Systems, and Society. The research will be presented at the International Conference on Learning Representations.
Machine-learning model could help scientists speed the development of new medicines.
The AI nanny is here! In a new feat for science, robots and AI can now be paired to optimise the creation of human life. In a Matrix-esque reality, robotics and artificial intelligence can now help to develop babies with algorithms and artificial wombs.
Reported by South China Morning Post, Chinese scientists in Suzhou have developed the new technology. However, there are worries surrounding the ethics of actually artificially growing human babies.
This video covers the world in 2080 and its future technologies. Watch this next video about the world in 2070: https://bit.ly/3nYXvjf.
âș BlockFi: Get Up To $250 In Bitcoin: https://bit.ly/3rPOf1V
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SOURCES:
âą https://www.futuretimeline.net.
âą The Future of Humanity (Michio Kaku): https://amzn.to/3Gz8ffA
âą The Singularity Is Near: When Humans Transcend Biology (Ray Kurzweil): https://amzn.to/3ftOhXI
âą Physics of the Future (Michio Kaku): https://amzn.to/33NP7f7
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One structure on the dwarf planet Ceres made big news but there is a hitch. It seems that the square-shaped form inside a larger triangle, located in a crater. Everyone else saw it, but the use of artificial intelligence might be a square peg not fitting a round hole. This remark by a Spanish neuropsychologist is questioning the veracity of depending on AI, which might be unsound by SETI.
Ceres is located in the main asteroid belt, a dwarf planet, and the biggest object too. One of its craters, Occator had bright lights which lead to several ideas of what it was. NASA sent the Dawn probe to go close enough to capture visual evidence of what these lights were and solve the mystery. These lights were from volcanic ice and salt eruptions, nothing more.
It gets more interesting as researchers based in the University of Cadiz (Spain) have examined images of these spots. Areas like them are called Vinalia Faculae, in an area where geometric contours are very evident for observers. It now serves as a template to compare how machines and humans perceive images on planetary surfaces in general. Tests like these will show artificial intelligence can see technosignatures of other lifeforms besides human-civilization.