Something seems to be missing from the universe, and the favored model of physics calls it “dark matter” – but despite a century of searching, it remains a no-show. A new paper proposes an alternative hypothesis, showing how gravity could exist without mass and produce many of the same effects we ascribe to dark matter.
Einstein’s theory of general relativity is still our best model for describing gravity. As you might remember from high school physics class, gravity is the force that arises from masses resting on the fabric of spacetime. The more mass an object has, the deeper the “dip” in spacetime and the stronger the gravitational pull.
But starting in the 1930s, some strange astronomical observations began to raise questions. Galaxy clusters seemed to be moving much too fast to stay stable based on visible matter, suggesting that far more matter was present than we could see. That led to the hypothesis that huge amounts of invisible stuff – which was dubbed dark matter – pervaded the universe. The idea has held surprisingly strong in observations in the decades since, backed up by the motions of stars within galaxies and the bending and magnifying of light through gravitational lenses.
A recent study reveals new insights into aurorae across Earth, Jupiter, and Saturn, highlighting the role of magnetic fields and solar winds in shaping these phenomena, with significant implications for space weather forecasting and planetary exploration.
The breathtaking aurorae, commonly known as the Northern and Southern Lights, have captivated human imagination for centuries. From May 10th to 12th, 2024, the most powerful aurora event in 21 years showcased the extraordinary beauty of these celestial light displays.
Recently, space physicists from the Department of Earth Sciences at The University of Hong Kong (HKU), including Professor Binzheng Zhang, Professor Zhonghua Yao, and Dr Junjie Chen, along with their international collaborators, have published a paper in Nature Astronomy that explores the fundamental laws governing the diverse aurorae observed across planets, such as Earth, Jupiter and Saturn. This work provides new insights into the interactions between planetary magnetic fields and solar wind, updating the textbook picture of giant planetary magnetospheres. Their findings can improve space weather forecasting, guide future planetary exploration, and inspire further comparative studies of magnetospheric environments.
Professor Donald Hoffman is a cognitive neuroscientist and the author of more than 90 scientific papers and three books, including Visual Intelligence and The Case Against Reality.
He is best known for his theory of consciousness, which combines evolutionary theory with mathematics to make a compelling case that the reality we see every day is an illusion created by our minds.
Researchers achieve advances in periodic oscillations and transportation for optical pulses, with potential for next-gen optical communications and signal processing.
Researchers have achieved significant advances in wave physics by conducting experiments on Super-Bloch Oscillations (SBOs), which demonstrate the potential for manipulating optical pulses. By applying both DC and nearly detuned AC electric fields, they not only observed SBO collapse for the first time but also extended these oscillations to arbitrary wave driving situations, paving the way for innovative optical communication technologies.
Scientists propose a new way of implementing a neural network with an optical system which could make machine learning more sustainable in the future. The researchers at the Max Planck Institute for the Science of Light have published their new method in Nature Physics, demonstrating a method much simpler than previous approaches.
Machine learning and artificial intelligence are becoming increasingly widespread with applications ranging from computer vision to text generation, as demonstrated by ChatGPT. However, these complex tasks require increasingly complex neural networks; some with many billion parameters. This rapid growth of neural network size has put the technologies on an unsustainable path due to their exponentially growing energy consumption and training times. For instance, it is estimated that training GPT-3 consumed more than 1,000 MWh of energy, which amounts to the daily electrical energy consumption of a small town. This trend has created a need for faster, more energy-and cost-efficient alternatives, sparking the rapidly developing field of neuromorphic computing. The aim of this field is to replace the neural networks on our digital computers with physical neural networks.
While AI has the potential to automate many tasks, there are certain jobs that require human skills and abilities that AI cannot replicate. These include jobs that require creativity, empathy, critical thinking, and human interaction. According to the World Economic Forum, AI is unlikely to be able to replace jobs requiring human skills such as judgement, creativity, physical dexterity and emotional intelligence. Some examples of jobs that AI cannot replace include psychologists, caregivers, most engineers, human resource managers, marketing strategists, and lawyers. In this video, Dr. Michio Kaku mentioned three specific types of jobs that AI cannot replace: blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. These types of jobs require human skills and abilities that are difficult for AI to replicate. For example, blue-collar jobs that are not repetitive often require physical dexterity and mobility. Emotional jobs require empathy and the ability to connect with others on a personal level. Jobs requiring imagination involve creativity and innovation. In conclusion, while AI has the potential to automate many tasks and change the job landscape, there are certain jobs that require human skills and abilities that AI cannot replicate. These include blue-collar jobs that are not repetitive, emotional jobs, and jobs requiring imagination. It is important for individuals to develop these skills in order to thrive in the future job market. Fair Use Disclaimer : Copyright disclaimer under section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, commenting, news reporting, teaching, scholarship and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. Disclaimer: The video and audio content used in this video is for educational purposes only and does not belong to me. I have given credit to the respective owners and creators of the content. This video is intended to provide information and knowledge to its viewers, and no copyright infringement is intended. I have made every effort to ensure that the content used in this video is properly credited and used in accordance with fair use guidelines. If you are the owner of any content used in this video and have any concerns, please contact me. Legal Disclaimer : The video clips incorporated into this project are the sole property of their respective owners and creators. I do not claim ownership or rights to any of the content used. All credit is attributed to the original sources. No copyright infringement is intended. Clips Provided by Cuckoo for Kaku Watch : https://youtu.be/JANGUKLJkPQ #shorts #shortsfeed #shortvideos #shortvideo #shortsvideo #shortsyoutube #shortsviral #viralshortsvideo #viralshorts #viral #viralvideo #viralvideos #space #spaceflightsimulator #deepspace #spaceship #spacelovers #spacesuit #spaceexploration #spacecraft #telescope #spacex #spacestation #universe #cosmos #nasa #viral #viralvideo #viralvideos #science #technology #physics #astronomy #astrophysics #astrophotography #cosmology #cosmos #jwst #jameswebbspacetelescope #jameswebb #hubble #hubbletelescope #video #videos #interstellar
Artificial neural networks—algorithms inspired by biological brains—are at the center of modern artificial intelligence, behind both chatbots and image generators. But with their many neurons, they can be black boxes, their inner workings uninterpretable to users.
Researchers have now created a fundamentally new way to make neural networks that in some ways surpasses traditional systems. These new networks are more interpretable and also more accurate, proponents say, even when they’re smaller. Their developers say the way they learn to represent physics data concisely could help scientists uncover new laws of nature.
Full coherent control of wave transport and localization is a long-sought goal in wave physics research, which encompasses many different areas from solid-state to matter-wave physics and photonics. One among the most important and fascinating coherent transport effects is Bloch oscillation (BO), which refers to the periodic oscillatory motion of electrons in solids under a direct current (DC)-driving electric field.
