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The rise of social media has changed our day to day lives. But more and more reports show that social media and especially social media can impact our brain. Social media addiction might also to a decline in mental health. How does social media changes us? And are the effects by social media addiction reversal?

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Social media has been developed to connect people. However, quite early, scientists found that social media (and social media addiction) can lead to changes in the brain such an enlarged amygdala. First reports surfaced showing that people compare their lives to lives they see on social media and report a decline of mental health upon heavy social media use. It seems like our brains cannot distinguish between social media and the real world. Social media also led to an attention span crisis meaning that we have a harder time to focus if we spend much time on social media. Moreover, social media is able to feed into the reward system of our brains. Everytime we perceive something good dopamine producing cells in the brain release dopamine which leads to a good feeling. Social media has used this mechanism to provide us with a constant stream of good feelings. Social media algorithms have been optimize to show more social media content in a shorter period of time leading to more dopamine. As a result, some argue that social media addiction should be recognized as a mental disorder.
Besides negatively impacting our brains on an individual level social media and social media addiction also impacts society. Last year, a sharp rise in tic symptoms have been reported among teenagers in the US. It seems like that tic-related content on tiktok together with anxiety caused by the COVID-19 pandemic led to this rise in tic-like symptoms. So what should we do about social media? And how can we ensure that our brains are not negatively impacted by the constant stream of dopamine? Well, sometimes the best thing is just to avoid social media for a while.

References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502424/
https://fortune.com/2022/03/31/teen-girls-tourette-tics-tiktok/
https://onlinelibrary.wiley.com/doi/full/10.1111/jpc.15932
https://www.sciencedirect.com/science/article/pii/S0960982214014870
https://onlinelibrary.wiley.com/doi/abs/10.1111/adb.12570
https://www.pnas.org/doi/abs/10.1073/pnas.0903620106
https://www.nature.com/articles/nn.2724?luicode=10000011&lfi.
2724.html.
https://psycnet.apa.org/record/2018-22977-002
https://www.liebertpub.com/doi/full/10.1089/cyber.2021.0324
https://link.springer.com/article/10.1007/s40519-017-0364-2
https://www.tandfonline.com/doi/pdf/10.1080/02673843.2019.1590851
https://www.tandfonline.com/doi/abs/10.1080/00224545.2019.1578725
https://movementdisorders.onlinelibrary.wiley.com/doi/10.1002/mds.28778
https://www.liebertpub.com/doi/abs/10.1089/cyber.2018.0701
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The only things that travel at the speed of light are photons. Nothing with any mass at all can travel at the speed of light because as it gets closer and closer to the speed of light, its mass increases. And if it were actually traveling at the speed of light, it would have an infinite mass. Light does not experience space or time. It’s not just a speed going through something. All of the universe shifts around this constant, the speed of light. Time and space itself stop when you go that speed.

MICHELLE THALLER: Dr. Michelle Thaller is an astronomer who studies binary stars and the life cycles of stars. She is Assistant Director of Science Communication at NASA. She went to college at Harvard University, completed a post-doctoral research fellowship at the California Institute of Technology (Caltech) in Pasadena, Calif. then started working for the Jet Propulsion Laboratory’s (JPL) Spitzer Space Telescope. After a hugely successful mission, she moved on to NASA’s Goddard Space Flight Center (GSFC), in the Washington D.C. area. In her off-hours often puts on about 30lbs of Elizabethan garb and performs intricate Renaissance dances. For more information, visit.
NASA.

TRANSCRIPT: MICHELLE THALLER: So, Tom, you asked the question, “How does mass increase as you go faster?” And this is really a wonderful part of Einstein’s theories. It actually is also relatively slippery and kind of complicated because to even answer this question at all, we have to ask the rather strange question: “What do you mean by mass? What is your definition of mass?” You may have heard that nothing with mass can possibly go at the speed of light. The only things that travel at the speed of light are photons pure energy, light, the speed of light. Nothing with any mass at all can travel at the speed of light because as it gets closer and closer to the speed of light, its mass increases. And if it were actually traveling at the speed of light, it would have an infinite mass. So think about that. Even if you had a tiny little particle that was, say, billions of times less massive than an electron just a tiny, tiny little piece of mass if for some reason, that tiny thing accelerated to the speed of light, it would have an infinite mass. And that’s a bit of a problem. So let’s talk about this. One of the things that you really have to realize is the speed of light is very, very special. It’s not just simply a speed of something moving through space. As you go faster and faster and closer to the speed of light, time itself begins to slow down. And space begins to contract. As you go close to the speed of light, the entire universe becomes smaller and smaller until it basically just becomes a single point when you’re going at the speed of light. And time, as you go closer to the speed of light, gets slower and slower until basically time is a single point at the speed of light. Light does not experience space or time. It’s not just a speed going through something. All of the universe shifts around this constant, the speed of light. Time and space itself stop when you go that speed. So the reason you can’t accelerate to the speed of light, and the reason we say you have an infinite mass is a little complicated because the idea that mass actually is a measurement of energy. You may remember Einstein’s famous equation, E equals MC squared. Energy equals mass times the speed of light squared. Energy and mass are equivalent. Mass is basically a measurement of how much energy there is in an object. When you’re moving, you have the energy of your motion, too. That’s called kinetic energy, energy of motion. So E equals MC squared, now your mass has not just the stuff that’s in you but also the energy of your motion. And that’s why mass seems to increase as you go faster, and faster, and closer to the speed of light. It’s not that you are actually getting any heavier. The increase in mass is something that’s only observed by people that are watching you go by. If you were on a spaceship going very fast at the speed of light, you don’t notice anything getting heavier. You are on your spaceship. You could jump up and down. You can skip rope. You can do whatever you want. You don’t notice any change at all. But if people try to measure your mass as you go by, they not only are measuring your rest mass — your mass when you were still — but this added energy of this h
For the full transcript, check out https://bigthink.com/videos/speed-of-light

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As fantastic as this may seem this is not an impossible occurrence.

Before Einstein, time travel was just a story, but his calculations led us into the quantum world and gave us a more complicated picture of time. Kurt Godel found that Einstein’s equations made it possible to go back in time. What’s up? None of the ideas about how to go back in time were ever physically possible.

Before sending a particle back in time, scientists from ETH Zurich, Argonne National Laboratory, and Moscow Institute of Physics and Technology asked, Why stick to physical grounds?

Many laws of physics treat the future and the past as if they are one thing. The second rule of thermodynamics says that in a closed system, order gives way to chaos (or entropy). When you scramble an egg to make an omelet, you add a lot of chaos to the egg, which was a closed system before.

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Researchers have crafted an artificial intelligence (AI) system capable of deciphering fragments of ancient Babylonian texts. Dubbed the “Fragmentarium,” the algorithm holds the potential to piece together some of the oldest stories ever written by humans, including the Epic of Gilgamesh.

The work comes from a team at Ludwig Maximilian University in Germany who have been attempting to digitize every surviving Babylonian cuneiform tablet since 2018.

The problem with understanding Babylonian texts is that the narratives are written on clay tablets, which today exist only in countless fragments. The fragments are stored at facilities that are continents away from each other, such as the British Museum in London and the Iraq Museum in Baghdad.

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Robotic systems have become increasingly sophisticated over the past decades, improving both in terms of precision and capabilities. This is gradually facilitating the partial automation of some surgical and medical procedures.

Researchers at Tsinghua University have recently developed a soft robotic tentacle that could potentially be used to improve the efficiency of some standard medical procedures. This tentacle, introduced in IEEE Transactions on Robotics, is controlled through their novel control algorithm, together with the so-called active cooling for , the actuating candidate for the robot.

“A neurosurgeon doctor one day came to our lab and asked about the possibility of developing a soft, controllable catheter for him to assist him in his neurosurgeries,” Huichan Zhao, one of the researchers who carried out the study, told Tech Xplore. “He would like this soft catheter to be extremely safe to the surroundings and be able to bend to different directions by a . Starting from these requirements, we developed a soft robotic tentacle.”

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Humans are innately able to reason about the behaviors of different physical objects in their surroundings. These physical reasoning skills are incredibly valuable for solving everyday problems, as they can help us to choose more effective actions to achieve specific goals.

Some computer scientists have been trying to replicate these reasoning abilities in (AI) , to improve their performance on . So far, however, a reliable approach to train and assess the physical reasoning capabilities of AI algorithms has been lacking.

Cheng Xue, Vimukthini Pinto, Chathura Gamage, and colleagues, a team of researchers at the Australian National University, recently introduced Phy-Q, a new designed to fill this gap in the literature. Their testbed, introduced in a paper in Nature Machine Intelligence, includes a series of scenarios that specifically assess an AI agent’s physical reasoning capabilities.

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Michael Levin, Tufts Center for Regenerative and Developmental Biology.
Bioelectric Networks: Taming the Collective Intelligence of Cells for Regenerative Medicine.

Michael Levin, Distinguished Professor in the Biology department and Vannevar Bush Chair, serves as director of the Tufts Center for Regenerative and Developmental Biology. Recent honors include the Scientist of Vision award and the Distinguished Scholar Award. His group’s focus is on understanding the biophysical mechanisms that implement decision-making during complex pattern regulation, and harnessing endogenous bioelectric dynamics toward rational control of growth and form. The lab’s current main directions are:

‱ Understanding how somatic cells form bioelectrical networks for storing and recalling pattern memories that guide morphogenesis;
‱ Creating next-generation AI tools for helping scientists understand top-down control of pattern regulation (a new bioinformatics of shape); and.
‱ Using these insights to enable new capabilities in regenerative medicine and engineering.

Prior to college, Michael Levin worked as a software engineer and independent contractor in the field of scientific computing. He attended Tufts University, interested in artificial intelligence and unconventional computation. To explore the algorithms by which the biological world implemented complex adaptive behavior, he got dual B.S. degrees, in CS and in Biology and then received a PhD from Harvard University. He did post-doctoral training at Harvard Medical School (1996−2000), where he began to uncover a new bioelectric language by which cells coordinate their activity during embryogenesis. His independent laboratory (2000−2007 at Forsyth Institute, Harvard; 2008-present at Tufts University) develops new molecular-genetic and conceptual tools to probe large-scale information processing in regeneration, embryogenesis, and cancer suppression.

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A team of researchers have come up with a machine learning-assisted way to detect the position of shapes including the poses of humans to an astonishing degree — using only WiFi signals.

In a yet-to-be-peer-reviewed paper, first spotted by Vice, researchers at Carnegie Mellon University came up with a deep learning method of mapping the position of multiple human subjects by analyzing the phase and amplitude of WiFi signals, and processing them using computer vision algorithms.

“The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input,” the team concluded in their paper.

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Artificial intelligence (AI) tools have achieved promising results on numerous tasks and could soon assist professionals in various settings. In recent years, computer scientists have been exploring the potential of these tools for detecting signs of different physical and psychiatric conditions.

Depression is one of the most widespread psychiatric disorders, affecting approximately 9.5% of American adults every year. Tools that can automatically detect signs of depression might help to reduce suicide rates, as they would allow doctors to promptly identify people in need of psychological support.

Researchers at Jinhua Advanced Research Institute and Harbin University of Science and Technology have recently developed a deep learning algorithm that could detect depression from a person’s speech. This model, introduced in a paper published in Mobile Networks and Applications, was trained to recognize emotions in by analyzing different relevant features.

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