For most stars, neutron stars and black holes are their final resting places. When a supergiant star runs out of fuel, it expands and then rapidly collapses on itself. This act creates a neutron star—an object denser than our sun crammed into a space 13 to 18 miles wide. In such a heavily condensed stellar environment, most electrons combine with protons to make neutrons, resulting in a dense ball of matter consisting mainly of neutrons. Researchers try to understand the forces that control this process by creating dense matter in the laboratory through colliding neutron-rich nuclei and taking detailed measurements.
The U.S. Air Force Test Pilot School and the Defense Advanced Research Projects Agency were finalists for the 2023 Robert J. Collier Trophy, a formal acknowledgement of recent breakthroughs that have launched the machine-learning era within the aerospace industry. The teams worked together to test breakthrough executions in artificial intelligence algorithms using the X-62A VISTA aircraft as part of DARPA’s Air Combat Evolution (ACE) program. In less than a calendar year the teams went from the initial installation of live AI agents into the X-62A’s systems, to demonstrating the first AI versus human within-visual-range engagements, otherwise known as a dogfight. In total, the team made over 100,000 lines of flight-critical software changes across 21 test flights. Dogfighting is a highly complex scenario that the X-62A utilized to successfully prove using non-deterministic artificial intelligence safely is possible within aerospace.
“The X-62A is an incredible platform, not just for research and advancing the state of tests, but also for preparing the next generation of test leaders. When ensuring the capability in front of them is safe, efficient, effective and responsible, industry can look to the results of what the X-62A ACE team has done as a paradigm shift,” said Col. James Valpiani, commandant of the Test Pilot School.
Measuring a photon’s angular momentum after it passes through optical devices teaches an algorithm to reconstruct the properties of the photon’s initial quantum state.
An easy-to-use technique could assist everyone from economists to sports analysts.
Pollsters trying to predict presidential election results and physicists searching for distant exoplanets have at least one thing in common: They often use a tried-and-true scientific technique called Bayesian inference.
Bayesian inference allows these scientists to effectively estimate some unknown parameter — like the winner of an election — from data such as poll results. But Bayesian inference can be slow, sometimes consuming weeks or even months of computation time or requiring a researcher to spend hours deriving tedious equations by hand.
Is time an illusion? If not, what is time? Why does time flow forward? You are watching this video. Your brain cells are firing in anticipation. A story is unfolding. Time is moving forward. Or is it? What if I told you that nothing is happening. There is no story unfolding. The story has already been told. The video has already been uploaded and seen by others. You are just watching it one second at a time, so there is a story unfolding for you only. What if your entire life was like this video upload, like a DVD. The story of your life is already on that DVD. The only difference is that you don’t have a forward and reverse button. You are forced to experience your DVD one moment at a time. There is some strong scientific evidence that this may be the true nature of reality. If so, that could mean that everything you think you know is utterly an illusion. Einstein’s theory of relativity supports something called the block universe, which is really a four dimensional space time structure. This means that every event has its own coordinates not only in space but in time. So for example, wherever you are right now corresponds to a location in 3 dimensions, like London, England — and a location in time, 2PM on Feb 2, 2019. But just like the space 10 feet ahead of you is as real as the space 10 feet behind you, so too is the moment 10 minutes into the future and 10 minutes into the past. In other words, the past and future exist just as much as the present. MIT physicist Max Tegmark says we can view the universe as a three dimensional space where stuff happens, or four dimensional block universe where nothing happens. If it is the latter, he says, then change is really an illusion, because nothing is changing. It’s all there – past, present and future – like a DVD. A drama maybe unfolding in the movie recorded on the dvd, but nothing about the DVD is changing in any way. We may have the illusion, at any given moment, that the past already happened and the future doesn’t yet exist, and that things are changing. But the only reason we may have a past is that our brain contains memories of the past. If we did not have any memories, would we have any sense of the past, or of a sense of time at all? Is it possible that time doesn’t actually exist except through our perception of it? Physics doesn’t help us when it comes to the arrow of time – it is time-agnostic. If time was running backwards, all the equations would still be valid. So mathematically, physics does not say that time goes forward or backward. It just says that time Time can not be zero, but it can run either forward or backward without violating any laws. Time is symmetric. But this is counterintuitive. Reality seems to be telling us that time does exist, and that its arrow points in only one direction — forward. Why doesn’t the arrow of time flow backward, if physics says it is equally likely. It would have been possible if it were not for one aspect of physics, and that is the law of entropy. Entropy is a measure of the disorderliness of the universe. Things always get more disorderly. You can scramble and egg, but you can’t unscramble it. This is entropy increasing. Entropy appears to be the only reason the arrow of time is what it is. But why is Entropy always becoming higher? Why doesn’t it become lower? There doesn’t appear to be any fundamental reason for that. Alan Guth, professor at MIT, who pioneered the idea of cosmic inflation, may have solved this riddle. He argues that information and entropy are almost the same thing. In order to know your past, you have to form memories. Adding memories means adding information. Adding information increases entropy. Therefore a conscious system can only be conscious in one direction – when entropy increases, which allows information to increase. This implies that we are conscious because we live in a universe of increasing entropy. Consciousness cannot exist in a universe where entropy decreases. So if entropy has been increasing since the beginning of time, it means that the universe must have started at the lowest possible state of entropy at the beginning…at the big bang. Why then did the universe start off this way, resulting in forward time? Alan Guth says that if the universe is infinitely large, then the total potential entropy of the universe is infinite. If that is the case, then any entropy you start with is low entropy. The entropy will increase from any given starting point he says. This means that it doesn’t matter what the entropy of the big bang was, it would always be the lowest entropy, because there will always be a larger entropy number that the universe can flow to. And seemingly, we exist because time has flowed in a favorable direction for causality to occur, namely, it has flowed forward in our universe. But what about the block universe, are we living inside a DVD?…watch the video for the answer.
What should we look for when trying to find life beyond Earth? Should it be the familiar green and blue colors that we see thriving on our small, blue planet, or something else entirely? This is what a recent study published in the Monthly Notices of the Royal Astronomical Society hopes to address as a team of researchers investigated how identifying purple colors on other worlds, as opposed to the aforementioned green and blue on Earth, could serve as an optimal method in the search for life beyond Earth since many bacteria exhibit purple pigmentation. This study holds the potential to help scientists better understand the criteria for identifying life beyond Earth, and specifically life as we don’t know it.
“Purple bacteria can thrive under a wide range of conditions, making it one of the primary contenders for life that could dominate a variety of worlds,” said Dr. Lígia Fonseca Coelho, a postdoctoral associate at the Carl Sagan Institute (CSI) and lead author of the study.
For the study, the researchers analyzed a myriad of purple sulfur and purple non-sulfur from various oxygenated and non-oxygenated environments with the goal of ascertaining how their physical properties compared with reflectance data derived from several Earth-sized exoplanets. In the end, they produced a data base that can be used to potentially locate purple-colored life on other worlds throughout the cosmos, including Earth analogs, water planets, frozen planets, and snowball planets. The goal of this data is to improve algorithms and additional search methods to identify purple colors instead of green, with the latter being the traditional search baseline.
Professor Jeongho Kwak’s from the Department of Electrical Engineering and Computer Science at DGIST has developed a learning model and resource optimization technology that combines accuracy and efficiency for 6G vision services. This technology is expected to be utilized to address the high levels of computing power and complex learning models required by 6G vision services.
6G mobile vision services are associated with innovative technologies such as augmented reality (AR) and autonomous driving, which are receiving significant attention in modern society. These services enable quick capturing of videos and images, and efficient understanding of their content through deep learning-based models.
However, this requires high-performance processors (GPUs) and accurate learning models. Previous technologies treated learning models and computing/networking resources as separate entities, failing to optimize performance and mobile device resource utilization.
The streaming audio giant’s suite of recommendation tools has grown over the years: Spotify Home feed, Discover Weekly,Blend, Daylist, and Made for You Mixes. And in recent years, there have been signs that it is working. According to data released by Spotify at its 2022 Investor Day, artist discoveries every month on Spotify had reached 22 billion, up from 10 billion in 2018, “and we’re nowhere near done,” the company stated at that time.
Over the past decade or more, Spotify has been investing in AI and, in particular, in machine learning. Its recently launched AI DJ may be its biggest bet yet that technology will allow subscribers to better personalize listening sessions and discover new music. The AI DJ mimics the vibe of radio by announcing the names of songs and lead-in to tracks, something aimed in part to help ease listeners into extending out of their comfort zones. An existing pain point for AI algorithms — which can be excellent at giving listeners what it knows they already like — is anticipating when you want to break out of that comfort zone.
