If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Kindly give this article a like or a comment, so I know that you are still reading. I'm a big believer in A.I.'s ability to enable human civilization to become a multi-planetary species. I think artificial intelligence will be critical in enabling us to make this jump in the brief window afforded to us by time and history since the risks of human extinction will become greater in the decades and centuries ahead. I'm always searching and on the hunt for big stories in how A.I. is shaping our understanding of the world and in terms of business innovation. Sometimes however you have to look up.
Scientists around the world are gearing up to study the first images taken by the James Webb Space Telescope, which are to be released on July 12. Some astronomers will be running machine-learning algorithms on the data to detect and classify galaxies in deep space at a level of detail never seen before. Brant Robertson, an astrophysics professor at the University of California, Santa Cruz, in the US believes the telescope's snaps will lead to breakthroughs that will help us better understand how the universe formed some 13.7 billion years ago. "The JWST data is exciting because it gives us an unprecedented window on the infrared universe, with a resolution that we've only dreamed about until now," he told The Register. Robertson helped develop Morpheus, a machine-learning model trained to pore over pixels and pick out blurry blob-shaped objects from the deep abyss of space and determine whether these structures are galaxies or not, and if so, of what type.
Classifying celestial objects is a long-standing problem. With sources at near unimaginable distances, sometimes it's difficult for researchers to distinguish between objects such as stars, galaxies, quasars or supernovae. Instituto de Astrofísica e Ciências do Espaço's (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classical problem by creating SHEEP, a machine-learning algorithm that determines the nature of astronomical sources. Andrew Humphrey (IA & University of Porto, Portugal) comments: "The problem of classifying celestial objects is very challenging, in terms of the numbers and the complexity of the universe, and artificial intelligence is a very promising tool for this type of task." The first author of the article, now published in the journal Astronomy & Astrophysics, Pedro Cunha, a Ph.D. student at IA and in the Dept. of Physics and the University of Porto, says, "This work was born as a side project from my MSc thesis. It combined the lessons learned during that time into a unique project."
Astronomers have at long last seen the center of the Milky Way galaxy, unmasking a giant black hole, a celestial vortex 26,000 light-years from Earth that should otherwise be hidden from sight. An international team of researchers released on Thursday a snapshot of the supermassive black hole known as Sagittarius A*, spied through the power of eight linked radio dishes from around the world that together can penetrate through murky dust in outer space. Though black holes are by definition unseeable -- light can't travel fast enough to escape their clutches -- Sagittarius A* revealed itself in the form of a black shadow surrounded by the bright glow of the gas and debris swirling around its perimeter. The photo showed a region in deep space reminiscent of a solar eclipse -- a darkened circle, wreathed in a radiant red-orange fuzz of light. The image was colorized so that human eyes could perceive it.
A new class of extremely large telescopes has evolved to collect vast amounts of data; The volume of data collected from an entire survey ten years ago can now be collected in one night. One example of these new generation telescopes is fifth Sloan Digital Sky Survey (SDSS-V), launched in 2020 and slated to collect optical and infrared spectra for more than six million objects during its five-year lifetime . Human inspection is wholly inadequate for dealing with millions of pieces of fuzzy and distorted images. Even traditional, centralized data processing systems can't keep up with the petabytes of astronomical data emerging from new surveys. The solution is a new field called Astro information, which astronomy with machine learning and AI to search for habitable exoplanets, estimate red shifts, and classify galaxies and supernovas .
Imagine if you could look at a snowflake at the South Pole and determine the size and the climate of all of Antarctica. Or study a randomly selected tree in the Amazon rain forest and, from that one tree--be it rare or common, narrow or wide, young or old--deduce characteristics of the forest as a whole. Or, what if, by looking at one galaxy among the hundred billion or so in the observable universe, one could say something substantial about the universe as a whole? A recent paper, whose lead authors include a cosmologist, a galaxy-formation expert, and an undergraduate named Jupiter (who did the initial work), suggests that this may be the case. The result at first seemed "crazy" to the paper's authors.
Rotten Tomatoes recently shared a list of DC Extended Universe (DCEU) movies to explain the timeline. However, they mistakenly featured the Marvel Cinematic Universe (MCU) movie, "Guardians of the Galaxy" title in that list. This mistake has not gone down well with director James Gunn, who was quick to take a dig at the list. Gunn reposted Rotten Tomatoes's tweet on Wednesday, and wrote, "I'm surprised Guardians is after Birds of Prey in the DCEU timeline." The comment came after the DCEU timeline featured a poster of Gunn's recently released TV series, "Peacemaker," but the title was written "Guardians of the Galaxy."
Cosmologist Francisco "Paco" Villaescusa-Navarro has a problem. "We are spending billions of dollars in ground and space telescopes to decipher the mysteries of the universe," he explains, "but we are missing most of the information that the surveys contain." The issue is that in any survey, most of the information is at the very smallest scales. For example, if you look at a picture of a forest, you'll get some information, like a rough idea of how many trees are in there. Once you zoom in a bit, you can see the individual trees and get more information – say, the different species and their heights.
A group of scientists may have stumbled upon a radical new way to do cosmology. Cosmologists usually determine the composition of the universe by observing as much of it as possible. But these researchers have found that a machine learning algorithm can scrutinize a single simulated galaxy and predict the overall makeup of the digital universe in which it exists--a feat analogous to analyzing a random grain of sand under a microscope and working out the mass of Eurasia. The machines appear to have found a pattern that might someday allow astronomers to draw sweeping conclusions about the real cosmos merely by studying its elemental building blocks. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
Citizen scientists have helped researchers discover new types of galaxies, design drugs to fight COVID-19, and map the bird world. The term describes a range of ways that the public can meaningfully contribute to scientific and engineering research, as well as environmental monitoring. As members of the Computing Community Consortium (CCC) recently argued in a Quadrennial Paper, "Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research," non-scientists can help advance science by "providing or analyzing data at spatial and temporal resolutions or scales and speeds that otherwise would be impossible given limited staff and resources." Recently, citizen scientists' efforts have found a new purpose: helping researchers develop machine learning models, using labeled data and algorithms, to train a computer to solve a specific task. This approach was pioneered by the crowdsourced astronomy project Galaxy Zoo, which started leveraging citizen scientists in 2007.