MAROKO133 Hot ai: The James Webb Appears to Have Spotted “Dark Star” Powered by Dark Matte

📌 MAROKO133 Update ai: The James Webb Appears to Have Spotted “Dark Star” Powered

Astronomers say NASA’s James Webb Space Telescope may have spotted the universe’s first “dark stars,” primordial bodies of hydrogen and helium that bear almost no resemblance to the nuclear fusion-powered stars we’ve come to know.

As detailed in a recent paper published in the journal Proceedings of the National Academy of Sciences, the team suggests that the very early days of the universe, mere hundreds of millions of years after the Big Bang, may have been home to supermassive dark stars, which were powered by dark matter that eventually led them to self-destruct.

“Supermassive dark stars are extremely bright, giant, yet puffy clouds made primarily out of hydrogen and helium, which are supported against gravitational collapse by the minute amounts of self-annihilating dark matter inside them,” said study lead and Colgate University astrophysicist Cosmin Ilie in a statement.

Dark matter is the invisible, hypothetical substance that’s believed to make up approximately 25 percent of the universe. Despite its expected abundance, scientists have yet to directly observe or detect it, since its existence is inferred from its gravitational effects on visible matter.

The existence of supermassive dark stars could explain why the JWST is finding bright and unexpectedly common galaxies in the furthest reaches of the universe. The supermassive black holes that result from these dark stars could also account for the formation of distant quasars, which are extremely bright galactic nuclei powered by black holes at the centers of galaxies.

The theory behind dark stars was first devised in the late 2000s. Since then, researchers have suggested that they could be the result of “Weakly Interacting Massive Particles,” a leading candidate for dark matter, that are believed to annihilate themselves and generate heat in a phenomenon that appears as brightly shining stars.

A few hundred million years after the Big Bang would have allowed for the right conditions for these dark stars to form, the researchers suggest.

“For the first time, we have identified spectroscopic supermassive dark star candidates in JWST, including the earliest objects at redshift 14, only 300 [million years] after the Big Bang,” said coauthor and The University of Texas at Austin astrophysicist Katherine Freese, in the statement.

“Weighing a million times as much as the Sun, such early dark stars are important not only in teaching us about dark matter but also as precursors to the early supermassive black holes seen in JWST that are otherwise so difficult to explain,” she added.

In 2023, Freese and her colleagues identified several supermassive dark star candidates in images taken by the JWST’s Near-Infrared Camera (NIRCam) instrument, which captures high-resolution images in the near-infrared range, to study the earliest galaxies.

Since then, data from the space telescope’s Near-Infrared Spectrograph (NIRSPec), which was designed primarily to study the very earliest days of the universe by measuring several near-infrared bands simultaneously, has become available for analysis.

The latest paper builds on the 2023 findings by using NIRSpec data to identify “four spectroscopic dark star candidates, one of them being the second most distant object ever observed.”

Ilie claims he and his colleagues may have found a “potential smoking gun signature of a dark star,” finding a “1640 Angstrom absorption dip” in the spectrum of one of the four candidates. Angstrom is a unit of length that’s equal to one hundred-millionth of a centimeter and is usually used to express wavelengths of light.

But plenty of research remains until we can confirm the existence of supermassive dark stars populating the earliest universe. The latest findings, however, could help scientists shed more light on the nature of dark matter as well, one of the thorniest and most elusive mysteries plaguing astronomers to date.

More on dark matter stars: James Webb Appears to Have Spotted Weird Stars Powered by Dark Matter

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📌 MAROKO133 Hot ai: New Paper Finds That When You Reward AI for Success on Social

AI bots are everywhere now, filling everything from online stores to social media.

But that sudden ubiquity could end up being a very bad thing, according to a new paper from Stanford University scientists who unleashed AI models into different environments — including social media — and found that when they were rewarded for success at tasks like boosting likes and other online engagement metrics, the bots increasingly engaged in unethical behavior like lying and spreading hateful messages or misinformation.

“Competition-induced misaligned behaviors emerge even when models are explicitly instructed to remain truthful and grounded,” wrote paper co-author and machine learning Stanford professor James Zou in a post on X-formerly-Twitter.

The troubling behavior underlines what can go wrong with our increasing reliance on AI models, which has already manifested in disturbing ways such as people shunning other humans for AI relationships and spiraling into mental health crises after becoming obsessed with chatbots.

The Stanford scientists dubbed the emergence of sociopathic behavior within AI bots with an ominous-sounding name: “Moloch’s Bargain for AI,” in a reference to a Rationalist concept called Moloch in which competing individuals optimize their actions towards a goal, but everybody loses in the end.

For the study, the scientists created three digital online environments with simulated audiences: online election drives directed towards voters, sale pitches for products directed towards consumers, and social media posts aimed at maximizing engagement. They used the AI models Qwen, developed by Alibaba Cloud, and Meta’s Llama to act as the AI agents interacting with these different audiences.

The result was striking: even with guardrails in place to try to prevent the bots from engaging in deceptive behavior, the AI models would become “misaligned” as they they started engaging in unethical behavior.

For example, in a social media environment, the models would share news article to online users, who would provide feedback in the form of actions such as likes and other online engagement. As the models received feedback, their incentive to increase engagement led to increasing misalignment.

“Using simulated environments across these scenarios, we find that, 6.3 percent increase in sales is accompanied by a 14 percent rise in deceptive marketing,” reads the paper. “[I]n elections, a 4.9 percent gain in vote share coincides with 22.3 percent more disinformation and 12.5 percent more populist rhetoric; and on social media, a 7.5 percent engagement boost comes with 188.6 percent more disinformation and a 16.3 percent increase in promotion of harmful behaviors.”

It’s clear from the study and real-world anecdotes that current guardrails are insufficient. “Significant social costs are likely to follow,” reads the paper.

“When LLMs compete for social media likes, they start making things up,” Zou wrote on X. “When they compete for votes, they turn inflammatory/populist.”

More on AI agents: Companies That Replaced Humans With AI Are Realizing Their Mistake

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