May 11, 2022 08:54 PM
(This post was last modified: May 12, 2022 03:17 PM by C C.)
Physicists found a way to trigger the strange glow of warp speed acceleration
https://www.sciencealert.com/physicists-...warp-speed
INTRO: Every time you take a step, space itself glows with a soft warmth. Called the Fulling–Davies–Unruh effect (or sometimes just Unruh effect if you're pushed for time), this eerie glow of radiation emerging from the vacuum is akin to the mysterious Hawking radiation that's thought to surround black holes.
Only in this case, it's the product of acceleration rather than gravity Can't feel it? There's a good reason for that. You'd need to move at an impossible speed to sense even the weakest of Unruh rays.
For now, the effect remains a purely theoretical phenomenon, far beyond our ability to measure. But that could soon change, following a discovery by researchers from the University of Waterloo in Canada and the Massachusetts Institute of Technology (MIT)... (MORE - details)
Powerful ‘machine scientists’ distill the laws of physics from raw data
https://www.quantamagazine.org/machine-s...-20220510/
EXCERPTS: . . . The researchers hadn’t spotted the crucial pattern in their data themselves. Rather, an unpublished invention of theirs — a digital assistant they called the “machine scientist” — had handed it to them. When writing up the result, Guimerà recalls thinking, “We can’t just say we fed it to an algorithm and this is the answer. No reviewer is going to accept that.”
[...] Four years later, this awkward situation is quickly becoming an accepted method of scientific discovery. Sales-Pardo and Guimerà are among a handful of researchers developing the latest generation of tools capable of a process known as symbolic regression.
Symbolic regression algorithms are distinct from deep neural networks, the famous artificial intelligence algorithms that may take in thousands of pixels, let them percolate through a labyrinth of millions of nodes, and output the word “dog” through opaque mechanisms. Symbolic regression similarly identifies relationships in complicated data sets, but it reports the findings in a format human researchers can understand: a short equation.
These algorithms resemble supercharged versions of Excel’s curve-fitting function, except they look not just for lines or parabolas to fit a set of data points, but billions of formulas of all sorts. In this way, the machine scientist could give the humans insight into why cells divide, whereas a neural network could only predict when they do.
Researchers have tinkered with such machine scientists for decades, carefully coaxing them into rediscovering textbook laws of nature from crisp data sets arranged to make the patterns pop out. But in recent years the algorithms have grown mature enough to ferret out undiscovered relationships in real data — from how turbulence affects the atmosphere to how dark matter clusters.
“No doubt about it,” said Hod Lipson, a roboticist at Columbia University who jump-started the study of symbolic regression 13 years ago. “The whole field is moving forward.”
Occasionally physicists arrive at grand truths through pure reasoning, as when Albert Einstein intuited the pliability of space and time by imagining a light beam from another light beam’s perspective. More often, though, theories are born from marathon data-crunching sessions.
After the 16th-century astronomer Tycho Brahe passed away, Johannes Kepler got his hands on the celestial observations in Brahe’s notebooks. It took Kepler four years to determine that Mars traces an ellipse through the sky rather than the dozens of other egglike shapes he considered. He followed up this “first law” with two more relationships uncovered through brute-force calculations. These regularities would later point Isaac Newton toward his law of universal gravitation.
The goal of symbolic regression is to speed up such Keplerian trial and error, scanning the countless ways of linking variables with basic mathematical operations to find the equation that most accurately predicts a system’s behavior.
[...] Of the growing band of machine scientists (another notable example is “AI Feynman,” created by Max Tegmark and Silviu-Marian Udrescu, physicists at the Massachusetts Institute of Technology), human researchers say the more the merrier. “We really need all these techniques,” Kutz said. “There’s not a single one that’s a magic bullet.”
Kutz believes machine scientists are bringing the field to the cusp of what he calls “GoPro physics,” where researchers will simply point a camera at an event and get back an equation capturing the essence of what’s going on. (Current algorithms still need humans to feed them a laundry list of potentially relevant variables like positions and angles.)
That’s what Lipson has been working on lately. In a December preprint, he and his collaborators described a procedure in which they first trained a deep neural network to take in a few frames of a video and predict the next few frames. The team then reduced how many variables the neural network was allowed to use until its predictions started to fail.
The algorithm was able to figure out how many variables were needed to model both simple systems like a pendulum and complicated setups like the flickering of a campfire — tongues of flames with no obvious variables to track.
“We don’t have names for them,” Lipson said. “They’re like the flaminess of the flame.” (MORE - missing details)
https://www.sciencealert.com/physicists-...warp-speed
INTRO: Every time you take a step, space itself glows with a soft warmth. Called the Fulling–Davies–Unruh effect (or sometimes just Unruh effect if you're pushed for time), this eerie glow of radiation emerging from the vacuum is akin to the mysterious Hawking radiation that's thought to surround black holes.
Only in this case, it's the product of acceleration rather than gravity Can't feel it? There's a good reason for that. You'd need to move at an impossible speed to sense even the weakest of Unruh rays.
For now, the effect remains a purely theoretical phenomenon, far beyond our ability to measure. But that could soon change, following a discovery by researchers from the University of Waterloo in Canada and the Massachusetts Institute of Technology (MIT)... (MORE - details)
Powerful ‘machine scientists’ distill the laws of physics from raw data
https://www.quantamagazine.org/machine-s...-20220510/
EXCERPTS: . . . The researchers hadn’t spotted the crucial pattern in their data themselves. Rather, an unpublished invention of theirs — a digital assistant they called the “machine scientist” — had handed it to them. When writing up the result, Guimerà recalls thinking, “We can’t just say we fed it to an algorithm and this is the answer. No reviewer is going to accept that.”
[...] Four years later, this awkward situation is quickly becoming an accepted method of scientific discovery. Sales-Pardo and Guimerà are among a handful of researchers developing the latest generation of tools capable of a process known as symbolic regression.
Symbolic regression algorithms are distinct from deep neural networks, the famous artificial intelligence algorithms that may take in thousands of pixels, let them percolate through a labyrinth of millions of nodes, and output the word “dog” through opaque mechanisms. Symbolic regression similarly identifies relationships in complicated data sets, but it reports the findings in a format human researchers can understand: a short equation.
These algorithms resemble supercharged versions of Excel’s curve-fitting function, except they look not just for lines or parabolas to fit a set of data points, but billions of formulas of all sorts. In this way, the machine scientist could give the humans insight into why cells divide, whereas a neural network could only predict when they do.
Researchers have tinkered with such machine scientists for decades, carefully coaxing them into rediscovering textbook laws of nature from crisp data sets arranged to make the patterns pop out. But in recent years the algorithms have grown mature enough to ferret out undiscovered relationships in real data — from how turbulence affects the atmosphere to how dark matter clusters.
“No doubt about it,” said Hod Lipson, a roboticist at Columbia University who jump-started the study of symbolic regression 13 years ago. “The whole field is moving forward.”
Occasionally physicists arrive at grand truths through pure reasoning, as when Albert Einstein intuited the pliability of space and time by imagining a light beam from another light beam’s perspective. More often, though, theories are born from marathon data-crunching sessions.
After the 16th-century astronomer Tycho Brahe passed away, Johannes Kepler got his hands on the celestial observations in Brahe’s notebooks. It took Kepler four years to determine that Mars traces an ellipse through the sky rather than the dozens of other egglike shapes he considered. He followed up this “first law” with two more relationships uncovered through brute-force calculations. These regularities would later point Isaac Newton toward his law of universal gravitation.
The goal of symbolic regression is to speed up such Keplerian trial and error, scanning the countless ways of linking variables with basic mathematical operations to find the equation that most accurately predicts a system’s behavior.
[...] Of the growing band of machine scientists (another notable example is “AI Feynman,” created by Max Tegmark and Silviu-Marian Udrescu, physicists at the Massachusetts Institute of Technology), human researchers say the more the merrier. “We really need all these techniques,” Kutz said. “There’s not a single one that’s a magic bullet.”
Kutz believes machine scientists are bringing the field to the cusp of what he calls “GoPro physics,” where researchers will simply point a camera at an event and get back an equation capturing the essence of what’s going on. (Current algorithms still need humans to feed them a laundry list of potentially relevant variables like positions and angles.)
That’s what Lipson has been working on lately. In a December preprint, he and his collaborators described a procedure in which they first trained a deep neural network to take in a few frames of a video and predict the next few frames. The team then reduced how many variables the neural network was allowed to use until its predictions started to fail.
The algorithm was able to figure out how many variables were needed to model both simple systems like a pendulum and complicated setups like the flickering of a campfire — tongues of flames with no obvious variables to track.
“We don’t have names for them,” Lipson said. “They’re like the flaminess of the flame.” (MORE - missing details)
