from the world's big
A neural network discovered Copernicus’ heliocentricity on its own
Can neural networks help scientists discover laws about more complex phenomena, like quantum mechanics?
- Scientists trained a neural network to predict the movements of Mars and the Sun.
- In the process, the network generated formulae that place the Sun at the center of our solar system.
- The case suggests that machine-learning techniques could help reveal new laws of physics.
A neural network was able to rediscover one of the most important paradigm shifts in scientific history: Earth and other planets revolve around the Sun. The accomplishment suggests machine-learning techniques could someday help to reveal new laws of physics, maybe even within the complex realm of quantum mechanics.
The results are set to appear in the journal Physical Review Letters, according to Nature.
The neural network — a machine-learning algorithm called SciNet — was shown measurements of how the Sun and Mars appear from Earth against the fixed-star background of the night sky. SciNet's task, assigned by a team of scientists at the Swiss Federal Institute of Technology, was to predict where the Sun and Mars would be at future points in time.
In the process, SciNet generated formulas that place the Sun at the center of our solar system. Remarkably, SciNet accomplished this in a way similar to how astronomer Nicolaus Copernicus discovered heliocentricity.
"In the 16th century, Copernicus measured the angles between a distant fixed star and several planets and celestial bodies and hypothesized that the Sun, and not the Earth, is in the centre of our solar system and that the planets move around the Sun on simple orbits," the team wrote in a paper published on the preprint repository arXiv. "This explains the complicated orbits as seen from Earth."
The team "encouraged" SciNet to come up with ways to predict the movements of the Sun and Mars in the simplest way possible. To do that, SciNet passes information back and forth between two sub-networks. One network "learns" from data, and the other uses that knowledge to make predictions and test their accuracy. These networks are connected to each other by only a few links, so when they communicate, information is compressed, resulting in "simpler" representations.
Renner et al.
SciNet decided that the simplest way to predict the movements of celestial bodies was through a model that places the Sun at the center of our solar system. So, the neural network didn't necessarily "discover" heliocentricity, but rather described it through mathematics that humans can interpret.
Building humanlike AI
In 2017, data scientist Brenden Lake and his colleagues wrote a paper describing what it will take to build machines that learn and think like people. One benchmark for doing so would be artificial intelligence that can describe the physical world. At the time, they said it "remains to be seen" whether "deep networks trained on physics-related data" could discover laws of physics on their own. In a narrow sense, SciNet passes this test.
"To summarize, the main aim of this work is to show that neural networks can be used to discover physical concepts without any prior knowledge," the SciNet team wrote. "To achieve this goal, we introduced a neural network architecture that models the physical reasoning process. The examples illustrate that this architecture allows us to extract physically relevant data from experiments, without imposing further knowledge about physics or mathematics."
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Richard Feynman once asked a silly question. Two MIT students just answered it.
Here's a fun experiment to try. Go to your pantry and see if you have a box of spaghetti. If you do, take out a noodle. Grab both ends of it and bend it until it breaks in half. How many pieces did it break into? If you got two large pieces and at least one small piece you're not alone.
But science loves a good challenge<p>The mystery remained unsolved until 2005, when French scientists <a href="http://www.lmm.jussieu.fr/~audoly/" target="_blank">Basile Audoly</a> and <a href="http://www.lmm.jussieu.fr/~neukirch/" target="_blank">Sebastien Neukirch </a>won an <a href="https://www.improbable.com/ig/" target="_blank">Ig Nobel Prize</a>, an award given to scientists for real work which is of a less serious nature than the discoveries that win Nobel prizes, for finally determining why this happens. <a href="http://www.lmm.jussieu.fr/spaghetti/audoly_neukirch_fragmentation.pdf" target="_blank">Their paper describing the effect is wonderfully funny to read</a>, as it takes such a banal issue so seriously. </p><p>They demonstrated that when a rod is bent past a certain point, such as when spaghetti is snapped in half by bending it at the ends, a "snapback effect" is created. This causes energy to reverberate from the initial break to other parts of the rod, often leading to a second break elsewhere.</p><p>While this settled the issue of <em>why </em>spaghetti noodles break into three or more pieces, it didn't establish if they always had to break this way. The question of if the snapback could be regulated remained unsettled.</p>
Physicists, being themselves, immediately wanted to try and break pasta into two pieces using this info<p><a href="https://roheiss.wordpress.com/fun/" target="_blank">Ronald Heisser</a> and <a href="https://math.mit.edu/directory/profile.php?pid=1787" target="_blank">Vishal Patil</a>, two graduate students currently at Cornell and MIT respectively, read about Feynman's night of noodle snapping in class and were inspired to try and find what could be done to make sure the pasta always broke in two.</p><p><a href="http://news.mit.edu/2018/mit-mathematicians-solve-age-old-spaghetti-mystery-0813" target="_blank">By placing the noodles in a special machine</a> built for the task and recording the bending with a high-powered camera, the young scientists were able to observe in extreme detail exactly what each change in their snapping method did to the pasta. After breaking more than 500 noodles, they found the solution.</p>
The apparatus the MIT researchers built specifically for the task of snapping hundreds of spaghetti sticks.
(Courtesy of the researchers)
What possible application could this have?<p>The snapback effect is not limited to uncooked pasta noodles and can be applied to rods of all sorts. The discovery of how to cleanly break them in two could be applied to future engineering projects.</p><p>Likewise, knowing how things fragment and fail is always handy to know when you're trying to build things. Carbon Nanotubes, <a href="https://bigthink.com/ideafeed/carbon-nanotube-space-elevator" target="_self">super strong cylinders often hailed as the building material of the future</a>, are also rods which can be better understood thanks to this odd experiment.</p><p>Sometimes big discoveries can be inspired by silly questions. If it hadn't been for Richard Feynman bending noodles seventy years ago, we wouldn't know what we know now about how energy is dispersed through rods and how to control their fracturing. While not all silly questions will lead to such a significant discovery, they can all help us learn.</p>
A study looks at the performance benefits delivered by asthma drugs when they're taken by athletes who don't have asthma.
- One on hand, the most common health condition among Olympic athletes is asthma. On the other, asthmatic athletes regularly outperform their non-asthmatic counterparts.
- A new study assesses the performance-enhancement effects of asthma medication for non-asthmatics.
- The analysis looks at the effects of both allowed and banned asthma medications.
WADA uncertainty<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMzUzNzU0OS9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMDc4NjUwN30.fFTvRR0yJDLtFhaYiixh5Fa7NK1t1T4CzUM0Yh6KYiA/img.jpg?width=980" id="01b1b" class="rm-shortcode" data-rm-shortcode-id="2fd91a47d91e4d5083449b258a2fd63f" data-rm-shortcode-name="rebelmouse-image" alt="urine sample for drug test" />
Image source: joel bubble ben/Shutterstock<p>When inhaled β-agonists first came out just before the 1972 Olympics, they were immediately banned altogether by the WADA as possible doping substances. Over the years, the WADA has reexamined their use and refined the organization's stance, evidence of the thorniness of finding an equitable position regarding their use. As of January 2020, only three β-agonists are allowed — salbutamol, formoterol, and salmeterol —and only in inhaled form. Oral consumption appears to have a greater effect on performance.</p>
The study<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMzUzNzU0Ny9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTY1MTIzMDQyMX0.Gk4v-7PCA7NohvJjw12L15p7SumPCY0tLdsSlMrLlGs/img.jpg?width=980" id="d3141" class="rm-shortcode" data-rm-shortcode-id="ebe7b30a315aeffcb4fe739095cf0767" data-rm-shortcode-name="rebelmouse-image" alt="runner at starting position on track" />
Image source: MinDof/Shutterstock<p>Of primary interest to the authors of the study is confirming and measuring the performance improvement to be gained from β-agonists when they're ingested by athletes who don't have asthma.</p><p>The researchers performed a meta-analysis of 34 existing studies documenting 44 randomized trials reporting on 472 participants. The pool of individuals included was broad, encompassing both untrained and elite athletes. In addition, lab tests, as opposed to actual competitions, tracked performance. The authors of the study therefore recommend taking its conclusions with just a grain of salt.</p><p>The effects of both WADA-banned and approved β-agonists were assessed.</p>
Approved β-agonists and non-asthmatic athletes<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMzUzNzU1MC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMzkxODk0M30.3RssFwk_tWkHRkEl_tIee02rdq2tLuAePifnngqcIr8/img.jpg?width=980" id="39a99" class="rm-shortcode" data-rm-shortcode-id="b1fe4a580c6d4f8a0fd021d7d6570e2a" data-rm-shortcode-name="rebelmouse-image" alt="vaulter clearing pole" />
Image source: Andrey Yurlov/Shutterstock<p>What the meta-analysis showed is that the currently approved β-agonists didn't significantly improve athletic performance among those without asthma — what very slight benefit they <em>may</em> produce is just enough to prompt the study's authors to write that "it is still uncertain whether approved doses improve anaerobic performance." They note that the tiny effect did increase slightly over multiple weeks of β-agonist intake.</p>
Banned β-agonist and non-asthmatic athletes<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yMzUzNzU1Mi9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYzNjI3ODU5Mn0.vyoxSE5EYjPGc2ZEbBN8d5F79nSEIiC6TUzTt0ycVqc/img.jpg?width=980" id="de095" class="rm-shortcode" data-rm-shortcode-id="02fdd42dfda8e3665a7b547bb88007ef" data-rm-shortcode-name="rebelmouse-image" alt="swimmer mid stroke" />
Image source: Nejron Photo/Shutterstock<p>The study found that for athletes without asthma, however, the use of currently banned β-agonists did indeed result in enhanced performance. The authors write, "Our meta-analysis shows that β2-agonists improve anaerobic performance by 5%, an improvement that would change the outcome of most athletic competitions."</p><p>That 5 percent is an average: 70-meter sprint performance was improved by 3 percent, while strength performance, MVC (maximal voluntary contraction), was improved by 6 percent.</p><p>The analysis also revealed that different results were produced by different methods of ingestion. The percentages cited above were seen when a β-agonist was ingested orally. The effect was less pronounced when the banned substances were inhaled.</p><p>Given the difference between the results for allowed and banned β-agonists, the study's conclusions suggest that the WADA has it about right, at least in terms of selection of allowable β-agonists, as well as the allowable dosage method.</p>
Takeaway<p>The study, say its authors, "should be of interest to WADA and anyone who is interested in equal opportunities in competitive sports." Its results clearly support vigilance, with the report concluding: "The use of β2-agonists in athletes should be regulated and limited to those with an asthma diagnosis documented with objective tests."</p>
Certain water beetles can escape from frogs after being consumed.
- A Japanese scientist shows that some beetles can wiggle out of frog's butts after being eaten whole.
- The research suggests the beetle can get out in as little as 7 minutes.
- Most of the beetles swallowed in the experiment survived with no complications after being excreted.