CPU’s the Universe and Everything

The image from an interesting article on the ultimate in cloud computing. Hubble image of the asymptotic giant branch star U Camelopardalis. This star, nearing the end of its life, is losing mass as it coughs out shells of gas. Credit: ESA/Hubble, NASA and H. Olofsson (Onsala Space Observatory).

Seems like there must have been a mash up of astrophysics/cosmology/cybernetics a couple of weeks ago there have been a series of articles about computers and the universe. One series pointing out that once could conceive of using the AGB stars in their ‘dusting mode’ (above) as a computing engine.

But on the other side there have been a couple of articles that touch on the metaphysical (philosophical basis of reality) concept that we and our universe, are one vast simulation.

…Oxford philosopher Nick Bostrom’s philosophical thought experiment that the universe is a computer simulation. If that were true, then fundamental physical laws should reveal that the universe consists of individual chunks of space-time, like pixels in a video game. “If we live in a simulation, our world has to be discrete,”….

From: New machine learning theory raises questions about nature of science

….a discrete field theory, which views the universe as composed of individual bits and differs from the theories that people normally create. While scientists typically devise overarching concepts of how the physical world behaves, computers just assemble a collection of data points…..

From: New machine learning theory raises questions about nature of science

…A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system….

… devised by a scientist at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions.

Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This program, along with an additional program known as a ‘serving algorithm,’ then made accurate predictions of the orbits of other planets in the solar system without using Newton’s laws of motion and gravitation. “Essentially, I bypassed all the fundamental ingredients of physics. I go directly from data to data,” Qin said. “There is no law of physics in the middle…

…”Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations,” said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. “What I’m doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law.”…

From: New machine learning theory raises questions about nature of science

Ok so now I am going to go a bit sideways and you may want to just go on about your internet day. But while I laude Qin and his team I have a bit of an issue with what he claims re the basis is Philosophy. Not the claim that the discrete field theory sparked his concept exploration. But that the actual system he developed has anything to say about that metaphysical theory.

Taking nothing away from the team what I see seems like a straightforward application of machine learning. In fact a relatively simple one though I would laude the whole idea of applying it to physics in general. A very interesting though, like many interesting insights, oddly obvious is retrospect. (Sorry for the repeated Though clauses…I absolutely see this as fascinating insight…and possibly extremely important…it just seems like D’oh in retrospect.)

As physics is very much aligned with mathematics (I think because the discovery of each was feedback on the other) and mathematics and cybernetics are also deeply intwined it should come as no surprise that computer systems designed to create black box solutions, when fed the right kind of data, will create a black box model of physical phenomena.

The output of science are tools that allow us to predict finite things about the universe we live in, repeatably and accurately. These tools are often used by engineers to enable technologyy that make life better for everyone.

But in many ways this is an engineers (relatively narrow) viewpoint. To some large degree an engineer does not care why the tool works, only that it does and how accurately. Counter to that, a strength of the theory based + mathematical model approach is that it gives you a tool to link the rest of reality to the ‘discrete’ piece you are working on right now. A jumping off point or a linking point to other theories that allows us to move onto other problems and link the

And/But (you knew it was coming) i wonder if this has anything to do with discrete field theory per se. Maybe if the learning algorithm used had that in it this would show something of that nature, but otherwise I do not see this as showing anything in particular other than the ability of learning systems which are in some sense continuous not discrete systems to develop predictive models directly from the data (as Qin says) rather than through the labor intensive methods of theory extraction and proof that has been the basis for scientific exploration since it first evolved in the Middle Ages.

Again BUT, it has been getting harder to develop these ‘deep’ theories. Look at the colliders and other tools that physicists use these days to probe the depths of our reality. In this world there are many things, like Qin’s next test with Nuclear Fusion, where an engineering model might be much more valuable than a ‘theory of this’ if it can be captured and used in a fraction of the time.

It’s all good, fascinating, wonderful…but let’s not get ahead of ourselves.

WOW! A cool SETI theory…

Figure: The Wow! Signal. The peak is 32 times the signal to noise ratio of the observations. Courtesy of Sam Morrell. (From the article)

Not much more to be said so I post the intro to the article from Centauri Dreams, about an article/Theory by James Benford. Cool…

Was the Wow! Signal Due to Power Beaming Leakage?


The Wow! signal has a storied history in the SETI community, a one-off detection at the Ohio State ‘Big Ear’ observatory in 1977 that Jim Benford, among others, considers the most interesting candidate signal ever received. A plasma physicist and CEO of Microwave Sciences, Benford returns to Centauri Dreams today with a closer look at the signal and its striking characteristics, which admit to a variety of explanations, though only one that the author believes fits all the parameters. A second reception of the Wow! might tell us a great deal, but is such an event likely? So far all repeat observations have failed and, as Benford points out, there may be reason to assume they must. The essay below is a shorter version of the paper Jim has submitted to Astrobiology.

A little air, a bit of heat, some light

What Global Warming? 148 New (2020) Scientific Papers Affirm Recent Non-Warming, A Degrees-Warmer Past at WattsUpWithThat

Climate Change Horror Porn is another tool of the apparat to frighten us. In realty there is an objective truth out there…none of us know it. Two sides largely aligned Left and Right though not precisely have taken sides and because the liberal left is ascendant and deeply intwined in academia and the media they are trying to ‘scare us straight.’ It might be well intentioned in many cases, but ideologues, abusers, users and grifters have gathered around a powerful ideological tool that can be used to manipulate the population.

  • The science such as it is….which is a lot…but not what you are told it is by the media and the ideologues who want to use it.
    • Climate science
      • What climate was/is/will be:
        • Is based on models of how the whole atmosphere, hydrosphere and lithosphere work.
          • Early simple models were very illuminating.
          • Complex models are horribly sensitive to incorrect knowledge and unknowns.
        • A lot of it is based on prior history comparing things like plant and sea life growth vs temperature, CO2 etc.
          • But most of this knowledge is based on proxies up until a decade or at most two ago.
          • Plus sparse and non technical accounts up until the modern era
          • Has a sparse and erratic technical record from about a century and a half.
          • Decent deep record for a couple of decades.
          • Can see what it is today in fair but not omniscient detail.
        • We model the future based on models that we ‘test’ against the past. Like the stock market sometimes these models can do an ok job. But that is only because of parameter fiddling to ‘match the curves.’ The models are by necessity highly simplified and often just plain wrong. For example:
          • recent discovery that cloud impact on surface temperature can increase not decrease surface temperature. And that it may depend on where you are in the world.
          • Recent discovery that CO2 concentration’s affect on green house is not linear and tapers quickly at higher concentrations.
          • That the planetary heat balance is highly affected by cooling at the poles, and that the magnetosphere/sun link into the climate also is highly linked at the poles.
          • Etc.
        • While the first climate models that brilliant men and women came up with less than a century ago have been proven to be largely correct, the details are practically, hardly better modeled today than they were in the 1950’s.
        • Today there are literally hundreds of complex computer models and that are run many times with many different start parameters. They generate families of predictions, effectively at random. Those predictions are never even close to right at a rate greater than chance.
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Icy moons, exciting targets of exploration

The Interior of Enceladus Looks Really Great for Supporting Life
Article in UniverseToday on one of Saturn’s moons

In the early days of space exploration it was the rocky planets, particularly Mars and Venus that held some hope of significant life. Though those with the tools of observation and analysis were pretty negative and life in the rest of the solar system looked impossible. But as our knowledge and tools expanded the icy moons quickly became of interest because as cold region natives know, ice is not a bad insulator and a couple of miles of it would protect a lake. These days it seems pretty clear that Icy Moons often have oceans, seas or lakes inside, and the heat that melts the ice from underneath (from orbital stresses and or radioactive decay) could quite conceivably support life.

The article linked discusses model based research based on data from earlier orbiters and flybys. It shows that notionally their are several mechanisms that could be feeding nutrients and energy sources into the ocean of Enceladus, at a rate suffient to support a significant biome.

There are lots of other interesting articles on space at universe today website, take a look.

Wow this is … Fantastic

20140201-170827.jpgA composite image showing jets and radio-emitting lobes emanating from Centaurus A’s central black hole. Credit: NASA/ESO/WFI
The photo-art and the article it goes with. The article Grey is the new black hole: is Stephen Hawking right? Jan 29, 2014 by Geraint Lewis at The Conversation. It is a great piece of science writing explaining the evolution of our understanding of Black Holes and the context of Hawking’s latest pronouncements

Talk about taking your breath away

When tectonics killed everything
by Johnny Bontemps

Permian Seafloor — More than 90 percent of ocean species vanished during the Permian extinction. Credit: University of Michigan Exhibit of Natural History


Late Permian (260 million years ago) — All the world’s lands had joined into a single supercontinent, Pangea, and all the world’s sea water had formed a global ocean, Panthalassa. Credit: Ron Blakey, NAU Geology

Some 300 million years ago, at the beginning of the Permian period, all the world’s lands had joined into a single supercontinent, Pangea, and all the world’s sea water had formed a global ocean, Panthalassa.

The formation of Pangea led to higher mountains and deeper oceans. According to an equilibrium principle, a giant continent should have a thicker crust than each scattered continent, and the oceans should become deeper. This recession of water away from the land would have eliminated a lot of the biodiversity that thrives in shallow water near the coasts. This recession would have also led to changes in ocean currents and wind patterns, initiating global climate changes.
What’s more, the inland region of one giant continent would become dry and arid, leading to the disappearance of much vegetation.

But something else also went on, deep within the Earth.

When the lands joined, some tectonic plates moved under others and sunk deep into the Earth’s mantle. That cooler material then may have reached all the way to the Earth’s core layer. Evidence for that includes the reversal of Earth’s magnetic field that occurred around that time, an event called the Illawarra magnetic reversal.

The accumulation of cool material near Earth’s core then could have led to the formation of a large mantle plume (by a process called thermo-convection), other researchers had suggested. That “super-plume” would eventually reach the Earth’s surface in two separate bursts—first with an eruption in China 260 million years ago, and then with the other in Russia 251 million years ago.

By that point, all life had nearly vanished.

Read more at: http://phys.org/news/2013-11-tectonics.html#jCp

newScientist: 6 months in the air? Swifts, natures endurance mini drone…

20131014-194818.jpgSwifts stay airborne for six months at a time: by Andy Coghlan. 08 October 2013
It’s possible young swifts don’t spend much or any time on the ground for three years.
Some theorize sleeping on the wing or shutting down half the brain. But it’s known that dolphins and suspected other large marine mammals can be awake for weeks at a time with no harm.