Posts Tagged ‘Physics’
“A cosmic mystery of immense proportions, once seemingly on the verge of solution, has deepened and left astronomers and astrophysicists more baffled than ever. The crux … is that the vast majority of the mass of the universe seems to be missing”*…
Quantum effects may not be just subatomic, Sabine Hossenfelder suggests; they might be expressed across galaxies, and solve the puzzle of dark matter…
Most of the matter in the Universe is invisible, composed of some substance that leaves no mark as it breezes through us – and through all of the detectors the scientists have created to catch it. But this dark matter might not consist of unseen particle clouds, as most theorists have assumed. Instead, it might be something even stranger: a superfluid that condensed to puddles billions of years ago, seeding the galaxies we observe today.
This new proposal has vast implications for cosmology and physics. Superfluid dark matter overcomes many of the theoretical problems with the particle clouds. It explains the long-running, increasingly frustrating failure to identify the individual constituents within these clouds. And it offers a concrete scientific path forward, yielding specific predictions that could soon be testable.
Superfluid dark matter has important conceptual implications as well. It suggests that the common picture of the Universe as a mass of individual particles bound together by forces – almost like a tinker toy model – misses much of the richness of nature. Most of the matter in the Universe might be utterly unlike the matter in your body: not composed of atoms, and not even built of particles as we normally understand them, but instead a coherent whole of vast extension…
Is dark matter composed of particles? Is it a fluid? Or is it both? Read On: “The superfluid Universe,” from @skdh in @aeonmag.
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As we deconstruct the dark, we might spare a thought for Richard Philips Feynman; he died on this date in 1988. A theoretical physicist, Feynman was probably the most brilliant, influential, and iconoclastic figure in his field in the post-WW II era.
Richard Feynman was a once-in-a-generation intellectual. He had no shortage of brains. (Relevantly to the piece above, in 1965 he won the Nobel Prize in Physics for his work on quantum electrodynamics.) He had charisma. (Witness this outtake [below] from his 1964 Cornell physics lectures [available in full here].) He knew how to make science and academic thought available, even entertaining, to a broader public. (See, for example, these two public TV programs hosted by Feynman here and here.) And he knew how to have fun.
– From Open Culture (where one can also find Feynman’s elegant and accessible 1.5 minute explanation of “The Key to Science.”)
“Simplicity, carried to the extreme, becomes elegance”*…
Jordana Cepelewicz on a very different approach to computing…
In 1936, the British mathematician Alan Turing came up with an idea for a universal computer. It was a simple device: an infinite strip of tape covered in zeros and ones, together with a machine that could move back and forth along the tape, changing zeros to ones and vice versa according to some set of rules. He showed that such a device could be used to perform any computation.
Turing did not intend for his idea to be practical for solving problems. Rather, it offered an invaluable way to explore the nature of computation and its limits. In the decades since that seminal idea, mathematicians have racked up a list of even less practical computing schemes. Games like Minesweeper or Magic: The Gathering could, in principle, be used as general-purpose computers. So could so-called cellular automata like John Conway’s Game of Life, a set of rules for evolving black and white squares on a two-dimensional grid.
In September 2023, Inna Zakharevich of Cornell University and Thomas Hull of Franklin & Marshall College showed that anything that can be computed can be computed by folding paper. They proved that origami is “Turing complete” — meaning that, like a Turing machine, it can solve any tractable computational problem, given enough time…
Read on for more on how folding paper can, in principle, be used to perform any possible computation: “How to Build an Origami Computer” from @jordanacep in @QuantaMagazine.
* Jon Franklin
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As we contemplate calculation, we might send entropic birthday greeting to Rolf Landauer; he was born on this date in 1927. A physicist, we made important contributions made important contributions in several areas of the thermodynamics of information processing, condensed matter physics, and the conductivity of disordered media… most of which important to the development of computing (of the electronic variety).
He is best known for his discovery and formulation of what’s known as Landauer’s principle: that in any logically irreversible operation that manipulates information, such as erasing a bit of memory, entropy increases and an associated amount of energy is dissipated as heat– a “thermodynamic cost of forgetting,” relevant to chip design (how closely packed elements can be on a chip and still handle the heat), reversible computing, quantum information, and quantum computing… but not an issue for origami.)
“What we need is the celestial fire to change the flint into the transparent crystal, bright and clear”*…
… or so it used to be. Scientists at Google DeepMind and the Lawrence Berkeley National Laboratory have applied AI to the task– with encouraging results…
Modern technologies from computer chips and batteries to solar panels rely on inorganic crystals. To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation.
… in a paper published in Nature, we share the discovery of 2.2 million new crystals – equivalent to nearly 800 years’ worth of knowledge. We introduce Graph Networks for Materials Exploration (GNoME), our new deep learning tool that dramatically increases the speed and efficiency of discovery by predicting the stability of new materials.
With GNoME, we’ve multiplied the number of technologically viable materials known to humanity. Of its 2.2 million predictions, 380,000 are the most stable, making them promising candidates for experimental synthesis. Among these candidates are materials that have the potential to develop future transformative technologies ranging from superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles.
GNoME shows the potential of using AI to discover and develop new materials at scale. External researchers in labs around the world have independently created 736 of these new structures experimentally in concurrent work. In partnership with Google DeepMind, a team of researchers at the Lawrence Berkeley National Laboratory has also published a second paper in Nature that shows how our AI predictions can be leveraged for autonomous material synthesis.
We’ve made GNoME’s predictions available to the research community. We will be contributing 380,000 materials that we predict to be stable to the Materials Project, which is now processing the compounds and adding them into its online database. We hope these resources will drive forward research into inorganic crystals, and unlock the promise of machine learning tools as guides for experimentation…
GNoME suggests that materials science may be the next frontier to be turbocharged by artificial intelligence (see this earlier example from biotech): “Millions of new materials discovered with deep learning.”
* Henry Wadsworth Longfellow
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As we drive discovery, we might recall that it was on this date in 1942 that a team of scientists led by Enrico Fermi, working inside an enormous tent on a squash court under the stands of the University of Chicago’s Stagg Field, achieved the first controlled nuclear fission chain reaction… laying the foundation for the atomic bomb and later, nuclear power generation– that’s to say, inaugurating the Atomic Age.
“…the Italian Navigator has just landed in the New World…”
– Coded telephone message confirming first self-sustaining nuclear chain reaction, December 2, 1942.
Indeed, exactly 15 years later, on this date in 1957, the world’s first full-scale atomic electric power plant devoted exclusively to peacetime uses, the Shippingport Atomic Power Station, reached criticality; the first power was produced 16 days later, after engineers integrated the generator into the distribution grid of Duquesne Light Company.
“Humanity is acquiring all the right technology for all the wrong reasons”*…
Further to yesterday’s post on the poverty created by manufacturing displacement, and in the wake of the sturm und drang occasioned by the coup at OpenAI, the estimable Rana Foroohar on the politics of AI…
… Consider that current politics in the developed world — from the rise of Donald Trump to the growth of far right and far left politics in Europe — stem in large part from disruptions to the industrial workforce due to technology and globalisation. The hollowing out of manufacturing work led to more populist and fractious politics, as countries tried (and often failed) to balance the needs of the global marketplace with those of voters.
Now consider that this past summer, the OECD warned that white-collar, skilled labour representing about a third of the workforce in the US and other rich countries is most at risk from disruption by AI. We are already seeing this happen in office work — with women and Asians particularly at risk since they hold a disproportionate amount of roles in question. As our colleague John Burn-Murdoch has charted [image above], online freelancers are especially vulnerable.
So, what happens when you add more than three times as many workers, in new subgroups, to the cauldron of angry white men that have seen their jobs automated or outsourced in recent decades? Nothing good. I’m always struck when CEOs like Elon Musk proclaim that we are headed towards a world without work as if this is a good thing. As academics like Angus Deaton and Anne Case have laid out for some time now, a world without work very often leads to “deaths of despair,” broken families, and all sorts of social and political ills.
Now, to be fair, Goldman Sachs has estimated that the productivity impact of AI could double the recent rate — mirroring the impact of the PC revolution. This would lead to major growth which could, if widely shared, do everything from cut child poverty to reduce our burgeoning deficit.
But that’s only if it’s shared. And the historical trend lines for technology aren’t good in that sense — technology often widens wealth disparities before labour movements and government regulation equalise things. (Think about the turn of the 20th century, up until the 1930s). But the depth and breadth of AI disruption may well cause unprecedented levels of global labour displacement and political unrest.
I am getting more and more worried that this is where we may be heading. Consider this new National Bureau of Economic Research working paper, which analyses why AI will be as transformative as the industrial revolution. It also predicts, however, that there is a very good chance that it lowers the labour share radically, even pushing it to zero, in lieu of policies that prevent this (the wonderful Daron Acemoglu and Simon Johnson make similar points, and lay out the history of such tech transformation in their book Power and Progress…
We can’t educate ourselves out of this problem fast enough (or perhaps at all). We also can’t count on universal basic income to fix everything, no matter how generous it could be, because people simply need work to function (as Freud said, it’s all about work and love). Economists and political scientists have been pondering the existential risks of AI — from nuclear war to a pandemic — for years. But I wonder if the real existential crisis isn’t a massive crisis of meaning, and the resulting politics of despair, as work is displaced faster than we can fix the problem…
Everyone’s worried about AI, but are we worried about the right thing? “The politics of AI,” from @RanaForoohar in @FT.
See also: Henry Farrell‘s “What OpenAI shares with Scientology” (“strange beliefs, fights over money, and bad science fiction”) and Dave Karpf‘s “On OpenAI: Let Them Fight.” (“It’s chaos… And that’s a good thing.”)
For a different point-of-view, see: “OpenAI and the Biggest Threat in the History of Humanity,” from Tomás Pueyo.
And for deep background, read Benjamin Labatut‘s remarkable The MANIAC.
* R. Buckminster Fuller
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As we equilibrate, we might recall that it was on this date in 1874 that electrical engineer, inventor, and physicist Ferdinand Braun published a paper in the Annalen der Physik und Chemie describing his discovery of the electrical rectifier effect, the original practical semiconductor device.
(Braun is better known for his contributions to the development of radio and television technology: he shared the 1909 Nobel Prize in Physics with Guglielmo Marconi “for their contributions to the development of wireless telegraphy” (Braun invented the crystal tuner and the phased-array antenna); was a founder of Telefunken, one of the pioneering communications and television companies; and (as the builder of the first cathode ray tube) has been called the “father of television” (shared with inventors like Paul Gottlieb Nipkow).
“The structure of the universe- I mean, of the heavens and the earth and the whole world- was arranged by one harmony through the blending of the most opposite principles”*…
… And as we undertake to understand that structure, we use the lens– the mental models and language– that we have. The redoubtable Anthony Grafton considers and early 16th century attempt: Heinrich Cornelius Agrippa‘s De Occulta Philosophia libri III, Agrippa’s encyclopedic study of magic that was, at the same time, an attempt to describe the structure of the universe, sketching a path that leads both upward and downward: up toward complete knowledge of God, and down into every order of being on earth…
Heinrich Cornelius Agrippa’s manual of learned magic, De occulta philosophia (1533), explicated the ways in which magicians understood and manipulated the cosmos more systematically than any of his predecessors. It was here that he mapped the entire network of forces that passed from angels and demons, stars and planets, downward into the world of matter. Agrippa laid his work out in three books, on the elementary, astrological, and celestial worlds. But he saw all of them as connected, weaving complex spider webs of influence that passed from high to low and low to high. With the zeal and learning of an encyclopedist imagined by Borges, Agrippa catalogued the parts of the soul and body, animals, minerals, and plants that came under the influence of any given planet or daemon. He then offered his readers a plethora of ways for averting evil influences and enhancing good ones. Some of these were originally simple remedies, many of them passed down from Roman times in the great encyclopedic work of Pliny the Younger and less respectable sources, and lacked any deep connection to learned magic.
[Grafton describes the many dimensions of Agrippa’s compilation of the then-current state of magic…]
But few of the dozens of manuscript compilations that transmitted magic through the Middle Ages reflected any effort to impose a system on the whole range of magical practices, as Agrippa’s book did. He made clear that each of the separate arts of magic, from the simplest form of herbal remedy to the highest forms of communication with angels, fitted into a single, lucid structure with three levels: the elementary or terrestrial realm, ruled by medicine and natural magic; the celestial realm, ruled by astrology; and the intellectual realm, ruled by angelic magic. Long tendrils of celestial and magical influence stitched these disparate realms into something like a single great being…
Agrippa offered, in other words, both a grand, schematic plan of the cosmos, rather like that of the London Underground, which laid out its structure as a whole, and a clutch of minutely detailed local Ordinance Survey maps, which made it possible to navigate through any specific part of the cosmos. Readers rapidly saw what Agrippa had to offer. The owner of a copy of On Occult Philosophy, now in Munich, made clear in his only annotation that he appreciated Agrippa’s systematic presentation of a universe in which physical forms revealed the natures of beings and their relations to one another: “Physiognomy, metoposcopy [the interpretation of faces], and chiromancy, and the arts of divination from the appearance and gestures of the human body work through signs.” Agrippa’s book not only became the manual of magical practice, but it also made the formal claim that magic was a kind of philosophy in its own right…
A 16th century attempt to understand the structure of the universe: “Marked by Stars- Agrippa’s Occult Philosophy,” from @scaliger in @PublicDomainRev.
* Aristotle
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As we take in the totality, we might send more modern birthday greetings to a rough contemporary of Agrippa’s, Evangelista Torricelli; he was born on this date in 1608. Even as Agrippa was trying to understand the world via magic, Torricelli, a student of Galileo, was using observation and reason to fuel the same quest. A physicist and mathematician, he is best known for his invention of the barometer, but is also known for his advances in optics, his work on the method of indivisibles, and “Torricelli’s Trumpet.” The torr, a unit of pressure, is named after him.
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