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    Singularity Hub Daily

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    Latest Episodes:
    The Most Powerful Space Telescope Ever Built Will Look Back in Time to the Dark Ages of the Universe Oct 24, 2021
    Show notes

    Some have called NASA’s James Webb Space Telescope the “telescope that ate astronomy.” It is the most powerful space telescope ever built and a complex piece of mechanical origami that has pushed the limits of human engineering. On Dec. 18, 2021, after years of delays and billions of dollars in cost overruns, the telescope is scheduled to launch into orbit and usher in the next era of astronomy. I’m an astronomer with a specialty in observational cosmology—I’ve been studying distant galaxies for 30 years. Some of the biggest unanswered questions about the universe relate to its early years just after the Big Bang. When did the first stars and galaxies form? Which came first, and why? I am incredibly excited that astronomers may soon uncover the story of how galaxies started because James Webb was built specifically to answer these very questions. The ‘Dark Ages’ of the Universe Excellent evidence shows that the universe started with an event called the Big Bang 13.8 billion years ago, which left it in an ultra-hot, ultra-dense state. The universe immediately began expanding after the Big Bang, cooling as it did so. One second after the Big Bang, the universe was a hundred trillion miles across with an average temperature of an incredible 18 billion degrees Fahrenheit (10 billion degrees Celsius). Around 400,000 years after the Big Bang, the universe was 10 million light-years across and the temperature had cooled to 5,500 degrees Fahrenheit (3,000 degrees Celsius). If anyone had been there to see it at this point, the universe would have been glowing dull red like a giant heat lamp. Throughout this time, space was filled with a smooth soup of high energy particles, radiation, hydrogen, and helium. There was no structure. As the expanding universe became bigger and colder, the soup thinned out and everything faded to black. This was the start of what astronomers call the Dark Ages of the universe. The soup of the Dark Ages was not perfectly uniform and due to gravity, tiny areas of gas began to clump together and become more dense. The smooth universe became lumpy and these small clumps of denser gas were seeds for the eventual formation of stars, galaxies, and everything else in the universe. Although there was nothing to see, the Dark Ages were an important phase in the evolution of the universe. Looking for the First light The Dark Ages ended when gravity formed the first stars and galaxies that eventually began to emit the first light. Although astronomers don’t know when first light happened, the best guess is that it was several hundred million years after the Big Bang. Astronomers also don’t know whether stars or galaxies formed first. Current theories based on how gravity forms structure in a universe dominated by dark matter suggest that small objects—like stars and star clusters—likely formed first and then later grew into dwarf galaxies and then larger galaxies like the Milky Way. These first stars in the universe were extreme objects compared to stars of today. They were a million times brighter but they lived very short lives. They burned hot and bright and when they died, they left behind black holes up to a hundred times the Sun’s mass, which might have acted as the seeds for galaxy formation. Astronomers would love to study this fascinating and important era of the universe, but detecting first light is incredibly challenging. Compared today’s massive, bright galaxies, the first objects were very small and due to the constant expansion of the universe, they’re now tens of billions of light-years away from Earth. Also, the earliest stars were surrounded by gas left over from their formation and this gas acted like fog that absorbed most of the light. It took several hundred million years for radiation to blast away the fog. This early light is very faint by the time it gets to Earth. But this is not the only challenge. As the universe expands, it continuously stretches the wavelength of light traveling through...


    Scientists Are on a Quest to Create the Perfect Cup of Coffee—Without the Beans Oct 22, 2021
    Show notes

    Ahh, coffee. Is there anything more delicious, more satisfying? It’s always there when you need it, be it first thing in the morning or for a mid-afternoon pick-me-up. According to the Sustainable Coffee Challenge, global consumption of this vital brew is around 600 billion cups per year (I know—I would have guessed higher, too). But as with many of the products we consume, there’s a cost beyond what we pay at the store. Producing coffee—like producing meat, or almonds, or corn, or pretty much anything—has an environmental cost, too. It’s that cost that’s led innovative entrepreneurs to seek a more Earth-friendly way to produce everything from beef to milk to salmon. Now coffee is joining the club, with startups in the US and Europe experimenting with new ways to make crave-worthy coffee—sans any coffee beans. One of these is Finland’s VTT Technical Research Centre. VTT uses a technique called cellular agriculture to grow its pseudo-coffee, filling bioreactors with cell cultures then adding nutrients that encourage growth. Heiko Rischer, VTT’s head of plant biotechnology, described one of the first cups brewed with his company’s product as tasting like something “in between a coffee and a black tea.” If Finland seems like a surprising location for one of the first artificial coffees to be made—I personally would have guessed Italy, or maybe Spain—it makes sense when you put together a couple key factors. First, Nordic countries tend to be a few steps ahead of the rest of the world in terms of environmentalism; from Greta Thunberg to electric car usage to Right to Repair laws, their help-the-planet game is strong. Also, Finland is actually the world’s biggest consumer of coffee per capita, with people throwing back an average of 26.45 pounds per year (as compared to the US average of 9.26 pounds per year). Like most crops, coffee production simultaneously impacts the climate crisis and is impacted by it. One of the big problems coffee demand is causing is deforestation, with more and more land being cleared of trees and natural ecosystems to make way for coffee plants. Those plants require pesticides and fertilizer, and their beans then need to be shipped across the world to caffeine-addicted consumers. VTT isn’t the only company working on making a more sustainable version of our favorite morning drink. Atomo Coffee, a startup based in Seattle, uses a different method than VTT, breaking down plant waste then converting the relevant compounds into a coffee-bean-like solid, and San Francisco-based Compound Foods uses microbes and fermentation to make bean-free coffee. According to The Guardian, Atomo’s facility currently produces enough of the fake bean to equal around 1,000 servings of coffee a day, and aims to get that up to 10,000 a day in the next year—so, about enough to fulfill the coffee needs of a tiny fraction of its home city’s population. That’s one of the major hurdles that companies producing synthetic foods will face; the supply chains, processes, and infrastructure serving our existing food production system grew and were refined over decades, and are able to meet consumer demand in their current form. Scaling production of lab-grown products to the level needed to continue meeting that demand—or, more likely, increased demand as the global middle class continues to grow—won’t be easy, even once fake meat tastes and feels just like real meat or lab-grown coffee goes down as smooth as the stuff that comes from plants. Speaking of which, “in between a coffee and a black tea” isn’t going to cut it for coffee-lovers. Until the synthetic stuff smells, tastes, and feels a lot more like the real thing, switching to cell-cultured coffee is going to be a very hard. sell (pun not intended). In addition, VTT’s coffee will need to be approved by regulatory bodies in Europe and the US before the company can bring its product to market. A final relevant issue is technological unemployment, which isn’t just a problem for peop...


    Would We Still See Ourselves as ‘Human’ if Other Hominin Species Hadn’t Gone Extinct? Oct 21, 2021
    Show notes

    In our mythologies, there’s often a singular moment when we became “human.” Eve plucked the fruit of the tree of knowledge and gained awareness of good and evil. Prometheus created men from clay and gave them fire. But in the modern origin story, evolution, there’s no defining moment of creation. Instead, humans emerged gradually, generation by generation, from earlier species. As with any other complex adaptation—a bird’s wing, a whale’s fluke, our own fingers—our humanity evolved step by step, over millions of years. Mutations appeared in our DNA, spread through the population, our ancestors slowly became something more like us and, finally, we appeared. Strange Apes, But Still Apes People are animals, but we’re unlike other animals. We have complex languages that let us articulate and communicate ideas. We’re creative: we make art, music, tools. Our imaginations let us think up worlds that once existed, dream up worlds that might yet exist, and reorder the external world according to those thoughts. Our social lives are complex networks of families, friends, and tribes, linked by a sense of responsibility towards each other. We also have awareness of ourselves and our universe: sentience, sapience, consciousness, whatever you call it. And yet the distinction between ourselves and other animals is, arguably, artificial. Animals are more like humans than we might think—or like to think. Almost all behavior we once considered unique to ourselves is seen in animals, even if they’re less well developed. That’s especially true of the great apes. Chimps, for example, have simple gestural and verbal communication. They make crude tools, even weapons, and different groups have different suites of tools—distinct cultures. Chimps also have complex social lives and cooperate with each other. As Darwin noted in Descent of Man, almost everything odd about Homo sapiens—emotion, cognition, language, tools, society—exists, in some primitive form, in other animals. We’re different, but less different than we think. And in the past, some species were far more like us than other apes: Ardipithecus, Australopithecus, Homo erectus, and Neanderthals. Homo sapiens is the only survivor of a once diverse group of humans and human-like apes, the hominins, which includes around 20 known species and probably dozens of unknown species. The extinction of those other hominins wiped out all the species that were intermediate between ourselves and other apes, creating the impression that some vast, unbridgeable gulf separates us from the rest of life on Earth. But the division would be far less clear if those species still existed. What looks like a bright, sharp dividing line is really an artefact of extinction. The discovery of these extinct species now blurs that line again and shows how the distance between us and other animals was crossed—gradually, over millennia. The Evolution of Humanity Our lineage probably split from the chimpanzees around six million years ago. These first hominins, members of the human line, would barely have seemed human, however. For the first few million years, hominin evolution was slow. The first big change was walking upright, which let hominins move away from forests into more open grassland and bush. But if they walked like us, nothing else suggests the first hominins were any more human than chimps or gorillas. Ardipithecus, the earliest well-known hominin, had a brain that was slightly smaller than a chimp’s, and there’s no evidence they used tools. In the next million years, Australopithecus appeared. Australopithecus had a slightly larger brain; larger than a chimp’s, still smaller than a gorilla’s. It made slightly more sophisticated tools than chimps, using sharp stones to butcher animals. Then came Homo habilis. For the first time, hominin brain size exceeded that of other apes. Tools like stone flakes, hammer stones, and “choppers” became much more complex. After that, around two million years ago, human evolu...


    AI-Savvy Criminals Pulled Off a $35 Million Deepfake Bank Heist Oct 20, 2021
    Show notes

    Thanks to the advance of deepfake technology, it’s becoming easier to clone peoples’ voices. Some uses of the tech, like creating voice-overs to fill in gaps in Roadrunner, the documentary about Anthony Bourdain released this past summer, are harmless (though even the ethics of this move were hotly debated when the film came out). In other cases, though, deepfaked voices are being used for ends that are very clearly nefarious—like stealing millions of dollars. An article published last week by Forbes revealed that a group of cybercriminals in the United Arab Emirates used deepfake technology as part of a bank heist that transferred a total of $35 million out of the country and into accounts all over the world. Money Heist, Voice Edition All you need to make a fake version of someone’s voice is a recording of that person speaking. As with any machine learning system whose output improves based on the quantity and quality of its input data, a deepfaked voice will sound more like the real thing if there are more recordings for the system to learn from. In this case, criminals used deepfake software to recreate the voice of an executive at a large company (details around the company, the software used, and the recordings to train said software don’t appear to be available). They then placed phone calls to a bank manager with whom the executive had a pre-existing relationship, meaning the bank manager knew the executive’s voice. The impersonators also sent forged emails to the bank manager confirming details of the requested transactions. Between the emails and the familiar voice, when the executive asked the manager to authorize transfer of millions of dollars between accounts, the manager saw no problem with going ahead and doing so. The fraud took place in January 2020, but a relevant court document was just filed in the US last week. Officials in the UAE are asking investigators in the US for help tracing $400,000 of the stolen money that went to US bank accounts at Centennial Bank. Our Voices, Our Selves The old-fashioned way (“old” in this context meaning before machine learning was as ubiquitous as it is today) to make a fake human voice was to record a real human voice, split that recording into many distinct syllables of speech, then paste those syllables together in countless permutations to form the words you wanted the voice to say. It was tedious and yielded a voice that didn’t sound at all realistic. It’s easy to differentiate the voices of people close to us, and to recognize famous voices—but we don’t often think through the many components that contribute to making a voice unique. There’s the timbre and pitch, which refer to where a voice falls on a span of notes from low to high. There’s the cadence, which is the speaker’s rhythm and variations in pitch and emphasis on different words or parts of a sentence. There’s pronunciation, and quirks like regional accents or lisps. In short, our voices are wholly unique—which makes it all the more creepy that they’re becoming easier to synthetically recreate. Fake Voices to Come Is the UAE bank heist a harbinger of crimes to come? Unfortunately, the answer is very likely yes. It’s not the first such attempt, but it’s the first to succeed at stealing such a large sum of money using a deepfaked voice. In 2019 a group of criminals faked the voice of a UK-based energy firm’s CEO to have $243,000 transferred to a Hungarian bank account. Many different versions of audio deepfake software are already commercially available, including versions from companies like Lyrebird (which needs just a one-minute recording to create a fake voice, albeit slightly halting and robot-like), Descript, Sonantic, and Veritone, to name just a few. These companies intend their products to be used for good, and some positive use cases certainly do exist; people with speech disabilities or paralysis could use the software to communicate with those around them, for example. Veritone is marketing its ...


    Super-Precise CRISPR Gene Editing Tool Could Tackle Tough Genetic Diseases Oct 19, 2021
    Show notes

    For all its supposed genetic editing finesse, CRISPR’s a brute. The Swiss Army knife of gene editing tools chops up DNA strands to insert genetic changes. What’s called “editing” is actually genetic vandalism—pick a malfunctioning gene, chop it up, and wait for the cell to patch and repair the rest. It’s a hasty, clunky process, prone to errors and other unintended and unpredictable effects. Back in 2019, researchers led by Dr. David Liu at Harvard decided to rework CRISPR from a butcher to a surgeon, one that lives up to its search-and-replace potential. The result is prime editing, an alternative version of CRISPR with the ability to “make virtually any targeted change in the genome of any living cell or organism.” It’s the nip-tuck of DNA editing: with just a small snip on one DNA chain, we have a whole menu of potential genetic changes at our fingertips. Prime editing was hailed as a fantastic “yay, science!” moment that could conceivably repair nearly 90 percent of over 75,000 diseases caused by genetic mutations. But even at its birth, Liu warned that CRISPR prime was only taking its first toddler steps into the big, wild world of changing a life form’s base code. “This first study is just the beginning—rather than the end—of a long-standing aspiration in the life sciences to be able to make any DNA change at any position in an organism,” he told Nature at the time. Flash forward two years. Liu’s gene editing ingénue took some stumbles. Despite its precise and effective nature, prime editing could only edit genes in certain types of cells, while being less effective and introducing errors in others. It also failed when trying to make large genetic edits, particularly those that require hundreds of DNA letters to be replaced to fix a disease-causing genetic mistake. But the good news? Toddlers grow up. This week, three separate studies advanced prime editing, helping the CRISPR tool grow into a more sophisticated DNA-editing genius. Two teams, based at the University of Massachusetts Medical School and the University of Washington, reworked the tool’s molecular makeup to precisely cut out up to 10,000 DNA letters in one go—a challenge for prime editing 1.0. A third study from the tool’s original inventor probed its inner molecular workings, identifying protein friends and foes inside the cell that control the tool’s genetic editing abilities. By promoting friendly interactions, the team increased prime editing’s efficiency in seven different cell types nearly eight-fold. Even better, the “foes” that block prime’s editing potential were identified using CRISPR—in other words, we’re witnessing a full circle of innovation whereby gene editing tools help build better gene editing tools. A Primer for CRISPR Prime Prime editing burst onto the gene editing scene for its dexterity and precision. If the original CRISPR-Cas9 is a dancer with two left feet, prime editing is a highly-trained ballerina. The two processes start similarly. Both rely on a molecular “zip code” to target the tool to a specific gene. In CRISPR, it’s called a guide RNA. For prime editing, it’s a slightly modified version dubbed pegRNA. Once the guides tether their respective dance partners to the gene, their routines differ. For CRISPR, the second component, Cas9, acts as a pair of scissors to snip both DNA strands. From here, cells can either throw out parts of a gene, or—when given a template—insert a healthy version of a gene to replace the original one. The cost is molecular surgery. Just as an incision might not fully heal, a double-stranded break to the DNA can introduce errors into the genetic code, leading to unexpected effects that vary between cells. Prime editing was the sophisticated upgrade set to fix that. Rather than cutting both DNA strands, it lightly nips one chain. From there, it can delete or insert genetic code based on a template without relying on the cell’s DNA repair mechanism. In other words, prime editing opened a new universe o...


    How Nanotechnology Will Help Us Probe the Brain in Unimaginable Detail Oct 18, 2021
    Show notes

    One of the biggest challenges when it comes to probing and manipulating the brain are the blunt tools we have at our disposal. But breakthroughs in nanotechnology could soon change that, say researchers. Neuroscience has experienced a technological revolution in the last couple decades thanks to rapid improvements in brain-machine interfaces and groundbreaking new methods like functional magnetic resonance imaging, which makes it possible to track neural activity across the whole brain, or optogenetics, which makes it possible to control individual neurons with light. But despite this progress, we are still a long way from being able to record or stimulate large parts of the brain at the single-neuron level. Being able to do so could have profound implications on our understanding of the brain, as well as our ability to augment its function and treat disease. The key to bridging this gap is the emerging field of “NanoNeuro,” say the authors of a new paper in Nature Methods. The unique properties and diminutive size of nanomaterials could make it possible to probe neural circuits in entirely new ways and at previously unimaginable scales, the researchers write. The most obvious application of nanotechnology is in simply reducing the size of the standard neuroscience toolbox. A host of recent designs for nanoprobes and nanoelectrodes, often exploiting the same processes that have powered the miniaturization of computer chips, are making it possible to record from orders of magnitude more neurons. These probes often come with other desirable properties too, such as flexibility, optical functionality, or chemical sensing. Other materials such as quartz, carbon nanotubes, and graphene are also being experimented with and each have their own unique properties. Perhaps most importantly, these tiny electrodes open the door to probing neural activity at the sub-cellular level. Given the powerful processing that goes on within neurons, this could significantly improve our understanding of critical aspects of brain function. Nanotechnology isn’t just about making things smaller, though. Physics operates on very different principles when you get down to the scale of atoms and molecules, which means nanomaterials can have exotic properties that enable entirely new functionality. For example, plasmonic nanoparticles have unique optical properties that can be easily tuned by simply varying their size and shape. These particles could be used to boost the sensitivity of existing optogenetic approaches, say the authors, and using light to excite and heat them up could also make it possible to trigger neurons to fire with very high precision. Even smaller “quantum dots”—nanoparticles that emit light in various colors when energy is applied to them—are a more durable and sensitive alternative to fluorescent dyes currently used for imaging. Their fluorescence is also modulated by electric fields, so they could potentially be used to give an optical readout on the activity of neurons. Another promising class of nanoparticles can absorb multiple low-energy electrons and convert them into a high-energy one. Researchers have used these so-called “upconverting nanoparticles” to let mice see in infrared by injecting them into the animals’ retinas, where they translate incoming signals into visible light. Potentially the most powerful application, though, could come from magnetic nanoparticles. The human body is almost entirely unaffected by magnetic fields, which makes it possible to send them deep into biological tissue with little impact. Nanoparticles that can convert magnetic fields into stimuli that trigger neurons could be a powerful tool to modulate brain activity. There’s still a long way to go, according to the authors. Effectively delivering nanoparticles to where we want them is challenging, as is producing large numbers of them without too much variability. And while early studies suggest many nanomaterials are biocompatible, proving they...


    Seismic 'Telescope' Reveals a Titanic, Tree-Like Plume Feeding Earth's Volcanoes Oct 17, 2021
    Show notes

    Some 75% of the world’s volcanoes live along the aptly name Ring of Fire. This makes sense. Hugging a boundary between tectonic pates, the Ring of Fire is an open seam on the planet’s interior. But then there’s Hawaii, a chain of volcanic islands smack in the middle of the Pacific plate, far from any boundaries. What feeds its fire? Scientists have long theorized that columns of superheated rock—piping hot plumes pushing through the mantle to the crust above—explain the Hawaiian islands and other areas like them. Where these columns touch the surface, volcanic hotspots form and the ground erupts. Over millions of years, inch by inch, the Earth’s tectonic plates drag new ground over hotspots and form long volcanic chains. The theory is old, but actually observing the mantle plumes feeding these hotspots in any detail is fairly new. “Theoretically, we know [plumes] have to exist,” Harriet Lau, a University of California, Berkeley geophysicist told Quanta Magazine. “But they’re just so hard to see seismically.” Now, however, in a particularly striking example, a team of scientists have completed a map of the underworld nearly a decade in the making. The result, beautifully visualized below for a feature in Quanta, is one of the most detailed snapshots yet—and it’s surprisingly complicated. Instead of a simple vertical column rising through the mantle, the structure is tree-like, with roots near the core, a trunk mid-mantle, and finer branching structures sprouting near the surface. The plume is feeding one the world’s most active volcanoes, Piton de la Fournaise, on the French island of Réunion in the Indian Ocean. But it’s also driving an intensely volcanic region in East Africa, some 3,000 kilometers away. Traveling back in time to when dinosaurs still ruled the planet, it ignited an area known as the Deccan Traps. Now in modern-day India, the Deccan Traps spilled enough lava to bury California, Montana, and Texas. Seeing Through the Ground Beneath Our Feet The Hubble Space Telescope is surely a wonder of the world. Imaging galaxies billions of light-years away is impressive—but how exactly does one see through thousands of kilometers of rock? In a sense, geophysicists build ‘telescopes’ too. But instead of sensing light, these systems collect and analyze the planet’s vibrations. “People have had a longer history and an easier time actually looking up at the stars,” University of Cambridge seismologist Sanne Cottaar told Quanta last year. “Looking down has actually been quite challenging.” To create this particular model of the underworld, the team drew on data from one of the largest such ‘telescopes’ to date. In 2012, ships dropped 57 seismometers into the ocean around Réunion. The entire array, which included 37 land-based sensors too, spanned some 2,000 kilometers. Over the next 13 months, the sensors recorded subtle vibrations from seismic activity occurring on the opposite side of the world. As earthquakes rattle the surface, they also ring the planet’s insides like a bell. By correlating a seismic event on one side of the world with the shiver it produces on the other, scientists infer what happened in between. Seismic vibrations tend to move more slowly through hotter areas than cooler areas, for example, so a mantle plume would slow their progress. With enough sensors and seismic events, researchers can construct a model. The model, in this case, was surprising. Scientist agree the mantle plumes underlying hotspots are so buoyant and quick-moving they should rise straight up. The diagonally branching paths in the data were unexpected. The team proposes they occur when temperature differences between hotter and cooler material make some areas of the plume more buoyant, pinching off blobs from the top of the trunk (or cusp) over time, one after another. These blobs do rise vertically but appear to form diagonal branches because older blobs have risen higher than younger ones. Nearer the surface, where the upper mantle...


    The World's Electronic Waste This Year Will Weigh More Than the Great Wall of China Oct 15, 2021
    Show notes

    It’s widely known that the world has a plastics problem. From landfills to the ocean, the stuff is everywhere, and our conscientious efforts to recycle don’t do nearly as much good as we think. What’s less widely known is that we have a similar problem with another kind of waste: electronics. A report published this week on WEEE Forum revealed that the total waste electronic and electrical equipment from 2021 will weigh an estimated 57.4 million tons. That’s heavier than China’s Great Wall, which is the heaviest man-made object on Earth. Not surprisingly, the amount of e-waste generated each year is steadily increasing. For one, as the global middle class grows, more people can afford to buy electronics (and to buy new ones when their old ones break, rather than getting the old ones repaired). Also, the prices of many electronic items tend to trend downwards as their manufacture is scaled up, their technology improves, supply chains are streamlined, etc. (given the global chip shortage, the next couple years may be an exception to this trend). E-waste appears to be growing by three to four percent per year. In 2019 the total reached 53.6 million tons; that was 21 percent higher than 2014’s total. If we stay on this trajectory, annual global e-waste will reach 74 tons by 2030. Product manufacturers aren’t helping the situation; building products with shorter life cycles, making repairs too expensive or difficult to undertake, and continually releasing new iterations means people are likely to either cast aside their perfectly-good iPhones/tablets/laptops for newer models, or decide that repairing a non-working device isn’t worth the trouble and opt for buying a brand-new one. Do you have at least one working (or partially-working) cell phone or laptop sitting in a drawer somewhere, untouched for months or years? Yeah, me too. “When you buy an expensive product, whether it’s a half-a-million-dollar tractor or a thousand-dollar phone, you are in a very real sense under the power of the manufacturer,” said Tim Wu, special assistant to the president for technology and competition policy within the National Economic Council. “And when they have repair specifications that are unreasonable, there’s not a lot you can do.” The Right to Repair movement thinks otherwise—or, is trying to get consumers and manufacturers to think otherwise. The movement is trying to make it easier for people to repair the devices they already own rather than having to buy new ones. Europe is several steps ahead of the US in this arena. In March of this year the EU implemented a law requiring appliances to be repairable for at least 10 years; new devices have to come with repair manuals and be compatible with conventional tools when their life cycle ends (so that people are more likely to break them down and recycle them). In Sweden, people even get tax breaks for appliance repairs done by technicians in their homes. Though there are no similar laws in place in the US yet, the Federal Trade Commission has been investigating repair restrictions as they relate to antitrust laws and consumer protection. Unsurprisingly, electronics manufacturers are largely against right to repair, claiming consumer safety could be jeopardized. But an FTC report from May of this year found there was limited evidence to support manufacturers’ justifications for restricting repairs, and that peoples’ device batteries aren’t actually that likely to burst into flames, nor their personal data likely to be compromised by repairing their devices. According to the WEEE Forum report, around 416,000 phones per day are thrown out in the US. That’s 151 million a year, and guess where they end up? Here’s a hint: 40 percent of heavy metals in landfills come from discarded electronics. Those metals could be recycled for use in new products, but there’s no system nor incentive in place to facilitate this. While small electronics like phones and laptops may have the fastest turnover, they’re n...


    Scientists Find the First Known Planet to Have Survived the Death of Its Star Oct 14, 2021
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    How will the solar system die? It’s a hugely important question that researchers have speculated a lot about, using our knowledge of physics to create complex theoretical models. We know that the sun will eventually become a “white dwarf,” a burnt stellar remnant whose dim light gradually fades into darkness. This transformation will involve a violent process that will destroy an unknown number of its planets. So which planets will survive the death of the sun? One way to seek the answer is to look at the fates of other similar planetary systems. This has proven difficult, however. The feeble radiation from white dwarfs makes it difficult to spot exoplanets (planets around stars other than our sun) which have survived this stellar transformation; they are literally in the dark. In fact, of the over 4,500 exoplanets that are currently known, just a handful have been found around white dwarfs, and the location of these planets suggests they arrived there after the death of the star. This lack of data paints an incomplete picture of our own planetary fate. Fortunately, we are now filling in the gaps. In our new paper, published in Nature, we report the discovery of the first known exoplanet to survive the death of its star without having its orbit altered by other planets moving around, circling a distance comparable to those between the sun and the solar system planets. A Jupiter-Like Planet This new exoplanet, which we discovered with the Keck Observatory in Hawaii, is particularly similar to Jupiter in both mass and orbital separation, and provides us with a crucial snapshot into planetary survivors around dying stars. A star’s transformation into a white dwarf involves a violent phase in which it becomes a bloated “red giant,” also known as a “giant branch” star, hundreds of times bigger than before. We believe that this exoplanet only just survived; if it was initially closer to its parent star, it would have been engulfed by the star’s expansion. When the sun eventually becomes a red giant, its radius will actually reach outwards to Earth’s current orbit. That means the sun will (probably) engulf Mercury and Venus, and possibly the Earth, but we are not sure. Jupiter, and its moons, have been expected to survive, although we previously didn’t know for sure. But with our discovery of this new exoplanet, we can now be more certain that Jupiter really will make it. Moreover, the margin of error in the position of this exoplanet could mean that it is almost half as close to the white dwarf as Jupiter currently is to the sun. If so, that is additional evidence for assuming that Jupiter and Mars will make it. So could any life survive this transformation? A white dwarf could power life on moons or planets that end up being very close to it (about one-tenth the distance between the sun and Mercury) for the first few billion years. After that, there wouldn’t be enough radiation to sustain anything. Asteroids and White Dwarfs Although planets orbiting white dwarfs have been difficult to find, what has been much easier to detect are asteroids breaking up close to the white dwarf’s surface. For exoasteroids to get so close to a white dwarf, they need to have enough momentum imparted to them by surviving exoplanets. Hence, exoasteroids have been long assumed to be evidence that exoplanets are there too. Our discovery finally provides confirmation of this. Although in the system being discussed in the paper, current technology does not allow us to see any exoasteroids, at least now we can piece together different parts of the puzzle of planetary fate by merging the evidence from different white dwarf systems. The link between exoasteroids and exoplanets also applies to our own solar system. Individual objects in the asteroid main belt and Kuiper belt (a disc in the outer solar system) are likely to survive the sun’s demise, but some will be moved by gravity by one of the surviving planets towards the white dwarf’s surface. Future Dis...


    Microsoft's Massive New Language AI Is Triple the Size of OpenAI’s GPT-3 Oct 13, 2021
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    Just under a year and a half ago OpenAI announced completion of GPT-3, its natural language processing algorithm that was, at the time, the largest and most complex model of its type. This week, Microsoft and Nvidia introduced a new model they’re calling “the world’s largest and most powerful generative language model.” The Megatron-Turing Natural Language Generation model (MT-NLG) is more than triple the size of GPT-3 at 530 billion parameters. GPT-3’s 175 billion parameters was already a lot; its predecessor, GPT-2, had a mere 1.5 billion parameters, and Microsoft’s Turing Natural Language Generation model, released in February 2020, had 17 billion. A parameter is an attribute a machine learning model defines based on its training data, and tuning more of them requires upping the amount of data the model is trained on. It’s essentially learning to predict how likely it is that a given word will be preceded or followed by another word, and how much that likelihood changes based on other words in the sentence. As you can imagine, getting to 530 billion parameters required quite a lot of input data and just as much computing power. The algorithm was trained using an Nvidia supercomputer made up of 560 servers, each holding eight 80-gigabyte GPUs. That’s 4,480 GPUs total, and an estimated cost of over $85 million. For training data, Megatron-Turing’s creators used The Pile, a dataset put together by open-source language model research group Eleuther AI. Comprised of everything from PubMed to Wikipedia to Github, the dataset totals 825GB, broken down into 22 smaller datasets. Microsoft and Nvidia curated the dataset, selecting subsets they found to be “of the highest relative quality.” They added data from Common Crawl, a non-profit that scans the open web every month and downloads content from billions of HTML pages then makes it available in a special format for large-scale data mining. GPT-3 was also trained using Common Crawl data. Microsoft’s blog post on Megatron-Turing says the algorithm is skilled at tasks like completion prediction, reading comprehension, commonsense reasoning, natural language inferences, and word sense disambiguation. But stay tuned—there will likely be more skills added to that list once the model starts being widely utilized. GPT-3 turned out to have capabilities beyond what its creators anticipated, like writing code, doing math, translating between languages, and autocompleting images (oh, and writing a short film with a twist ending). This led some to speculate that GPT-3 might be the gateway to artificial general intelligence. But the algorithm’s variety of talents, while unexpected, still fell within the language domain (including programming languages), so that’s a bit of a stretch. However, given the tricks GPT-3 had up its sleeve based on its 175 billion parameters, it’s intriguing to wonder what the Megatron-Turing model may surprise us with at 530 billion. The algorithm likely won’t be commercially available for some time, so it’ll be a while before we find out. The new model’s creators, though, are highly optimistic. “We look forward to how MT-NLG will shape tomorrow’s products and motivate the community to push the boundaries of natural language processing even further,” they wrote in the blog post. “The journey is long and far from complete, but we are excited by what is possible and what lies ahead.” Image Credit: Kranich17 from Pixabay


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