scientificamerican--2020-12-03--When Same-Sex Mating Makes Reproductive Sense

When a male sand-sifting sea star in the coastal waters of Australia reaches out a mating arm to its nearest neighbor , sometimes that neighbor is also male .
Undaunted , the pair assume their species ’ pseudocopulation position and forge ahead with spawning .
Mating , pseudo or otherwise , with a same-sex neighbor obviously does not transfer a set of genes to the next generation—yet several sea star and other echinoderm species persist with the practice .
They are not alone .
From butterflies to birds to beetles , many animals exhibit same-sex sexual behaviors despite their offering @ @ @ @ @ @ @ the energy expense and risk of being eaten that mating attempts can involve , why do these behaviors persist ?
One hypothesis , hotly debated among biologists , suggests this represents an ancient evolutionary strategy that could ultimately enhance an organism ’ s chances to reproduce .
In results published recently in Nature Ecology & Evolution , Brian Lerch and Maria R. Servedio , from the University of North Carolina , Chapel Hill , offer theoretical support for this proposed explanation .
They created a mathematical model that calculated scenarios in which mating @ @ @ @ @ @ @ might be worth it .
The results predicted that , depending on life span and mating chances , indiscriminate mating with any available candidates could in fact yield a better reproductive payoff than spending precious time and energy sorting out one sex from the other .
Although this study does not address sexual orientation or attraction , both of which are common among vertebrate species , it does get at some persistent evolutionary questions : when did animals start distinguishing mates by sex , based on specific cues , and why do some @ @ @ @ @ @ @ ?
“ What is probably going to get lost in translation for most people is that same-sex sexual behavior doesn ’ t necessarily equal same-sex sexual orientation , ” says Paul Vasey , professor of psychology and research chair at University of Lethbridge , Alberta , who was not involved in the new study .
Indiscriminate mating implies a random process between animals that do not transmit any sex-specific signals—whether chemical , sensory or behavioral .
Evolutionary biologists have proposed several explanations for indiscriminate mating attempts that include both same-sex and different-sex sexual @ @ @ @ @ @ @ s work adds a new theoretical underpinning to the literature .
To predict how time , life span , and sex-specific cues might affect reproductive success , they established a model that had two sexes , one dubbed the “ searcher ” and one the “ target.
” They also set some adjustable factors : sex signals from the target could range from “ nonexistent ” to “ always present , ” and could be detectable by searchers in a range from “ never ” to “ always.
” If the signal were @ @ @ @ @ @ @ it , then indiscriminate mating would be nonexistent .
But with no signals or weak ones , and with high risks involved in searching , mating with any available partner might tilt the scale toward evolutionary benefit .
The model also suggested an effect involving death and time : for species with short lives , the indiscriminate approach might be the best use of time , maximizing odds of at least one success .
Species with the longest lives would likely have more mating opportunities .
But indiscriminate mating might benefit them as @ @ @ @ @ @ @ a gamble , these animals might boost reproductive success by taking every mating opportunity that comes along and still be able to compensate for misfires .
Long-lived echinoderms such as the sand-sifting sea star are perhaps an example of the latter .
Echinoderms lie just outside the vertebrate family tree and are probably the closest non-bony relatives of animals with backbones .
For this reason , scientists often use this animal group as the evolutionary stand-in for a common ancestor of vertebrates .
These spiky creatures have rudimentary structures that can detect light @ @ @ @ @ @ @ animals send or receive sex-specific visual signals .
And , Lerch and Servedio write , echinoderms show little evidence of emitting sex-specific chemical cues over a distance .
The mathematical model would predict that animals like these , with a relatively long life and no apparent sex signals , might benefit from indiscriminate mating .
In the case of the sand-sifting sea star , real-world observation confirms that at least some pair up for mating regardless of sex .
Sea urchins do , too .
Lerch and Servedio ’ s model “ gives @ @ @ @ @ @ @ test just how prevalent indiscriminate sexual behaviors are , ( and ) my guess is , pretty darn prevalent , ” says Max Lambert , a conservation biologist and postdoctoral researcher at the University of California , Berkeley , who was not involved in the new work but co-authored a 2019 paper on the subject .
Yet single examples bearing out a theoretical prediction do not prove or disprove that same-sex sexual behaviors sit at the root of the vertebrate family tree .
Evolution has been known to shape , dispense with , @ @ @ @ @ @ @ features over millions of years .
It is possible that some species showing these behaviors , including many insects and spiders , might do so from mistaken identity—an imperfect read of a sex-specific signal—rather than by way of a reproductive gamble .
After all , some of these animals even occasionally choose the wrong species .
Yet another suggested explanation is the undertaking of noncommittal trial runs in preparation for the real thing .
Vasey says multiple evolutionary pathways probably generate same-sex sexual behaviors .
“ You can certainly see in a particular @ @ @ @ @ @ @ , mate with it—might be a good one , ” Vasey says .
But “ whether you can jump from there to say it was the ancestral pattern ?
That ’ s another question that will require more real-world studies to answer.
” For the moment , debate on the topic continues .
Lambert , with first author Julia Monk and colleagues , last year published a paper in Nature Ecology & Evolution proposing that some degree of same-sex activity could have been part of an ancestral repertoire of behaviors , after which @ @ @ @ @ @ @ selective mating .
But this proposition highlights a chicken-or-egg question about which came first : sex-specific signals , or different-sex sexual behavior .
In the absence of eyewitnesses , the best scientists can do is start with plausible ideas and devise ways to test them .
The testable parts of these ideas often rely on the kinds of mathematical models that Lerch and Servedio developed in their paper .
“ We were hoping in part to stimulate exactly this type of research , so I was happy to see someone building that argument @ @ @ @ @ @ @ Monk , a Ph.D. candidate and ecologist at the Yale School of the Environment .
A group of researchers wrote a critical response to the hypothesis that Monk and her colleagues proposed , calling for less theory and more testing .
Vasey agrees about the need for more hard evidence .
“ I personally would like to see more data collection to accompany all of this theorizing , ” he says .
“ We need to see what real animals are doing in the real world , because the models are only as @ @ @ @ @ @ @ .
”

When a male sand-sifting sea star in the coastal waters of Australia reaches out a mating arm to its nearest neighbor, sometimes that neighbor is also male. Undaunted, the pair assume their species’ pseudocopulation position and forge ahead with spawning. Mating, pseudo or otherwise, with a same-sex neighbor obviously does not transfer a set of genes to the next generation—yet several sea star and other echinoderm species persist with the practice.
They are not alone. From butterflies to birds to beetles, many animals exhibit same-sex sexual behaviors despite their offering zero chance of reproductive success. Given the energy expense and risk of being eaten that mating attempts can involve, why do these behaviors persist?
One hypothesis, hotly debated among biologists, suggests this represents an ancient evolutionary strategy that could ultimately enhance an organism’s chances to reproduce. In results published recently in Nature Ecology & Evolution, Brian Lerch and Maria R. Servedio, from the University of North Carolina, Chapel Hill, offer theoretical support for this proposed explanation. They created a mathematical model that calculated scenarios in which mating attempts, regardless of partner sex, might be worth it. The results predicted that, depending on life span and mating chances, indiscriminate mating with any available candidates could in fact yield a better reproductive payoff than spending precious time and energy sorting out one sex from the other.
Although this study does not address sexual orientation or attraction, both of which are common among vertebrate species, it does get at some persistent evolutionary questions: when did animals start distinguishing mates by sex, based on specific cues, and why do some animals apparently remain indiscriminate in their choices?
“What is probably going to get lost in translation for most people is that same-sex sexual behavior doesn’t necessarily equal same-sex sexual orientation,” says Paul Vasey, professor of psychology and research chair at University of Lethbridge, Alberta, who was not involved in the new study. Indiscriminate mating implies a random process between animals that do not transmit any sex-specific signals—whether chemical, sensory or behavioral.
Evolutionary biologists have proposed several explanations for indiscriminate mating attempts that include both same-sex and different-sex sexual behaviors, and Lerch and Servedio’s work adds a new theoretical underpinning to the literature. To predict how time, life span, and sex-specific cues might affect reproductive success, they established a model that had two sexes, one dubbed the “searcher” and one the “target.” They also set some adjustable factors: sex signals from the target could range from “nonexistent” to “always present,” and could be detectable by searchers in a range from “never” to “always.” If the signal were always present and the searcher always detected it, then indiscriminate mating would be nonexistent. But with no signals or weak ones, and with high risks involved in searching, mating with any available partner might tilt the scale toward evolutionary benefit.
The model also suggested an effect involving death and time: for species with short lives, the indiscriminate approach might be the best use of time, maximizing odds of at least one success. Species with the longest lives would likely have more mating opportunities. But indiscriminate mating might benefit them as well—with the luxury of time to take a gamble, these animals might boost reproductive success by taking every mating opportunity that comes along and still be able to compensate for misfires.
Long-lived echinoderms such as the sand-sifting sea star are perhaps an example of the latter. Echinoderms lie just outside the vertebrate family tree and are probably the closest non-bony relatives of animals with backbones. For this reason, scientists often use this animal group as the evolutionary stand-in for a common ancestor of vertebrates. These spiky creatures have rudimentary structures that can detect light, but it is unlikely that the animals send or receive sex-specific visual signals. And, Lerch and Servedio write, echinoderms show little evidence of emitting sex-specific chemical cues over a distance. The mathematical model would predict that animals like these, with a relatively long life and no apparent sex signals, might benefit from indiscriminate mating. In the case of the sand-sifting sea star, real-world observation confirms that at least some pair up for mating regardless of sex. Sea urchins do, too.
Lerch and Servedio’s model “gives us a deeper theoretical understanding to empirically test just how prevalent indiscriminate sexual behaviors are, (and) my guess is, pretty darn prevalent,” says Max Lambert, a conservation biologist and postdoctoral researcher at the University of California, Berkeley, who was not involved in the new work but co-authored a 2019 paper on the subject.
Yet single examples bearing out a theoretical prediction do not prove or disprove that same-sex sexual behaviors sit at the root of the vertebrate family tree. Evolution has been known to shape, dispense with, and yet again shape similar tactics and features over millions of years. It is possible that some species showing these behaviors, including many insects and spiders, might do so from mistaken identity—an imperfect read of a sex-specific signal—rather than by way of a reproductive gamble. After all, some of these animals even occasionally choose the wrong species. Yet another suggested explanation is the undertaking of noncommittal trial runs in preparation for the real thing.
Vasey says multiple evolutionary pathways probably generate same-sex sexual behaviors. “You can certainly see in a particular ecological context how that tactic—if it moves, mate with it—might be a good one,” Vasey says. But “whether you can jump from there to say it was the ancestral pattern? That’s another question that will require more real-world studies to answer.”
For the moment, debate on the topic continues. Lambert, with first author Julia Monk and colleagues, last year published a paper in Nature Ecology & Evolution proposing that some degree of same-sex activity could have been part of an ancestral repertoire of behaviors, after which sex-based signals arose and allowed for more selective mating.
But this proposition highlights a chicken-or-egg question about which came first: sex-specific signals, or different-sex sexual behavior. In the absence of eyewitnesses, the best scientists can do is start with plausible ideas and devise ways to test them. The testable parts of these ideas often rely on the kinds of mathematical models that Lerch and Servedio developed in their paper.
“We were hoping in part to stimulate exactly this type of research, so I was happy to see someone building that argument and testing it theoretically,” says Monk, a Ph.D. candidate and ecologist at the Yale School of the Environment.
A group of researchers wrote a critical response to the hypothesis that Monk and her colleagues proposed, calling for less theory and more testing. Vasey agrees about the need for more hard evidence. “I personally would like to see more data collection to accompany all of this theorizing,” he says. “We need to see what real animals are doing in the real world, because the models are only as good as the assumptions that underlie them.”


scientificamerican--2020-12-03--Light-based Quantum Computer Exceeds Fastest Classical Supercomputers

For the first time , a quantum computer made from photons—particles of light—has outperformed even the fastest classical supercomputers .
Physicists led by Chao-Yang Lu and Jian-Wei Pan of the University of Science and Technology of China ( USTC ) in Shanghai performed a technique called Gaussian boson sampling with their quantum computer , named Jiŭzhāng .
The result , reported in the journal Science , was 76 detected photons—far above and beyond the previous record of five detected photons and the capabilities of classical supercomputers .
Unlike a traditional computer built from silicon processors , Jiŭzhāngis an elaborate tabletop @ @ @ @ @ @ @ and photon detectors .
It is not a universal computer that could one day send e-mails or store files , but it does demonstrate the potential of quantum computing .
Last year , Google captured headlines when its quantum computer Sycamore took roughly three minutes to do what would take a supercomputer three days ( or 10,000 years , depending on your estimation method ) .
In their paper , the USTC team estimates that it would take the Sunway TaihuLight , the third most powerful supercomputer in the world , a staggering @ @ @ @ @ @ @ calculation as Jiŭzhāng .
This is only the second demonstration of quantum primacy , which is a term that describes the point at which a quantum computer exponentially outspeeds any classical one , effectively doing what would otherwise essentially be computationally impossible .
It is not just proof of principle ; there are also some hints that Gaussian boson sampling could have practical applications , such as solving specialized problems in quantum chemistry and math .
More broadly , the ability to control photons as qubits is a prerequisite for any large-scale quantum @ @ @ @ @ @ @ quantum bit , analogous to the bits used to represent information in classical computing . )
“ It was not obvious that this was going to happen , ” says Scott Aaronson , a theoretical computer scientist now at the University of Texas at Austin who along with then-student Alex Arkhipov first outlined the basics of boson sampling in 2011 .
Boson sampling experiments were , for many years , stuck at around three to five detected photons , which is “ a hell of a long way ” from quantum primacy , @ @ @ @ @ @ @ up is hard , ” he says .
“ Hats off to them.
” Over the past few years , quantum computing has risen from an obscurity to a multibillion dollar enterprise recognized for its potential impact on national security , the global economy and the foundations of physics and computer science .
In 2019 , the the U.S. National Quantum Initiative Act was signed into law to invest more than $ 1.2 billion in quantum technology over the next 10 years .
The field has also garnered a fair amount of hype @ @ @ @ @ @ @ about quantum computers making classical computers entirely obsolete .
This latest demonstration of quantum computing ’ s potential from the USTC group is critical because it differs dramatically from Google ’ s approach .
Sycamore uses superconducting loops of metal to form qubits ; in Jiŭzhāng , the photons themselves are the qubits .
Independent corroboration that quantum computing principles can lead to primacy even on totally different hardware “ gives us confidence that in the long term , eventually , useful quantum simulators and a fault-tolerant quantum computer will become feasible , @ @ @ @ @ @ @ computers have enormous potential ?
Consider the famous double-slit experiment , in which a photon is fired at a barrier with two slits , A and B .
The photon does not go through A , or through B .
Instead , the double-slit experiment shows that the photon exists in a “ superposition , ” or combination of possibilities , of having gone through both A and B .
In theory , exploiting quantum properties like superposition allows quantum computers to achieve exponential speedups over their classical counterparts when applied to certain @ @ @ @ @ @ @ 2000s were interested in exploiting the quantum properties of photons to make a quantum computer , in part because photons can act as qubits at room temperatures , so there is no need for the costly task of cooling one ’ s system to a few kelvins ( about –455 degrees Fahrenheit ) as with other quantum computing schemes .
But it quickly became apparent that building a universal photonic quantum computer was infeasible .
To even build a working quantum computer would require millions of lasers and other optical devices .
As @ @ @ @ @ @ @ seemed out of reach .
Then , in 2011 , Aaronson and Arkhipov introduced the concept of boson sampling , showing how it could be done with a limited quantum computer made from just a few lasers , mirrors , prisms and photon detectors .
Suddenly , there was a path for photonic quantum computers to show that they could be faster than classical computers .
The setup for boson sampling is analogous to the toy called a bean machine , which is just a peg-studded board covered with a sheet of clear @ @ @ @ @ @ @ rows of pegs from the top .
On their way down , they bounce off of the pegs and each other until they land in slots at the bottom .
Simulating the distribution of balls in slots is relatively easy on a classical computer .
Instead of balls , boson sampling uses photons , and it replaces pegs with mirrors and prisms .
Photons from the lasers bounce off of mirrors and through prisms until they land in a “ slot ” to be detected .
Unlike the classical balls , the photon @ @ @ @ @ @ @ exponentially increasing number of possible distributions .
The problem boson sampling solves is essentially “ What is the distribution of photons ?
” Boson sampling is a quantum computer that solves itself by being the distribution of photons .
Meanwhile , a classical computer has to figure out the distribution of photons by computing what ’ s called the “ permanent ” of a matrix .
For an input of two photons , this is just a short calculation with a two-by-two array .
But as the number of photonic inputs and detectors @ @ @ @ @ @ @ array grows , exponentially increasing the problem ’ s computational difficulty .
Last year the USTC group demonstrated boson sampling with 14 detected photons—hard for a laptop to compute , but easy for a supercomputer .
To scale up to quantum primacy , they used a slightly different protocol , Gaussian boson sampling .
According to Christine Silberhorn , an quantum optics expert at the University of Paderborn in Germany and one of the co-developers of Gaussian boson sampling , the technique was designed to avoid the unreliable single photons used in Aaronson @ @ @ @ @ @ @ boson sampling .
“ I really wanted to make it practical , ” she says “ It 's a scheme which is specific to what you can do experimentally.
” Even so , she acknowledges that the USTC setup is dauntingly complicated .
Jiŭzhāng begins with a laser that is split so it strikes 25 crystals made of potassium titanyl phosphate .
After each crystal is hit , it reliably spits out two photons in opposite directions .
The photons are then sent through 100 inputs , where they race through a track @ @ @ @ @ @ @ .
Finally , the photons land in 100 slots where they are detected .
Averaging over 200 seconds of runs , the USTC group detected about 43 photons per run .
But in one run , they observed 76 photons—more than enough to justify their quantum primacy claim .
It is difficult to estimate just how much time would be needed for a supercomputer to solve a distribution with 76 detected photons—in large part because it is not exactly feasible to spend 2.5 billion years running a supercomputer to directly check it .
@ @ @ @ @ @ @ time it takes to classically calculate for smaller numbers of detected photons .
At best , solving for 50 photons , the researchers claim , would take a supercomputer two days , which is far slower than the 200-second run time of Jiŭzhāng .
Boson sampling schemes have languished at low numbers of photons for years because they are incredibly difficult to scale up .
To preserve the sensitive quantum arrangement , the photons must remain indistinguishable .
Imagine a horse race where the horses all have to be released from the starting @ @ @ @ @ @ @ finish at the same time as well .
Photons , unfortunately , are a lot more unreliable than horses .
As photons in Jiŭzhāng travel a 22-meter path , their positions can differ by no more than 25 nanometers .
That is the equivalent of 100 horses going 100 kilometers and crossing the finish line with no more than a hair ’ s width between them , Lu says .
The USTC quantum computer takes its name , Jiŭzhāng , from Jiŭzhāng Suànshù , or “ The Nine Chapters on the Mathematical Art @ @ @ @ @ @ @ an impact comparable to Euclid ’ s Elements .
Quantum computing , too , has many twists and turns ahead .
Outspeeding classical computers is not a one-and-done deal , according to Lu , but will instead be a continuing competition to see if classical algorithms and computers can catch up , or if quantum computers will maintain the primacy they have seized .
Things are unlikely to be static .
At the end of October , researchers at the Canadian quantum computing start-up Xanadu found an algorithm that quadratically cut the classical @ @ @ @ @ @ @ .
In other words , if 50 detected photons sufficed for quantum primacy before , you would now need 100 .
For theoretical computer scientists like Aaronson , the result is exciting because it helps give further evidence against the extended Church-Turing thesis , which holds that any physical system can be efficiently simulated on a classical computer .
“ At the very broadest level , if we thought of the universe as a computer , then what kind of computer is it ?
” Aaronson says .
“ Is it a classical @ @ @ @ @ @ @ computer ?
” So far , the universe , like the computers we are attempting to make , seems to be stubbornly quantum .

For the first time, a quantum computer made from photons—particles of light—has outperformed even the fastest classical supercomputers.
Physicists led by Chao-Yang Lu and Jian-Wei Pan, both at the University of Science and Technology of China (USTC) in Hefei, performed a technique called Gaussian boson sampling with their quantum computer, named Jiŭzhāng.* The result, reported in the journal Science, was 76 detected photons—far above and beyond the previous record of five detected photons and the capabilities of classical supercomputers.
Unlike a traditional computer built from silicon processors, Jiŭzhāng is an elaborate tabletop setup of lasers, mirrors, prisms and photon detectors. It is not a universal computer that could one day send e-mails or store files, but it does demonstrate the potential of quantum computing.
Last year, Google captured headlines when its quantum computer Sycamore took roughly three minutes to do what would take a supercomputer three days (or 10,000 years, depending on your estimation method). In their paper, the USTC team estimates that it would take the Sunway TaihuLight, the third most powerful supercomputer in the world, a staggering 2.5 billion years to perform the same calculation as Jiŭzhāng.
This is only the second demonstration of quantum primacy, which is a term that describes the point at which a quantum computer exponentially outspeeds any classical one, effectively doing what would otherwise essentially be computationally impossible. It is not just proof of principle; there are also some hints that Gaussian boson sampling could have practical applications, such as solving specialized problems in quantum chemistry and math. More broadly, the ability to control photons as qubits is a prerequisite for any large-scale quantum internet. (A qubit is a quantum bit, analogous to the bits used to represent information in classical computing.)
“It was not obvious that this was going to happen,” says Scott Aaronson, a theoretical computer scientist now at the University of Texas at Austin who along with then-student Alex Arkhipov first outlined the basics of boson sampling in 2011. Boson sampling experiments were, for many years, stuck at around three to five detected photons, which is “a hell of a long way” from quantum primacy, according to Aaronson. “Scaling it up is hard,” he says. “Hats off to them.”
Over the past few years, quantum computing has risen from an obscurity to a multibillion dollar enterprise recognized for its potential impact on national security, the global economy and the foundations of physics and computer science. In 2019, the the U.S. National Quantum Initiative Act was signed into law to invest more than $1.2 billion in quantum technology over the next 10 years. The field has also garnered a fair amount of hype, with unrealistic timelines and bombastic claims about quantum computers making classical computers entirely obsolete.
This latest demonstration of quantum computing’s potential from the USTC group is critical because it differs dramatically from Google’s approach. Sycamore uses superconducting loops of metal to form qubits; in Jiŭzhāng, the photons themselves are the qubits. Independent corroboration that quantum computing principles can lead to primacy even on totally different hardware “gives us confidence that in the long term, eventually, useful quantum simulators and a fault-tolerant quantum computer will become feasible,” Lu says.
A LIGHT SAMPLING
Why do quantum computers have enormous potential? Consider the famous double-slit experiment, in which a photon is fired at a barrier with two slits, A and B. The photon does not go through A, or through B. Instead, the double-slit experiment shows that the photon exists in a “superposition,” or combination of possibilities, of having gone through both A and B. In theory, exploiting quantum properties like superposition allows quantum computers to achieve exponential speedups over their classical counterparts when applied to certain specific problems.
Physicists in the early 2000s were interested in exploiting the quantum properties of photons to make a quantum computer, in part because photons can act as qubits at room temperatures, so there is no need for the costly task of cooling one’s system to a few kelvins (about –455 degrees Fahrenheit) as with other quantum computing schemes. But it quickly became apparent that building a universal photonic quantum computer was infeasible. To even build a working quantum computer would require millions of lasers and other optical devices. As a result, quantum primacy with photons seemed out of reach.
Then, in 2011, Aaronson and Arkhipov introduced the concept of boson sampling, showing how it could be done with a limited quantum computer made from just a few lasers, mirrors, prisms and photon detectors. Suddenly, there was a path for photonic quantum computers to show that they could be faster than classical computers.
The setup for boson sampling is analogous to the toy called a bean machine, which is just a peg-studded board covered with a sheet of clear glass. Balls are dropped into the rows of pegs from the top. On their way down, they bounce off of the pegs and each other until they land in slots at the bottom. Simulating the distribution of balls in slots is relatively easy on a classical computer.
Instead of balls, boson sampling uses photons, and it replaces pegs with mirrors and prisms. Photons from the lasers bounce off of mirrors and through prisms until they land in a “slot” to be detected. Unlike the classical balls, the photon’s quantum properties lead to an exponentially increasing number of possible distributions.
The problem boson sampling solves is essentially “What is the distribution of photons?” Boson sampling is a quantum computer that solves itself by being the distribution of photons. Meanwhile, a classical computer has to figure out the distribution of photons by computing what’s called the “permanent” of a matrix. For an input of two photons, this is just a short calculation with a two-by-two array. But as the number of photonic inputs and detectors goes up, the size of the array grows, exponentially increasing the problem’s computational difficulty.
Last year the USTC group demonstrated boson sampling with 14 detected photons—hard for a laptop to compute, but easy for a supercomputer. To scale up to quantum primacy, they used a slightly different protocol, Gaussian boson sampling.
According to Christine Silberhorn, an quantum optics expert at the University of Paderborn in Germany and one of the co-developers of Gaussian boson sampling, the technique was designed to avoid the unreliable single photons used in Aaronson and Arkhipov’s “vanilla” boson sampling.
“I really wanted to make it practical,” she says “It's a scheme which is specific to what you can do experimentally.”
Even so, she acknowledges that the USTC setup is dauntingly complicated. Jiŭzhāng begins with a laser that is split so it strikes 25 crystals made of potassium titanyl phosphate. After each crystal is hit, it reliably spits out two photons in opposite directions. The photons are then sent through 100 inputs, where they race through a track made of 300 prisms and 75 mirrors. Finally, the photons land in 100 slots where they are detected. Averaging over 200 seconds of runs, the USTC group detected about 43 photons per run. But in one run, they observed 76 photons—more than enough to justify their quantum primacy claim.
It is difficult to estimate just how much time would be needed for a supercomputer to solve a distribution with 76 detected photons—in large part because it is not exactly feasible to spend 2.5 billion years running a supercomputer to directly check it. Instead, the researchers extrapolate from the time it takes to classically calculate for smaller numbers of detected photons. At best, solving for 50 photons, the researchers claim, would take a supercomputer two days, which is far slower than the 200-second run time of Jiŭzhāng.
Boson sampling schemes have languished at low numbers of photons for years because they are incredibly difficult to scale up. To preserve the sensitive quantum arrangement, the photons must remain indistinguishable. Imagine a horse race where the horses all have to be released from the starting gate at exactly the same time and finish at the same time as well. Photons, unfortunately, are a lot more unreliable than horses.
As photons in Jiŭzhāng travel a 22-meter path, their positions can differ by no more than 25 nanometers. That is the equivalent of 100 horses going 100 kilometers and crossing the finish line with no more than a hair’s width between them, Lu says.
QUANTUM QUESTING
The USTC quantum computer takes its name, Jiŭzhāng, from Jiŭzhāng Suànshù, or “The Nine Chapters on the Mathematical Art,” an ancient Chinese text with an impact comparable to Euclid’s Elements.
Quantum computing, too, has many twists and turns ahead. Outspeeding classical computers is not a one-and-done deal, according to Lu, but will instead be a continuing competition to see if classical algorithms and computers can catch up, or if quantum computers will maintain the primacy they have seized.
Things are unlikely to be static. At the end of October, researchers at the Canadian quantum computing start-up Xanadu found an algorithm that quadratically cut the classical simulation time for some boson sampling experiments. In other words, if 50 detected photons sufficed for quantum primacy before, you would now need 100.
For theoretical computer scientists like Aaronson, the result is exciting because it helps give further evidence against the extended Church-Turing thesis, which holds that any physical system can be efficiently simulated on a classical computer.
“At the very broadest level, if we thought of the universe as a computer, then what kind of computer is it?” Aaronson says. “Is it a classical computer? Or is it a quantum computer?”
So far, the universe, like the computers we are attempting to make, seems to be stubbornly quantum.
*Editor’s Note (12/20/21): This sentence was edited after posting to correct the location of the University of Science and Technology of China.


scientificamerican--2020-12-03--Major Companies Call on Biden to Act on Climate Change

Some major companies are angling to line up with the Biden administration on climate next year , as lawmakers and lobbyists gird for renewed policy fights in Washington .
A big slice of corporate America—including utilities , banks and auto manufacturers—signed onto a statement yesterday calling on President-elect Joe Biden to work with Congress on “ ambitious , durable , bipartisan climate solutions.
” While the statement doesn ’ t offer support for a specific policy , its signatories represent a cross section of the U.S. economy , including Amazon.com Inc. , Bank of America Corp. , BP PLC , @ @ @ @ @ @ @ “ To achieve a net-zero economy , the United States must establish durable national policies that harness market forces , mobilize investment and innovation , and provide the certainty needed to plan for the long term , ” they wrote .
Organized by the Center for Climate and Energy Solutions , or C2ES , the statement comes amid a raft of corporate signaling on climate , as Biden enters office with an ambitious clean energy platform .
It ’ s signaling that could be indicative of the policy fights to come in the @ @ @ @ @ @ @ for carbon pricing and progressives increasingly looking to regulation and spending with the prospect of divided government looming next year .
“ We faced , I think it goes without saying , some head winds with the past administration on a lot of these fronts , '' ” Hugh Welsh , president and general counsel of DSM North America , said during a virtual event with the CEO Climate Dialogue yesterday .
“ I think we all believe that we ’ ll enjoy some tail wind going forward.
” General Motors Co. , @ @ @ @ @ @ @ week it would end its support for the Trump administration ’ s clean car standards rollback and instead support Biden ’ s efforts to deploy electric vehicles ( E & E News PM , Nov. 23 ) .
Meanwhile , the Business Roundtable , a major trade organization , announced earlier this year that it supports carbon pricing , aligning itself with various energy companies and business groups like the CEO Climate Dialogue ( Greenwire , Sept. 16 ) .
And more recently , the U.S. Chamber of Commerce , long a foe @ @ @ @ @ @ @ circulated a questionnaire to certain members last week asking their opinions on a price on carbon and what should be done with the revenue , Welsh said .
“ Just being asked that question for the U.S. Chamber I found to be encouraging , ” said Welsh , who has been active in recent years in pushing for pro-climate advocacy from within the organization .
Welsh ’ s company and a wide selection of others publicly support carbon pricing , but it remains to be seen where the major trade groups , which @ @ @ @ @ @ @ Capitol , will land .
But the Chamber , in particular , has gradually shifted its stance in the last two years , coming out in favor of the Paris climate agreement and forming a “ Task Force on Climate Actions ” to explore its members ’ opinions on carbon pricing and other policy questions .
Chamber spokesman Matt Letourneau didn ’ t confirm the carbon pricing questionnaire but said the group “ engaged in substantive dialogue with our members on ways the business community can continue to advance climate and sustainability priorities.
@ @ @ @ @ @ @ share specific internal member discussions , we can confirm that we regularly communicate and seek input on a range of issues with our members through a variety of means and will continue to do so , ” Letourneau said .
Still , Welsh and other executives said yesterday that they see a price on carbon as inevitable , even as progressives and climate activists increasingly are turning away from the policy in favor of heavy spending on clean energy and infrastructure .
“ I don ’ t know that it ’ s doable @ @ @ @ @ @ @ next couple of Congresses it will be , so we ’ re going to keep pushing , ” Calpine Corp. CEO Thad Hill said .
While the incoming Biden administration has prompted some additional climate wrangling in the corporate world , the landscape has shifted dramatically with the economy under the Trump administration .
Under pressure from activists and investors , a wide range of companies have set goals in recent years to slash emissions from their own operations .
Sea-level rise , hurricanes and wildfires—as well as the pandemic-induced oil crash earlier @ @ @ @ @ @ @ among economists about how climate change could affect coastal property and fossil fuel assets .
Environmental , social and governance ( ESG ) investing and divestment from fossil fuels have in recent years gone from largely political statements by a handful of institutions to a serious consideration in the financial community , said C2ES President Bob Perciasepe .
“ I don ’ t think it ’ s a boutique thing anymore , ” Perciasepe said during the CEO Climate Dialogue event .
“ I think the sophistication of thinking ahead to the future @ @ @ @ @ @ @ prepared for that economy in 2050 , as opposed to the economy in 1950 , is going to be on the minds of a lot of people.
” Welsh , for example , said that DSM has heard more about climate change from investors and financial institutions in the last 18 months than he had in the previous 18 years .
During quarterly shareholder calls , Welsh said , DSM rarely got questions about its sustainability or climate initiatives .
“ That began to change about a year ago , where we went @ @ @ @ @ @ @ analysts asking quite a few questions about how our different innovations were working out , where we were going to invest money in the future that was consistent with our climate strategy , ” Welsh said .
“ And we came to learn that over the course of the last year , our shareholding group is now self-identified as about 43 % ESG shareholders , which is remarkable for a company like ours that ’ s been around for 116 years , ” he said .
Reprinted from Climatewire with permission from E @ @ @ @ @ @ @ provides daily coverage of essential energy and environmental news at www.eenews.net .

Some major companies are angling to line up with the Biden administration on climate next year, as lawmakers and lobbyists gird for renewed policy fights in Washington.
A big slice of corporate America—including utilities, banks and auto manufacturers—signed onto a statement yesterday calling on President-elect Joe Biden to work with Congress on “ambitious, durable, bipartisan climate solutions.”
While the statement doesn’t offer support for a specific policy, its signatories represent a cross section of the U.S. economy, including Amazon.com Inc., Bank of America Corp., BP PLC, Walmart Inc., DSM and Exelon Corp.
“To achieve a net-zero economy, the United States must establish durable national policies that harness market forces, mobilize investment and innovation, and provide the certainty needed to plan for the long term,” they wrote.
Organized by the Center for Climate and Energy Solutions, or C2ES, the statement comes amid a raft of corporate signaling on climate, as Biden enters office with an ambitious clean energy platform.
It’s signaling that could be indicative of the policy fights to come in the next few years, with companies pushing for carbon pricing and progressives increasingly looking to regulation and spending with the prospect of divided government looming next year.
“We faced, I think it goes without saying, some head winds with the past administration on a lot of these fronts,"”Hugh Welsh, president and general counsel of DSM North America, said during a virtual event with the CEO Climate Dialogue yesterday. “I think we all believe that we’ll enjoy some tail wind going forward.”
General Motors Co., which signed the statement, said last week it would end its support for the Trump administration’s clean car standards rollback and instead support Biden’s efforts to deploy electric vehicles (E&E News PM, Nov. 23).
Meanwhile, the Business Roundtable, a major trade organization, announced earlier this year that it supports carbon pricing, aligning itself with various energy companies and business groups like the CEO Climate Dialogue (Greenwire, Sept. 16).
And more recently, the U.S. Chamber of Commerce, long a foe of climate policy on Capitol Hill, circulated a questionnaire to certain members last week asking their opinions on a price on carbon and what should be done with the revenue, Welsh said.
“Just being asked that question for the U.S. Chamber I found to be encouraging,” said Welsh, who has been active in recent years in pushing for pro-climate advocacy from within the organization.
Welsh’s company and a wide selection of others publicly support carbon pricing, but it remains to be seen where the major trade groups, which have historically held heavy sway in the Capitol, will land.
But the Chamber, in particular, has gradually shifted its stance in the last two years, coming out in favor of the Paris climate agreement and forming a “Task Force on Climate Actions” to explore its members’ opinions on carbon pricing and other policy questions.
Chamber spokesman Matt Letourneau didn’t confirm the carbon pricing questionnaire but said the group “engaged in substantive dialogue with our members on ways the business community can continue to advance climate and sustainability priorities.”
“While we don’t share specific internal member discussions, we can confirm that we regularly communicate and seek input on a range of issues with our members through a variety of means and will continue to do so,” Letourneau said.
Still, Welsh and other executives said yesterday that they see a price on carbon as inevitable, even as progressives and climate activists increasingly are turning away from the policy in favor of heavy spending on clean energy and infrastructure.
“I don’t know that it’s doable this Congress, but hopefully within the next couple of Congresses it will be, so we’re going to keep pushing,” Calpine Corp. CEO Thad Hill said.
Financial shift
While the incoming Biden administration has prompted some additional climate wrangling in the corporate world, the landscape has shifted dramatically with the economy under the Trump administration.
Under pressure from activists and investors, a wide range of companies have set goals in recent years to slash emissions from their own operations.
Sea-level rise, hurricanes and wildfires—as well as the pandemic-induced oil crash earlier this year—have also set off alarm bells among economists about how climate change could affect coastal property and fossil fuel assets.
Environmental, social and governance (ESG) investing and divestment from fossil fuels have in recent years gone from largely political statements by a handful of institutions to a serious consideration in the financial community, said C2ES President Bob Perciasepe.
“I don’t think it’s a boutique thing anymore,” Perciasepe said during the CEO Climate Dialogue event. “I think the sophistication of thinking ahead to the future, and who is going to be prepared for that economy in 2050, as opposed to the economy in 1950, is going to be on the minds of a lot of people.”
Welsh, for example, said that DSM has heard more about climate change from investors and financial institutions in the last 18 months than he had in the previous 18 years.
During quarterly shareholder calls, Welsh said, DSM rarely got questions about its sustainability or climate initiatives.
“That began to change about a year ago, where we went from zero questions on these initiatives to analysts asking quite a few questions about how our different innovations were working out, where we were going to invest money in the future that was consistent with our climate strategy,” Welsh said.
“And we came to learn that over the course of the last year, our shareholding group is now self-identified as about 43% ESG shareholders, which is remarkable for a company like ours that’s been around for 116 years,” he said.
Reprinted from Climatewire with permission from E&E News. E&E provides daily coverage of essential energy and environmental news at www.eenews.net.


scientificamerican--2020-12-03--Inventing Us: How Inventions Shaped Humanity

This is Scientific American ’ s Science Talk .
I ’ m Steve Mirsky .
On this episode : That ’ s Ainissa Ramirez .
She ’ s a materials scientist with a doctorate from Stanford and a science writer .
And she ’ s the author of the 2020 book The Alchemy of Us : How Humans and Matter Transformed One Another .
The book has recently been named one of Smithsonian Magazine ’ s Ten Best Science Books of 2020 , an Amazon Best Science Books of 2020 , and , even though it ’ s written for @ @ @ @ @ @ @ 2021 Young Adult Science Book Award .
I called her at her home in New Haven , Connecticut .
That ’ s it for this episode , get your science news at our website , www.scientificamerican.com .
Where all of our coronavirus coverage is out from behind the paywall , available free .
And follow us on Twitter , where you ’ ll get a tweet whenever a new item hits the website .
Our twitter name is @ sciam .
For Scientific American ’ s Science Talk , I ’ m Steve @ @ @ @ @ @ @ .

Full Transcript
This is Scientific American’s Science Talk. I’m Steve Mirsky. On this episode:
[RAMIREZ CLIP]
That’s Ainissa Ramirez. She’s a materials scientist with a doctorate from Stanford and a science writer. And she’s the author of the 2020 book The Alchemy of Us: How Humans and Matter Transformed One Another. The book has recently been named one of Smithsonian Magazine’s Ten Best Science Books of 2020. It’s also been named an Amazon Best Science Books of 2020. And even though it’s written for adults, it’s a finalist for the AAAS/Subaru 2021 Young Adult Science Book Award. I called her at her home in New Haven, Conn.
[RAMIREZ SEGMENT]
Full transcript to come.
That’s it for this episode. Get your science news at our Web site (www.ScientificAmerican.com), where all of our coronavirus coverage is out from behind the paywall, available free.
And follow us on Twitter, where you’ll get a tweet whenever a new item hits the Web site. Our twitter name is @sciam. For Scientific American’s Science Talk, I’m Steve Mirsky. Thanks for clicking on us.


scientificamerican--2020-12-04--Bionic Eye Tech Learns Its ABCs

Bionic Eye Tech Learns Its ABCs

Jens Naumann was 17 when an accident sent a fragment of metal from a railway line flying into his left eye .
Three years later , a metal sliver from a snowmobile clutch destroyed his right eye , plunging him into total darkness .
Naumann ’ s book Search for Paradise recounts his desperate quest back to the light , primarily as biomedical engineer William Dobelle ’ s “ patient alpha.
” In the 1970s , Dobelle had shown that electrically stimulating visual brain areas ( the visual cortex ) caused people to perceive spots of light , or “ @ @ @ @ @ @ @ was to develop a “ bionic eye.
” The apparatus would consist of a head-mounted camera that feeds video to a computer processor , which would then send electrical signals to electrodes implanted in the visual cortex , generating visual perceptions .
Naumann became Dobelle ’ s most famous patient after traveling to Portugal for surgery in 2002 ; the FDA had banned the procedure in the U.S. as not having been proven to be safe .
His anecdotal accounts of perceiving crude outlines remained the only evidence researchers had that perception of @ @ @ @ @ @ @ , because data from these procedures were never published .
The device degraded after a few months , and Naumann ’ s newfound visual world faded away , but he has continued to campaign through the years to move the technology forward .
A more sophisticated version that moves the technology closer to routine practical use in people has now been built and tested in monkeys .
A team led by neuroscientist Pieter Roelfsema , of the Netherlands Institute for Neuroscience , has demonstrated perception of position , orientation , motion , and @ @ @ @ @ @ @ .
The study , published December 3 in Science “ is a technical tour de force , ” says neurosurgeon Daniel Yoshor , who was not involved but co-wrote an accompanying commentary .
The device needs further development before it is ready for use in humans , but the work brings nearer the dream of restoring vision to people who have none .
The approach is the only possible treatment for people without functioning cells in the eye—a group that includes some glaucoma and diabetes patients and those who have experienced a physical @ @ @ @ @ @ @ the team used 16 arrays , each a grid of 64 electrodes , for a total of 1024 electrodes .
“ We tiled a large fraction of the surface of the cortex , thereby creating an interface with a large fraction of this map of visual space , ” Roelfsema says .
The visual cortex has a property known as “ retinotopy , ” which means that visual space physically maps onto areas of the cortex , allowing researchers to generate phosphenes at specific points in space .
A series of experiments showed @ @ @ @ @ @ @ of individual phosphenes , the orientation of lines consisting of two phosphenes , and the direction of motion implied by stimulating two sequentially .
Finally , monkeys who had been trained to recognize letters seemed to be able to identify letters generated from between eight and 15 phosphenes .
The phosphenes were generated without using cameras by directly stimulating electrodes , and the monkeys indicated responses using eye movements .
Other groups are working on the same problem , and one is already testing devices in humans .
A California company , Second @ @ @ @ @ @ @ Orion in six blind people in an FDA-approved clinical trial .
Like Dobelle ’ s system , Orion uses electrodes that sit on the brain ’ s surface that avoids the tissue damage and inflammation that arises when using “ penetrating ” implants , which results in a loss of performance over time .
A disadvantage of this method is that currents required are relatively high , limiting the number of electrodes that can be used safely .
“ You don ’ t want to induce epilepsy , ” Roelfsema says .
The @ @ @ @ @ @ @ penetrating electrodes , which require less current .
“ We used thin needles , so we can activate just a few cells , with relatively mild currents , a hundred times smaller than you need with a surface electrode , ” Roelfsema says .
Most significantly , the new device has far higher resolution .
Where the new system has 1,024 electrodes , the Orion implant has 60 , limiting recipients to detecting areas of dark and light .
The vision this device might generate would be crude compared to the richness of @ @ @ @ @ @ @ substantial benefit .
“ When you start at nothing , 10 , 20 percent is game-changing , ” says Neena Haider of Harvard Medical School , who was not involved in the work .
“ It gives you a window into how to navigate the world.
” But obstacles remain before this technology sees human use .
First , implants must be wireless—and other groups are making efforts to develop wireless brain implants .
Next steps also need to measure the physiological consequences of penetrating implants , Haider says .
“ What cellular @ @ @ @ @ @ @ ” she asks , regarding both acute and long-term effects .
“ Biocompatibility ” remains an issue , but solutions may be at hand .
“ We ’ re working with groups that develop thin , flexible electrodes , ” pushed into the brain using rods that are then retracted , Roelfsema says .
“ First impressions are these new materials are very stable , but there ’ s still work to do.
” Better understanding how the brain processes visual information will also help .
“ There ’ s a hardware challenge @ @ @ @ @ @ @ says .
“ Sometimes people neglect the software element , which is how we stimulate the brain.
” Yoshor is one of two neurosurgeons who have implanted Orion in patients , and he and colleagues recently published a study exploring its capabilities .
“ It ’ s easy to make patients see spots , ” Yoshor says .
“ But when we try to combine them , like stadium lights , it ’ s much harder to get patients to perceive a coherent form.
” The research team wondered whether exploiting the brain @ @ @ @ @ @ @ stimulation might help .
“ If we stimulated six electrodes simultaneously , patients saw inconsistent blobs , ” Yoshor says .
“ But when we swept across the brain , patients were immediately able to detect visual forms or letters.
” The processing performed by the visual system is incredibly complex , but deeper understanding of this processing , together with more sophisticated stimulation technologies , will continue to advance these devices .
Yoshor likens the problem to playing music .
“ It 's like the difference between playing a chord and banging @ @ @ @ @ @ @ he says .
“ If you input information crudely , you produce a cacophony ; it has to be done in a way that ’ s musical .
”

Jens Naumann was 17 when an accident sent a fragment of metal from a railway line flying into his left eye. Three years later, a metal sliver from a snowmobile clutch destroyed his right eye, plunging him into total darkness. Naumann’s book Search for Paradise recounts his desperate quest back to the light, primarily as biomedical engineer William Dobelle’s “patient alpha.” In the 1970s, Dobelle had shown that electrically stimulating visual brain areas (the visual cortex) caused people to perceive spots of light, or “phosphenes.”
The engineer’s goal was to develop a “bionic eye.” The apparatus would consist of a head-mounted camera that feeds video to a computer processor, which would then send electrical signals to electrodes implanted in the visual cortex, generating visual perceptions. Naumann became Dobelle’s most famous patient after traveling to Portugal for surgery in 2002; the FDA had banned the procedure in the U.S. as not having been proven to be safe. His anecdotal accounts of perceiving crude outlines remained the only evidence researchers had that perception of shapes was possible using such a device, because data from these procedures were never published. The device degraded after a few months, and Naumann’s newfound visual world faded away, but he has continued to campaign through the years to move the technology forward.
A more sophisticated version that moves the technology closer to routine practical use in people has now been built and tested in monkeys. A team led by neuroscientist Pieter Roelfsema, of the Netherlands Institute for Neuroscience, has demonstrated perception of position, orientation, motion, and letter shapes, in two sighted monkeys. The study, published December 3 in Science “is a technical tour de force,” says neurosurgeon Daniel Yoshor, who was not involved but co-wrote an accompanying commentary. The device needs further development before it is ready for use in humans, but the work brings nearer the dream of restoring vision to people who have none. The approach is the only possible treatment for people without functioning cells in the eye—a group that includes some glaucoma and diabetes patients and those who have experienced a physical trauma.
In the new study, the team used 16 arrays, each a grid of 64 electrodes, for a total of 1024 electrodes. “We tiled a large fraction of the surface of the cortex, thereby creating an interface with a large fraction of this map of visual space,” Roelfsema says. The visual cortex has a property known as “retinotopy,” which means that visual space physically maps onto areas of the cortex, allowing researchers to generate phosphenes at specific points in space. A series of experiments showed that the monkeys could identify the position of individual phosphenes, the orientation of lines consisting of two phosphenes, and the direction of motion implied by stimulating two sequentially. Finally, monkeys who had been trained to recognize letters seemed to be able to identify letters generated from between eight and 15 phosphenes. The phosphenes were generated without using cameras by directly stimulating electrodes, and the monkeys indicated responses using eye movements.
Other groups are working on the same problem, and one is already testing devices in humans. A California company, Second Sight, is developing a system called Orion in six blind people in an FDA-approved clinical trial. Like Dobelle’s system, Orion uses electrodes that sit on the brain’s surface that avoids the tissue damage and inflammation that arises when using “penetrating” implants, which results in a loss of performance over time. A disadvantage of this method is that currents required are relatively high, limiting the number of electrodes that can be used safely. “You don’t want to induce epilepsy,” Roelfsema says. The new study from the Netherlands group used penetrating electrodes, which require less current. “We used thin needles, so we can activate just a few cells, with relatively mild currents, a hundred times smaller than you need with a surface electrode,” Roelfsema says. Most significantly, the new device has far higher resolution. Where the new system has 1,024 electrodes, the Orion implant has 60, limiting recipients to detecting areas of dark and light.
The vision this device might generate would be crude compared to the richness of natural vision, but still provide a substantial benefit. “When you start at nothing, 10, 20 percent is game-changing,” says Neena Haider of Harvard Medical School, who was not involved in the work. “It gives you a window into how to navigate the world.” But obstacles remain before this technology sees human use. First, implants must be wireless—and other groups are making efforts to develop wireless brain implants. Next steps also need to measure the physiological consequences of penetrating implants, Haider says. “What cellular responses are happening in the brain?” she asks, regarding both acute and long-term effects. “Biocompatibility” remains an issue, but solutions may be at hand. “We’re working with groups that develop thin, flexible electrodes,” pushed into the brain using rods that are then retracted, Roelfsema says. “First impressions are these new materials are very stable, but there’s still work to do.”
Better understanding how the brain processes visual information will also help. “There’s a hardware challenge and a software challenge,” Yoshor says. “Sometimes people neglect the software element, which is how we stimulate the brain.” Yoshor is one of two neurosurgeons who have implanted Orion in patients, and he and colleagues recently published a study exploring its capabilities. “It’s easy to make patients see spots,” Yoshor says. “But when we try to combine them, like stadium lights, it’s much harder to get patients to perceive a coherent form.” The research team wondered whether exploiting the brain’s propensity for detecting changes in stimulation might help. “If we stimulated six electrodes simultaneously, patients saw inconsistent blobs,” Yoshor says. “But when we swept across the brain, patients were immediately able to detect visual forms or letters.”
The processing performed by the visual system is incredibly complex, but deeper understanding of this processing, together with more sophisticated stimulation technologies, will continue to advance these devices. Yoshor likens the problem to playing music. “It's like the difference between playing a chord and banging a piano with your fists,” he says. “If you input information crudely, you produce a cacophony; it has to be done in a way that’s musical.”


scientificamerican--2020-12-04--Who Is My Doctor? Some Hospital Patients Never Know

Even after being hospitalized for an entire week , my friend Aidan never got an answer to a major question : Who is my doctor ?
As a healthy 26-year-old , he didn ’ t know much about the hospital—a place that I work every day .
He learned a lot after a lung infection forced him into the infirmary for seven long nights .
Aidan now knows the excruciating pain of having plastic hoses inserted into the chest to drain pus from around the lungs ; the definition of an empyema ( which he still refers to as his @ @ @ @ @ @ @ dignity that occurs when physicians probe into your personal life ; how disorienting and lonely the hospital can be .
He called me from his hospital bed with updates and a series of queries .
But he had one question that never got answered : Who , in fact , was his doctor ?
Despite a steady march of white-coated figures in and out of his room—sometimes at 5 A.M. , seldom introducing themselves—he never found out .
They would often face each other instead of addressing him , and use only technical @ @ @ @ @ @ @ his body .
During these months of pandemic fear , friends , old classmates , previous professors and even strangers have reached out to me because of my role as an emergency medicine physician .
With loved ones in or about to be in the hospital , they send a flurry of questions to my inbox .
How do they get in contact with their loved one ?
What questions should they know to ask ?
What does this test result mean ?
Is it safe to go home ?
Who is my @ @ @ @ @ @ @ hospital can be one of the most frightening , dizzying and unpleasant life experiences , admission rarely comes with an orientation packet .
It ’ s a confusing place with an uncountable number of moving parts , roles and features , and when patients are whisked from one place to another by busy people who already know the system well , a clear explanation of what to expect often falls between the cracks .
Communication is even more limited with the visitor limitations COVID-19 has forced into place .
The advocates that patients @ @ @ @ @ @ @ children , concerned friends—aren ’ t there to process information as it comes in spurts .
Important family decisions are reduced to Zoom calls on propped-up tablets .
It takes a clear head to sift through medical jargon , but we expect patients to interpret this new language in the height of discomfort , in a new environment and often with pain , anxiety and illness clouding their ability to ask questions about their care .
Aidan is a smart guy .
He ’ s Ivy League–educated , with friends in the medical @ @ @ @ @ @ @ moment ’ s notice , but he still felt immensely uninformed and unequipped to process what exactly was going on with his care on a day-to-day basis .
He , like a number of other people in this pandemic , had never had reason to interface with the hospital and was forced into the steep learning curve of illness without warning or preparation .
Aidan can ’ t imagine how much worse the process would have been if he , like so many of our patients , didn ’ t possess impeccable English @ @ @ @ @ @ @ support .
Getting Aidan ’ s call reminded me how muddled and opaque a hospital stay can be .
Though the following is by no means comprehensive , I ’ m hoping that by offering a short glossary of physician roles and a brief summary of the hospital stay process , people who are experiencing hospitalization or sending care to hospitalized loved ones will have a better understanding of what to expect .
Most patients get admitted to the hospital after being seen and stabilized in the emergency department .
Sometimes it can @ @ @ @ @ @ @ what is happening multiple times , but generally it ’ s because there are several steps that happen to make sure a patient is being appropriately placed and evaluated in the emergency room .
Often , patients will undergo a brief triage evaluation , wait in the waiting room , then undergo an initial nursing evaluation that is sometimes conducted in parallel to a medical resident physician evaluation .
The resident will then present the clinical history and findings to the senior/attending physician ( simply “ attending , ” in hospital jargon ) @ @ @ @ @ @ @ formal evaluation as well .
In almost every case , care in the emergency department is a team sport that takes a number of different people to help the process run smoothly .
Because shift changes don ’ t always align for attending physicians , resident physicians , medical students and nurses , this means a patient in the emergency room can be cared for by several different people during their stay .
When there are shift changes , major facts of each patient case and plan are communicated from the original care @ @ @ @ @ @ @ process we call “ sign out.
” If a patient is admitted overnight to the hospital for care and monitoring , the admitting physician from the wards ( the “ inpatient ” side of the hospital ) will reevaluate the individual to come up with a medical assessment and plan for the patient ’ s stay .
( This includes , for example , ordering medications that the patient has previously been prescribed , deciding on further necessary tests and calling specialists for consultation . )
In the morning , new labs ( @ @ @ @ @ @ @ clinical information .
Residents usually arrive in the morning before their attendings in order to “ pre-round , ” or complete a fresh physical exam and assessment of each patient .
Sometimes , medical students , interns and senior residents will all “ pre-round ” on patients , which can result in multiple people in white coats coming in and out of a room .
What ’ s more , if a patient is being followed by consulting teams , each team will also do their own exam .
This can mean multiple @ @ @ @ @ @ @ , often before 6 or 7 A.M.
It ’ s not pleasant to be woken before the sun rises by hands that usually push and inspect exactly where it hurts most .
It ’ s definitely not enjoyable to have rest and privacy disrupted , or to be jarred into the day by freezing stethoscopes and slamming doors .
But these exams and new laboratory results are used to discuss each patient ’ s developments or improvements during “ rounds ” with the attending later in the morning .
During “ rounds , @ @ @ @ @ @ @ for the day are made with the whole team present , though discussions involving specialty care can be completed later during the day .
Overall , and unfortunately , these processes favor a system where communication about new information and decision-making about patient care happens chiefly among physicians , instead of between patients and doctors .
Though providers should introduce themselves and explain their clinical role clearly , this rarely happens .
It can be helpful to keep a notebook and pen around to keep track of questions , notes and physicians .
@ @ @ @ @ @ @ trust is being updated with medical decisions and plans each day .
It ’ s always good to have a second set of ears listening in and clarifying any confusing points .
Most importantly , ask questions .
It ’ s important that you as a patient understand and are on-board with your own medical plan .
WHO DOES WHAT Medical Students : Third- and fourth-year medical students working towards their M.D .
participate in four- to-12-week rotations in the hospital , where they are involved with patient care as part of their @ @ @ @ @ @ @ learn , not work , and as novices they usually “ carry ” ( are responsible for ) a fewer number of patients .
Because of this , they have more time to spend on each patient and can be important sources of advocacy and communication .
They can put in orders for medications and procedures with the supervision of senior physicians .
Interns/Residents : Residents are graduated and practicing doctors in the middle of residency—a three-to-seven-year specialty training program required to become an independent , licensed physician .
Interns are simply physicians @ @ @ @ @ @ @ Residents and interns are responsible for evaluating patients and placing medication and procedure orders , under supervision of the attending physician .
Often , residents and interns are the physicians with whom patients are in most contact and communication , since they remain on the wards for the entirety of the day and night and are the first call for any arising medical issues or questions .
Attending : The attending physician is the senior and supervising doctor of the team .
Major medical decisions are made by the attending , who also @ @ @ @ @ @ @ .
Many attendings are scheduled to be “ on service ” for inpatient wards for one to two weeks at a time .
This means , depending on scheduling and duration of stay ( often , attendings switch on and off service on Sundays or Mondays ) , patients may be under the primary care of multiple or different attending physicians from week to week .
Primary Team : The primary team is responsible for a patient ’ s overall care and makes final clinical decisions , sometimes with the input of Consulting @ @ @ @ @ @ @ in , for example , pulmonology , geriatrics , psychiatry , orthopedics .
When called , consulting physicians will evaluate patients and offer recommendations on specialty care through written notes and discussions with primary team members .
Fellow : Fellows are in between residents and attendings in seniority , as they are physicians who have graduated from medical residency and are pursuing extra training in a subspecialty ( For example , infectious disease , critical care , cardiology , endocrinology , trauma ) .
They can work within a primary or consulting team @ @ @ @ @ @ @ academic medical centers , fellows and residents on rotation operate as the arm of a consulting team and will do an initial evaluation/daily examinations before reporting back to the specialty attending and communicating recommendations to the primary team .
Aidan ’ s experience is not unique , and it is a reminder that taking the time to communicate compassionately is a major part of patient care , comfort and well-being .
I imagine that , in the emergency department , it is awful to see your doctor walk out the door with their @ @ @ @ @ @ @ or further explanation .
While sign-out is often a rushed time , I am trying harder to introduce myself , my role on the team , the next steps of the medical process and who I am taking over for to every patient whose care is transferred into my hands .
The hospital is a terrifying place even without a novel pandemic .
Tell your patients who you are before you touch them .
Know that everyone is hungry for answers ; be generous with information .
Many people who come to the @ @ @ @ @ @ @ discomfort , suffering and fear , which makes it ever more important to help them process difficult and technical information and to take the time to communicate with family , who are often waiting anxiously across phone lines for updates on their loved ones .
We must recognize these small actions as integral to ethical and appropriate patient care .
Everyone is busy , but it is necessary to make time for this .
As the poet Marge Piercy tells us as a bracing reminder : The work of the world is common @ @ @

Even after being hospitalized for an entire week, my friend Aidan never got an answer to a major question: Who is my doctor?
As a healthy 26-year-old, he didn’t know much about the hospital—a place that I work every day. He learned a lot after a lung infection forced him into the infirmary for seven long nights.
Aidan now knows the excruciating pain of having plastic hoses inserted into the chest to drain pus from around the lungs; the definition of an empyema (which he still refers to as his lung empanadas); the violation of dignity that occurs when physicians probe into your personal life; how disorienting and lonely the hospital can be.
He called me from his hospital bed with updates and a series of queries. But he had one question that never got answered: Who, in fact, was his doctor?
Despite a steady march of white-coated figures in and out of his room—sometimes at 5 A.M., seldom introducing themselves—he never found out. They would often face each other instead of addressing him, and use only technical language when pulling back blankets to examine his body.
During these months of pandemic fear, friends, old classmates, previous professors and even strangers have reached out to me because of my role as an emergency medicine physician. With loved ones in or about to be in the hospital, they send a flurry of questions to my inbox. How do they get in contact with their loved one? What questions should they know to ask? What does this test result mean? Is it safe to go home? Who is my doctor?
Though staying overnight in the hospital can be one of the most frightening, dizzying and unpleasant life experiences, admission rarely comes with an orientation packet. It’s a confusing place with an uncountable number of moving parts, roles and features, and when patients are whisked from one place to another by busy people who already know the system well, a clear explanation of what to expect often falls between the cracks.
Communication is even more limited with the visitor limitations COVID-19 has forced into place. The advocates that patients usually have around them—worried parents, frightened children, concerned friends—aren’t there to process information as it comes in spurts. Important family decisions are reduced to Zoom calls on propped-up tablets. It takes a clear head to sift through medical jargon, but we expect patients to interpret this new language in the height of discomfort, in a new environment and often with pain, anxiety and illness clouding their ability to ask questions about their care.
Aidan is a smart guy. He’s Ivy League–educated, with friends in the medical system he can call on at a moment’s notice, but he still felt immensely uninformed and unequipped to process what exactly was going on with his care on a day-to-day basis. He, like a number of other people in this pandemic, had never had reason to interface with the hospital and was forced into the steep learning curve of illness without warning or preparation. Aidan can’t imagine how much worse the process would have been if he, like so many of our patients, didn’t possess impeccable English, above-average health literacy, and family support.
Getting Aidan’s call reminded me how muddled and opaque a hospital stay can be. Though the following is by no means comprehensive, I’m hoping that by offering a short glossary of physician roles and a brief summary of the hospital stay process, people who are experiencing hospitalization or sending care to hospitalized loved ones will have a better understanding of what to expect.
ADMISSION
Most patients get admitted to the hospital after being seen and stabilized in the emergency department. Sometimes it can be frustrating to repeat your story about what is happening multiple times, but generally it’s because there are several steps that happen to make sure a patient is being appropriately placed and evaluated in the emergency room. Often, patients will undergo a brief triage evaluation, wait in the waiting room, then undergo an initial nursing evaluation that is sometimes conducted in parallel to a medical resident physician evaluation. The resident will then present the clinical history and findings to the senior/attending physician (simply “attending,” in hospital jargon), who will often perform their own formal evaluation as well.
In almost every case, care in the emergency department is a team sport that takes a number of different people to help the process run smoothly. Because shift changes don’t always align for attending physicians, resident physicians, medical students and nurses, this means a patient in the emergency room can be cared for by several different people during their stay. When there are shift changes, major facts of each patient case and plan are communicated from the original care provider to the incoming team in a process we call “sign out.”
If a patient is admitted overnight to the hospital for care and monitoring, the admitting physician from the wards (the “inpatient” side of the hospital) will reevaluate the individual to come up with a medical assessment and plan for the patient’s stay. (This includes, for example, ordering medications that the patient has previously been prescribed, deciding on further necessary tests and calling specialists for consultation.)
In the morning, new labs (blood work) are drawn for updated clinical information. Residents usually arrive in the morning before their attendings in order to “pre-round,” or complete a fresh physical exam and assessment of each patient. Sometimes, medical students, interns and senior residents will all “pre-round” on patients, which can result in multiple people in white coats coming in and out of a room. What’s more, if a patient is being followed by consulting teams, each team will also do their own exam.
This can mean multiple examinations happen very early in the morning, often before 6 or 7 A.M. It’s not pleasant to be woken before the sun rises by hands that usually push and inspect exactly where it hurts most. It’s definitely not enjoyable to have rest and privacy disrupted, or to be jarred into the day by freezing stethoscopes and slamming doors. But these exams and new laboratory results are used to discuss each patient’s developments or improvements during “rounds” with the attending later in the morning. During “rounds,” most major scheduling and clinical decisions for the day are made with the whole team present, though discussions involving specialty care can be completed later during the day.
Overall, and unfortunately, these processes favor a system where communication about new information and decision-making about patient care happens chiefly among physicians, instead of between patients and doctors. Though providers should introduce themselves and explain their clinical role clearly, this rarely happens. It can be helpful to keep a notebook and pen around to keep track of questions, notes and physicians. If possible, make sure someone you trust is being updated with medical decisions and plans each day. It’s always good to have a second set of ears listening in and clarifying any confusing points. Most importantly, ask questions. It’s important that you as a patient understand and are on-board with your own medical plan.
WHO DOES WHAT
Medical Students: Third- and fourth-year medical students working towards their M.D. participate in four- to-12-week rotations in the hospital, where they are involved with patient care as part of their education. Their main role is to learn, not work, and as novices they usually “carry” (are responsible for) a fewer number of patients. Because of this, they have more time to spend on each patient and can be important sources of advocacy and communication. They can put in orders for medications and procedures with the supervision of senior physicians.
Interns/Residents: Residents are graduated and practicing doctors in the middle of residency—a three-to-seven-year specialty training program required to become an independent, licensed physician. Interns are simply physicians in the first year of residency. Residents and interns are responsible for evaluating patients and placing medication and procedure orders, under supervision of the attending physician. Often, residents and interns are the physicians with whom patients are in most contact and communication, since they remain on the wards for the entirety of the day and night and are the first call for any arising medical issues or questions.
Attending: The attending physician is the senior and supervising doctor of the team. Major medical decisions are made by the attending, who also supervises residents during medical procedures and rounds. Many attendings are scheduled to be “on service” for inpatient wards for one to two weeks at a time. This means, depending on scheduling and duration of stay (often, attendings switch on and off service on Sundays or Mondays), patients may be under the primary care of multiple or different attending physicians from week to week.
Primary Team: The primary team is responsible for a patient’s overall care and makes final clinical decisions, sometimes with the input of Consulting Teams or Consultants who have special expertise in, for example, pulmonology, geriatrics, psychiatry, orthopedics. When called, consulting physicians will evaluate patients and offer recommendations on specialty care through written notes and discussions with primary team members.
Fellow: Fellows are in between residents and attendings in seniority, as they are physicians who have graduated from medical residency and are pursuing extra training in a subspecialty (For example, infectious disease, critical care, cardiology, endocrinology, trauma). They can work within a primary or consulting team. Sometimes, and especially in large academic medical centers, fellows and residents on rotation operate as the arm of a consulting team and will do an initial evaluation/daily examinations before reporting back to the specialty attending and communicating recommendations to the primary team.
Aidan’s experience is not unique, and it is a reminder that taking the time to communicate compassionately is a major part of patient care, comfort and well-being. I imagine that, in the emergency department, it is awful to see your doctor walk out the door with their bag and coat without giving you warning or further explanation. While sign-out is often a rushed time, I am trying harder to introduce myself, my role on the team, the next steps of the medical process and who I am taking over for to every patient whose care is transferred into my hands.
The hospital is a terrifying place even without a novel pandemic. Tell your patients who you are before you touch them. Know that everyone is hungry for answers; be generous with information. Many people who come to the hospital are understandably distracted by pain, discomfort, suffering and fear, which makes it ever more important to help them process difficult and technical information and to take the time to communicate with family, who are often waiting anxiously across phone lines for updates on their loved ones. We must recognize these small actions as integral to ethical and appropriate patient care. Everyone is busy, but it is necessary to make time for this.
As the poet Marge Piercy tells us as a bracing reminder: The work of the world is common as mud.


scientificamerican--2020-12-04--This Robot Can Rap--Really

This Robot Can Rap--Really

What if your digital assistant could battle rap ?
That may sound far-fetched , but Gil Weinberg , a music technologist at the Georgia Institute of Technology , has adapted a musical robot called Shimon to compose lyrics and perform in real time .
That means it can engage in rap “ conversations ” with humans , and maybe even help them compose their own lyrics .
Shimon , which was intentionally designed to sound machinelike ( listen here ) , is meant to be a one-of-a-kind musical collaborator—or an inhuman rap-battle opponent .
Computer-generated music dates back to the @ @ @ @ @ @ @ to compose melodies .
Modern robots can use machine learning to ad-lib on instruments including the flute and drums .
One such machine was an earlier version of Shimon , which could play the marimba and sing .
The recently updated robot looks the same ; it still consists of a ball-shaped “ head , ” with saucy movable eyebrows above visor-covered eyes , perched at the end of a mechanical arm .
But now Weinberg claims Shimon is the first improvising robot to foray into rap , with its distinct stylistic features @ @ @ @ @ @ @ crowning glory of rap lies in the lyrics .
On top of semantic content , the words need to adhere to an aesthetically pleasing beat and rhythm , all while delivering multiple layers of poetic complexity .
In a recent paper , published in the proceedings of the 11th International Conference on Computational Creativity 2020 , Weinberg ’ s research team outlines the technical advances that brought a rapping Shimon to life .
When Shimon battle raps , software converts its human opponent ’ s spoken lyrics into text .
The robot ’ @ @ @ @ @ @ @ and generates new lyrics based on several custom data sets of words that Shimon has been trained on ( using deep-learning models ) .
These data sets can come from any text : the work of Lil Wayne , JAY-Z or other rappers ; lyrics from other genres ; or even nonmusical literary works .
Imagine how Shakespeare or Jane Austen might sound if they rapped ; Shimon could simulate that for you .
One novel element of Shimon ’ s design , its creators say , is the additional use of phoneme @ @ @ @ @ @ @ Phonemes are the distinct units of pronunciation that make up the sound of a word .
Breaking down keywords into these units is the most effective way to integrate rhyme into the lyrics , says Richard Savery , the first author of the paper on Shimon and a music technologist at Georgia Tech .
“ The way phonemes relate between words is really important , ” Savery explains , sometimes even “ more important than the actual meaning of the words.
” The training data set of phonemes enables Shimon to churn out @ @ @ @ @ @ @ robot then layers a rhythmic beat onto its speech .
Shimon ’ s systems must be fast enough to respond in real time without compromising performance quality .
To achieve this , the researchers made several tough programming decisions , such as capping Shimon ’ s response vocabulary at around 3,000 words and truncating the length of time Shimon will “ listen ” to its opponent .
So far Shimon can rap a comeback in less than seven seconds , while improvising gestures such as head bobbing and eyebrow waggling .
Hardware upgrades @ @ @ @ @ @ @ processing unit , will eventually make the process speedier .
No individual component of Shimon ’ s technology is completely new—but this particular assembly of parts is , says Prem Seetharaman , a research scientist at the tech start-up Descript , who was not involved in the project .
“ Generally , the field is pretty siloed into different things like speech-to-text , text-to-speech , music , ” Seetharaman says .
“ The field is approaching a good [ enough ] level of complexity so that people are able to take these [ @ @ @ @ @ @ @ really interesting interactive systems.
” Beyond Shimon ’ s novelty value , Weinberg wants his robot to provide opportunities for people to experiment with new kinds of music .
“ It ’ s not interesting to me if [ Shimon ] does its thing without humans … as a completely autonomous musical system , ” he says .
His goal is to see his robots “ communicating and interacting with [ humans ] and inspiring them in surprising ways.
” Weinberg had never written lyrics before , but says Shimon enabled him to @ @ @ @ @ @ @ He adds that he has even received requests for help from lyricists afflicted by writer ’ s block .
Seetharaman , a recreational musician himself , also says he is excited by the possibilities Shimon ’ s technology might offer nonmusicians .
“ Tools that use AI can reduce the barrier to entry … to making art , ” he says .
“ People do it all the time : you see people make Instagram Stories and TikTok [ videos ] .
” Professionals , however , have some reservations .
Rhys Langston @ @ @ @ @ @ @ was not involved in the project , says he would be keen on rapping with Shimon , especially since the COVID-19 pandemic has limited the in-person interactions from which Langston derives most of his inspiration .
He says it is impressive what artificial intelligence can achieve—but also suggests that robots simply can not access the inspiration that sometimes serendipitously arises from things like human error .
During a recording session , Langston explains , mistakes occasionally end up in a final recording because they sound surprisingly good .
Accidents “ unlock possibilities because @ @ @ @ @ @ @ is planned out , ” he says .
“ Can you teach a machine to make mistakes ?
”

What if your digital assistant could battle rap? That may sound far-fetched, but Gil Weinberg, a music technologist at the Georgia Institute of Technology, has adapted a musical robot called Shimon to compose lyrics and perform in real time. That means it can engage in rap “conversations” with humans, and maybe even help them compose their own lyrics. Shimon, which was intentionally designed to sound machinelike (listen here), is meant to be a one-of-a-kind musical collaborator—or an inhuman rap-battle opponent.
Computer-generated music dates back to the 1950s, when early computers used algorithms to compose melodies. Modern robots can use machine learning to ad-lib on instruments including the flute and drums. One such machine was an earlier version of Shimon, which could play the marimba and sing. The recently updated robot looks the same; it still consists of a ball-shaped “head,” with saucy movable eyebrows above visor-covered eyes, perched at the end of a mechanical arm. But now Weinberg claims Shimon is the first improvising robot to foray into rap, with its distinct stylistic features that pose unique programming challenges.
The crowning glory of rap lies in the lyrics. On top of semantic content, the words need to adhere to an aesthetically pleasing beat and rhythm, all while delivering multiple layers of poetic complexity. In a recent paper, published in the proceedings of the 11th International Conference on Computational Creativity 2020, Weinberg’s research team outlines the technical advances that brought a rapping Shimon to life.
When Shimon battle raps, software converts its human opponent’s spoken lyrics into text. The robot’s system identifies keywords from this, and generates new lyrics based on several custom data sets of words that Shimon has been trained on (using deep-learning models). These data sets can come from any text: the work of Lil Wayne, JAY-Z or other rappers; lyrics from other genres; or even nonmusical literary works. Imagine how Shakespeare or Jane Austen might sound if they rapped; Shimon could simulate that for you.
One novel element of Shimon’s design, its creators say, is the additional use of phoneme data sets to conceive new lyrics. Phonemes are the distinct units of pronunciation that make up the sound of a word. Breaking down keywords into these units is the most effective way to integrate rhyme into the lyrics, says Richard Savery, the first author of the paper on Shimon and a music technologist at Georgia Tech. “The way phonemes relate between words is really important,” Savery explains, sometimes even “more important than the actual meaning of the words.” The training data set of phonemes enables Shimon to churn out keyword-centric phrases in rhyme, and the robot then layers a rhythmic beat onto its speech.
Shimon’s systems must be fast enough to respond in real time without compromising performance quality. To achieve this, the researchers made several tough programming decisions, such as capping Shimon’s response vocabulary at around 3,000 words and truncating the length of time Shimon will “listen” to its opponent. So far Shimon can rap a comeback in less than seven seconds, while improvising gestures such as head bobbing and eyebrow waggling. Hardware upgrades, such as a more powerful graphics processing unit, will eventually make the process speedier.
No individual component of Shimon’s technology is completely new—but this particular assembly of parts is, says Prem Seetharaman, a research scientist at the tech start-up Descript, who was not involved in the project. “Generally, the field is pretty siloed into different things like speech-to-text, text-to-speech, music,” Seetharaman says. “The field is approaching a good [enough] level of complexity so that people are able to take these [components] and connect them together into really interesting interactive systems.”
Beyond Shimon’s novelty value, Weinberg wants his robot to provide opportunities for people to experiment with new kinds of music. “It’s not interesting to me if [Shimon] does its thing without humans … as a completely autonomous musical system,” he says. His goal is to see his robots “communicating and interacting with [humans] and inspiring them in surprising ways.” Weinberg had never written lyrics before, but says Shimon enabled him to produce songs for the first time. He adds that he has even received requests for help from lyricists afflicted by writer’s block.
Seetharaman, a recreational musician himself, also says he is excited by the possibilities Shimon’s technology might offer nonmusicians. “Tools that use AI can reduce the barrier to entry … to making art,” he says. “People do it all the time: you see people make Instagram Stories and TikTok [videos].”
Professionals, however, have some reservations. Rhys Langston, a rapper and multimedia artist who was not involved in the project, says he would be keen on rapping with Shimon, especially since the COVID-19 pandemic has limited the in-person interactions from which Langston derives most of his inspiration. He says it is impressive what artificial intelligence can achieve—but also suggests that robots simply cannot access the inspiration that sometimes serendipitously arises from things like human error. During a recording session, Langston explains, mistakes occasionally end up in a final recording because they sound surprisingly good. Accidents “unlock possibilities because not everything [in a recording] is planned out,” he says. “Can you teach a machine to make mistakes?”