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AI-Powered Brain Implant Eases Severe Depression With a Zap of Electricity Oct 12, 2021

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Sarah hadn’t laughed in five years. At 36 years old, the avid home cook has struggled with depression since early childhood. She tried the whole range of antidepressant medications and therapy for decades. Nothing worked. One night, five years ago, driving home from work, she had one thought in her mind: this is it. I’m done. Luckily she made it home safe. And soon she was offered an intriguing new possibility to tackle her symptoms—a little chip, implanted into her brain, that captures the unique neural signals encoding her depression. Once the implant detects those signals, it zaps them away with a brief electrical jolt, like adding noise to an enemy’s digital transmissions to scramble their original message. When that message triggers depression, hijacking neural communications is exactly what we want to do. Flash forward several years, and Sarah has her depression under control for the first time in her life. Her suicidal thoughts evaporated. After quitting her tech job due to her condition, she’s now back on her feet, enrolled in data analytics classes and taking care of her elderly mother. “For the first time,” she said, “I’m finally laughing.” Sarah’s recovery is just one case. But it signifies a new era for the technology underlying her stunning improvement. It’s one of the first cases in which a personalized “brain pacemaker” can stealthily tap into, decipher, and alter a person’s mood and introspection based on their own unique electrical brain signatures. And while those implants have achieved stunning medical miracles in other areas—such as allowing people with paralysis to walk again—Sarah’s recovery is some of the strongest evidence yet that a computer chip, in a brain, powered by AI, can fundamentally alter our perception of life. It’s the closest to reading and repairing a troubled mind that we’ve ever gotten. “We haven’t been able to do this kind of personalized therapy previously in psychiatry,” said study lead Dr. Katherine Scangos at UCSF. “This success in itself is an incredible advancement in our knowledge of the brain function that underlies mental illness.” Brain Pacemaker The key to Sarah’s recovery is a brain-machine interface. Roughly the size of a matchbox, the implant sits inside the brain, silently listening to and decoding its electrical signals. Using those signals, it’s possible to control other parts of the brain or body. Brain implants have given people with lower body paralysis the ability to walk again. They’ve allowed amputees to control robotic hands with just a thought. They’ve opened up a world of sensations, integrating feedback from cyborg-like artificial limbs that transmit signals directly into the brain. But Sarah’s implant is different. Sensation and movement are generally controlled by relatively well-defined circuits in the outermost layer of the brain: the cortex. Emotion and mood are also products of our brain’s electrical signals, but they tend to stem from deeper neural networks hidden at the center of the brain. One way to tap into those circuits is called deep brain stimulation (DBS), a method pioneered in the ’80s that’s been used to treat severe Parkinson’s disease and epilepsy, particularly for cases that don’t usually respond to medication. Sarah’s neural implant takes this route: it listens in on the chatter between neurons deep within the brain to decode mood. But where is mood in the brain? One particular problem, the authors explained, is that unlike movement, there is no “depression brain region.” Rather, emotions are regulated by intricate, intertwining networks across multiple brain regions. Adding to that complexity is the fact that we’re all neural snowflakes—each of us have uniquely personalized brain network connections. In other words, zapping my circuit to reduce depression might not work for you. DBS, for example, has previously been studied for treating depression. But despite decades of research, it’s not federally approved due to inconsistent result...

Intel's Brain-Inspired Loihi 2 Chip Can Hold a Million Artificial Neurons Oct 11, 2021

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Computer chips that recreate the brain’s structure in silicon are a promising avenue for powering the smart robots of the future. Now Intel has released an updated version of its Loihi neuromorphic chip, which it hopes will bring that dream closer. Despite frequent comparisons, the neural networks that power today’s leading AI systems operate very differently than the brain. While the “neurons” used in deep learning shuttle numbers back and forth between one another, biological neurons communicate in spikes of electrical activity whose meaning is tied up in their timing. That is a very different language from the one spoken by modern processors, and it’s been hard to efficiently implement these kinds of spiking neurons on conventional chips. To get around this roadblock, so-called “neuromorphic” engineers build chips that mimic the architecture of biological neural networks to make running these spiking networks easier. The field has been around for a while, but in recent years it’s piqued the interest of major technology companies like Intel, IBM, and Samsung. Spiking neural networks (SNNs) are considerably less developed than the deep learning algorithms that dominate modern AI research. But they have the potential to be far faster and more energy-efficient, which makes them promising for running AI on power-constrained edge devices like smartphones or robots. Intel entered the fray in 2017 with its Loihi neuromorphic chip, which could emulate 125,000 spiking neurons. But now the company has released a major update that can implement one million neurons and is ten times faster than its predecessor. “Our second-generation chip greatly improves the speed, programmability, and capacity of neuromorphic processing, broadening its usages in power and latency constrained intelligent computing applications,” Mike Davies, director of Intel’s Neuromorphic Computing Lab, said in a statement. Loihi 2 doesn’t only significantly boost the number of neurons, it greatly expands their functionality. As outlined by IEEE Spectrum, the new chip is much more programmable, allowing it to implement a wide range of SNNs rather than the single type of model the previous chip was capable of. It’s also capable of supporting a wider variety of learning rules that should, among other things, make it more compatible with the kind of backpropagation-based training approaches used in deep learning. Faster circuits also mean the chip can now run at 5,000 times the speed of biological neurons, and improved chip interfaces make it easier to get several of them working in concert. Perhaps the most significant changes, though, are to the neurons themselves. Each neuron can run its own program, making it possible to implement a variety of different kinds of neurons. And the chip’s designers have taken it upon themselves to improve on Mother Nature’s designs by allowing the neurons to communicate using both spike timing and strength. The company doesn’t appear to have any plans to commercialize the chips, though, and for the time being they will only be available over the cloud to members of the Intel Neuromorphic Research Community. But the company does seem intent on building up the neuromorphic ecosystem. Alongside the new chip, it has also released a new open-source software framework called LAVA to help researchers build “neuro-inspired” applications that can run on any kind of neuromorphic hardware or even conventional processors. “LAVA is meant to help get neuromorphic [programming] to spread to the wider computer science community,” Davies told Ars Technica. That will be a crucial step if the company ever wants its neuromorphic chips to be anything more than a novelty for researchers. But given the broad range of applications for the kind of fast, low-power intelligence they could one day provide, it seems like a sound investment. Image Credit: Intel

This Asteroid May Be the Shard of a Dead Protoplanet—and Have More Metal Than All the Reserves on Earth Oct 10, 2021

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It’s often said Earth’s resources are finite. This is true enough. But shift your gaze skyward for a moment. Up there, amid the stars, lurks an invisible bonanza of epic proportions. Many of the materials upon which modern civilization is built exist in far greater amounts throughout the rest of the solar system. Earth, after all, was formed from the same cosmic cloud as all the other planets, comets, and asteroids—and it hardly cornered the market when it comes to the valuable materials we use to make smartphone batteries or raise skyscrapers. A recent study puts it in perspective. Lead author Juan Sanchez and a team of scientists analyzed the spectrum of asteroid 1986 DA, a member of a rare class of metal-rich, near-Earth asteroids. They found the surface of this particular space rock to be 85% metallic, likely including iron, nickel, cobalt, copper, gold, and platinum group metals prized for industrial uses, from cars to electronics. With the exception of gold and copper, they estimate the mass of these metals would exceed their global reserves on Earth—in some cases by an order of magnitude (or more). The team also put a dollar figure on the asteroid’s economic value. If mined and marketed over a period of 50 years, 1986 DA’s precious metals would bring in some $233 billion a year for a total haul of $11.65 trillion. (That takes into account the deflationary effect the flood of new supply would have on the market.) It probably wouldn’t make sense to bring home metals like iron, nickel, and cobalt, which are common on Earth, but they could be used to build infrastructure in orbit and on the moon and Mars. In short, mining one nearby asteroid could yield a precious metals jackpot. And there are greater prizes lurking further afield in the asteroid belt. Of course, asteroid mining is hardly a new idea. The challenging (and expensive) parts are traveling to said asteroids, stripping them of their precious ore, and shipping it out. But before we even get to the hard parts, we need to prospect the claim. This study, combined with future NASA missions to the asteroid belt, should help bring the true extent of space resources into sharper focus. The Priceless Cores of Dead Protoplanets What makes 1986 DA particularly interesting is its proximity to Earth. Most metal-rich asteroids live way out in the asteroid belt, between Mars and Jupiter. Famous among these is 16 Psyche, a hulking, 140-mile-wide asteroid first discovered in 1852. The asteroid belt was once thought to be the remnants of a planet, but its origins are less certain now. Still, scientists speculate Psyche may be the exposed core of a shattered planet-in-the-making. And indeed, smaller metal-rich asteroids may also be the shards of a protoplanetary core. Under this theory, developing planets in the asteroid belt grew large enough to differentiate rocky mantles and metal cores. These later suffered a series of collisions, leaving their shattered rocky remains and broken metal hearts to wander the belt. We may never observe Earth’s core in person, so, if the theory is true, Psyche could be our next best alternative. Also, the existence of so much exposed metal in one place is tantalizing for those who would extend humanity’s presence beyond Earth. In either case, we have yet only managed to assemble a basic portrait of Psyche. It’s simply too far away to study in any great detail. Which is where 1986 DA and 2016 ED85 (another asteroid in the study) come in. Keeping Up With the Joneses Both 1986 DA and 2016 ED85 are classified as near-Earth asteroids. That is, they live in our neighborhood. At some point in the past, gravitational interactions with Jupiter nudged them out of the asteroid belt and into near-Earth orbits. So, a key motivation of the study was to trace the asteroids’ lineage. Because they’re closer, we can observe them in more detail and infer the characteristics of their distant family members, including Psyche. According to the study, spectral analysis...

This Bipedal Drone Robot Can Walk, Fly, Skateboard, and Slackline Oct 08, 2021

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Most animals are limited to either walking, flying, or swimming, with a handful of lucky species whose physiology allows them to cross over. A new robot took inspiration from them, and can fly like a bird just as well as it can walk like a (weirdly awkward, metallic, tiny) person. It also happens to be able to skateboard and slackline, two skills most humans will never pick up. Described in a paper published this week in Science Robotics, the robot’s name is Leo, which is short for Leonardo, which is short for LEgs ONboARD drOne. The name makes it sound like a drone with legs, but it has a somewhat humanoid shape, with multi-joint legs, propeller thrusters that look like arms, a “body” that contains its motors and electronics, and a dome-shaped protection helmet. Leo was built by a team at Caltech, and they were particularly interested in how the robot would transition between walking and flying. The team notes that they studied the way birds use their legs to generate thrust when they take off, and applied similar principles to the robot. In a video that shows Leo approaching a staircase, taking off, and gliding over the stairs to land near the bottom, the robot’s motions are seamlessly graceful. “There is a similarity between how a human wearing a jet suit controls their legs and feet when landing or taking off and how LEO uses synchronized control of distributed propeller-based thrusters and leg joints,” said Soon-Jo Chung, one of the paper’s authors a professor at Caltech. “We wanted to study the interface of walking and flying from the dynamics and control standpoint.” Leo walks at a speed of 20 centimeters (7.87 inches) per second, but can move faster by mixing in some flying with the walking. How wide our steps are, where we place our feet, and where our torsos are in relation to our legs all help us balance when we walk. The robot uses its propellers to help it balance, while its leg actuators move it forward. To teach the robot to slackline—which is much harder than walking on a balance beam—the team overrode its feet contact sensors with a fixed virtual foot contact centered just underneath it, because the sensors weren’t able to detect the line. The propellers played a big part as well, helping keep Leo upright and balanced. For the robot to ride a skateboard, the team broke the process down into two distinct components: controlling the steering angle and controlling the skateboard’s acceleration and deceleration. Placing Leo’s legs in specific spots on the board made it tilt to enable steering, and forward acceleration was achieved by moving the bot’s center of mass backward while pitching the body forward at the same time. So besides being cool (and a little creepy), what’s the goal of developing a robot like Leo? The paper authors see robots like Leo enabling a range of robotic missions that couldn’t be carried out by ground or aerial robots. “Perhaps the most well-suited applications for Leo would be the ones that involve physical interactions with structures at a high altitude, which are usually dangerous for human workers and call for a substitution by robotic workers,” the paper’s authors said. Examples could include high-voltage line inspection, painting tall bridges or other high-up surfaces, inspecting building roofs or oil refinery pipes, or landing sensitive equipment on an extraterrestrial object. Next up for Leo is an upgrade to its performance via a more rigid leg design, which will help support the robot’s weight and increase the thrust force of its propellers. The team also wants to make Leo more autonomous, and plans to add a drone landing control algorithm to its software, ultimately aiming for the robot to be able to decide where and when to walk versus fly. Leo hasn’t quite achieved the wow factor of Boston Dynamics’ dancing robots (or its Atlas that can do parkour), but it’s on its way. Image Credit: Caltech Center for Autonomous Systems and Technologies/Science Robotics

How Musicologists and Scientists Used AI to Complete Beethoven’s Unfinished 10th Symphony Oct 07, 2021

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When Ludwig van Beethoven died in 1827, he was three years removed from the completion of his Ninth Symphony, a work heralded by many as his magnum opus. He had started work on his 10th Symphony but, due to deteriorating health, wasn’t able to make much headway: All he left behind were some musical sketches. Ever since then, Beethoven fans and musicologists have puzzled and lamented over what could have been. His notes teased at some magnificent reward, albeit one that seemed forever out of reach. Now, thanks to the work of a team of music historians, musicologists, composers and computer scientists, Beethoven’s vision will come to life. I presided over the artificial intelligence side of the project, leading a group of scientists at the creative AI startup Playform AI that taught a machine both Beethoven’s entire body of work and his creative process. A full recording of Beethoven’s 10th Symphony is set to be released on Oct. 9, 2021, the same day as the world premiere performance scheduled to take place in Bonn, Germany—the culmination of a two-year-plus effort. Past Attempts Hit a Wall Around 1817, the Royal Philharmonic Society in London commissioned Beethoven to write his ninth and 10th symphonies. Written for an orchestra, symphonies often contain four movements: the first is performed at a fast tempo, the second at a slower one, the third at a medium or fast tempo, and the last at a fast tempo. Beethoven completed his Ninth Symphony in 1824, which concludes with the timeless “Ode to Joy.” But when it came to the 10th Symphony, Beethoven didn’t leave much behind, other than some musical notes and a handful of ideas he had jotted down. There have been some past attempts to reconstruct parts of Beethoven’s 10th Symphony. Most famously, in 1988, musicologist Barry Cooper ventured to complete the first and second movements. He wove together 250 bars of music from the sketches to create what was, in his view, a production of the first movement that was faithful to Beethoven’s vision. Yet the sparseness of Beethoven’s sketches made it impossible for symphony experts to go beyond that first movement. Assembling the Team In early 2019, Dr. Matthias Röder, the director of the Karajan Institute, an organization in Salzburg, Austria, that promotes music technology, contacted me. He explained that he was putting together a team to complete Beethoven’s 10th Symphony in celebration of the composer’s 250th birthday. Aware of my work on AI-generated art, he wanted to know if AI would be able to help fill in the blanks left by Beethoven. The challenge seemed daunting. To pull it off, AI would need to do something it had never done before. But I said I would give it a shot. Röder then compiled a team that included Austrian composer Walter Werzowa. Famous for writing Intel’s signature bong jingle, Werzowa was tasked with putting together a new kind of composition that would integrate what Beethoven left behind with what the AI would generate. Mark Gotham, a computational music expert, led the effort to transcribe Beethoven’s sketches and process his entire body of work so the AI could be properly trained. The team also included Robert Levin, a musicologist at Harvard University who also happens to be an incredible pianist. Levin had previously finished a number of incomplete 18th-century works by Mozart and Johann Sebastian Bach. The Project Takes Shape In June 2019, the group gathered for a two-day workshop at Harvard’s music library. In a large room with a piano, a blackboard and a stack of Beethoven’s sketchbooks spanning most of his known works, we talked about how fragments could be turned into a complete piece of music and how AI could help solve this puzzle, while still remaining faithful to Beethoven’s process and vision. The music experts in the room were eager to learn more about the sort of music AI had created in the past. I told them how AI had successfully generated music in the style of Bach. However, this was only a harm...

NASA's Mission to Crash a Spacecraft Into an Asteroid Launches Next Month Oct 06, 2021

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In March of this year, a quarter-mile-wide asteroid flew through space at a speed of 77,000 miles per hour. It was five times farther from Earth than the moon, but that’s actually considered pretty close when the context is the whole Milky Way galaxy. There’s not a huge risk of an asteroid hitting Earth anytime in the foreseeable future. But NASA wants to be ready, just in case. In April the space agency led a simulated asteroid impact scenario, testing how well federal agencies, international space agencies, and other decision-makers, scientific institutions, and emergency managers could work together to avert catastrophe. Now another asteroid-deflecting initiative is underway, but this time, it’s getting much more real. There’s still no danger of anything colliding with Earth or threatening human lives. But NASA’s DART mission plans to purposely crash a spacecraft into an asteroid to try to alter its path. DART stands for Double Asteroid Redirection Test, and NASA has just set its launch date for November 23 at 10:20 pm Pacific time. The spacecraft will launch on a SpaceX Falcon 9 rocket from Vandenberg Air Force Base, located near the California coast about 160 miles north of Los Angeles. From there, it will travel to an asteroid called Didymos, taking about a year to arrive (it’s seven million miles away) and using roll-out solar arrays to power its electric propulsion system. Didymos is 2,560 feet wide and completes a rotation every 2.26 hours. It has a secondary body, or moonlet, named Dimorphos that’s 525 feet wide. The two bodies are just over half a mile apart, and the moonlet revolves about the primary once every 11.9 hours. Using an onboard camera and autonomous navigation software, the spacecraft will crash itself into the moonlet at a speed of almost 15,000 miles per hour. NASA estimates that the collision will change Dimorphos’ speed in its orbit around Didymos by just a fraction of one percent, but that’s enough to alter Dimorphos’ orbital period by several minutes, enough to be observed and measured from telescopes on Earth. NASA plans to capture the whole thing on video. Ten days before DART’s asteroid impact, the agency will launch a miniaturized satellite, called LICIACube, equipped with two optical cameras. The goal will be for the cubesat to fly past Dimorphos around three minutes after DART hits its moonlet, allowing the cameras to capture images of the impact’s effects. And that’s not all the observation the mission will get. The European Space Agency plans to launch its Hera spacecraft (named for the Greek goddess of marriage!) in 2024 to see the effects of DART up close and in detail. As the agency notes in its description of the Hera mission, “By the time Hera reaches Didymos, in 2026, Dimorphos will have achieved historic significance: the first object in the solar system to have its orbit shifted by human effort in a measurable way.” You can watch NASA’s coverage of the DART launch on NASA TV via the agency’s app and website. Image Credit: NASA

Moonshot Project Aims to Understand and Beat Cancer Using Protein Maps Oct 05, 2021

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Understanding cancer is like assembling IKEA furniture. Hear me out. Both start with individual pieces that make up the final product. For a cabinet, it’s a list of labeled precut plywood. For cancer, it’s a ledger of genes that—through the Human Genome Project and subsequent studies—we know are somehow involved in cells mutating, spreading, and eventually killing their host. Yet without instructions, pieces of wood can’t be assembled into a cabinet. And without knowing how cancer-related genes piece together, we can’t decipher how they synergize to create one of our fiercest medical foes. It’s like we have the first page of an IKEA manual, said Dr. Trey Ideker at UC San Diego. But “how these genes and gene products, the proteins, are tied together is the rest of the manual—except there’s about a million pages worth of it. You need to understand those pages if you’re really going to understand disease.” Ideker’s comment, made in 2017, was strikingly prescient. The underlying idea is seemingly simple, yet a wild shift from previous attempts at cancer research: rather than individual genes, let’s turn the spotlight on how they fit together into networks to drive cancer. Together with Dr. Nevan Krogan at UC San Francisco, a team launched the Cancer Cell Map Initiative (CCMI), a moonshot that peeks into the molecular “phone lines” within cancer cells that guide their growth and spread. Snip them off, the theory goes, and it’s possible to nip tumors in the bud. This week, three studies in Science led by Ideker and Krogan showcased the power of that radical change in perspective. At its heart is protein-protein interactions: that is, how the cell’s molecular “phone lines” rewire and fit together as they turn to the cancerous dark side. One study mapped the landscape of protein networks to see how individual genes and their protein products coalesce to drive breast cancer. Another traced the intricate web of genetic connections that promote head and neck cancer. Tying everything together, the third study generated an atlas of protein networks involved in various types of cancer. By looking at connections, the map revealed new mutations that likely give cancer a boost, while also pointing out potential weaknesses ripe for target-and-destroy. For now, the studies aren’t yet a comprehensive IKEA-like manual of how cancer components fit together. But they’re the first victories in a sweeping framework for rethinking cancer. “For many cancers, there is an extensive catalog of genetic mutations, but a consolidated map that organizes these mutations into pathways that drive tumor growth is missing,” said Drs. Ran Cheng and Peter Jackson at Stanford University, who weren’t involved in the studies. Knowing how those work “will simplify our search for effective cancer therapies.” Cellular Chatterbox Every cell is an intricate city, with energy, communications systems, and waste disposal needs. Their secret sauce for everything humming along nicely? Proteins. Proteins are indispensable workhorses with many tasks and even more identities. Some are builders, tirelessly laying down “railway” tracks to connect different parts of a cell; others are carriers, hauling cargo down those protein rails. Enzymes allow cells to generate energy and perform hundreds of other life-sustaining biochemical reactions. But perhaps the most enigmatic proteins are the messengers. These are often small in size, allowing them to zip around the cell and between different compartments. If a cell is a neighborhood, these proteins are mailmen, shuttling messages back and forth. Rather than dropping off mail, however, they deliver messages by physically tagging onto other protein. These “handshakes” are dubbed protein-protein interactions (PPIs), and are critical to a cell’s function. PPIs are basically the cell’s supply chain, communications cable, and energy economy rolled into one massive infrastructure. Destroying just one PPI can lead a thriving cell to die. PPIs ar...

How Quantum Computers Can Be Used to Build Better Quantum Computers Oct 04, 2021

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Using computer simulations to design new chips played a crucial role in the rapid improvements in processor performance we’ve experienced in recent decades. Now Chinese researchers have extended the approach to the quantum world. Electronic design automation tools started to become commonplace in the early 1980s as the complexity of processors rose exponentially, and today they are an indispensable tool for chip designers. More recently, Google has been turbocharging the approach by using artificial intelligence to design the next generation of its AI chips. This holds the promise of setting off a process of recursive self-improvement that could lead to rapid performance gains for AI. Now, New Scientist has reported on a team from the University of Science and Technology of China in Shanghai that has applied the same ideas to another emerging field of computing: quantum processors. In a paper posted to the arXiv pre-print server, the researchers describe how they used a quantum computer to design a new type of qubit that significantly outperformed their previous design. “Simulations of high-complexity quantum systems, which are intractable for classical computers, can be efficiently done with quantum computers,” the authors wrote. “Our work opens the way to designing advanced quantum processors using existing quantum computing resources.” At the heart of the idea is the fact that the complexity of quantum systems grows exponentially as they increase in size. As a result, even the most powerful supercomputers struggle to simulate fairly small quantum systems. This was the basis for Google’s groundbreaking display of “quantum supremacy” in 2019. The company’s researchers used a 53-qubit processor to run a random quantum circuit a million times and showed that it would take roughly 10,000 years to simulate the experiment on the world’s fastest supercomputer. This means that using classical computers to help in the design of new quantum computers is likely to hit fundamental limits pretty quickly. Using a quantum computer, however, sidesteps the problem because it can exploit the same oddities of the quantum world that make the problem complex in the first place. This is exactly what the Chinese researchers did. They used an algorithm called a variational quantum eigensolver to simulate the kind of superconducting electronic circuit found at the heart of a quantum computer. This was used to explore what happens when certain energy levels in the circuit are altered. Normally this kind of experiment would require them to build large numbers of physical prototypes and test them, but instead the team was able to rapidly model the impact of the changes. The upshot was that the researchers discovered a new type of qubit that was more powerful than the one they were already using. Any two-level quantum system can act as a qubit, but most superconducting quantum computers use transmons, which encode quantum states into the oscillations of electrons. By tweaking the energy levels of their simulated quantum circuit, the researchers were able to discover a new qubit design they dubbed a plasonium. It is less than half the size of a transmon, and when the researchers fabricated it they found that it holds its quantum state for longer and is less prone to errors. It still works on similar principles to the transmon, so it’s possible to manipulate it using the same control technologies. The researchers point out that this is only a first prototype, so with further optimization and the integration of recent progress in new superconducting materials and surface treatment methods they expect performance to increase even more. But the new qubit the researchers have designed is probably not their most significant contribution. By demonstrating that even today’s rudimentary quantum computers can help design future devices, they’ve opened the door to a virtuous cycle that could significantly speed innovation in this field. Image Credit: Pete Linfor...

The Music of Proteins Is Made Audible Through a Computer Program That Learns From Chopin Oct 03, 2021

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With the right computer program, proteins become pleasant music. There are many surprising analogies between proteins, the basic building blocks of life, and musical notation. These analogies can be used not only to help advance research, but also to make the complexity of proteins accessible to the public. We’re computational biologists who believe that hearing the sound of life at the molecular level could help inspire people to learn more about biology and the computational sciences. While creating music based on proteins isn’t new, different musical styles and composition algorithms had yet to be explored. So we led a team of high school students and other scholars to figure out how to create classical music from proteins. The Musical Analogies of Proteins Proteins are structured like folded chains. These chains are composed of small units of 20 possible amino acids, each labeled by a letter of the alphabet. A protein chain can be represented as a string of these alphabetic letters, very much like a string of music notes in alphabetical notation. Protein chains can also fold into wavy and curved patterns with ups, downs, turns, and loops. Likewise, music consists of sound waves of higher and lower pitches, with changing tempos and repeating motifs. Protein-to-music algorithms can thus map the structural and physiochemical features of a string of amino acids onto the musical features of a string of notes. Enhancing the Musicality of Protein Mapping Protein-to-music mapping can be fine-tuned by basing it on the features of a specific music style. This enhances musicality, or the melodiousness of the song, when converting amino acid properties, such as sequence patterns and variations, into analogous musical properties, like pitch, note lengths, and chords. For our study, we specifically selected 19th-century Romantic period classical piano music, which includes composers like Chopin and Schubert, as a guide because it typically spans a wide range of notes with more complex features such as chromaticism, like playing both white and black keys on a piano in order of pitch, and chords. Music from this period also tends to have lighter and more graceful and emotive melodies. Songs are usually homophonic, meaning they follow a central melody with accompaniment. These features allowed us to test out a greater range of notes in our protein-to-music mapping algorithm. In this case, we chose to analyze features of Chopin’s Fantaisie-Impromptu to guide our development of the program. To test the algorithm, we applied it to 18 proteins that play a key role in various biological functions. Each amino acid in the protein is mapped to a particular note based on how frequently they appear in the protein, and other aspects of their biochemistry correspond with other aspects of the music. A larger-sized amino acid, for instance, would have a shorter note length, and vice versa. The resulting music is complex, with notable variations in pitch, loudness, and rhythm. Because the algorithm was completely based on the amino acid sequence and no two proteins share the same amino acid sequence, each protein will produce a distinct song. This also means that there are variations in musicality across the different pieces, and interesting patterns can emerge. For example, music generated from the receptor protein that binds to the hormone and neurotransmitter oxytocin has some recurring motifs due to the repetition of certain small sequences of amino acids. On the other hand, music generated from tumor antigen p53, a protein that prevents cancer formation, is highly chromatic, producing particularly fascinating phrases where the music sounds almost toccata-like, a style that often features fast and virtuoso technique. By guiding analysis of amino acid properties through specific music styles, protein music can sound much more pleasant to the ear. This can be further developed and applied to a wider variety of music styles, including pop and jazz. P...

Scientists Created Holograms You Can Touch—You Could Soon Shake a Virtual Colleague’s Hand Oct 01, 2021

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The TV show Star Trek: The Next Generation introduced millions of people to the idea of a holodeck: an immersive, realistic 3D holographic projection of a complete environment that you could interact with and even touch. In the 21st century, holograms are already being used in a variety of ways, such as medical systems, education, art, security and defense. Scientists are still developing ways to use lasers, modern digital processors, and motion-sensing technologies to create several different types of holograms that could change the way we interact. My colleagues and I working in the University of Glasgow’s bendable electronics and sensing technologies research group have now developed a system of holograms of people using “aerohaptics,” creating feelings of touch with jets of air. Those jets of air deliver a sensation of touch on peoples’ fingers, hands, and wrists. In time, this could be developed to allow you to meet a virtual avatar of a colleague on the other side of the world and really feel their handshake. It could even be the first step towards building something like a holodeck. To create this feeling of touch we use affordable, commercially available parts to pair computer-generated graphics with carefully-directed and controlled jets of air. In some ways, it’s a step beyond the current generation of virtual reality, which usually requires a headset to deliver 3D graphics and smart gloves or handheld controllers to provide haptic feedback, a stimulation that feels like touch. Most of the wearable gadgets-based approaches are limited to controlling the virtual object that is being displayed. Controlling a virtual object doesn’t give the feeling that you would experience when two people touch. The addition of an artificial touch sensation can deliver the additional dimension without having to wear gloves to feel objects, and so feels much more natural. Using Glass and Mirrors Our research uses graphics that provide the illusion of a 3D virtual image. It’s a modern variation on a 19th-century illusion technique known as Pepper’s Ghost, which thrilled Victorian theatergoers with visions of the supernatural onstage. The systems uses glass and mirrors to make a two-dimensional image appear to hover in space without the need for any additional equipment. And our haptic feedback is created with nothing but air. The mirrors making up our system are arranged in a pyramid shape with one open side. Users put their hands through the open side and interact with computer-generated objects which appear to be floating in free space inside the pyramid. The objects are graphics created and controlled by a software program called Unity Game Engine, which is often used to create 3D objects and worlds in videogames. Located just below the pyramid is a sensor that tracks the movements of users’ hands and fingers, and a single air nozzle, which directs jets of air towards them to create complex sensations of touch. The overall system is directed by electronic hardware programmed to control nozzle movements. We developed an algorithm which allowed the air nozzle to respond to the movements of users’ hands with appropriate combinations of direction and force. One of the ways we’ve demonstrated the capabilities of the “aerohaptic” system is with an interactive projection of a basketball, which can be convincingly touched, rolled, and bounced. The touch feedback from air jets from the system is also modulated based on the virtual surface of the basketball, allowing users to feel the rounded shape of the ball as it rolls from their fingertips when they bounce it and the slap in their palm when it returns. Users can even push the virtual ball with varying force and sense the resulting difference in how a hard bounce or a soft bounce feels in their palm. Even something as apparently simple as bouncing a basketball required us to work hard to model the physics of the action and how we could replicate that familiar sensation with jets of air. S...

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