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#435140 This Week’s Awesome Stories From ...

GENETICS
Gene Therapy Might Have Its First Blockbuster
Antonio Regalado | MIT Technology Review
“…drug giant Novartis expects to win approval to launch what it says will be the first ‘blockbuster’ gene-replacement treatment. A blockbuster is any drug with more than $1 billion in sales each year. The treatment, called Zolgensma, is able to save infants born with spinal muscular atrophy (SMA) type 1, a degenerative disease that usually kills within two years.”

ARTIFICIAL INTELLIGENCE
AI Took a Test to Detect Lung Cancer. It Got an A.
Denise Grady | The New York Times
“Computers were as good or better than doctors at detecting tiny lung cancers on CT scans, in a study by researchers from Google and several medical centers. The technology is a work in progress, not ready for widespread use, but the new report, published Monday in the journal Nature Medicine, offers a glimpse of the future of artificial intelligence in medicine.”

ROBOTICS
The Rise and Reign of Starship, the World’s First Robotic Delivery Provider
Luke Dormehl | Digital Trends
“[Starship’s] delivery robots have travelled a combined 200,000 miles, carried out 50,000 deliveries, and been tested in over 100 cities in 20 countries. It is a regular fixture not just in multiple neighborhoods but also university campuses.”

SPACE
Elon Musk Just Ignited the Race to Build the Space Internet
Jonathan O’Callaghan | Wired
“It’s estimated that about 3.3 billion people lack access to the internet, but Elon Musk is trying to change that. On Thursday, May 23—after two cancelled launches the week before—SpaceX launched 60 Starlink satellites on a Falcon 9 rocket from Cape Canaveral, in Florida, as part of the firm’s mission to bring low-cost, high-speed internet to the world.”

VIRTUAL REALITY
The iPod of VR Is Here, and You Should Try It
Mark Wilson | Fast Company
“In nearly 15 years of writing about cutting-edge technology, I’ve never seen a single product line get so much better so fast. With [the Oculus] Quest, there are no PCs required. There are no wires to run. All you do is grab the cloth headset and pull it around your head.”

FUTURE OF FOOD
Impossible Foods’ Rising Empire of Almost Meat
Chris Ip | Engadget
“Impossible says it wants to ultimately create a parallel universe of ersatz animal products from steak to eggs. …Yet as Impossible ventures deeper into the culinary uncanny valley, it also needs society to discard a fundamental cultural idea that dates back millennia and accept a new truth: Meat doesn’t have to come from animals.”

LONGEVITY
Can We Live Longer but Stay Younger?
Adam Gopnik | The New Yorker
“With greater longevity, the quest to avoid the infirmities of aging is more urgent than ever.”

PRIVACY
Facial Recognition Has Already Reached Its Breaking Point
Lily Hay Newman | Wired
“As facial recognition technologies have evolved from fledgling projects into powerful software platforms, researchers and civil liberties advocates have been issuing warnings about the potential for privacy erosions. Those mounting fears came to a head Wednesday in Congress.”

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#435106 Could Artificial Photosynthesis Help ...

Plants are the planet’s lungs, but they’re struggling to keep up due to rising CO2 emissions and deforestation. Engineers are giving them a helping hand, though, by augmenting their capacity with new technology and creating artificial substitutes to help them clean up our atmosphere.

Imperial College London, one of the UK’s top engineering schools, recently announced that it was teaming up with startup Arborea to build the company’s first outdoor pilot of its BioSolar Leaf cultivation system at the university’s White City campus in West London.

Arborea is developing large solar panel-like structures that house microscopic plants and can be installed on buildings or open land. The plants absorb light and carbon dioxide as they photosynthesize, removing greenhouse gases from the air and producing organic material, which can be processed to extract valuable food additives like omega-3 fatty acids.

The idea of growing algae to produce useful materials isn’t new, but Arborea’s pitch seems to be flexibility and affordability. The more conventional approach is to grow algae in open ponds, which are less efficient and open to contamination, or in photo-bioreactors, which typically require CO2 to be piped in rather than getting it from the air and can be expensive to run.

There’s little detail on how the technology deals with issues like nutrient supply and harvesting or how efficient it is. The company claims it can remove carbon dioxide as fast as 100 trees using the surface area of just a single tree, but there’s no published research to back that up, and it’s hard to compare the surface area of flat panels to that of a complex object like a tree. If you flattened out every inch of a tree’s surface it would cover a surprisingly large area.

Nonetheless, the ability to install these panels directly on buildings could present a promising way to soak up the huge amount of CO2 produced in our cities by transport and industry. And Arborea isn’t the only one trying to give plants a helping hand.

For decades researchers have been working on ways to use light-activated catalysts to split water into oxygen and hydrogen fuel, and more recently there have been efforts to fuse this with additional processes to combine the hydrogen with carbon from CO2 to produce all kinds of useful products.

Most notably, in 2016 Harvard researchers showed that water-splitting catalysts could be augmented with bacteria that combines the resulting hydrogen with CO2 to create oxygen and biomass, fuel, or other useful products. The approach was more efficient than plants at turning CO2 to fuel and was built using cheap materials, but turning it into a commercially viable technology will take time.

Not everyone is looking to mimic or borrow from biology in their efforts to suck CO2 out of the atmosphere. There’s been a recent glut of investment in startups working on direct-air capture (DAC) technology, which had previously been written off for using too much power and space to be practical. The looming climate change crisis appears to be rewriting some of those assumptions, though.

Most approaches aim to use the concentrated CO2 to produce synthetic fuels or other useful products, creating a revenue stream that could help improve their commercial viability. But we look increasingly likely to surpass the safe greenhouse gas limits, so attention is instead turning to carbon-negative technologies.

That means capturing CO2 from the air and then putting it into long-term storage. One way could be to grow lots of biomass and then bury it, mimicking the process that created fossil fuels in the first place. Or DAC plants could pump the CO2 they produce into deep underground wells.

But the former would take up unreasonably large amounts of land to make a significant dent in emissions, while the latter would require huge amounts of already scant and expensive renewable power. According to a recent analysis, artificial photosynthesis could sidestep these issues because it’s up to five times more efficient than its natural counterpart and could be cheaper than DAC.

Whether the technology will develop quickly enough for it to be deployed at scale and in time to mitigate the worst effects of climate change remains to be seen. Emissions reductions certainly present a more sure-fire way to deal with the problem, but nonetheless, cyborg plants could soon be a common sight in our cities.

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#435070 5 Breakthroughs Coming Soon in Augmented ...

Convergence is accelerating disruption… everywhere! Exponential technologies are colliding into each other, reinventing products, services, and industries.

In this third installment of my Convergence Catalyzer series, I’ll be synthesizing key insights from my annual entrepreneurs’ mastermind event, Abundance 360. This five-blog series looks at 3D printing, artificial intelligence, VR/AR, energy and transportation, and blockchain.

Today, let’s dive into virtual and augmented reality.

Today’s most prominent tech giants are leaping onto the VR/AR scene, each driving forward new and upcoming product lines. Think: Microsoft’s HoloLens, Facebook’s Oculus, Amazon’s Sumerian, and Google’s Cardboard (Apple plans to release a headset by 2021).

And as plummeting prices meet exponential advancements in VR/AR hardware, this burgeoning disruptor is on its way out of the early adopters’ market and into the majority of consumers’ homes.

My good friend Philip Rosedale is my go-to expert on AR/VR and one of the foremost creators of today’s most cutting-edge virtual worlds. After creating the virtual civilization Second Life in 2013, now populated by almost 1 million active users, Philip went on to co-found High Fidelity, which explores the future of next-generation shared VR.

In just the next five years, he predicts five emerging trends will take hold, together disrupting major players and birthing new ones.

Let’s dive in…

Top 5 Predictions for VR/AR Breakthroughs (2019-2024)
“If you think you kind of understand what’s going on with that tech today, you probably don’t,” says Philip. “We’re still in the middle of landing the airplane of all these new devices.”

(1) Transition from PC-based to standalone mobile VR devices

Historically, VR devices have relied on PC connections, usually involving wires and clunky hardware that restrict a user’s field of motion. However, as VR enters the dematerialization stage, we are about to witness the rapid rise of a standalone and highly mobile VR experience economy.

Oculus Go, the leading standalone mobile VR device on the market, requires only a mobile app for setup and can be transported anywhere with WiFi.

With a consumer audience in mind, the 32GB headset is priced at $200 and shares an app ecosystem with Samsung’s Gear VR. While Google Daydream are also standalone VR devices, they require a docked mobile phone instead of the built-in screen of Oculus Go.

In the AR space, Lenovo’s standalone Microsoft’s HoloLens 2 leads the way in providing tetherless experiences.

Freeing headsets from the constraints of heavy hardware will make VR/AR increasingly interactive and transportable, a seamless add-on whenever, wherever. Within a matter of years, it may be as simple as carrying lightweight VR goggles wherever you go and throwing them on at a moment’s notice.

(2) Wide field-of-view AR displays

Microsoft’s HoloLens 2 leads the AR industry in headset comfort and display quality. The most significant issue with their prior version was the limited rectangular field of view (FOV).

By implementing laser technology to create a microelectromechanical systems (MEMS) display, however, HoloLens 2 can position waveguides in front of users’ eyes, directed by mirrors. Subsequently enlarging images can be accomplished by shifting the angles of these mirrors. Coupled with a 47 pixel per degree resolution, HoloLens 2 has now doubled its predecessor’s FOV. Microsoft anticipates the release of its headset by the end of this year at a $3,500 price point, first targeting businesses and eventually rolling it out to consumers.

Magic Leap provides a similar FOV but with lower resolution than the HoloLens 2. The Meta 2 boasts an even wider 90-degree FOV, but requires a cable attachment. The race to achieve the natural human 120-degree horizontal FOV continues.

“The technology to expand the field of view is going to make those devices much more usable by giving you bigger than a small box to look through,” Rosedale explains.

(3) Mapping of real world to enable persistent AR ‘mirror worlds’

‘Mirror worlds’ are alternative dimensions of reality that can blanket a physical space. While seated in your office, the floor beneath you could dissolve into a calm lake and each desk into a sailboat. In the classroom, mirror worlds would convert pencils into magic wands and tabletops into touch screens.

Pokémon Go provides an introductory glimpse into the mirror world concept and its massive potential to unite people in real action.

To create these mirror worlds, AR headsets must precisely understand the architecture of the surrounding world. Rosedale predicts the scanning accuracy of devices will improve rapidly over the next five years to make these alternate dimensions possible.

(4) 5G mobile devices reduce latency to imperceptible levels

Verizon has already launched 5G networks in Minneapolis and Chicago, compatible with the Moto Z3. Sprint plans to follow with its own 5G launch in May. Samsung, LG, Huawei, and ZTE have all announced upcoming 5G devices.

“5G is rolling out this year and it’s going to materially affect particularly my work, which is making you feel like you’re talking to somebody else directly face to face,” explains Rosedale. “5G is critical because currently the cell devices impose too much delay, so it doesn’t feel real to talk to somebody face to face on these devices.”

To operate seamlessly from anywhere on the planet, standalone VR/AR devices will require a strong 5G network. Enhancing real-time connectivity in VR/AR will transform the communication methods of tomorrow.

(5) Eye-tracking and facial expressions built in for full natural communication

Companies like Pupil Labs and Tobii provide eye tracking hardware add-ons and software to VR/AR headsets. This technology allows for foveated rendering, which renders a given scene in high resolution only in the fovea region, while the peripheral regions appear in lower resolution, conserving processing power.

As seen in the HoloLens 2, eye tracking can also be used to identify users and customize lens widths to provide a comfortable, personalized experience for each individual.

According to Rosedale, “The fundamental opportunity for both VR and AR is to improve human communication.” He points out that current VR/AR headsets miss many of the subtle yet important aspects of communication. Eye movements and microexpressions provide valuable insight into a user’s emotions and desires.

Coupled with emotion-detecting AI software, such as Affectiva, VR/AR devices might soon convey much more richly textured and expressive interactions between any two people, transcending physical boundaries and even language gaps.

Final Thoughts
As these promising trends begin to transform the market, VR/AR will undoubtedly revolutionize our lives… possibly to the point at which our virtual worlds become just as consequential and enriching as our physical world.

A boon for next-gen education, VR/AR will empower youth and adults alike with holistic learning that incorporates social, emotional, and creative components through visceral experiences, storytelling, and simulation. Traveling to another time, manipulating the insides of a cell, or even designing a new city will become daily phenomena of tomorrow’s classrooms.

In real estate, buyers will increasingly make decisions through virtual tours. Corporate offices might evolve into spaces that only exist in ‘mirror worlds’ or grow virtual duplicates for remote workers.

In healthcare, accuracy of diagnosis will skyrocket, while surgeons gain access to digital aids as they conduct life-saving procedures. Or take manufacturing, wherein training and assembly will become exponentially more efficient as visual cues guide complex tasks.

In the mere matter of a decade, VR and AR will unlock limitless applications for new and converging industries. And as virtual worlds converge with AI, 3D printing, computing advancements and beyond, today’s experience economies will explode in scale and scope. Prepare yourself for the exciting disruption ahead!

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#435056 How Researchers Used AI to Better ...

A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.

We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.

Hassabis is about to be proven right again.

Last week, two studies independently tapped into the power of ANNs to solve a 70-year-old neuroscience mystery: how does our visual system perceive reality?

The first, published in Cell, used generative networks to evolve DeepDream-like images that hyper-activate complex visual neurons in monkeys. These machine artworks are pure nightmare fuel to the human eye; but together, they revealed a fundamental “visual hieroglyph” that may form a basic rule for how we piece together visual stimuli to process sight into perception.

In the second study, a team used a deep ANN model—one thought to mimic biological vision—to synthesize new patterns tailored to control certain networks of visual neurons in the monkey brain. When directly shown to monkeys, the team found that the machine-generated artworks could reliably activate predicted populations of neurons. Future improved ANN models could allow even better control, giving neuroscientists a powerful noninvasive tool to study the brain. The work was published in Science.

The individual results, though fascinating, aren’t necessarily the point. Rather, they illustrate how scientists are now striving to complete the virtuous circle: tapping AI to probe natural intelligence. Vision is only the beginning—the tools can potentially be expanded into other sensory domains. And the more we understand about natural brains, the better we can engineer artificial ones.

It’s a “great example of leveraging artificial intelligence to study organic intelligence,” commented Dr. Roman Sandler at Kernel.co on Twitter.

Why Vision?
ANNs and biological vision have quite the history.

In the late 1950s, the legendary neuroscientist duo David Hubel and Torsten Wiesel became some of the first to use mathematical equations to understand how neurons in the brain work together.

In a series of experiments—many using cats—the team carefully dissected the structure and function of the visual cortex. Using myriads of images, they revealed that vision is processed in a hierarchy: neurons in “earlier” brain regions, those closer to the eyes, tend to activate when they “see” simple patterns such as lines. As we move deeper into the brain, from the early V1 to a nub located slightly behind our ears, the IT cortex, neurons increasingly respond to more complex or abstract patterns, including faces, animals, and objects. The discovery led some scientists to call certain IT neurons “Jennifer Aniston cells,” which fire in response to pictures of the actress regardless of lighting, angle, or haircut. That is, IT neurons somehow extract visual information into the “gist” of things.

That’s not trivial. The complex neural connections that lead to increasing abstraction of what we see into what we think we see—what we perceive—is a central question in machine vision: how can we teach machines to transform numbers encoding stimuli into dots, lines, and angles that eventually form “perceptions” and “gists”? The answer could transform self-driving cars, facial recognition, and other computer vision applications as they learn to better generalize.

Hubel and Wiesel’s Nobel-prize-winning studies heavily influenced the birth of ANNs and deep learning. Much of earlier ANN “feed-forward” model structures are based on our visual system; even today, the idea of increasing layers of abstraction—for perception or reasoning—guide computer scientists to build AI that can better generalize. The early romance between vision and deep learning is perhaps the bond that kicked off our current AI revolution.

It only seems fair that AI would feed back into vision neuroscience.

Hieroglyphs and Controllers
In the Cell study, a team led by Dr. Margaret Livingstone at Harvard Medical School tapped into generative networks to unravel IT neurons’ complex visual alphabet.

Scientists have long known that neurons in earlier visual regions (V1) tend to fire in response to “grating patches” oriented in certain ways. Using a limited set of these patches like letters, V1 neurons can “express a visual sentence” and represent any image, said Dr. Arash Afraz at the National Institute of Health, who was not involved in the study.

But how IT neurons operate remained a mystery. Here, the team used a combination of genetic algorithms and deep generative networks to “evolve” computer art for every studied neuron. In seven monkeys, the team implanted electrodes into various parts of the visual IT region so that they could monitor the activity of a single neuron.

The team showed each monkey an initial set of 40 images. They then picked the top 10 images that stimulated the highest neural activity, and married them to 30 new images to “evolve” the next generation of images. After 250 generations, the technique, XDREAM, generated a slew of images that mashed up contorted face-like shapes with lines, gratings, and abstract shapes.

This image shows the evolution of an optimum image for stimulating a visual neuron in a monkey. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“The evolved images look quite counter-intuitive,” explained Afraz. Some clearly show detailed structures that resemble natural images, while others show complex structures that can’t be characterized by our puny human brains.

This figure shows natural images (right) and images evolved by neurons in the inferotemporal cortex of a monkey (left). Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world,” said study author Carlos Ponce. “We were seeing something that was more like the language cells use with each other.”

This image was evolved by a neuron in the inferotemporal cortex of a monkey using AI. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
Although IT neurons don’t seem to use a simple letter alphabet, it does rely on a vast array of characters like hieroglyphs or Chinese characters, “each loaded with more information,” said Afraz.

The adaptive nature of XDREAM turns it into a powerful tool to probe the inner workings of our brains—particularly for revealing discrepancies between biology and models.

The Science study, led by Dr. James DiCarlo at MIT, takes a similar approach. Using ANNs to generate new patterns and images, the team was able to selectively predict and independently control neuron populations in a high-level visual region called V4.

“So far, what has been done with these models is predicting what the neural responses would be to other stimuli that they have not seen before,” said study author Dr. Pouya Bashivan. “The main difference here is that we are going one step further and using the models to drive the neurons into desired states.”

It suggests that our current ANN models for visual computation “implicitly capture a great deal of visual knowledge” which we can’t really describe, but which the brain uses to turn vision information into perception, the authors said. By testing AI-generated images on biological vision, however, the team concluded that today’s ANNs have a degree of understanding and generalization. The results could potentially help engineer even more accurate ANN models of biological vision, which in turn could feed back into machine vision.

“One thing is clear already: Improved ANN models … have led to control of a high-level neural population that was previously out of reach,” the authors said. “The results presented here have likely only scratched the surface of what is possible with such implemented characterizations of the brain’s neural networks.”

To Afraz, the power of AI here is to find cracks in human perception—both our computational models of sensory processes, as well as our evolved biological software itself. AI can be used “as a perfect adversarial tool to discover design cracks” of IT, said Afraz, such as finding computer art that “fools” a neuron into thinking the object is something else.

“As artificial intelligence researchers develop models that work as well as the brain does—or even better—we will still need to understand which networks are more likely to behave safely and further human goals,” said Ponce. “More efficient AI can be grounded by knowledge of how the brain works.”

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#434854 New Lifelike Biomaterial Self-Reproduces ...

Life demands flux.

Every living organism is constantly changing: cells divide and die, proteins build and disintegrate, DNA breaks and heals. Life demands metabolism—the simultaneous builder and destroyer of living materials—to continuously upgrade our bodies. That’s how we heal and grow, how we propagate and survive.

What if we could endow cold, static, lifeless robots with the gift of metabolism?

In a study published this month in Science Robotics, an international team developed a DNA-based method that gives raw biomaterials an artificial metabolism. Dubbed DASH—DNA-based assembly and synthesis of hierarchical materials—the method automatically generates “slime”-like nanobots that dynamically move and navigate their environments.

Like humans, the artificial lifelike material used external energy to constantly change the nanobots’ bodies in pre-programmed ways, recycling their DNA-based parts as both waste and raw material for further use. Some “grew” into the shape of molecular double-helixes; others “wrote” the DNA letters inside micro-chips.

The artificial life forms were also rather “competitive”—in quotes, because these molecular machines are not conscious. Yet when pitted against each other, two DASH bots automatically raced forward, crawling in typical slime-mold fashion at a scale easily seen under the microscope—and with some iterations, with the naked human eye.

“Fundamentally, we may be able to change how we create and use the materials with lifelike characteristics. Typically materials and objects we create in general are basically static… one day, we may be able to ‘grow’ objects like houses and maintain their forms and functions autonomously,” said study author Dr. Shogo Hamada to Singularity Hub.

“This is a great study that combines the versatility of DNA nanotechnology with the dynamics of living materials,” said Dr. Job Boekhoven at the Technical University of Munich, who was not involved in the work.

Dissipative Assembly
The study builds on previous ideas on how to make molecular Lego blocks that essentially assemble—and destroy—themselves.

Although the inspiration came from biological metabolism, scientists have long hoped to cut their reliance on nature. At its core, metabolism is just a bunch of well-coordinated chemical reactions, programmed by eons of evolution. So why build artificial lifelike materials still tethered by evolution when we can use chemistry to engineer completely new forms of artificial life?

Back in 2015, for example, a team led by Boekhoven described a way to mimic how our cells build their internal “structural beams,” aptly called the cytoskeleton. The key here, unlike many processes in nature, isn’t balance or equilibrium; rather, the team engineered an extremely unstable system that automatically builds—and sustains—assemblies from molecular building blocks when given an external source of chemical energy.

Sound familiar? The team basically built molecular devices that “die” without “food.” Thanks to the laws of thermodynamics (hey ya, Newton!), that energy eventually dissipates, and the shapes automatically begin to break down, completing an artificial “circle of life.”

The new study took the system one step further: rather than just mimicking synthesis, they completed the circle by coupling the building process with dissipative assembly.

Here, the “assembling units themselves are also autonomously created from scratch,” said Hamada.

DNA Nanobots
The process of building DNA nanobots starts on a microfluidic chip.

Decades of research have allowed researchers to optimize DNA assembly outside the body. With the help of catalysts, which help “bind” individual molecules together, the team found that they could easily alter the shape of the self-assembling DNA bots—which formed fiber-like shapes—by changing the structure of the microfluidic chambers.

Computer simulations played a role here too: through both digital simulations and observations under the microscope, the team was able to identify a few critical rules that helped them predict how their molecules self-assemble while navigating a maze of blocking “pillars” and channels carved onto the microchips.

This “enabled a general design strategy for the DASH patterns,” they said.

In particular, the whirling motion of the fluids as they coursed through—and bumped into—ridges in the chips seems to help the DNA molecules “entangle into networks,” the team explained.

These insights helped the team further develop the “destroying” part of metabolism. Similar to linking molecules into DNA chains, their destruction also relies on enzymes.

Once the team pumped both “generation” and “degeneration” enzymes into the microchips, along with raw building blocks, the process was completely autonomous. The simultaneous processes were so lifelike that the team used a metric commonly used in robotics, finite-state automation, to measure the behavior of their DNA nanobots from growth to eventual decay.

“The result is a synthetic structure with features associated with life. These behaviors include locomotion, self-regeneration, and spatiotemporal regulation,” said Boekhoven.

Molecular Slime Molds
Just witnessing lifelike molecules grow in place like the dance move running man wasn’t enough.

In their next experiments, the team took inspiration from slugs to program undulating movements into their DNA bots. Here, “movement” is actually a sort of illusion: the machines “moved” because their front ends kept regenerating, whereas their back ends degenerated. In essence, the molecular slime was built from linking multiple individual “DNA robot-like” units together: each unit receives a delayed “decay” signal from the head of the slime in a way that allowed the whole artificial “organism” to crawl forward, against the steam of fluid flow.

Here’s the fun part: the team eventually engineered two molecular slime bots and pitted them against each other, Mario Kart-style. In these experiments, the faster moving bot alters the state of its competitor to promote “decay.” This slows down the competitor, allowing the dominant DNA nanoslug to win in a race.

Of course, the end goal isn’t molecular podracing. Rather, the DNA-based bots could easily amplify a given DNA or RNA sequence, making them efficient nano-diagnosticians for viral and other infections.

The lifelike material can basically generate patterns that doctors can directly ‘see’ with their eyes, which makes DNA or RNA molecules from bacteria and viruses extremely easy to detect, the team said.

In the short run, “the detection device with this self-generating material could be applied to many places and help people on site, from farmers to clinics, by providing an easy and accurate way to detect pathogens,” explained Hamaga.

A Futuristic Iron Man Nanosuit?
I’m letting my nerd flag fly here. In Avengers: Infinity Wars, the scientist-engineer-philanthropist-playboy Tony Stark unveiled a nanosuit that grew to his contours when needed and automatically healed when damaged.

DASH may one day realize that vision. For now, the team isn’t focused on using the technology for regenerating armor—rather, the dynamic materials could create new protein assemblies or chemical pathways inside living organisms, for example. The team also envisions adding simple sensing and computing mechanisms into the material, which can then easily be thought of as a robot.

Unlike synthetic biology, the goal isn’t to create artificial life. Rather, the team hopes to give lifelike properties to otherwise static materials.

“We are introducing a brand-new, lifelike material concept powered by its very own artificial metabolism. We are not making something that’s alive, but we are creating materials that are much more lifelike than have ever been seen before,” said lead author Dr. Dan Luo.

“Ultimately, our material may allow the construction of self-reproducing machines… artificial metabolism is an important step toward the creation of ‘artificial’ biological systems with dynamic, lifelike capabilities,” added Hamada. “It could open a new frontier in robotics.”

Image Credit: A timelapse image of DASH, by Jeff Tyson at Cornell University. Continue reading

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