science

Festo’s latest biomimetic robots are a flying feathered bird and ball-bottomed helper arm

Posted by | biomimetic, festo, Gadgets, hardware, robotics, robots, science | No Comments

You could be excused for thinking that German robotics company Festo does nothing but put together fabulous prototype robots built to resemble kangaroos, jellyfish, and other living things. They do in fact actually make real industrial robots, but it’s hard not to marvel at their biomimetic experiments; Case in point, the feathered BionicSwift and absurd BionicMobileAssistant motile arm.

Festo already has a flying bird robot — I wrote about it almost 10 years ago. They even made a flying bat as a follow-up. But the BionicSwift is more impressive than both because, in an effort to more closely resemble its avian inspiration, it flies using artificial feathers.

Image Credits: Festo

“The individual lamellae [i.e. feathers] are made of an ultralight, flexible but very robust foam and lie on top of each other like shingles. Connected to a carbon quill, they are attached to the actual hand and arm wings as in the natural model,” Festo writes in its description of the robot.

The articulating lamellae allow the wing to work like a bird’s, forming a powerful scoop on the downstroke to push against the air, but separating on the upstroke to produce less resistance. Everything is controlled on-board, including the indoor positioning system that the bird was ostensibly built to demonstrate. Flocks of BionicSwifts can fly in close quarters and avoid each other using an ultra wideband setup.

Festo’s BionicMobileAssistant seems like it would be more practical, and in a way it is, but not by much. The robot is basically an arm emerging from a wheeled base — or rather a balled one. The spherical bottom is driven by three “omniwheels,” letting it move easily in any direction while minimizing its footprint.

The hand is a showcase of modern robotic gripper design, with all kinds of state of the art tech packed in there — but the result is less than the sum of its parts. What makes a robotic hand good these days is less that it has a hundred sensors in the palm and fingers and huge motility for its thumb, but rather intelligence about what it is gripping. An unadorned pincer may be a better “hand” than one that looks like the real thing because of the software that backs it up.

Not to mention the spherical movement strategy makes for something of an unstable base. It’s telling that the robot is transporting scarves and not plates of food or parts.

Of course, it’s silly to criticize such a machine, which is aspirational rather than practical. But it’s important to understand that these fascinating creations from Festo are hints at a possible future more than anything.

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Drone-deployed sterile mosquitoes could check spread of insect-borne illnesses

Posted by | aerospace, drones, Gadgets, hardware, Health, science, UAVs | No Comments

Drone deployment of sterile mosquitoes could accelerate efforts to control their populations and reduce insect-borne disease, according to a proof of concept experiment by a multi-institutional research team. The improved technique could save thousands of lives.

Mosquitoes are a public health hazard around the world, spreading infections like malaria to millions and causing countless deaths and health crises. Although traps and netting offer some protection, the proactive approach of reducing the number of insects has also proven effective. This is accomplished by sterilizing male mosquitoes and releasing them into the wild, where they compete with the other males for food and mates but produce no offspring.

The problem with this approach is it is fairly hands-on, requiring people to travel through mosquito-infested areas to make regular releases of treated males. Some aerial and other dispersal methods have been attempted, but this project from French, Swiss, British, Brazilian, Senegalese and other researchers seems to be the most effective and practical yet.

Mosquitoes grown in bulk and sterilized by radiation are packed at low temperatures (“chilled” mosquitoes don’t fly or bite) into cartridges. These cartridges are kept refrigerated until they can be brought to a target site, where they’re loaded onto a drone.

Thousands of chilled, marked mosquitoes ready for deployment. Image Credit: Bouyer et al.

This drone ascends to a set altitude and travels over the target area, steadily releasing thousands of sterile males as it goes. By staging at the center of a town, the drone operators can reload the craft with new cartridges and send it in more directions, accomplishing dispersal over a huge and perhaps difficult to navigate space more quickly and easily than manual techniques.

The experiment used mosquitoes marked with fluorescent dyes that let the researchers track the effectiveness of their air-dropped mosquitoes, and the new technique shows great improvement over manual methods (on the order of 50% better) — without even getting into the reductions in time and labor. New methods for sterilizing, packing and meting out the insects further gild the results.

The researchers point out that while there are of course plenty of applications for this technique in ordinary times, the extraordinary times of this pandemic present new dangers and opportunities. Comorbidity of COVID-19 and mosquito-borne illnesses is practically unstudied, and disruptions to supply chains and normal insect suppression efforts is likely to lead to spikes in the likes of malaria and dengue fever.

Work like this could lead to improved general health for billions. The researchers’ work appeared in the journal Science Robotics.

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Lidar helps uncover an ancient, kilometer-long Mayan structure

Posted by | archaeology, Gadgets, Lidar, science | No Comments

Lidar is fast becoming one of the most influential tools in archaeology, revealing things in a few hours what might have taken months of machete wielding and manual measurements otherwise. The latest such discovery is an enormous Mayan structure, more than a kilometer long, 3,000 years old, and seemingly used for astronomical observations.

Takeshi Inomata of the University of Arizona is the lead author of the paper describing the monumental artificial plateau, published in the journal Nature. This unprecedented structure — by far the largest and oldest of its type — may remind you of another such discovery, the “Mayan megalopolis” found in Guatemala two years ago.

Such huge structures, groups of foundations, and other evidence of human activity may strike you as obvious. But when you’re on the ground they’re not nearly as obvious as you’d think — usually because they’re covered by both a canopy of trees and thick undergrowth.

“I have spent thousands of hours of fieldwork walking behind a local machete-wielding man who would cut straight lines through the forest,” wrote anthropologist Patricia McAnany, who was not involved in the research, for an commentary that also appeared in Nature. “This time-consuming process has required years, often decades, of fieldwork to map a large ancient Maya city such as Tikal in Guatemala and Caracol in Belize.”

You can see an aerial view of the site below. If you didn’t know there was something there, you might not notice anything more than some slightly geometric hills.

Lidar detects the distance to objects and surfaces by bouncing lasers off them. Empowered by powerful computational techniques, it can see through the canopy and find the level of the ground beneath, producing a detailed height map of the surface.

In this case the researchers picked a large area of the Tabasco region of Mexico, on the Guatemalan border, known to have been occupied by early Mayan civilization. A large-scale, low-resolution lidar scan of the area produced some leads, and smaller areas were then scanned at higher resolution, producing the images you see here.

What emerged was an enormous ceremonial center now called Aguada Fénix, the largest feature of which is an artificial plateau more than 10 meters tall and 1.4 kilometers in length. It is theorized that these huge plateaus, of which Aguada Fénix is the oldest and largest, were used to track the movement of the sun through the seasons and perform various rites.

The high-resolution lidar map also helped accelerate other findings, such as that, owing to the lack of statues or sculptures in honor of contemporary leaders, the community that built Aguada Fénix “probably did not have marked social inequality” comparable to others in the 1,000-800 B.C. timeframe (calculated from carbon dating). That such an enormous project could have been accomplished without the backing and orders of a rich central authority — and at a time when Mayan communities were supposed to be small and not yet stationary — could upend existing doctrine regarding the development of Mayan culture.

All because of advances in laser scanning technology that most think of as a way for self-driving cars to avoid pedestrians. You can read more about Aguada Fénix in Nature and this National Geographic article.

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The Great Reset

Posted by | Column, coronavirus, COVID-19, Entertainment, Extra Crunch, food, Gaming, Health, Market Analysis, Media, science, Social, Startups, Transportation, Venture Capital | No Comments
Ann Miura-Ko
Contributor

Ann Miura-Ko is a co-founding partner at Floodgate, a seed-stage VC firm. A repeat member of the Forbes Midas List and the New York Times Top 20 Venture Capitalists Worldwide, she earned a PhD in math modeling of cybersecurity at Stanford University.

Talk of an economic downturn can be frightening, especially one precipitated by a pervasive health crisis. At times, I’m overwhelmed by the images of countless patients on life-support and the near-endless streams of statistics regurgitating bad news.

Having started in venture at the beginning of two recessions, I’ve seen how the startup industry functions during economic trouble. My second day of work at Charles River Ventures was September 11th, 2001. My first project, analyzing the VC industry, propelled the firm to return more than 60% of its fund to investors, going from a $1.2 billion fund to $450 million. In May 2008, Mike Maples and I founded Floodgate in the midst of the Great Recession. We learned that great founders won’t wait for a better economic moment to start a company.

While we are currently embroiled in personal and professional circumstances unimaginable even three months ago, these very challenges will form the basis of incredibly innovative ideas. In order for the world to move forward, we need our greatest minds to imagine a brighter future and create solutions to make it a reality.

When I analyze our society and novel health situation, one thing is certain: COVID-19 is a paradigm-shifting event, creating massively accelerated social and economic change.

The Great Reset is not just another economic event

Our current situation is unique. It’s not merely a cyclical economic event, nor is it a standalone health crisis. What we are experiencing is not just an inflection point: it’s a societal phase-change unlike anything we have ever seen. We face an epic choice of how we move forward, and the decisions we make today will shape an entire generation.

Here’s why: COVID-19 is prompting us to reset many of our most fundamental behaviors. These changes are impacting our financial system, with effects visible throughout our homes, businesses and even the concept of “workplace” itself.

COVID-19 is pervasive

As a global pandemic, the virus itself has spread to nearly every country in the world.

Between February 20 and March 26, 100% of the world’s 20 largest economies implemented government-mandated social distancing. Globally, the number of scheduled airline flights is down 64%. In some countries, like Spain and Germany, flight numbers are down by more than 90%.

Since the timeline for lifting government restrictions is unclear — and even then, scientists are uncertain how the virus will spread — the question lingers: How long will this go on?

COVID-19’s impact is uncertain, long-term and potentially undulating, affecting every facet of our lives. You can’t simply wait it out with the expectation that industries will rebound. In 2001, September 11 felt pervasive, but its economic impact ultimately stemmed from just one single incident and the resulting fear… and that one single incident still cost more than three trillion dollars. How much larger will COVID-19 be?

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MIT develops a way to use wireless signals from in-home appliances to better understand your health

Posted by | ambient intelligence, articles, artificial intelligence, Gadgets, hardware, Health, IoT, Massachusetts Institute of Technology, MIT, science, TC, technology | No Comments

Having a holistic picture of your health might not mean just wearing a device like an Apple Watch that can monitor your biometrics — researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have developed a new system that can figure out when and where in-home appliances like hair dryers, stoves, microwaves and washing machines are being used, and they believe that info could help inform healthcare practitioners about the habits and challenges of people under their care.

The researchers devised a system called “Sapple” that uses just two sensors placed in a person’s home to determine use patterns of devices including stoves, hair dryers and more. There’s one location sensor that works using radio signals to figure out placement, with a user able to calibrate it to cover their area by simply walking the bounds of their space. A second sensor measures energy usage through the home, and combines that data with movement information to matching energy use signals with physical locations of specific applicants, to provide data both when a person is using the appliances around the house, and for how long.

This gets around a lot of the issues raised by similar systems, including more simple voltage meters used on their own. While appliances do tend to have specific energy use patterns that mean you can identify them just based on consumption, it’s hard to tell when and how they’re being used with that data on its own. This info can let health professionals know if a patient is taking proper care of hygiene, food preparation and intake and more.

Of course, the system does sound like one that has a lot of potential privacy pitfalls, but its intended use is for specific cases, like providing supervised care of aging populations that need it while also still preserving resources and enabling better distancing, which is actually a more urgent need right now as we continue to figure out how to address caregiving in the context of the COVID-19 pandemic.

It’s a clever system in that it doesn’t require any special smart IoT devices to work, beyond the two simple sensors, and essentially also doesn’t require any technical expertise on the part of the patients receiving care.

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Air Force gives a rare look at the research going to orbit in its X-37B spaceplane

Posted by | aerospace, Air Force, air force research lab, Boeing, Defense Department, department of defense, Gadgets, Government, hardware, military, NASA, science, Space, Space Force, TC, x-37b | No Comments

The X-37B spaceplane sounds like something out of a sci-fi novel, and its mysterious past is equally evocative. What does the military put in this long-term orbital vehicle? Turns out it’s exactly the kind of neat, but not mind-blowing, science you’d expect to find in such a thing — though solar-powered masers do sound pretty cool.

Also known as the Orbital Test Vehicle, the Boeing -designed X-37B has performed five prior missions, amounting to a total of nearly eight years in orbit; the last mission alone was 780 days. But while the craft’s owners (the Air Force, though it is used by many others) are proud to tout its remarkable longevity and reliability, they rarely if ever admit what they’re sending up, or what (if anything) it brings down.

While it’s fun to think that it may be truly top secret Area 51-type stuff, it’s much more likely that it’s just run-of-the-mill classified military research. The Defense Department bankrolls an enormous amount of basic science as well as advanced technology, and some of that is bound to require testing in space. While we love and respect our Russian friends with whom we share the ISS, the Pentagon would seem to prefer they didn’t run its experiments, so they have the X-37B.

On one occasion the Air Force said that the craft tests “advanced guidance, navigation and control, thermal protection systems, avionics, high temperature structures and seals, conformal reusable insulation, lightweight electromechanical flight systems, advanced propulsion systems, advanced materials and autonomous orbital flight, reentry and landing,” which narrows it down a bit.

For the spaceplane’s sixth mission, the various departments involved have broken tradition and given details on the payloads. That’s no small feat, given it’s an operation combining the resources of the Air Force, Space Force, Naval Research Lab and NASA.

The most broadly interesting experiment has to be a solar-powered microwave laser, or maser, built by the NRL. The device “will transform solar power into radio frequency microwave energy which could then be transmitted to the ground.”

Image Credits: U.S. Air Force courtesy photo

The key word there is could, as this type of wireless energy transmission has been pursued for decades. It’s doubtful that a foot-wide solar cell can produce enough energy to be beamed to the surface in measurable levels, but proving the concept piece by piece is something that has to be done in space. And for all we know they’ve already sent multiple precursor device up there on previous missions.

Don’t worry that this is some kind of orbital beam weapon that fries surface-dwellers: The total amount of energy collected by a foot-wide cell would be difficult to change into a form that’s harmful a few feet away, let alone 200 miles up through the entire atmosphere. It could, however, be used to beam power to receptive spacecraft or (conceivably) to interfere with poorly protected adversary spacecraft.

Two other experiments on board are from NASA, and they have to do with seeing how various items react to being exposed to the space environment. “One is a sample plate evaluating the reaction of select significant materials to the conditions in space. The second studies the effect of ambient space radiation on seeds,” said Air Force Secretary Barbara Barrett.

Last — that we know of — is FalconSat-8, an Air Force Academy satellite that will be performing its own unspecified experiments once released into its own orbit by the X-37B. It is itself “an educational platform that will carry five experimental payloads for USAFA to operate

This rather large number of items being brought to space is made possible by a “service module” attached for the first time to the aft of the craft and containing some of the hardware.

It’s unknown how long this mission will be, but if it’s anything like the others, it will be on the order of months or years.

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The future of deep-reinforcement learning, our contemporary AI superhero

Posted by | artificial intelligence, Column, deep-reinforcement learning, Extra Crunch, Gaming, Market Analysis, robotics, science | No Comments
Rish Joshi
Contributor

Rish is an entrepreneur and investor. Previously, he was a VC at Gradient Ventures (Google’s AI fund), co-founded a fintech startup building an analytics platform for SEC filings and worked on deep-learning research as a graduate student in computer science at MIT.

It was not long ago that the world watched World Chess Champion Garry Kasparov lose a decisive match against a supercomputer. IBM’s Deep Blue embodied the state of the art in the late 1990s, when a machine defeating a world (human) champion at a complex game such as chess was still unheard of.

Fast-forward to today, and not only have supercomputers greatly surpassed Deep Blue in chess, they have managed to achieve superhuman performance in a string of other games, often much more complex than chess, ranging from Go to Dota to classic Atari titles.

Many of these games have been mastered just in the last five years, pointing to a pace of innovation much quicker than the two decades prior. Recently, Google released work on Agent57, which for the first time showcased superior performance over existing benchmarks across all 57 Atari 2600 games.

The class of AI algorithms underlying these feats — deep-reinforcement learning — has demonstrated the ability to learn at very high levels in constrained domains, such as the ones offered by games.

The exploits in gaming have provided valuable insights (for the research community) into what deep-reinforcement learning can and cannot do. Running these algorithms has required gargantuan compute power as well as fine-tuning of the neural networks involved in order to achieve the performance we’ve seen.

Researchers are pursuing new approaches such as multi-environment training and the use of language modeling to help enable learning across multiple domains, but there remains an open question of whether deep-reinforcement learning takes us closer to the mother lode — artificial general intelligence (AGI) — in any extensible way.

While the talk of AGI can get quite philosophical quickly, deep-reinforcement learning has already shown great performance in constrained environments, which has spurred its use in areas like robotics and healthcare, where problems often come with defined spaces and rules where the techniques can be effectively applied.

In robotics, it has shown promising results in using simulation environments to train robots for the real world. It has performed well in training real-world robots to perform tasks such as picking and how to walk. It’s being applied to a number of use cases in healthcare, such as personalized medicine, chronic care management, drug discovery and resource scheduling and allocation. Other areas that are seeing applications have included natural language processing, computer vision, algorithmic optimization and finance.

The research community is still early in fully understanding the potential of deep-reinforcement learning, but if we are to go by how well it has done in playing games in recent years, it’s likely we’ll be seeing even more interesting breakthroughs in other areas shortly.

So what is deep-reinforcement learning?

If you’ve ever navigated a corn maze, your brain at an abstract level has been using reinforcement learning to help you figure out the lay of the land by trial and error, ultimately leading you to find a way out.

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The ‘PuffPacket’ could help researchers learn when, how and why people vape

Posted by | cornell, cornell university, Gadgets, Health, science, vaping | No Comments

Vaping is a controversial habit: it certainly has its downsides, but anecdotally it’s a fantastic smoking cessation aid. The thing is, until behavioral scientists know a bit more about who does it, when, how much and other details, its use will continue to be something of a mystery. That’s where the PuffPacket comes in.

Designed by Cornell engineers, the PuffPacket is a small device that attaches to e-cigarettes (or vape pens, or whatever you call yours) and precisely measures their use, sharing that information with a smartphone app for the user, and potentially researchers, to review later.

Some vaping devices are already set up with something like this, to tell a user when the cartridge is running low or a certain limit has been reached. But generally when vaping habits are studied, they rely on self-report data, not proprietary apps.

“The lack of continuous and objective understanding of vaping behaviors led us to develop PuffPacket to enable proper measurement, monitoring, tracking and recording of e-cigarette use, as opposed to inferring it from location and activity data, or self-reports,” said PhD student Alexander Adams, who led the creation of the device, in a Cornell news release.

The device fits a number of e-cigarette types, fitting between the mouthpiece and the heating element. It sits idle until the user breathes in, which activates the e-cigarette’s circuits, and the PuffPacket’s as well. By paying attention to the voltage, it can tell how much liquid is being vaporized, as well as simpler measurements like the duration and timing of the inhalation.

An example using real data of how location and activity could be correlated with vaping

This data is sent to the smartphone app via Bluetooth, where it is cross-referenced with other information, like location, motion and other metadata. This may lead to identifiable patterns, like that someone vapes frequently when they walk in the morning but not the afternoon, or after coffee but not meals, or far more at the bar than at home — that sort of thing. Perhaps even (with the proper permissions) it could track use of certain apps — Instagram and vape? Post-game puff?

Some of these might be obvious, others not so much — but either way, it helps to have them backed up by real data rather than asking a person to estimate their own usage. They may not know, understand or wish to admit their own habits.

“Getting these correlations between time of day, place and activity is important for understanding addiction. Research has shown that if you can keep people away from the paths of their normal habits, it can disrupt them,” said Adams.

No one is expecting people to voluntarily stick these things on their vape pens and share their info, but the design — which is being released as open source — could be used by researchers performing more formal studies. You can read the paper describing PuffPacket here.

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R&D Roundup: Sweat power, Earth imaging, testing ‘ghostdrivers’

Posted by | artificial intelligence, autonomous systems, coronavirus, COVID-19, cybernetics, esa, Extra Crunch, Gadgets, Health, imaging, Lidar, machine learning, MIT, National Science Foundation, plastics, satellite imagery, science, self-driving car, Space, TC, technology, telemedicine | No Comments

I see far more research articles than I could possibly write up. This column collects the most interesting of those papers and advances, along with notes on why they may prove important in the world of tech and startups.

This week: one step closer to self-powered on-skin electronics; people dressed as car seats; how to make a search engine for 3D data; and a trio of Earth imaging projects that take on three different types of disasters.

Sweat as biofuel

Monitoring vital signs is a crucial part of healthcare and is a big business across fitness, remote medicine and other industries. Unfortunately, powering devices that are low-profile and last a long time requires a bulky battery or frequent charging is a fundamental challenge. Wearables powered by body movement or other bio-derived sources are an area of much research, and this sweat-powered wireless patch is a major advance.

A figure from the paper showing the device and interactions happening inside it.

The device, described in Science Robotics, uses perspiration as both fuel and sampling material; sweat contains chemical signals that can indicate stress, medication uptake, and so on, as well as lactic acid, which can be used in power-generating reactions.

The patch performs this work on a flexible substrate and uses the generated power to transmit its data wirelessly. It’s reliable enough that it was used to control a prosthesis, albeit in limited fashion. The market for devices like this will be enormous and this platform demonstrates a new and interesting direction for researchers to take.

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What is contact tracing?

Posted by | Apple, contact tracing, coronavirus, COVID-19, Google, Health, MIT, Mobile, science, TC, WTF is | No Comments

One of the best tools we have to slow the spread of the coronavirus is, as you have no doubt heard by now, contact tracing. But what exactly is contact tracing, who does it and how, and do you need to worry about it?

In short, contact tracing helps prevent the spread of a virus by proactively finding people at higher risk than others due to potential exposure, notifying them if possible, and quarantining them if necessary. It’s a proven technique, and smartphones could help make it even more effective — but only if privacy and other concerns can be overcome.

Contact tracing, from memory to RAM

Contact tracing has been done in some form or another as long as the medical establishment has understood the nature of contagious diseases. When a person is diagnosed with an infectious disease, they are asked whom they have been in contact with over the previous weeks, both in order to determine who may have been infected by them and perhaps where they themselves were infected.

Until very recently, however, the process has relied heavily on the recall of people who are in a highly stressful situation and, until prompted, were probably not paying special attention to their movements and interactions.

This results in a list of contacts that is far from complete, though still very helpful. If those people can be contacted and their contacts likewise traced, a network of potential infections can be built up without a single swab or blood drop, and lives can be saved or important resources better allocated.

You might think that has all changed now what with modern technology and all, but in fact contact tracing being done at hospitals right now is almost all still of the memory-based kind — the same we might have used a hundred years ago.

It certainly seems as if the enormous digital surveillance apparatus that has been assembled around us over the last decade should be able to accomplish this kind of contact tracing easily, but in fact it’s surprisingly useless for anything but tracking what you are likely to click on or buy.

While it would be nice to be able to piece together a contagious person’s week from a hundred cameras spread throughout the city and background location data collected by social media, the potential for abuse of such a system should make us thankful it is not so easy as that. In other, less dire circumstances the ability to track the exact movements and interactions of a person from their digital record would be considered creepy at best, and perhaps even criminal.

But it’s one thing when an unscrupulous data aggregator uses your movements and interests to target you with ads without your knowledge or consent — and quite another when people choose to use the forbidden capabilities of everyday technology in an informed and limited way to turn the tide of a global pandemic. And that’s what modern digital contact tracing is intended to do.

Bluetooth beacons

All modern mobile phones use wireless radios to exchange data with cell towers, Wi-Fi routers, and each other. On their own, these transmissions aren’t a very good way to tell where someone is or who they’re near — a Wi-Fi signal can travel 100 to 200 feet reliably, and a cell signal can go miles. Bluetooth, on the other hand, has a short range by design, less than 30 feet for good reception and with a swiftly attenuating signal that makes it unlikely to catch a stray contact from much further out than that.

We all know Bluetooth as the way our wireless earbuds receive music from our phones, and that’s a big part of its job. But Bluetooth, by design, is constantly reaching out and touching other Bluetooth-enabled devices — it’s how your car knows you’ve gotten into it, or how your phone detects a smart home device nearby.

Bluetooth chips also make brief contact without your knowledge with other phones and devices you pass nearby, and if they aren’t recognized, they delete each other from their respective memories as soon as possible. But what if they didn’t?

The type of contact tracing being tested and deployed around the world now uses Bluetooth signals very similar to the ones your phone already transmits and receives constantly. The difference is it just doesn’t automatically forget the other devices it comes into contact with.

Assuming the system is working correctly, what would happen when a person presents at the hospital with COVID-19 is basically just a digitally enhanced version of manual contact tracing. Instead of querying the person’s fallible memory, they query the phone’s much more reliable one, which has dutifully recorded all the other phones it has recently been close enough to connect to. (Anonymously, as we’ll see.)

Those devices — and it’s important to note that it’s devices, not people — would be alerted within seconds that they had recently been in contact with someone who has now been diagnosed with COVID-19. The notification they receive will contain information on what the affected person can do next: Download an app or call a number for screening, for instance, or find a nearby location for testing.

The ease, quickness, and comprehensiveness of this contact tracing method make it an excellent opportunity to help stem the spread of the virus. So why aren’t we all using it already?

Successes and potential worries

In fact digital contact tracing using the above method (or something very like it) has already been implemented with millions of users, apparently to good effect, in east Asia, which of course was hit by the virus earlier than the U.S. and Europe.

In Singapore the TraceTogether app was promoted by the government as the official means for contact tracing. South Korea saw the voluntary adoption of a handful of apps that tracked people known to be diagnosed. Taiwan was able to compare data from its highly centralized healthcare system to a contact tracing system it began work on during the SARS outbreak years ago. And mainland China has implemented a variety of tracking procedures through mega-popular services like WeChat and Alipay.

While it would be premature to make conclusions on the efficacy of these programs while they’re still underway, it seems at least anecdotally to have improved the response and potentially limited the spread of the virus.

But east Asia is a very different place from the U.S.; we can’t just take Taiwan’s playbook and apply it here (or in Europe, or Africa, etc.), for myriad reasons. There are also valid questions of privacy, security, and other matters that need to be answered before people, who for good reason are skeptical of the intentions of both the government and the private sector, will submit to this kind of tracking.

Right now there are a handful of efforts being made in the U.S., the largest profile by far being the collaboration between arch-rivals Apple and Google, which have proposed a cross-platform contact tracing method that can be added to phones at the operating system.

The system they have suggested uses Bluetooth as described above, but importantly does not tie it to a person’s identity in any way. A phone would have a temporary ID number of its own, and as it made contact with other devices, it exchanges numbers. These lists of ID numbers are collected and stored locally, not synced with the cloud or anything. And the numbers also change frequently so no single one can be connected to your device or location.

If and only if a person is determined to be infected with the virus, a hospital (not the person) is authorized to activate the contact tracing app, which will send a notification to all the ID numbers stored in the person’s phone. The notification will say that they were recently near a person now diagnosed with COVID-19 — again, these are only ID numbers generated by a phone and are not connected with any personal information. As discussed earlier, the people notified can then take whatever action seems warranted.

MIT has developed a system that works in a very similar way, and which some states are reportedly beginning to promote among their residents.

Naturally even this straightforward, decentralized, and seemingly secure system has its flaws; this article at the Markup gives a good overview, and I’ve summarized them below:

  • It’s opt-in. This is a plus and a minus, of course, but means that many people may choose not to take part, limiting how comprehensive the list of recent contacts really is.
  • It’s vulnerable to malicious interference. Bluetooth isn’t particularly secure, which means there are several ways this method could be taken advantage of, should there be any attacker depraved enough to do so. Bluetooth signals could be harvested and imitated, for instance, or a phone driven through the city to “expose” it to thousands of others.
  • It could lead to false positives or negatives. In order to maintain privacy, the notifications sent to others would contain a minimum of information, leading them to wonder when and how they might have been exposed. There will be no details like “you stood next to this person in line 4 days ago for about 5 minutes” or “you jogged past this person on Broadway.” This lack of detail may lead to people panicking and running to the ER for no reason, or ignoring the alert altogether.
  • It’s pretty anonymous, but nothing is truly anonymous. Although the systems seem to work with a bare minimum of data, that data could still be used for nefarious purposes if someone got their hands on it. De-anonymizing large sets of data is practically an entire domain of study in data science now and it’s possible that these records, however anonymous they appear, could be cross-referenced with other data to out infected persons or otherwise invade one’s privacy.
  • It’s not clear what happens to the data. Will this data be given to health authorities later? Will it be sold to advertisers? Will researcher be able to access it, and how will they be vetted? Questions like these could very well be answered satisfactorily, but right now it’s a bit of a mystery.

Contact tracing is an important part of the effort to curb the spread of the coronavirus, and whatever method or platform is decided on in your area — it may be different state to state or even between cities — it is important that as many people as possible take part in order to make it as effective as possible.

There are risks, yes, but the risks are relatively minor and the benefits would appear to outweigh them by orders of magnitude. When the time comes to opt in, it is out of consideration for the community at large that one should make the decision to do so.

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