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A Chinese rocket is falling back to Earth—but we don’t know where it will land

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A Chinese rocket is falling back to Earth—but we don't know where it will land


The probability that parts of the booster could hit populated land is admittedly quite low—it’s much more likely to land in the ocean somewhere. But that probability is not zero. Case in point: the CZ-5B booster’s debut last year for a mission on May 5, 2020. The same problem arose back then as well: the core booster ended up in an uncontrolled orbit before eventually reentering Earth’s atmosphere. Debris landed in villages across Ivory Coast. It was enough to elicit a notable rebuke from the NASA administrator at the time, Jim Bridenstine.

The same story is playing out this time, and we’re playing the same waiting game because of how difficult it is to predict when and where this thing will reenter. The first reason is the booster’s speed: it’s currently traveling at nearly 30,000 kilometers per hour, orbiting the planet about once every 90 minutes. The second reason has to do with the amount of drag the booster is experiencing. Although technically it’s in space, the booster is still interacting with the upper edges of the planet’s atmosphere.

That drag varies from day to day with changes in upper-atmosphere weather, solar activity, and other phenomena. In addition, the booster isn’t just zipping around smoothly and punching through the atmosphere cleanly—it’s tumbling, which creates even more unpredictable drag. 

Given those factors, we can establish a window for when and where we think the booster will reenter Earth’s atmosphere. But a change of even a couple of minutes can put its location thousands of miles away. “It can be difficult to model precisely, meaning we are left with some serious uncertainties when it comes to the space object’s reentry time,” says Thomas G. Roberts, an adjunct fellow at the CSIS Aerospace Security Project. 

This also depends on how well the structure of the booster holds up to heating caused by friction with the atmosphere. Some materials might hold up better than others, but drag will increase as the structure breaks up and melts. The flimsier the structure, the more it will break up, and the more drag will be produced, causing it to fall out of orbit more quickly. Some parts may hit the ground earlier or later than others.

By the morning of reentry, the estimate of when it will land should have narrowed to just a few hours. Several different groups around the world are tracking the booster, but most experts are following data provided by the US Space Force through its Space Track website. Jonathan McDowell, an astrophysicist at the Harvard-Smithsonian Center for Astrophysics, hopes that by the morning of reentry, the timing window will have shrunk to just a couple of hour where the booster orbits Earth maybe two more times. By then we should have a sharper sense of the route those orbits are taking and what regions of the Earth may be at risk from a shower of debris.

The Space Force’s missile early warning systems will already be tracking the infrared flare from the disintegrating rocket when reentry starts, so it will know where the debris is headed. Civilians won’t know for a while, of course, because that data is sensitive—it will take a few hours to work through the bureaucracy before an update is made to the Space Track site. If the remnants of the booster have landed in a populated area, we might already know thanks to reports on social media.

In the 1970s, these were common hazards after missions. “Then people started to feel it wasn’t appropriate to have large chunks of metal falling out of the sky,” says McDowell. NASA’s 77-ton Skylab space station was something of a wake-up call—its widely watched uncontrolled deorbit in 1979 led to large debris hitting Western Australia. No one was hurt and there was no property damage, but the world was eager to avoid any similar risks of large spacecraft uncontrollably reentering the atmosphere (not a problem with smaller boosters, which just burn up safely).

As a result, after the core booster gets into orbit and separates from the secondary boosters and payload, many launch providers quickly do  a deorbit burn that brings it back into the atmosphere and sets it on a controlled crash course for the ocean, eliminating the risk it would pose if left in space. This can be accomplished with either a restartable engine or an added second engine designed for deorbit burns specifically. The remnants of these boosters are sent to a remote part of the ocean, such as the South Pacific Ocean Uninhabited Area, where other massive spacecraft like Russia’s former Mir space station have been dumped. 

Another approach which was, used during space shuttle missions and is currently used by large boosters like Europe’s Ariane 5, is to avoid putting the core stage in orbit entirely and simply switch it off a few seconds early while it’s still in Earth’s atmosphere. Smaller engines then fire to take the payload the short extra distance to space, while the core booster is dumped in the ocean.

None of these options are cheap, and they create some new risks (more engines mean more points of failure), but “it’s what everyone does, since they don’t want to create this type of debris risk,” says McDowell. “It’s been standard practice around the world to avoid leaving these boosters in orbit. The Chinese are an outlier of this.”

Why? “Space safety is just not China’s priority,” says Roberts. “With years of space launch operations under its belt, China is capable of avoiding this weekend’s outcome, but chose not to.” 

The past few years have seen a number of rocket bodies from Chinese launches that have been allowed to fall back to land, destroying buildings in villages and exposing people to toxic chemicals. “It’s no wonder that they would be willing to roll the dice on an uncontrolled atmospheric reentry, where the threat to populated areas pales in comparison,” says Roberts. “I find this behavior totally unacceptable, but not surprising.”

McDowell also points to what happened during the space shuttle Columbia disaster, when damage to the wing caused the spacecraft’s entry to become unstable and break apart. Nearly 38,500 kilograms of debris landed in Texas and Louisiana. Large chunks of the main engine ended up in a swamp—had it broken up a couple of minutes earlier, those parts could have hit a major city, slamming into skyscrapers in, say, Dallas. “I think people don’t appreciate how lucky we were that there weren’t casualties on the ground,” says McDowell. “We’ve been in these risky situations before and been lucky.” 

But you can’t always count on luck. The CZ-5B variant of the Long March 5B is slated for two more launches in 2022 to help build out the rest of the Chinese space station. There’s no indication yet whether China plans to change its blueprint for those missions. Perhaps that will depend on what happens this weekend.

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Transforming health care at the edge

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Transforming health care at the edge


Edge computing, through on-site sensors and devices, as well as last-mile edge equipment that connects to those devices, allows data processing and analysis to happen close to the digital interaction. Rather than using centralized cloud or on-premises infrastructure, these distributed tools at the edge offer the same quality of data processing but without latency issues or massive bandwidth use.

“The real-time feedback loop required for things like remote monitoring of a patient’s heart and respiratory metrics is only possible with something like edge computing,” Mirchandani says. “If all that information took several seconds or a minute to get processed somewhere else, it’s useless.”

Opportunities and challenges at the health-care edge

The sky’s the limit when it comes to the opportunities to use edge computing in health care, says Paul Savill, senior vice president of product management and services at technology company Lumen, especially as health systems work to reduce costs by shifting testing and treatment out of hospitals and into clinics, retail locations, and homes.

“A lot of patient care now happens at retail drugstores, whether it is blood work, scans, or other assessments,” Savill says. “With edge computing capabilities and tools, that can now take place on-site, on a real-time basis, so you don’t have to send things to a lab and wait a day or week to get results back.”

The arrival of 5G technology, the new standard for broadband cellular networks, will also drive opportunities, as it works with edge computing tools to support the internet of things and machine learning, adds Mirchandani. “It’s the combination of this super-low-latency network and computing at the edge that will help these powerful new applications take flight,” he says. Take robotic surgeries—it’s crucial for the surgeon to have nearly instant, sub-millisecond sensory feedback. “That’s not possible in any other way than through technologies such as edge computing and 5G,” he says.

“A lot of patient care now happens at retail drugstores. With edge computing capabilities and tools, that can now take place on-site, on a real-time basis, so you don’t have to send things to a lab and wait a day or week to get results back.”

Paul Savill, Senior Vice President, Product Management and Services, Lumen

Data security, however, is a particular challenge for any health-care-related technology because of HIPAA, the US health information privacy law, and other regulations. The real-time data transmission edge computing provides will be under significant scrutiny, Mirchandani explains, which may affect widespread adoption. “There needs to be an almost 100% guarantee that the information you generate from a heart monitor, pulse oximeter, blood glucose monitor, or any other device will not be intercepted or disrupted in any way,” he says.

Still, edge computing technologies, paired with the right security standards and tools, are often more secure and reliable than the on-premises environment a business could implement on its own, Savill points out. “It’s about understanding the entire threat landscape down to the network level.”

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Anti-vaxxers are weaponizing Yelp to punish bars that require vaccine proof

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Anti-vaxxers are weaponizing Yelp to punish bars that require vaccine proof


Smith’s Yelp reviews were shut down after the sudden flurry of activity on its page, which the company labels “unusual activity alerts,” a stopgap measure for both the business and Yelp to filter through a flood of reviews and pick out which are spam and which aren’t. Noorie Malik, Yelp’s vice president of user operations, said Yelp has a “team of moderators” that investigate pages that get an unusual amount of traffic. “After we’ve seen activity dramatically decrease or stop, we will then clean up the page so that only firsthand consumer experiences are reflected,” she said in a statement.

It’s a practice that Yelp has had to deploy more often over the course of the pandemic: According to Yelp’s 2020 Trust & Safety Report, the company saw a 206% increase over 2019 levels in unusual activity alerts. “Since January 2021, we’ve placed more than 15 unusual activity alerts on business pages related to a business’s stance on covid-19 vaccinations,” said Malik.

The majority of those cases have been since May, like the gay bar C.C. Attles in Seattle, which got an alert from Yelp after it made patrons show proof of vaccination at the door. Earlier this month, Moe’s Cantina in Chicago’s River North neighborhood got spammed after it attempted to isolate vaccinated customers from unvaccinated ones.

Spamming a business with one-star reviews is not a new tactic. In fact, perhaps the best-known case is Colorado’s Masterpiece bakery, which won a 2018 Supreme Court battle for refusing to make a wedding cake for a same-sex couple, after which it got pummeled by one-star reviews. “People are still writing fake reviews. People will always write fake reviews,” Liu says.

But he adds that today’s online audience know that platforms use algorithms to detect and flag problematic words, so bad actors can mask their grievances by blaming poor restaurant service like a more typical negative review to ensure the rating stays up — and counts.

That seems to have been the case with Knapp’s bar. His Yelp review included comments like “There was hair in my food” or alleged cockroach sightings. “Really ridiculous, fantastic shit,” Knapp says. “If you looked at previous reviews, you would understand immediately that this doesn’t make sense.” 

Liu also says there is a limit to how much Yelp can improve their spam detection, since natural language — or the way we speak, read, and write — “is very tough for computer systems to detect.” 

But Liu doesn’t think putting a human being in charge of figuring out which reviews are spam or not will solve the problem. “Human beings can’t do it,” he says. “Some people might get it right, some people might get it wrong. I have fake reviews on my webpage and even I can’t tell which are real or not.”

You might notice that I’ve only mentioned Yelp reviews thus far, despite the fact that Google reviews — which appear in the business description box on the right side of the Google search results page under “reviews” — is arguably more influential. That’s because Google’s review operations are, frankly, even more mysterious. 

While businesses I spoke to said Yelp worked with them on identifying spam reviews, none of them had any luck with contacting Google’s team. “You would think Google would say, ‘Something is fucked up here,’” Knapp says. “These are IP addresses from overseas. It really undermines the review platform when things like this are allowed to happen.”

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These creepy fake humans herald a new age in AI

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Once viewed as less desirable than real data, synthetic data is now seen by some as a panacea. Real data is messy and riddled with bias. New data privacy regulations make it hard to collect. By contrast, synthetic data is pristine and can be used to build more diverse data sets. You can produce perfectly labeled faces, say, of different ages, shapes, and ethnicities to build a face-detection system that works across populations.

But synthetic data has its limitations. If it fails to reflect reality, it could end up producing even worse AI than messy, biased real-world data—or it could simply inherit the same problems. “What I don’t want to do is give the thumbs up to this paradigm and say, ‘Oh, this will solve so many problems,’” says Cathy O’Neil, a data scientist and founder of the algorithmic auditing firm ORCAA. “Because it will also ignore a lot of things.”

Realistic, not real

Deep learning has always been about data. But in the last few years, the AI community has learned that good data is more important than big data. Even small amounts of the right, cleanly labeled data can do more to improve an AI system’s performance than 10 times the amount of uncurated data, or even a more advanced algorithm.

That changes the way companies should approach developing their AI models, says Datagen’s CEO and cofounder, Ofir Chakon. Today, they start by acquiring as much data as possible and then tweak and tune their algorithms for better performance. Instead, they should be doing the opposite: use the same algorithm while improving on the composition of their data.

Datagen also generates fake furniture and indoor environments to put its fake humans in context.

DATAGEN

But collecting real-world data to perform this kind of iterative experimentation is too costly and time intensive. This is where Datagen comes in. With a synthetic data generator, teams can create and test dozens of new data sets a day to identify which one maximizes a model’s performance.

To ensure the realism of its data, Datagen gives its vendors detailed instructions on how many individuals to scan in each age bracket, BMI range, and ethnicity, as well as a set list of actions for them to perform, like walking around a room or drinking a soda. The vendors send back both high-fidelity static images and motion-capture data of those actions. Datagen’s algorithms then expand this data into hundreds of thousands of combinations. The synthesized data is sometimes then checked again. Fake faces are plotted against real faces, for example, to see if they seem realistic.

Datagen is now generating facial expressions to monitor driver alertness in smart cars, body motions to track customers in cashier-free stores, and irises and hand motions to improve the eye- and hand-tracking capabilities of VR headsets. The company says its data has already been used to develop computer-vision systems serving tens of millions of users.

It’s not just synthetic humans that are being mass-manufactured. Click-Ins is a startup that uses synthetic AI to perform automated vehicle inspections. Using design software, it re-creates all car makes and models that its AI needs to recognize and then renders them with different colors, damages, and deformations under different lighting conditions, against different backgrounds. This lets the company update its AI when automakers put out new models, and helps it avoid data privacy violations in countries where license plates are considered private information and thus cannot be present in photos used to train AI.

Click-Ins renders cars of different makes and models against various backgrounds.

CLICK-INS

Mostly.ai works with financial, telecommunications, and insurance companies to provide spreadsheets of fake client data that let companies share their customer database with outside vendors in a legally compliant way. Anonymization can reduce a data set’s richness yet still fail to adequately protect people’s privacy. But synthetic data can be used to generate detailed fake data sets that share the same statistical properties as a company’s real data. It can also be used to simulate data that the company doesn’t yet have, including a more diverse client population or scenarios like fraudulent activity.

Proponents of synthetic data say that it can help evaluate AI as well. In a recent paper published at an AI conference, Suchi Saria, an associate professor of machine learning and health care at Johns Hopkins University, and her coauthors demonstrated how data-generation techniques could be used to extrapolate different patient populations from a single set of data. This could be useful if, for example, a company only had data from New York City’s more youthful population but wanted to understand how its AI performs on an aging population with higher prevalence of diabetes. She’s now starting her own company, Bayesian Health, which will use this technique to help test medical AI systems.

The limits of faking it

But is synthetic data overhyped?

When it comes to privacy, “just because the data is ‘synthetic’ and does not directly correspond to real user data does not mean that it does not encode sensitive information about real people,” says Aaron Roth, a professor of computer and information science at the University of Pennsylvania. Some data generation techniques have been shown to closely reproduce images or text found in the training data, for example, while others are vulnerable to attacks that make them fully regurgitate that data.

This might be fine for a firm like Datagen, whose synthetic data isn’t meant to conceal the identity of the individuals who consented to be scanned. But it would be bad news for companies that offer their solution as a way to protect sensitive financial or patient information.

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