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No one can find the animal that gave people covid-19



No one can find the animal that gave people covid-19

One problem with the lab leak theory is that it presumes the Chinese are lying or hiding facts, a position incompatible with a joint scientific effort. This may have been why the WHO team, for instance, never asked to see the offline database. Peter Daszak, president of the EcoHealth Alliance, which collaborated with the Wuhan lab for many years and funded some of its work, says there is “no evidence” whatsoever to back the lab theory. “If you just firmly believe [that] what we hear from our Chinese colleagues over there in the labs is not going to be true, we will never be able to rule it out,” he said of the lab theory. “That is the problem. In its essence, that theory is not a conspiracy theory. But people have put it forward as such, saying the Chinese side conspired to cover up evidence.”

To those who believe a lab accident is likely, including Jamie Metzl, a technology and national security fellow at the Atlantic Council, the WHO team isn’t set up to carry out the sort of forensic probe he believes is necessary. “Everyone on earth is a stakeholder in this,” he says. “It’s crazy that a year into this, there is no full investigation into the origins of the pandemic.” In February, Metzl published a statement in which he said he was “appalled” by the investigators’ quick rebuttal of the lab hypothesis and called for Daszak to be removed from the team. Several days later, the WHO director general, Tedros Adhanom Ghebreyesus, appeared to rebuke the origins team in a speech in which he said, “I want to clarify that all hypotheses remain open and require further study.”

The scenario the WHO-China team said it considers most probable is the “intermediary” theory, in which a bat virus infected another wild animal that was then caught or farmed for food. The intermediary theory does have the strongest precedents. Not only is there the case of SARS, but in 2012 researchers discovered Middle East respiratory syndrome (MERS), a deadly lung infection caused by another coronavirus, and quickly traced it to dromedary camels.

The trouble with this hypothesis is that Chinese researchers have not succeeded in finding a “direct progenitor” of this virus in any animal they’ve looked at. Liang said China had tested 50,000 animal specimens, including 1,100 bats in Hubei province, where Wuhan is located. But no luck: a matching virus still hasn’t been found.

The Chinese team appears to strongly favor a twist on the intermediate-animal idea: that the virus could have reached Wuhan on a frozen food shipment that included a frozen wild animal. This “cold chain” hypothesis may have appeal because it would mean the virus came from thousands of miles away, even outside China. “We think that is a valid option,” says Marion Koopmans, a Dutch virologist who traveled with the group. She said China had tested 1.5 million frozen samples and found the virus 30 times. “That may not be surprising in the middle of an outbreak, when many people are handling these products,” Koopmans says. “But the WHO did request studies, spiked the virus onto fish, froze and thawed it, and could culture the virus. So it’s possible. You cannot rule it out.”

Blame game

The WHO-China team, in its eventual report, is expected to suggest further research that needs to be carried out. This is one reason the report matters; it may determine which questions get asked and which don’t.

There is likely to be a larger effort to trace the wild-animal trade, including supply chains of frozen products. In addition to animal evidence, Ben Embarek also said China should make a greater effort to locate people who were infected by covid-19 early on, but perhaps were asymptomatic or didn’t get tested. That could be done by hunting through samples in blood banks, using newer, more sensitive technology to locate antibodies. “We need to keep looking for material that could give insight into the early days of the events,” Ben Embarek said. As well, the report is likely to call for the creation of a master database that includes all the data collected so far.

WHO officer Peter Ben Embarek (right) and Liang Wannian shake hands after a press conference in Wuhan, China, on Feb. 9, 2021 in which they ranked four theories for how the covid-19 pandemic began.


Ultimately, in seeking the cause of the covid-19 disaster, we don’t just want to know what happened. We’re also looking for something—or someone—to blame. And each hypothesis points to a different culprit. To ecologists, the lesson of the pandemic is nearly a foregone conclusion: humans should stop encroaching on wild areas. “We have come to recognize how this kind of investigation is not just about illness in humans—nor indeed just about an interface between humans and animals—but feeds into an altogether wider discussion about how we use the world,” says John Watson, the British epidemiologist.

The Chinese authorities, meanwhile, are already taking action on the intermediary theory by putting responsibility on wild-animal farmers and traders. Last February, according to NPR, China’s legislature started taking steps to “uproot the pernicious habit of eating wild animals.” At the behest of President Xi Jinping, they have already banned the hunting, trade, and consumption of a large number of “terrestrial wild animals,” a step never fully implemented after the original SARS outbreak. According to a report in Nature, the Chinese government has already closed 12,000 businesses, purged a million websites with information about wildlife trading, and banned the farming of bamboo rats and civets, among other species.

Then there is the chance covid-19 is the result of a laboratory accident. If that’s true, it would bring the sharpest consequences, especially for scientists like those in charge of finding the virus’s origin. If the pandemic was caused by ambitious, high-tech research on dangerous germs, it would mean China’s fast rise as a biotech powerhouse is a threat to the globe. It would mean this type of science should be severely restricted, or even banned, in China and everywhere else. More than any other hypothesis, a government-sponsored technology program run amok—along with early efforts to conceal news of the outbreak—would establish a case for retribution. “If this is a man-made catastrophe,” says Miles Yu, an analyst with the conservative Hudson Institute, “I think the world should seek reparations.”

According to some former virus chasers, what’s actually in the WHO-China origins report may be different from what we’ve heard so far. Schnur says the Chinese probably already know much more than we think, so the role of the team could be to find ways to push those facts into the light. It is a process he calls “part diplomacy and part epidemiology.” He believes China’s investigation was likely very thorough and that the foreign visitors may also have stronger views than they have let on so far.

As he points out, “What you say in a press conference may be different than what you put in a report once you have left the country.”


Transforming health care at the edge



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



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



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.


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 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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