Facial recognition software is a powerful technology that poses serious threats to civil liberties. It’s also a booming business. Today, dozens of startups and tech giants are selling face recognition services to hotels, retail stores—even schools and summer camps. The business is flourishing thanks to new algorithms that can identify people with far more precision than even five years ago. In order to improve these algorithms, companies trained them on billions of faces—often without asking anyone’s permission. Indeed, chances are good that your own face is part of a “training set” used by a facial recognition firm or part of a company’s customer database.

Consumers may be surprised at some of the tactics companies have used to harvest their faces. In at least three cases, for instance, firms have obtained millions of images by harvesting them via photo apps on people’s phones. For now, there are few legal restrictions on facial recognition software, meaning there is little people can do to stop companies using their face in this manner.

In 2018, a camera collected the faces of passengers as they hurried down an airport jetway near Washington, D.C. In reality, neither the jetway nor the passengers were real; the entire structure was merely a set for the National Institute for Standards and Technology (NIST) to demonstrate how it could collect faces “in the wild.” The faces would become part of a recurring NIST competition that invites companies across the globe to test their facial recognition software.

In the jetway exercise, volunteers gave the agency consent to use their faces. This is how it worked in the early days of facial recognition; academic researchers took pains to get permission to include faces in their data sets. Today, companies are at the forefront of facial recognition, and they’re unlikely to ask for explicit consent to use someone’s face—if they bother with permission at all.

The companies, including industry leaders like Face++ and Kairos, are competing in a market for facial recognition software that is growing by 20% each year and is expected to be worth $9 billion a year by 2022, according to Market Research Future. Their business model involves licensing software to a growing body of customers—from law enforcement to retailers to high schools—which use it run facial recognition programs of their own.

In the race to produce the best software, the winners will be companies whose algorithms can identify faces with a high degree of accuracy without producing so-called false positives. As in other areas of artificial intelligence, creating the best facial recognition algorithm means amassing a big collection of data—faces, in this case—as a training tool. While companies are able to use the sanctioned collections compiled by government and universities, such as the Yale Face Database, these training sets are relatively small and contain no more than a few thousand faces.

These official data sets have other limitations. Many lack racial diversity or fail to depict conditions—such as shadows or hats or make-up—that can change how faces appear in the real world. In order to build facial recognition technology capable of spotting individuals “in the wild,” companies needed more images. Lots more.

“Hundreds are not enough, thousands are not enough. You need millions of images. If you don’t train the database with people with glasses or people of color, you won’t get accurate results,” says Peter Trepp, the CEO of FaceFirst, a California-based facial recognition company that helps retailers screen for criminals entering their stores.

An App for That

Where might a company obtain millions of images to train its software? One source has been databases of police mug shots, which are publicly available from state agencies and are also for sale by private companies. California-based Vigilant Solutions, for instance, offers a collection of 15 million faces as part of its facial recognition “solution.”

Some startups, however, have found an even better source of faces: personal photo album apps. These apps, which compile photos stored on a person’s phone, typically contain multiple images of the same person in a wide variety of poses and situations—a rich source of training data.

“We have consumers who tag the same person in thousands of different scenarios. Standing in the shadows, with hats-on, you name it,” says Doug Aley, the CEO of Ever AI, a San Francisco facial recognition startup that launched in 2012 as EverRoll, an app to help consumers manage their bulging photo collections.

Ever AI, which has raised $29 million from Khosla Ventures and other Silicon Valley venture capital firms, entered NIST’s most recent facial recognition competition, and placed second in the contest’s “Mugshots” category and third in “Faces in the Wild.” Aley credits the success to the company’s immense photo database, which Ever AI estimates to number 13 billion images.

In its earlier days, when Ever AI was a mere photo app, its aggressive marketing practices created controversy and temporarily led Apple to ban EverRoll from the App Store in 2016. Notably, the app induced users to send promotional links to all of their phone contacts, a tactic known as “growth hacking” in Silicon Valley parlance. Users also accused it of gobbling their data.

“The first thing it does even as it is installing is to harvest all your phone numbers and immediately message everybody… This thing then starts to pull all your photos and put them into the cloud,” wrote Greg Miller, a Texas-based portrait studio owner, in a 2015 Facebook review.

Four years later, Miller was dismayed to discover that the app once known as EverRoll still had his photos, and that it was now a facial recognition company.

“No, I was not aware of that, and I don’t agree with it one bit,” Miller tells Fortune. “All of this being tracked is a real problem. Nothing is private anymore and that just scares the hell out of me.”

Aley, the Ever AI CEO, says the company doesn’t share identifying information about individuals in its database, and only uses the photos to train its software. He added the company is akin to a social media network from which people can opt out. Aley also denied that Ever AI had intended to become a facial recognition company from the get-go, saying the move away from the now-shuttered photo app was a business decision. Currently, Ever AI’s customers are using it for a range of activities, including corporate ID management, retail, telecommunications, and law enforcement.

EverRoll is not the only facial recognition company that once offered a consumer photo app. Another example is Orbeus, a San Francisco-based startup quietly acquired by Amazon in 2016, which once offered a popular picture organizer called PhotoTime.

According to a longtime Orbeus employee, the startup’s A.I. technology and its large collection of photos with people in public settings made it an appealing acquisition target.

“Amazon was looking for that capability. They acquired everything, then shut down the app,” says the employee, who declined to be identified, citing non-disclosure agreements.

Today the PhotoTime app no longer exists, though Amazon continues to sell another Orbeus product known as Rekognition. The product is a type of facial recognition software used by law enforcement and other organizations.

Amazon declined to provide details about the extent to which Orbeus’s photo app was used to train the Rekognition software, only stating it obtains data for its A.I. projects, including facial recognition, from a variety of sources. The company added it does not use its customers’ Prime photo service to train its algorithms.

Another company that uses a consumer photo app to train its facial recognition algorithm is Real Networks. The Seattle-based company, once known for its 1990s-era online video player, today specializes in software that can recognize children’s faces in schools. At the same time, it offers a smartphone app aimed at families called RealTimes, which one critic says has served as a pretext to obtain facial data.

“The app allows users to make video slideshows of their own photos. Imagine mom putting together a video slide show to send to grandma, and those images being used to train a dataset to use on young faces. It’s pretty horrible,” says Clare Garvie, a Georgetown University professor who published an influential report on facial recognition technology.

Real Networks confirmed the photo app helps improve its facial recognition tool, but added that it uses additional data sources for the purpose.

In all of these cases where companies used a photo app to harvest faces for training data, they didn’t ask for consumers’ explicit permission. Instead, the firms appear to have obtained legal consent through their terms of service agreements.

This is, however, more than what some other facial recognition companies have done. According to Patrick Grother, who runs the face competitions at NIST, it’s common for facial recognition companies to write programs that “scrape” pictures from websites like SmugMug or Tumblr. In these cases, there is not even a pretext of consent from those whose faces end up in training sets.

This “help yourself” approach was underscored by a recent NBC News report detailing how IBM siphoned more than one million faces from the photo sharing site Flickr as part of the company’s artificial intelligence research. (John Smith, who oversees AI technology for IBM’s research division, told NBC News that the company was committed to “protecting the privacy of individuals” and would work with those who sought removal from the dataset.)

All of this raises questions about what companies are doing to safeguard the facial data they collect, and whether governments should provide more oversight. The issues will only be become more pressing as facial recognition spreads to more areas of society, and powers the business of companies large and small.

From Shops to Schools

Facial recognition software is not new. Primitive versions of the technology have existed since the 1980s when American mathematicians began defining faces as a series of numerical values, and used probability models to find a match. Security personnel in Tampa, Fla. deployed it at the 2001 Super Bowl and casinos have used it for years. But in the last few years, something changed.

“Facial recognition is undergoing something of a revolution,” says Grother of NIST, adding the change is most pronounced with fleeting or poor quality images. “The underlying technology has changed. The old tech has been replaced by a new generation of algorithms, and they’re remarkably effective.”

This revolution in facial recognition comes thanks to two factors that are transforming the field of artificial intelligence more broadly. The first is the emerging science of deep learning, a pattern recognition system that resembles the human brain. The second is an unprecedented glut of data that can be stored and parsed at low cost with the aid of cloud computing.

The first companies to take full advantage of these new developments, unsurprisingly, were Google and Facebook. In 2014, the social network launched a program called DeepFace that could discern if two faces belonged to the same person with an accuracy rate of 97.25%—a rate equivalent to what humans scored on the same test. A year later, Google topped this with its FaceNet program, which obtained a 100% accuracy score, according to security firm Gemalto.

Today, those companies and other tech giants like Microsoft are leaders in facial recognition—in no small part because of their access to large databases of faces. A growing number of startups, though, are also posting high accuracy scores as they seek a niche in a growing market for face software.

In the U.S. alone, there are more than a dozen such startups, including Kairos and FaceFirst. Silicon Valley has been flocking to the sector, according to market researcher PitchBook, which reveals dozens of investment deals taking place in the last few years. The average total investment in the last three years is $78.7 million, according to PitchBook. This is not an eye-popping number by Silicon Valley standards, but reflects a significant bet by venture capitalists that at least a few facial recognition startups will mushroom into major companies.

Business models for facial recognition companies are still emerging. Today, most revolve around licensing software to organizations. According to data from Crunchbase, annual revenue for startups like Ever AI and FaceFirst is relatively modest, ranging from $2 million to $8 million. Amazon and the other tech giants, meanwhile, have not disclosed how much of their revenue comes from licensing facial recognition.

For years, the most avid paying customers for facial recognition has been law enforcement agencies. More recently, though, a growing number of organizations, including Wal-Mart, are using the software to identify and learn more about the people who enter their physical premises.

This is certainly the case for customers of California-based FaceFirst, which sells facial recognition software to hundreds of retailers, including dollar stores and pharmacies. Its CEO, Trepp, says the bulk of his clients use the technology to screen for criminals coming into their stores but, increasingly, retailers are testing it for other purposes such as recognizing VIP customers or identifying employees.

Amazon, meanwhile, appears to be casting a wide net in its efforts to find a business models for face recognition. In addition to selling to police departments, the retail giant is reportedly working with hotels to help them expedite check-in procedures.

“Companies from all over are coming to Amazon and saying, ‘This what we’d like you to do’. Then you figure out that’s your sweet spot. The interest is all over the place,” says the unnamed person who joined Amazon when the company acquired Orbeus, the facial recognition firm.

These efforts, in the case of Amazon, have not been without controversy. Last July, the ACLU tested the company’s software by running the faces of every member of Congress against a database of convicted felons. The test resulted in 28 false positives, the majority of which comprised Congressional members of color. In response, the ACLU called for a ban on the use of facial recognition technology by law enforcement. Meanwhile, Amazon’s own employees have pressed the company to justify the sale of the software to police departments and to U.S. Immigration and Customs Enforcement.

Some members of Congress, including Rep. Jerrold Nadler (D-N.Y.) and Sen. Ron Wyden (D-Ore.), have since asked the Government Accountability Office to investigate the use of facial recognition software. Corporate leaders are also uneasy about the technology’s applications. Among them: Microsoft president Brad Smith, who in December called for government regulation.

But even as concern mounts, use of facial recognition technology is expanding as companies find new and novel applications for which to sell it. These include Real Networks, the maker of the family photo app, which is offering its software for free to K-12 schools across the country. The company says hundreds of schools are now using it. In an interview with Wired magazine, CEO Rob Glaser says he began the initiative as a non-partisan solution to the debate over school safety and gun control. Currently, Real Networks’ website is touting its technology as a way for event hosts to “recognize every fan, customer, employee, or guest”—even if their face is covered.

Real Networks isn’t the only facial recognition company with products that focus on children. A Texas-based startup called Waldo is supplying the technology to hundreds of schools, as well as kids’ sports leagues and summer camps. In practice, this involves using Waldo’s software to scan images taken by video cameras or official photographers, then match children’s faces to a database of images provided by parents. Those parents who don’t wish to participate can opt out.

According to CEO Rodney Rice, schools take tens of thousands of photos every year and only a handful of them up being seen in a yearbook. Facial recognition, he says, is an efficient way to distribute the remaining ones to those who would like to have them.

“Instead of buying popcorn or wrapping paper, you can get a photo stream to your kids’ grandparents,” says Rice, explaining that Waldo has a 50-50 revenue sharing arrangement with public schools. The service is now doing business in more than 30 U.S. states.

The growth of Waldo and FaceFirst show how businesses are helping to normalize facial recognition, which not long ago was the stuff of science fiction. And as the technology spreads to more sectors of the American economy, more companies will collect copies of our faces—either to train their algorithms or to recognize customers and criminals—even as the potential for mistakes or misuse grow.

The Future of Your Face

In a 2017 episode of the techno-dystopian TV series Black Mirror, an anxious mother frets over images of a ne’er-do-well carrying on with her daughter. To identify him, she uploads an image of his face to a consumer facial identification service. The software promptly displays his name and place of work, and she goes to confronts him.

Such a scenario, once far-fetched, feels close at hand today. While fears over facial recognition have focused on its use by governments, its deployment by private companies or even individuals—Black Mirror-style—poses obvious privacy risks.

As more companies start to sell facial recognition, and as our faces end up in more databases, the software could catch on with voyeurs and stalkers. Merchants and landlords could also use it to identify those they deem to be undesirable, and quietly withhold housing or services.

“Anybody with a video camera and a place with a lot of foot traffic can start to compile a databases of images, and then use this analytic software to see if there’s a match with what you’ve compiled,” says Jay Stanley, a policy analyst of the ACLU.

There’s also the risk of hacking. Andrei Barysevich of Gemini Advisors, a cybersecurity firm, says he has seen profiles stolen from India’s national biometrics database for sale on “dark web” Internet sites. He has yet to see databases of American faces for sale, but added, “It’s just a matter of time.” If such a thing were to occur, a stolen collection of customer faces from a hotel or retailer could help criminals carry out fraud or identity theft.

As the technology spreads with little government oversight, the best hope to limit its misuse may lie with the software makers themselves. In interviews with Fortune, the CEOs of facial recognition startups all stated they were deeply attuned to privacy perils. A number, including the CEO of FaceFirst, cited the spread of face surveillance systems in China as a cautionary tale.

The CEOs also offered two ways the industry can limit misuse of their technology. The first is by working closely with the purchasers of their software to ensure clients don’t deploy it willy-nilly. Aley of Ever AI, for instance, says his company follows a higher standard than Amazon, which he claims furnishes its Rekognition tool to nearly all comers.

In response to a question of how it polices misuse, Amazon provided a previously published statement by Matt Wood, who overseas artificial intelligence services at Amazon Web Services, pointing to a company policy prohibiting activity that is illegal or harmful to others.

The other potential privacy safeguard cited by facial recognition executives is the use of technical measures to ensure the faces identified in their databases can’t be hacked.

Rice, the CEO of the Waldo, says faces are stored in the form of alphanumeric hashes. This means that, even in the event of a data breach, privacy would not be compromised because a hacker would not be able to use the hashes to reconstruct the faces and their identities. The point was echoed by others.

Rice is also wary that lawmakers could do more harm than good by making rules for the use of facial technology. “Throwing the baby out with the bathwater, and creating a bunch of crazy regulations, that would be a travesty,” he says.

Meanwhile, some companies that make facial recognition software are using new techniques that may reduce the need for large collections of faces to train their algorithms. This is the case with Kairos, a Miami-based facial recognition startup that names a major hotel chain among its clients. According to chief security officer Stephen Moore, Kairos is creating “synthetic” facial data to replicate a wide variety of facial expressions and lighting conditions. He says these “artificial faces” means the company can rely on smaller sets of real world faces to build its products.

All of these measures—oversight of facial recognition customers, sound data security, and synthetic training tools—could allay some of the privacy concerns related to companies’ use of our faces. At the same time, Trepp of FaceFirst believes anxiety over the technology will diminish as we become more familiar with it. He even argues that facial recognition scenes in the 2002 sci-fi movie Minority Report will start to feel normal.

“Millennials are much more willing to hand over their face. That [Minority Report] world is coming,” he says. “Done properly, I think people are going to enjoy it and it’s going to be a positive experience. It won’t feel creepy.”

Others, including the ACLU, are less sanguine. Still, despite the growing controversy around the technology, there is, for now, almost nothing in the way of laws to limit the use of your face. The only exception comes from a trio of states—Illinois, Texas, and Washington—that require a degree of consent before the use of someone’s face. These laws have not really been tested, with one exception: Illinois, where consumers can bring lawsuits to enforce the right.

Currently, the Illinois law is the subject of a high profile appeals court case involving Facebook, which claims that restrictions on obtaining faces does not extend to digital scans. In 2017, Facebook and Google ran an unsuccessful lobbying campaign to persuade Illinois lawmakers to dilute the law. In late January, the law’s supporters got a boost when the Illinois Supreme Court ruled that consumers do not have to show real-world harm if they want to sue over the unauthorized use of their biometrics.

Other states are considering biometrics laws of their own. At the federal level, lawmakers have so far devoted little attention to the matter. This may be changing, however, as Senators Brian Schatz (D-Ha.) and Roy Blount (R.MO) this month introduced a bill that would require companies to get permission before using facial recognition in public places and or share face data with third parties.

Garvie, the Georgetown researcher, is in favor of laws to oversee the technology. But she says it has been difficult for lawmakers to keep up.

“One challenge of facial recognition is it’s been incredibly quick on the uptake because of legacy databases. There are so many instances where our faces were captured,” she says. “Unlike fingerprints, where there have long been rules on how and when they’re collected, there are no rules for face technology.”

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Just like we witnessed during the last recession, major retailers are laying off tens of thousands of workers, and it looks like this will be the worst year for store closings in all of U.S. history.  Many are referring to this as “the retail apocalypse”, and without a doubt this is one of the toughest stretches for retailers that we have ever seen.  But many believe that what we have witnessed so far is just the beginning.  After all, if retailers are struggling this much now, how bad will things be once the next recession really gets rolling?

Of course the truth is that things have been rocky for the retail industry for quite a few years, but the numbers are telling us that this crisis is really starting to accelerate.

According to Challenger, Gray & Christmas, retail layoffs were up a whopping 92 percent in January and February compared to the same period a year ago.  The following comes from NBC News…

More than 41,000 people have lost their jobs in the retail industry so far this year — a 92 percent spike in layoffs since the same time last year, according to a new report.

And the layoffs continue to mount, with JCPenney announcing this week it would be closing 18 stores in addition to three previously announced closures, as part of a “standard annual review.”

Yes, competition from Internet commerce is hurting the traditional retail industry, but it certainly doesn’t explain a 92 percent increase.

And very few retailers have been able to avoid this downsizing trend.  At this point, even the largest retailer in the entire country has begun “quietly closing stores”…

Walmart is closing at least 11 US stores across eight states.

The stores include one Walmart Supercenter in Lafayette, Louisiana, and Walmart Neighborhood Market stores in Arizona, California, Kansas, South Carolina, Tennessee, Virginia, and Washington.

For decades, Wal-Mart has been expanding extremely aggressively.

They have plenty of cash, and so the only way that it would make sense for them to close stores is if they anticipated that we are heading into a recession.

Here is a list of the addresses where Wal-Mart stores are closing…

6085 W. Chandler Blvd., Chandler, Arizona
3900 W. Ina Road, Tucson, Arizona
1600 Saratoga Ave., San Jose, California
712 N. Western Ave., Liberal, Kansas
1229 NE. Evangeline Trwy., Lafayette, Louisiana
3603 Broad River Road, Columbia, South Carolina
1757 W. Andrew Johnson Hwy., Morristown, Tennessee
2501 University Commons Way, Knoxville, Tennessee
7000 Iron Bridge Road, North Chesterfield, Virginia
2864 Virginia Beach Blvd., Virginia Beach, Virginia
7809 NE. Vancouver Plaza Dr., Vancouver, Washington

Of course Wal-Mart is in far better shape than almost everyone else in the industry.

One of Wal-Mart’s key competitors, Shopko, has just announced that they will be shutting down all of their stores…

Shopko will liquidate its assets and close all of its remaining locations by mid-June.

The company was unable to find a buyer for the retail business and will begin winding down its operations beginning this week, the company said in statement released Monday. The decision to liquidate will bring an end to the brick-and-mortar business that began in 1962 with one location in Green Bay, Wisconsin.

And personally I was very saddened to learn that Lifeway Christian Bookstores has also decided to close all their brick and mortar stores…

Lifeway Christian Bookstores announced last week it would be closing the doors of all 170 brick and mortar stores, in a pivot to focusing on digital and e-commerce.

“The decision to close our local stores is a difficult one,” said Lifeway Chief Executive Officer Brad Waggoner. “While we had hoped to keep some stores open, current market projections show this is no longer a viable option.”

Whenever I do an article like this, I always have some readers that try to convince me that this is only happening because of the growth of Internet retailing.

And yes, Internet retailing has been growing, but it still accounts for less than 10 percent of all U.S. retail sales.  In addition, it is important to point out that Internet retailers had a very disappointing holiday season just like brick and mortar retailers did.

Ultimately, the truth is that the U.S. economy has been steadily slowing down in recent months.

During the months of December, January and February, the amount of stuff being moved around the country by truck, rail and air was lower than during all of those same months a year earlier.  The following comes from Wolf Richter…

Now it’s the third month in a row, and the red flag is getting more visible and a little harder to ignore about the goods-based economy: Freight shipment volume in the US across all modes of transportation – truck, rail, air, and barge – in February fell 2.1% from February a year ago, according to the Cass Freight Index, released today. The three months in a row of year-over-year declines are the first such declines since the transportation recession of 2015 and 2016.

I have a feeling that when we get the final numbers for March that they will show that this streak has now extended to four months.

Right now, unsold goods are starting to pile up in U.S. warehouses at a rate that we haven’t seen since the last recession.  Many retailers that are barely clinging to life will simply not survive if economic conditions continue to deteriorate.

Unfortunately, it appears that things are only going to get rougher for the U.S. economy in the months ahead.

So more retail workers are going to get laid off, more stores are going to close, and there are going to be a lot more stories about our ongoing “retail apocalypse” in the mainstream media.

About the author: Michael Snyder is a nationally-syndicated writer, media personality and political activist. He is the author of four books including Get Prepared Now, The Beginning Of The End and Living A Life That Really Matters. His articles are originally published on The Economic Collapse Blog, End Of The American Dream and The Most Important News. From there, his articles are republished on dozens of other prominent websites. If you would like to republish his articles, please feel free to do so. The more people that see this information the better, and we need to wake more people up while there is still time.

It’s a scenario that’s all too familiar for many parents: Your child is acting differently and feeling unwell, so you take his temperature. If he has a fever, you’ll weigh the pros and cons of waiting it out versus bringing him to the doctor’s. Then, just a short time later, your child is running around like usual as if nothing ever happened, and his temperature is totally normal again.

Experienced moms and dads know that this happens sometimes, so when an Arizona mother saw that her son’s fever went away and she decided not to bring him to the hospital, she had no idea it would end with Child Services stepping in and a SWAT team bringing the boy to the hospital.

It all went down in dramatic fashion in Chandler, Arizona, after a mother, Sarah Beck, brought her 2-year-old to a naturopathic doctor because he had a fever of 105 degrees. The doctor instructed her to bring him to the emergency room to rule out meningitis.

However, by the time the family left the doctor’s office, the boy was playing with his siblings and laughing. His temperature had gone back down to almost normal, so they decided to head home rather than to the ER. Beck called the doctor to say everything was fine. The doctor believed she should go to the hospital anyway, but she ultimately decided not to because she was worried her son’s unvaccinated status would be problematic – a decision that led to an unbelievable turn of events.

When the doctor learned that the parents had never showed up at Cardon Children’s Hospital, she called the Arizona Department of Child Safety to report the incident. Police arrived at the family’s home to check on the boy, and his parents wouldn’t let them inside. Speaking to an officer over the phone, the boy’s father, Brooks Bryce, said he would not bring the boy to the hospital and incur an expensive ER bill because he was fine.

The officers came back with a court order for temporary custody of the boy. Surveillance video shows that officers, wearing tactical gear, broke down the front door and stormed into the home. Their other children, aged 4 and 6, were also taken to the hospital.

Although the parents weren’t arrested, investigators are determining whether criminal charges will be filed. The child, by the way, had an upper respiratory infection and did not end up having meningitis.

Although the boy’s symptoms had improved, 105 degrees is a rather high fever and it’s reasonable to suggest that a follow-up would have been a good idea. However, the reaction of authorities was completely over the top, and all three children are likely to be left with some serious trauma from the experience. They’re now staying with their grandparents, who have been granted temporary custody. Their parents are hoping to get them back after a hearing set for next month.

Abuse of power

Although the doctor’s concerns are understandable, the authorities’ reaction is not. It’s absolutely ridiculous to treat a 2-year-old like a dangerous fugitive. There is no reason to smash down a family’s doors and barge in with weapons drawn to take a child who had a fever that has since gone away. The kids in this family were essentially kidnapped, and many suspect the fact that they’re unvaccinated played a role in how these parents were treated.

Arizona House Rep. Kelly Townsend told KNXV that she considered the children’s removal an “abuse of power” on the part of law enforcement and DCS, adding: “I think we need to re-think where we draw the line when it comes to disagreements between doctors and parents and what level we’re going to go to to keep the child safe.”

Sources for this article include:

Reason.com

ABC13.com

Israel’s occupation army has introduced a new crowd dispersal weapon to be used against Palestinian protesters in the Gaza Strip, Israeli media reported on Sunday. According to Wallah, the weapon uses radio waves to make loud bomb-like sounds.

Apparently non-lethal, the weapon was developed in Israel. Soldiers using it on the first anniversary of the Great March of Return protests said that “it was very effective”.

During the latest Israeli crackdown on Palestinian protesters who are demanding their legitimate right of return to their land usurped by Israel since 1948, the occupation troops killed four people, three of whom were just 17 years old, and wounded 316 others. Those wounded included 86 children, 29 women, three paramedics and seven journalists.

Since the start of the Great March of Return protests on 30 March last year, Israel has killed around 280 protesters and wounded more than 30,000 others.

A bill introduced in the Massachusetts House would take a first step toward limiting the impact of federal programs that militarize local police.

Rep. Denise Provost (D-Middlesex)  and Rep. James Hawkins (D-Attleboro) introduced House Bill 2119 (H2119) earlier this year. The legislation would prohibit law enforcement agencies from acquiring a long list of military equipment from federal surplus programs without local government approval.

The law would apply both to the well-known 1033 program, along with any other military surplus program operated by the federal government. The legislation covers an extensive list of military items including unmanned aerial vehicles, armored vehicles, bayonets, bombs, directed-energy weapons, grenade launchers, international mobile subscriber identity catchers, launch vehicles, mines, missiles, radioactive or nuclear weapons, rockets, silencers, torpedoes, toxicological agents, including chemical agents, biological agents, and associated equipment, or ultrasonic devices used for crowd dispersal.

Police departments often obtain military equipment from the federal government in complete secrecy. Requiring local government approval would bring the process into the open and provide an opportunity for concerned residents to stop the acquisition through their local representatives.

FEDERAL SURPLUS AND GRANT MONEY

Through the federal 1033 Program, local police departments procure military grade weapons. Police can also get military equipment through the Department of Homeland Security via the (DHS) “Homeland Security Grant Program.” In 2013, DHS gave more than $900 million in counterterrorism funds to state and local police. According to a 2012 Senate report, this money has been used to purchase tactical vehicles, drones, and even tanks with little obvious benefit to public safety. And, according to ProPublica, “In 1994, the Justice Department and the Pentagon-funded a five-year program to adapt military security and surveillance technology for local police departments that they would otherwise not be able to afford.”

In August 2017, President Trump issued an executive order that gave a push to local police militarization. Trump’s action rescinded an Obama-era policy meant to provide greater transparency and oversight around the Department of Defense 1033 program and other federal resources that provide military weapons to local police.

Pasage of H2119  would create a framework for accountability and transparency for police militarization programs in Massachusetts. It would also create a foundation for the public to stop their local police from obtaining this type of gear.

COMMAND AND CONTROL

Arming ‘peace officers’ like they’re ready to occupy an enemy city is totally contrary to the society envisioned by the founders. They’ve turned ‘protect and serve’ into ‘command and control.’

In the 1980s, the federal government began arming, funding and training local police forces, turning peace officers into soldiers to fight in its unconstitutional “War on Drugs.” The militarization went into hyper-drive after 9/11 when a second front opened up – the “War on Terror.”

By making it more difficult for local police to get this military-grade gear and surveillance technology, and ensuring they can’t do it in secret, it makes them less likely to cooperate with the feds and removes incentives for partnerships. Passage of H2119 would take a first step toward limiting police militarization in Massachusetts.

WHAT’S NEXT

H2119 was referred to the Joint Public Safety and Homeland Security Committee where it will need to pass by a majority vote before moving forward in the legislative process.

Artificial Intelligence is widely seen as a strategic technology and has thus engendered national development plans from countries across the globe to promote its progress. For quite some time, the United States has been conspicuously on the sideline. The American AI research community has, by and large, bemoaned the lack of Federal leadership in this space with many calling for a Kennedy-esque “moonshot” plan to come out of Washington to cement American leadership in the AI space. This past month, the Trump White House released the Executive Order on Maintaining American Leadership in Artificial Intelligence to provide direction and support for technological advancement in this space.

The response to the EO from the research community has been muted at best. Far from receiving the requested moonshot with the requisite funding and supporting agencies, this EO mostly lays out principles for AI development to be followed by Federal departments. It directs agencies towards prioritization of AI by using existing funds and to seek out collaboration efforts. Further, it directs the National Science and Technology Council Select Committee on Artificial Intelligence (among other agencies) to draw up plans and coordinate efforts among Federal agencies to develop grants, project proposals, and reports to be written on areas of development and collaboration within the next 180 days. Beyond that, there is very little concrete action laid out in the EO, which is why it has been criticized so heavily by those in the research community.

In other words, the research community apparently feels the EO doesn’t go far enough.

In truth, however, even this limited EO goes much too far.

Yes, AI has been hailed as a transformative family of technologies that will bring about a radically improved standard of living for people around the globe. On the other hand, there are numerous potential uses of this technology to inflict incredible harm on individuals, and the risk of abuse becomes greater the more AI research is funded or directed by government agencies. Malinvestment (or “malscience,” in this case) is encouraged by political funding of R&D, and the Pentagon has explicitly stated they seek to use this technology in military applications. Rather than not going far enough, Trump’s Executive Order has already gone too far by explicitly bringing this technology into the political realm because the state is simply too dangerous to grant a leading role in AI development.

Chinese AI Spectre

The obvious backdrop to Trump’s new executive order is the plethora of articles and reports coming out of China regarding the Chinese Communist Party’s (CCP) high-profile investments and multi-year plans to AI dominance. Most notable is China’s New Generation Artificial Intelligence Development Plan (AIDP) released in July 2017 which makes the technology a key priority for government funding.

Citing challenges from an aging population, environmental concerns, and economic growth, the report sees AI as a technology which, “brings new opportunities for social construction.” The report then lays out milestones over the next few years: by 2020 they will be on par with the global leaders in AI development with; 2025 plans to see Chinese companies achieving their own innovative breakthroughs in algorithmic development; 2030 they plan to be the undisputed leader in AI technology. Each milestone contains targets for the core industry and the supporting businesses that comprise the wider, industrial AI ecosystem.

On top of the proposed industrial targets, the Chinese plan lays out a series of research areas such as swarm intelligence, virtual and augmented reality, security, natural language processing, AI hardware and infrastructure among others to focus their efforts on.

The Chinese plan is widely seen as the gold standard in national AI plans; it is comprehensive, lays out specific targets, and discusses ways to accelerate adoption including by forcing enterprises and cities to adopt the technology. Moreover, resources have already been allocated to achieving these goals such as the $15.7 fund earmarked by the city of Tianjin to develop AI and the $500 million lead Chinese AI startups have over US-based startups in receiving VC funding. China’s AI plan and the top-down approach the CCP is pursuing is a major reason why Kai-Fu Lu’s recent book, AI Superpowers: China, Silicon Valley and the New World Order , sees China steam rolling to success on the global stage.

AI Winters and Government Funding

The calls for more government funding by AI researchers, however, appear to lack historical perspective. Federal funding of AI projects is hardly anything new. The Advanced Research Projects Agency (now known as DARPA) provided $2.2 million grants (equivalent to $18.2 million in 2019) to MIT, Carnegie Mellon University, and Stanford University in 1963.¹ These institutions became the pioneers of AI research and had great, early success with new models such as an early precursor to today’s artificial neural networks called the perceptron . Additional optimism came from success in translating Russian to English via machine translation. However, the early optimism of AI dried up quickly when, in 1966, it was reported that machine translation was incapable of sufficiently capturing the nuances of Russian.2  Second, Marvin Minksy’s book Perceptrons proved that these simple, early models were incapable of learning very much at all. This killed research into neural networks for the next decade. The vast majority of funding was government related, and once it was pulled, plunged the field into the first AI winter.

A resurgence came later in the 1980’s as companies in the US, Britain, and Japan began to invest in expert systems – rule-based programs that are a precursor to today’s Robotic Process Automation (RPA) movement. This led to a rise in AI labs throughout many companies which were able to automate many simple decisions and reap the rewards associated with greater efficiency. Although the private AI market grew to roughly $1 billion by the end of the decade, these systems were shown to be brittle and unable to extend to more challenging domains.

In 1982, the Japanese Ministry of International Trade and Industry funded and established the Fifth Generation Computer Systems based on a 10-year plan to develop new computer systems and set Japan as the international leader. This spawned a series of international competitors and plans to stay ahead of the Japanese most notably the European Strategic Program on Research and Information Technology, the Strategic Computing Initiative and Microelectronics and Computer Technology Corporation in the US. The Japanese project failed to deliver on its lofty goals of developing a new computing system as it was overtaken by Sun Systems and Intel despite over $400 billion in funding ($730 billion in 2019).

The Fifth Generation’s competing projects ended in similar results. For example, the Strategic Computing Initiative spent $1 billion from 1983-1993 ($2.2 billion in 2019) to achieve machine intelligence.3  Although there were some successful spin-off projects (e.g. Carnegie Mellon’s Navlab), the Initiative itself was a failure and was a major contributor to the second AI winter when it collapsed.

If only inefficiency was the only concern of large-scale, government funded AI research! Unfortunately, the White House, Pentagon, Congress, and others have indicated that they have much more destructive ends in mind.

Weaponization of AI

Trump’s Executive Order refers to AI in the context of “national security” nearly a dozen times. The day after the Executive Order was released, the Pentagon released its AI strategy document calling for rapid AI adoption throughout all aspects of the military. The Pentagon had previously contracted with Google to develop Project Maven, a computer vision system to assist object recognition for drone warfare. Google agreed not to renew the contract after dozens of employees resigned in protest and thousands more petitioned the company to refrain from developing weaponized AI tools. This led to Google restricting its defense work , but military projects are still permitted.

Despite the push back that Google has received, other big tech companies such as Microsoft, Amazon, and IBM continue to engage in work supporting the weaponization of AI with the Pentagon. These companies too have had to deal with internal discontent and have resorted to moving employees to different projects when they object. Despite the internal challenges, Amazon continues to work with the government on controversial facial recognition programs and Microsoft has continued with a cloud-computing project known as JEDI .

For now, the Pentagon has been unable to attract the talent required to develop AI technologies internally and thus has had to resort to partnering with the likes of the big tech firms. In China, the funding and control of these companies is linked directly to the CCP, which has given rise to the Sesame Credit system. At the risk of sounding cliche, Sesame Credit is an Orwellian system of social control predicated on the complicity of big tech companies and AI to track the citizenry’s purchases and behaviors, incentivizing them to behave in party-approved ways and punishing them when they veer away. Punishments include restrictions on travel, loans, housing, business, and banking. The official goal of the program is to encompass all citizens and , “allow the trustworthy to roam everywhere under heaven, while making it hard for the discredited to take a single step.”

Perhaps most insidiously, it encourages social ostracization by reducing your score based on the score of those in your network. Thus you are encouraged to drop friends and family from your life if they aren’t behaving in a party-approved manner.

While the US is not China, we are living in a world where the NSA has obliterated 4th Amendment rights and where US presidents assert the legal authority to kill US citizens without charges or a trial. A national AI strategy that further links the US government with the tech companies and researchers puts us one step closer to China, but not as a leader in AI, rather as a country that leverages this technology to suppress people. One can only hope that backlash associated with Project Maven grows with respect to other Pentagon projects leaving the Department of Defense without the capabilities to weaponize AI as they seek.

• 1.Crevier, Daniel, AI: The Tumultuous Search for Artificial Intelligence, (New York, NY: BasicBooks, 1993) pg. 64-65
• 2.“The famous retranslation of ‘the spirit is willing but the flesh is weak’ as ‘the vodka is good but the meat is rotten’ illustrates the difficulties encountered.” Russell, Stuart J., Norvig, Peter, Artificial Intelligence: A Modern Approach , (Upper Saddle River, New Jersey: Prentice Hall 2009) pg. 21
• 3.Roland, Alex, Shirman, Philip, and Aspray, William, Strategic Computing: DARPA and the Quest for Machine Intelligence, 1983-1993(Cambridge, MA: MIT Press, 2002) pg. 2

Christian Hubbs is a PhD candidate at Carnegie Mellon University working in machine learning and optimization. He blogs at datahubbs.com and co-hosts a podcast at artificiallyintelligent.tech.

Last week we reported that the yield curve on US Treasurys had inverted after the yield on the 10-year fell below the yield on 3-year bonds for the first time since 2007 – the cusp of the Great Recession. This has historically been an early-warning sign signaling a recession.

Now we have some more bad news for bond markets – this time on a global scale. The amount of government debt with negative yields has vaulted back above the $10 trillion mark and now makes up a full one-fifth of the global bond market.

According to Bloomberg, the amount of debt trading at nominal yields below zero has hit $10.7 trillion. That’s nearly double from a low of $5.7 trillion in early 2018.

The Federal Reserve’s sudden policy reversal and the “Powell Pause” has pushed yields lower worldwide. According to the Financial Times, “The Federal Reserve’s unexpectedly downbeat outlook exacerbated concerns over the health of the global economy and sent investors scurrying for the apparent safety of sovereign bonds.”

Bond yields have sagged lower for much of 2019, as fixed-income investors have remained skeptical that growth will pick up again. With economic data still weak and inflation at bay, central banks have been forced to abandon moves to tighten monetary policy.”

It’s basically a function of supply and demand. As more people seek the “safety” of government bonds fearing economic turmoil, bond prices rise. Inversely, yields fall.

Bank of America Merrill Lynch senior strategist told the FT he found it puzzling that the Fed went even more dovish during the March FOMC meeting than it had been in January.

Investors are starting to ask what the Fed might know about the economy that the market does not?”

The Fed is not alone in its dovishness. In March, the European Central Bank relaunched a crisis-era bank lending program. Meanwhile, the yield on the German 10-year bond dropped below zero as the country’s economy appears to be on the cusp of a recession.

Late last year, the ECB announced the end of its QE program. The central bank’s QE purchases totaled somewhere in the neighborhood of  2.6 trillion euros. The bank also pushed interest rates below zero. So, what did the EU get for all this stimulus? Not a whole lot. We highlighted the “successes” of ECB QE. And now that the European Central Bank is winding down the stimulus, it already looks like Germany – and a lot of other EU countries – is slipping toward an economic downturn.

Here’s the $64,000 question: why are people pouring money into negative yielding bonds? At some point they are going to figure out losing money over time isn’t a great investing strategy. Perhaps at that point, they will turn to precious metals.

Pundits often knock gold because it is a “non-yielding” asset. And yet, gold actually outperformed the S&P 500 in 2018. Holding a “yielding” asset that is yielding a loss isn’t helping your portfolio.

The growing pile of negative yielding debt is yet another sign we are hurtling toward a crisis. At some point, people will figure it out.

As Peter Schiff has pointed out, given the enormity of the budget deficits and the ever-upward spiraling debt, the Fed had no choice but to call off the tightening. You can’t raise interest rates in an economy built on piles of debt. But the Fed can’t tell the markets that. They will have to figure it out on their own. So far, they seem pretty clueless. But eventually, they will and that’s when the bottom is really going to fall out of the dollar.

When the Fed has to go back to zero, which it will be doing relatively soon, when the Fed has to go back to quantitative easing, nobody is going to believe that it is temporary again. Nobody is going to buy the Fed’s BS about how interest rates are going to stay low only temporarily and then we’re going to normalize them, and we’re going to shrink our balance sheet. We’re not monetizing the debt. After the recession is over we’re going to shrink our balance sheet back down to where it was before the recession. No one’s going to believe that. They couldn’t shrink a $4 trillion balance sheet. They won’t be able to shrink an $8 trillion balance sheet. If they couldn’t raise rates when the national debt was $22 trillion, they sure as hell can’t raise them when the national debt is $30 trillion.”

Burns should be addressed immediately to prevent infections and to offer relief to the injured individual. But when SHTF and you don’t have access to emergency medical services, you can treat mild or moderate burns using the home remedies detailed below. (h/t to DoomAndBloom.net)

Burn wound treatment basics

When treating burns after a survival scenario, you must first cool the wound with running water for about 10 to 15 minutes for best results. If you don’t have access to clean, running water, submerge the burned area in cool water.

Never use ice on burned skin. The injured skin is traumatized and using ice on the patient’s skin will constrict blood vessels and decrease blood circulation to an area that desperately needs it.

Burns, especially severe ones, should be treated by medical experts. But when you only have access to limited supplies or if the burn is minor, these natural remedies will do in a pinch.

Aloe vera

As a succulent plant, aloe vera has some thick and fleshy parts. These viscous parts allow aloe vera to retain water in its natural habitat. Aloe vera likes warm climates, but it can be grown successfully indoors in pots.

According to studies, aloe vera helps maintain moist conditions in damaged skin. This plant is anti-inflammatory, and it encourages faster healing. Additionally, aloe vera promotes circulation and inhibits the growth of bacteria.

Soothing aloe vera is an important remedy because it’s very simple to use. Just cut a leaf, scoop out the slimy flesh from the leaf, then apply it to a minor burn. Reapply aloe vera several times daily, with or without a bandage covering the burn.

Oatmeal

Use oatmeal to relieve itching that may occur when a burn starts to heal. Prepare a lukewarm bath, then add a cup of uncooked oatmeal to the water.

Help the patient get into the tub, which may be slippery because of the oatmeal. Let the patient soak in the oatmeal bath for about 10 to 20 minutes so that a thin coating of oatmeal remains on their skin.

Raw, unprocessed honey

Honey, a common remedy for various kind of wounds, is also used to treat burns. However, it is best to use raw, unprocessed honey since it retains its antibacterial activity and hydrating properties. (Related: Survival medicine: Are you familiar with the medicinal uses of honey?)

Follow the steps below to use honey as a burn remedy:

1. Cool down the burned area with running water, then apply a generous amount of honey over the burned skin.
2. Spread some honey on a waterproof dressing so it doesn’t stick to the patient’s skin. Cover the entire area.
3. The dressing over the burned skin may start to fill up with fluid oozing out from the wound. This will require regular, frequent dressing changes to keep the wound clean.
4. If you used cling wrap on the burned area, let it stay in place for the first 48 hours, then check and add more honey to the damaged skin. After 48 hours, cover the burned area with new cling wrap or non-stick dressings for another 48 hours. Repeat this step for at least eight to 10 days.
5. Do not remove or wash off the honey from the burned area for the first 20 days, or earlier if the wound has already healed.
6. Reapply the honey regularly and fill up any deeper areas of severe burns as needed. When applying honey, make sure it extends over the edges of the burn to prevent exposure to air until healing is completed. Remember, even air contains bacteria that may infect the wound.

Witch hazel

You can use witch hazel to make a cooling compress for burns. Witch hazel extract contains tannins, and these chemicals can help ease inflammation and soothe first degree burns. Apply witch hazel directly to the skin to reduce swelling, repair damaged skin, and prevent infection.

Soak a compress in full-strength witch hazel, then apply to the burned area. Reapply the witch hazel compress as frequently as desired.

When disaster strikes, use these natural remedies to address mild burns.

Sources include:

DoomAndBloom.net

Healthline.com

Last week, the Florida House passed a bill that would expand a law passed last year to help facilitate healthcare freedom outside of government regulatory schemes.

Rep. Wyman Duggan (R-Jacksonville) filed House Bill 7 (H7) on Feb. 14. Last year, Florida enacted a law specifying that direct primary care agreements (sometimes called medical retainer agreements) do not constitute insurance, thereby freeing doctors and patients from the onerous requirements and regulations under the state insurance code. The law also stipulates that a primary care provider, or an agent of a primary care provider, is not required to obtain a certificate of authority or license under the Florida Insurance Code to market, sell, or offer to sell a direct primary care agreement. H7 would expand the law to include all licensed healthcare providers, not just primary care physicians.

On March 27, the House passed H7 by a 90-24 vote.

Final passage of the bill would open the door for specialists and other healthcare providers to open practices based on the direct care model.

According to Michigan Capitol Confidential, by removing a third party payer from the equation, medical retainer agreements help both physicians and patients minimize costs. Jack Spencer writes:

“Under medical retainer agreements, patients make monthly payments to a physician who in return agrees to provide a menu of routine services at no extra charge. Because no insurance company stands between patient and doctor, the hassles and expense of bureaucratic red tape are eliminated, which have resulted in dramatic cost reductions. Routine primary care services (and the bureaucracy required to reimburse them) are estimated to consume 40 cents out of every dollar spent on insurance policies, so lower premiums for a given amount of coverage are another potential benefit.”

This represents the kind of cost control Obamacare promised but failed to deliver. In 2015, Tom Woods interviewed a Kansas doctor who utilizes the direct primary care model. Dr. Josh Umbehr’s practice demonstrates the cost savings possible when doctors are unfettered from the bureaucratic health insurance system.

Under Obamacare, regulations define such programs as a primary care service and not a health insurance plan, and current IRS policy treats these monthly fee arrangements just like another health plan.

A SECOND STEP STEP

Introduction of H7 illustrates an important strategic point. Passing bills that take a step forward sets the stage, even if they aren’t perfect. Once the door is open, the way is cleared for additional steps. You can’t take the second step before you take the first.

Even while controlling both houses of Congress, the Republicans never did repeal Obamacare. And if they had passed one of their bills, the changes to the ACA proposed by the GOP would have arguably made things worse. Even with the penalty for not buying health insurance repealed by the Republican tax plan, all other Obamacare rules and regulations remain in place. Regardless, state actions can help completely bring down the Affordable Care Act, or any national healthcare plan the Congress comes up with in the future.

Oftentimes, supporters of Obamacare criticize opponents for not having any alternative. Direct health care agreements offer one.

These direct patient/doctor agreements allow a system uncontrolled by government regulations to develop. It makes doctors responsive to patients, not insurance company bureaucrats or government rule-makers. Allowing patients to contract directly with doctors via medical retainer agreements opens the market. Under such agreements, market forces will set price for services based on demand instead of relying on central planners with a political agenda. The end result will be better care delivered at a lower cost.

By incentivizing creative healthcare solutions, the market will naturally provide better options, such as the Surgery Center of Oklahoma, This facility operates completely outside of the insurance system, providing a low-cost alternative for many surgical procedures.

A more open healthcare marketplace within a state will help spur de facto nullification the federal program by providing an affordable alternative. As patients flock to these arrangements and others spurred by ingenuity and market forces, the old system will begin to crumble.

Final passage of H7 would take another step toward healthcare freedom in Florida and create a stepping stone to further action to nullify the onerous Affordable Care act. With this new law in place, the people of Florida could take further steps to fully extricate themselves from Obamacare for good.

WHAT’S NEXT

H7 will now move to the Senate for further consideration. At the time of this report, it had not been referred to a Senate committee.