Five ways your safety depends on machine learning

Machine learning, which actively protects you from all sorts of dangers, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.

Five Ways Your Safety Depends on Machine Learning – The Dr. Data Show
  • Your safety depends on machine learning.
  • It's not a cure-all -- unfortunately, there's no way to achieve 100% guaranteed security in this life.
  • Here are some example insights that help predict peril, which were told to us by data.

Your safety depends on machine learning. This technology protects you from harm every day by guiding the maintenance of bridges, buildings, and vehicles, and by guiding healthcare providers and law enforcement officers.

This puts you in good hands. Hospitals, companies, and the government use machine learning to combat risk, actively protecting you from all sorts of dangers and hazards, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime. And I thought lions and tigers and bears were bad!

Predictive prevention with machine learning

The technology for this job is machine learning, when computers learn from the experience encoded in data. Given data on, say, the history of many bridges and which ones deteriorated to become risky, the computer learns to predict which bridges should be flagged for inspection ASAP. When deployed for safety purposes and for other business and government purposes, machine learning is also known as predictive analytics.

It's not a cure-all — unfortunately, there's no way to achieve 100 percent guaranteed security in this life — but machine learning contributes a singular improvement. It stands as a unique, novel approach to lowering risk, tipping the odds in favor of more safety. Since predictive prevention is different from other risk management approaches, it always potentially helps, regardless of what other approaches are also being adopted. Machine learning, as it's used in general, improves the efficiency of all kinds of processes — and when applied to procedures that protect, this translates to lower risk.

So let's give due respect and appreciation for data, and in particular its pricelessly predictive power, which delivers this and other tremendous benefits. Here are some example insights that help predict peril, which were told to us by data. Hurricanes with female names such as Katrina and Maria are more deadly. A study of the most damaging hurricanes in the U.S. in recent decades showed that those with more feminine names killed almost three times as many people as those with more masculine names. Psychological research suggests this may result from implicit sexism — people perceive "female" hurricanes as less risky, underestimating the danger and taking fewer precautions.

Speaking of hurricanes, Walmart's data shows that Strawberry Pop-Tart sales blow up by a factor of about seven just before a hurricane. This is thought to be people stocking up on nonperishable comfort foods.

And people with low credit ratings are more likely to crash their car, according to insurance companies. Experts theorize this is 'cause your financial responsibility could reflect your responsibility behind the wheel, although that's not conclusive. In any case, it's another example of data predicting mishaps.

O.K. now, as promised, here are five ways that machine learning keeps you safer every day. By the way, you can actually find the details about most of these examples in the notes for my book, Predictive Analytics. The notes are available for free at PredictiveNotes.com.

Fortifying buildings, bridges, and other infrastructure

Number one, fortify buildings, bridges, and infrastructure in general. Lives are saved by prioritizing inspections according to the risk level calculated for each structure. The New York City Fire Department uses predictive analytics to flag buildings with the highest risk of fire, Con Edison identifies manholes with five times the average risk of dangerous incidents like explosions or fires, and researchers in civil engineering predict which bridges are deteriorating, in part by using machine learning to automatically detect cracks in the concrete from automatically scanned images of bridges.

The City of Chicago has identified homes that have more than double the risk of lead poisoning incidents than average. This serves to proactively flag, rather than the more common reactive steps taken after poisoning has been detected.

Prevent transporation mishaps

Number two, prevent traffic accidents and other transportation mishaps. Car companies and the military use machine learning to make driving safer — to detect when a vehicle's driver is not alert due to distraction, fatigue, or intoxication, and to predict when vehicle parts will fail in order to proactively plan maintenance.

And there's no stopping autonomous vehicles, a development largely driven by the promise of improved safety records, in comparison to the recent, century-long experiment during which we allowed humans to drive them. Self-driving cars run on machine learning, which identifies objects in the vicinity, predicts their movements, and optimizes navigation.

Train companies are also on the right track. They predict broken tracks — which is the leading cause of severe train accidents — and individual wheel failures.

And the maritime industry stays afloat by predicting which large ships will experience a dangerous incident. Each risk level is calculated by the vessel's age, type, carrying capacity, origin, ownership, management, and other factors.

Avert workplace injuries

Number three, stave off workplace injuries. For each team of workers at their oil refineries, globally, the company Shell predicts the number of safety incidents that will transpire and assesses which factors make the biggest difference, such as a how measurably engaged employees are — which the company believes has a big impact on decreasing accidents.

Another factor, which applies to working environments in general: Accident Fund Insurance found that certain medical conditions such as obesity and diabetes are predictive of which occupational injuries will be highest in cost, in order to target workers accordingly for preventative measures. And researchers at the National Institute for Occupational Safety and Health apply machine learning to determine which preventative practices — be they ergonomic or concerning trips and falls — are most important for each industry.

Strengthen health care

Number four, strengthen healthcare. Predictive medicine is an exciting and rapidly developing application area for machine learning, which is used to diagnose conditions and also predict clinical outcomes. For diagnosis, a machine learning model inputs all kinds of clinical features, test results, and even entire MRIs or other medical images to assess the probability of various diseases — one model per disease — such as diabetic retinopathy, which is the fastest growing cause of blindness, as well as various kinds of cancer.

Often, it does so as well as — or even better than — doctors. As for predicting outcomes, machine learning foretells surgical infections, sepsis, HIV progression, premature births, hospital readmissions, and even death. In fact, there's an entire episode of The Dr. Data Show on predicting death, which you can find at TheDoctorDataShow.com. By flagging high-risk cases, additional precautions can be targeted accordingly.

And, even before you need to go to the hospital at all, city governments. such as those of Boston and Seattle, preemptively safeguard you from food poisoning by predicting which restaurants will have health code violations in order to prioritize inspections. In some cases, they're able to improve these predictions by inputting Yelp! reviews — things people write about a restaurant can sometimes reveal that it's not up to snuff in the kitchen.

Toughen crime-fighting

And finally, number five, toughen crime-fighting. If the rule of law is the cornerstone of society, enforcing it as effectively as possible is foundational. Predictive policing deploys machine learning to guide law enforcement decisions such as whether to investigate or detain, how long to sentence, and whether to parole.

In making such decisions, judges and officers take into consideration the probability — output by a predictive model — that a suspect or defendant will be convicted for a crime in the future. These models base their calculations on factors such as the defendant's prior convictions, income level, employment status, family background, neighborhood, education level, and the behavior of family and friends.

Machine learning also drives rehabilitation. The Florida Department of Juvenile Justice makes rehabilitation assignments based in part on the predicted risk of future offenses.

Note: This article is based on a transcript of The Dr. Data Show episode, "Five Ways Your Safety Depends on Machine Learning" About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics. Click here to view more episodes and to sign up for future episodes of The Dr. Data Show.

Iron Age discoveries uncovered outside London, including a ‘murder’ victim

A man's skeleton, found facedown with his hands bound, was unearthed near an ancient ceremonial circle during a high speed rail excavation project.

Photo Credit: HS2
Culture & Religion
  • A skeleton representing a man who was tossed face down into a ditch nearly 2,500 years ago with his hands bound in front of his hips was dug up during an excavation outside of London.
  • The discovery was made during a high speed rail project that has been a bonanza for archaeology, as the area is home to more than 60 ancient sites along the planned route.
  • An ornate grave of a high status individual from the Roman period and an ancient ceremonial circle were also discovered during the excavations.
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Are we really addicted to technology?

Fear that new technologies are addictive isn't a modern phenomenon.

Credit: Rodion Kutsaev via Unsplash
Technology & Innovation

This article was originally published on our sister site, Freethink, which has partnered with the Build for Tomorrow podcast to go inside new episodes each month. Subscribe here to learn more about the crazy, curious things from history that shaped us, and how we can shape the future.

In many ways, technology has made our lives better. Through smartphones, apps, and social media platforms we can now work more efficiently and connect in ways that would have been unimaginable just decades ago.

But as we've grown to rely on technology for a lot of our professional and personal needs, most of us are asking tough questions about the role technology plays in our own lives. Are we becoming too dependent on technology to the point that it's actually harming us?

In the latest episode of Build for Tomorrow, host and Entrepreneur Editor-in-Chief Jason Feifer takes on the thorny question: is technology addictive?

Popularizing medical language

What makes something addictive rather than just engaging? It's a meaningful distinction because if technology is addictive, the next question could be: are the creators of popular digital technologies, like smartphones and social media apps, intentionally creating things that are addictive? If so, should they be held responsible?

To answer those questions, we've first got to agree on a definition of "addiction." As it turns out, that's not quite as easy as it sounds.

If we don't have a good definition of what we're talking about, then we can't properly help people.

LIAM SATCHELL UNIVERSITY OF WINCHESTER

"Over the past few decades, a lot of effort has gone into destigmatizing conversations about mental health, which of course is a very good thing," Feifer explains. It also means that medical language has entered into our vernacular —we're now more comfortable using clinical words outside of a specific diagnosis.

"We've all got that one friend who says, 'Oh, I'm a little bit OCD' or that friend who says, 'Oh, this is my big PTSD moment,'" Liam Satchell, a lecturer in psychology at the University of Winchester and guest on the podcast, says. He's concerned about how the word "addiction" gets tossed around by people with no background in mental health. An increased concern surrounding "tech addiction" isn't actually being driven by concern among psychiatric professionals, he says.

"These sorts of concerns about things like internet use or social media use haven't come from the psychiatric community as much," Satchell says. "They've come from people who are interested in technology first."

The casual use of medical language can lead to confusion about what is actually a mental health concern. We need a reliable standard for recognizing, discussing, and ultimately treating psychological conditions.

"If we don't have a good definition of what we're talking about, then we can't properly help people," Satchell says. That's why, according to Satchell, the psychiatric definition of addiction being based around experiencing distress or significant family, social, or occupational disruption needs to be included in any definition of addiction we may use.

Too much reading causes... heat rashes?

But as Feifer points out in his podcast, both popularizing medical language and the fear that new technologies are addictive aren't totally modern phenomena.

Take, for instance, the concept of "reading mania."

In the 18th Century, an author named J. G. Heinzmann claimed that people who read too many novels could experience something called "reading mania." This condition, Heinzmann explained, could cause many symptoms, including: "weakening of the eyes, heat rashes, gout, arthritis, hemorrhoids, asthma, apoplexy, pulmonary disease, indigestion, blocking of the bowels, nervous disorder, migraines, epilepsy, hypochondria, and melancholy."

"That is all very specific! But really, even the term 'reading mania' is medical," Feifer says.

"Manic episodes are not a joke, folks. But this didn't stop people a century later from applying the same term to wristwatches."

Indeed, an 1889 piece in the Newcastle Weekly Courant declared: "The watch mania, as it is called, is certainly excessive; indeed it becomes rabid."

Similar concerns have echoed throughout history about the radio, telephone, TV, and video games.

"It may sound comical in our modern context, but back then, when those new technologies were the latest distraction, they were probably really engaging. People spent too much time doing them," Feifer says. "And what can we say about that now, having seen it play out over and over and over again? We can say it's common. It's a common behavior. Doesn't mean it's the healthiest one. It's just not a medical problem."

Few today would argue that novels are in-and-of-themselves addictive — regardless of how voraciously you may have consumed your last favorite novel. So, what happened? Were these things ever addictive — and if not, what was happening in these moments of concern?

People are complicated, our relationship with new technology is complicated, and addiction is complicated — and our efforts to simplify very complex things, and make generalizations across broad portions of the population, can lead to real harm.

JASON FEIFER HOST OF BUILD FOR TOMORROW

There's a risk of pathologizing normal behavior, says Joel Billieux, professor of clinical psychology and psychological assessment at the University of Lausanne in Switzerland, and guest on the podcast. He's on a mission to understand how we can suss out what is truly addictive behavior versus what is normal behavior that we're calling addictive.

For Billieux and other professionals, this isn't just a rhetorical game. He uses the example of gaming addiction, which has come under increased scrutiny over the past half-decade. The language used around the subject of gaming addiction will determine how behaviors of potential patients are analyzed — and ultimately what treatment is recommended.

"For a lot of people you can realize that the gaming is actually a coping (mechanism for) social anxiety or trauma or depression," says Billieux.

"Those cases, of course, you will not necessarily target gaming per se. You will target what caused depression. And then as a result, If you succeed, gaming will diminish."

In some instances, a person might legitimately be addicted to gaming or technology, and require the corresponding treatment — but that treatment might be the wrong answer for another person.

"None of this is to discount that for some people, technology is a factor in a mental health problem," says Feifer.

"I am also not discounting that individual people can use technology such as smartphones or social media to a degree where it has a genuine negative impact on their lives. But the point here to understand is that people are complicated, our relationship with new technology is complicated, and addiction is complicated — and our efforts to simplify very complex things, and make generalizations across broad portions of the population, can lead to real harm."

Behavioral addiction is a notoriously complex thing for professionals to diagnose — even more so since the latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the book professionals use to classify mental disorders, introduced a new idea about addiction in 2013.

"The DSM-5 grouped substance addiction with gambling addiction — this is the first time that substance addiction was directly categorized with any kind of behavioral addiction," Feifer says.

"And then, the DSM-5 went a tiny bit further — and proposed that other potentially addictive behaviors require further study."

This might not sound like that big of a deal to laypeople, but its effect was massive in medicine.

"Researchers started launching studies — not to see if a behavior like social media use can be addictive, but rather, to start with the assumption that social media use is addictive, and then to see how many people have the addiction," says Feifer.

Learned helplessness

The assumption that a lot of us are addicted to technology may itself be harming us by undermining our autonomy and belief that we have agency to create change in our own lives. That's what Nir Eyal, author of the books Hooked and Indistractable, calls 'learned helplessness.'

"The price of living in a world with so many good things in it is that sometimes we have to learn these new skills, these new behaviors to moderate our use," Eyal says. "One surefire way to not do anything is to believe you are powerless. That's what learned helplessness is all about."

So if it's not an addiction that most of us are experiencing when we check our phones 90 times a day or are wondering about what our followers are saying on Twitter — then what is it?

"A choice, a willful choice, and perhaps some people would not agree or would criticize your choices. But I think we cannot consider that as something that is pathological in the clinical sense," says Billieux.

Of course, for some people technology can be addictive.

"If something is genuinely interfering with your social or occupational life, and you have no ability to control it, then please seek help," says Feifer.

But for the vast majority of people, thinking about our use of technology as a choice — albeit not always a healthy one — can be the first step to overcoming unwanted habits.

For more, be sure to check out the Build for Tomorrow episode here.

Why the U.S. and Belgium are culture buddies

The Inglehart-Welzel World Cultural map replaces geographic accuracy with closeness in terms of values.

According to the latest version of the Inglehart-Welzel World Cultural Map, Belgium and the United States are now each other's closest neighbors in terms of cultural values.

Credit: World Values Survey, public domain.
Strange Maps
  • This map replaces geography with another type of closeness: cultural values.
  • Although the groups it depicts have familiar names, their shapes are not.
  • The map makes for strange bedfellows: Brazil next to South Africa and Belgium neighboring the U.S.
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