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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.
- 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.
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.
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Inventions with revolutionary potential made by a mysterious aerospace engineer for the U.S. Navy come to light.
- U.S. Navy holds patents for enigmatic inventions by aerospace engineer Dr. Salvatore Pais.
- Pais came up with technology that can "engineer" reality, devising an ultrafast craft, a fusion reactor, and more.
- While mostly theoretical at this point, the inventions could transform energy, space, and military sectors.
The U.S. Navy controls patents for some futuristic and outlandish technologies, some of which, dubbed "the UFO patents," came to life recently. Of particular note are inventions by the somewhat mysterious Dr. Salvatore Cezar Pais, whose tech claims to be able to "engineer reality." His slate of highly-ambitious, borderline sci-fi designs meant for use by the U.S. government range from gravitational wave generators and compact fusion reactors to next-gen hybrid aerospace-underwater crafts with revolutionary propulsion systems, and beyond.
Of course, the existence of patents does not mean these technologies have actually been created, but there is evidence that some demonstrations of operability have been successfully carried out. As investigated and reported by The War Zone, a possible reason why some of the patents may have been taken on by the Navy is that the Chinese military may also be developing similar advanced gadgets.
Among Dr. Pais's patents are designs, approved in 2018, for an aerospace-underwater craft of incredible speed and maneuverability. This cone-shaped vehicle can potentially fly just as well anywhere it may be, whether air, water or space, without leaving any heat signatures. It can achieve this by creating a quantum vacuum around itself with a very dense polarized energy field. This vacuum would allow it to repel any molecule the craft comes in contact with, no matter the medium. Manipulating "quantum field fluctuations in the local vacuum energy state," would help reduce the craft's inertia. The polarized vacuum would dramatically decrease any elemental resistance and lead to "extreme speeds," claims the paper.
Not only that, if the vacuum-creating technology can be engineered, we'd also be able to "engineer the fabric of our reality at the most fundamental level," states the patent. This would lead to major advancements in aerospace propulsion and generating power. Not to mention other reality-changing outcomes that come to mind.
Among Pais's other patents are inventions that stem from similar thinking, outlining pieces of technology necessary to make his creations come to fruition. His paper presented in 2019, titled "Room Temperature Superconducting System for Use on a Hybrid Aerospace Undersea Craft," proposes a system that can achieve superconductivity at room temperatures. This would become "a highly disruptive technology, capable of a total paradigm change in Science and Technology," conveys Pais.
High frequency gravitational wave generator.
Credit: Dr. Salvatore Pais
Another invention devised by Pais is an electromagnetic field generator that could generate "an impenetrable defensive shield to sea and land as well as space-based military and civilian assets." This shield could protect from threats like anti-ship ballistic missiles, cruise missiles that evade radar, coronal mass ejections, military satellites, and even asteroids.
Dr. Pais's ideas center around the phenomenon he dubbed "The Pais Effect". He referred to it in his writings as the "controlled motion of electrically charged matter (from solid to plasma) via accelerated spin and/or accelerated vibration under rapid (yet smooth) acceleration-deceleration-acceleration transients." In less jargon-heavy terms, Pais claims to have figured out how to spin electromagnetic fields in order to contain a fusion reaction – an accomplishment that would lead to a tremendous change in power consumption and an abundance of energy.
According to his bio in a recently published paper on a new Plasma Compression Fusion Device, which could transform energy production, Dr. Pais is a mechanical and aerospace engineer working at the Naval Air Warfare Center Aircraft Division (NAWCAD), which is headquartered in Patuxent River, Maryland. Holding a Ph.D. from Case Western Reserve University in Cleveland, Ohio, Pais was a NASA Research Fellow and worked with Northrop Grumman Aerospace Systems. His current Department of Defense work involves his "advanced knowledge of theory, analysis, and modern experimental and computational methods in aerodynamics, along with an understanding of air-vehicle and missile design, especially in the domain of hypersonic power plant and vehicle design." He also has expert knowledge of electrooptics, emerging quantum technologies (laser power generation in particular), high-energy electromagnetic field generation, and the "breakthrough field of room temperature superconductivity, as related to advanced field propulsion."
Suffice it to say, with such a list of research credentials that would make Nikola Tesla proud, Dr. Pais seems well-positioned to carry out groundbreaking work.
A craft using an inertial mass reduction device.
Credit: Salvatore Pais
The patents won't necessarily lead to these technologies ever seeing the light of day. The research has its share of detractors and nonbelievers among other scientists, who think the amount of energy required for the fields described by Pais and his ideas on electromagnetic propulsions are well beyond the scope of current tech and are nearly impossible. Yet investigators at The War Zone found comments from Navy officials that indicate the inventions are being looked at seriously enough, and some tests are taking place.
If you'd like to read through Pais's patents yourself, check them out here.
Laser Augmented Turbojet Propulsion System
Credit: Dr. Salvatore Pais
New data have set the particle physics community abuzz.
- The first question ever asked in Western philosophy, "What's the world made of?" continues to inspire high energy physicists.
- New experimental results probing the magnetic properties of the muon, a heavier cousin of the electron, seem to indicate that new particles of nature may exist, potentially shedding light on the mystery of dark matter.
- The results are a celebration of the human spirit and our insatiable curiosity to understand the world and our place in it.
If brute force doesn't work, then look into the peculiarities of nothingness. This may sound like a Zen koan, but it's actually the strategy that particle physicists are using to find physics beyond the Standard Model, the current registry of all known particles and their interactions. Instead of the usual colliding experiments that smash particles against one another, exciting new results indicate that new vistas into exotic kinds of matter may be glimpsed by carefully measuring the properties of the quantum vacuum. There's a lot to unpack here, so let's go piecemeal.
It is fitting that the first question asked in Western philosophy concerned the material composition of the world. Writing around 350 BCE, Aristotle credited Thales of Miletus (circa 600 BCE) with the honor of being the first Western philosopher when he asked the question, "What is the world made of?" What modern high energy physicists do, albeit with very different methodology and equipment, is to follow along the same philosophical tradition of trying to answer this question, assuming that there are indivisible bricks of matter called elementary particles.
Deficits in the Standard Model
Jumping thousands of years of spectacular discoveries, we now have a very neat understanding of the material composition of the world at the subatomic level: a total of 12 particles and the Higgs boson. The 12 particles of matter are divided into two groups, six leptons and six quarks. The six quarks comprise all particles that interact via the strong nuclear force, like protons and neutrons. The leptons include the familiar electron and its two heavier cousins, the muon and the tau. The muon is the star of the new experiments.
For all its glory, the Standard Model described above is incomplete. The goal of fundamental physics is to answer the most questions with the least number of assumptions. As it stands, the values of the masses of all particles are parameters that we measure in the laboratory, related to how strongly they interact with the Higgs. We don't know why some interact much stronger than others (and, as a consequence, have larger masses), why there is a prevalence of matter over antimatter, or why the universe seems to be dominated by dark matter — a kind of matter we know nothing about, apart from the fact that it's not part of the recipe included in the Standard Model. We know dark matter has mass since its gravitational effects are felt in familiar matter, the matter that makes up galaxies and stars. But we don't know what it is.
Whatever happens, new science will be learned.
Physicists had hoped that the powerful Large Hadron Collider in Switzerland would shed light on the nature of dark matter, but nothing has come up there or in many direct searches, where detectors were mounted to collect dark matter that presumably would rain down from the skies and hit particles of ordinary matter.
Could muons fill in the gaps?
Enter the muons. The hope that these particles can help solve the shortcomings of the Standard Model has two parts to it. The first is that every particle, like a muon, that has an electric charge can be pictured simplistically as a spinning sphere. Spinning spheres and disks of charge create a magnetic field perpendicular to the direction of the spin. Picture the muon as a tiny spinning top. If it's rotating counterclockwise, its magnetic field would point vertically up. (Grab a glass of water with your right hand and turn it counterclockwise. Your thumb will be pointing up, the direction of the magnetic field.) The spinning muons will be placed into a doughnut-shaped tunnel and forced to go around and around. The tunnel will have its own magnetic field that will interact with the tiny magnetic field of the muons. As the muons circle the doughnut, they will wobble about, just like spinning-tops wobble on the ground due to their interaction with Earth's gravity. The amount of wobbling depends on the magnetic properties of the muon which, in turn, depend on what's going on with the muon in space.
Credit: Fabrice Coffrini / Getty Images
This is where the second idea comes in, the quantum vacuum. In physics, there is no empty space. The so-called vacuum is actually a bubbling soup of particles that appear and disappear in fractions of a second. Everything fluctuates, as encapsulated in Heisenberg's Uncertainty Principle. Energy fluctuates too, what we call zero-point energy. Since energy and mass are interconvertible (E=mc2, remember?), these tiny fluctuations of energy can be momentarily converted into particles that pop out and back into the busy nothingness of the quantum vacuum. Every particle of matter is cloaked with these particles emerging from vacuum fluctuations. Thus, a muon is not only a muon, but a muon dressed with these extra fleeting bits of stuff. That being the case, these extra particles affect a muon's magnetic field, and thus, its wobbling properties.
About 20 years ago, physicists at the Brookhaven National Laboratory detected anomalies in the muon's magnetic properties, larger than what theory predicted. This would mean that the quantum vacuum produces particles not accounted for by the Standard Model: new physics! Fast forward to 2017, and the experiment, at four times higher sensitivity, was repeated at the Fermi National Laboratory, where yours truly was a postdoctoral fellow a while back. The first results of the Muon g-2 experiment were unveiled on 7-April-2021 and not only confirmed the existence of a magnetic moment anomaly but greatly amplified it.
To most people, the official results, published recently, don't seem so exciting: a "tension between theory and experiment of 4.2 standard deviations." The gold standard for a new discovery in particle physics is a 5-sigma variation, or one part in 3.5 million. (That is, running the experiment 3.5 million times and only observing the anomaly once.) However, that's enough for plenty of excitement in the particle physics community, given the remarkable precision of the experimental measurements.
A time for excitement?
Now, results must be reanalyzed very carefully to make sure that (1) there are no hidden experimental errors; and (2) the theoretical calculations are not off. There will be a frenzy of calculations and papers in the coming months, all trying to make sense of the results, both on the experimental and theoretical fronts. And this is exactly how it should be. Science is a community-based effort, and the work of many compete with and complete each other.
Whatever happens, new science will be learned, even if less exciting than new particles. Or maybe, new particles have been there all along, blipping in and out of existence from the quantum vacuum, waiting to be pulled out of this busy nothingness by our tenacious efforts to find out what the world is made of.
- Benjamin Franklin wrote essays on a whole range of subjects, but one of his finest was on how to be a nice, likable person.
- Franklin lists a whole series of common errors people make while in the company of others, like over-talking or storytelling.
- His simple recipe for being good company is to be genuinely interested in others and to accept them for who they are.
Think of the nicest person you know. The person who would fit into any group configuration, who no one can dislike, or who makes a room warmer and happier just by being there.
What makes them this way? Why are they so amiable, likeable, or good-natured? What is it, you think, that makes a person good company?
There are really only two things that make someone likable.
This is the kind of advice that comes from one of history's most famously good-natured thinkers: Benjamin Franklin. His essay "On Conversation" is full of practical, surprisingly modern tips about how to be a nice person.
Franklin begins by arguing that there are really only two things that make someone likable. First, they have to be genuinely interested in what others say. Second, they have to be willing "to overlook or excuse Foibles." In other words, being good company means listening to people and ignoring their faults. Being witty, well-read, intelligent, or incredibly handsome can all make a good impression, but they're nothing without these two simple rules.
The sort of person nobody likes
From here, Franklin goes on to give a list of the common errors people tend to make while in company. These are the things people do that makes us dislike them. We might even find, with a sinking feeling in our stomach, that we do some of these ourselves.
1) Talking too much and becoming a "chaos of noise and nonsense." These people invariably talk about themselves, but even if "they speak beautifully," it's still ultimately more a soliloquy than a real conversation. Franklin mentions how funny it can be to see these kinds of people come together. They "neither hear nor care what the other says; but both talk on at any rate, and never fail to part highly disgusted with each other."
2) Asking too many questions. Interrogators are those people who have an "impertinent Inquisitiveness… of ten thousand questions," and it can feel like you're caught between a psychoanalyst and a lawyer. In itself, this might not be a bad thing, but Franklin notes it's usually just from a sense of nosiness and gossip. The questions are only designed to "discover secrets…and expose the mistakes of others."
3) Storytelling. You know those people who always have a scripted story they tell at every single gathering? Utterly painful. They'll either be entirely oblivious to how little others care for their story, or they'll be aware and carry on regardless. Franklin notes, "Old Folks are most subject to this Error," which we might think is perhaps harsh, or comically honest, depending on our age.
4) Debating. Some people are always itching for a fight or debate. The "Wrangling and Disputing" types inevitably make everyone else feel like they need to watch what they say. If you give even the lightest or most modest opinion on something, "you throw them into Rage and Passion." For them, the conversation is a boxing fight, and words are punches to be thrown.
5) Misjudging. Ribbing or mocking someone should be a careful business. We must never mock "Misfortunes, Defects, or Deformities of any kind", and should always be 100% sure we won't upset anyone. If there's any doubt about how a "joke" will be taken, don't say it. Offense is easily taken and hard to forget.
On practical philosophy
Franklin's essay is a trove of great advice, and this article only touches on the major themes. It really is worth your time to read it in its entirety. As you do, it's hard not to smile along or to think, "Yes! I've been in that situation." Though the world has changed dramatically in the 300 years since Franklin's essay, much is exactly the same. Basic etiquette doesn't change.
If there's only one thing to take away from Franklin's essay, it comes at the end, where he revises his simple recipe for being nice:
"Be ever ready to hear what others say… and do not censure others, nor expose their Failings, but kindly excuse or hide them"
So, all it takes to be good company is to listen and accept someone for who they are.
Philosophy doesn't always have to be about huge questions of truth, beauty, morality, art, or meaning. Sometimes it can teach us simply how to not be a jerk.
A recent study analyzed the skulls of early Homo species to learn more about the evolution of primate brains.