♪ I love it when you call me big data ♪
Welcome to The Dr. Data Show, I'm Eric Siegel.
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.
This works by using data to predict when
and where these hazards are most likely
and deploy inspections
and preventative measures accordingly.
That is, flag the riskiest buildings,
bridges, vehicles, and restaurants for a check-up.
This kind of predictive prevention is more
and more becoming a safety standard.
I'm gonna tell you about five ways
that these data driven safety measures keep you safer.
But first, a bit more on how it works.
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 A-S-A-P.
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
a 100% 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 like Katrina
and Maria are more deadly.
A study of the most damaging hurricanes in the US
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 showed 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.
Okay, 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.
Number one, fortify bridges, buildings,
and infrastructure in general.
Lives are saved by prioritizing inspections
according to the risk levels calculated
for each of these kinds of structures.
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 fire.
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.
Also, 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.
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 movement, and optimizes navigation.
Train companies are also on the right track.
They predict broken tracks,
which is the leading cause of severe 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.
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.
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 to predict 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 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 a decision,
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.
(quirky electronic music)
And that wraps up another high-stakes
episode of The Dr. Data Show.
I'm Eric Siegel, thanks for watching.
Hit like and share this video
if you think your friends would also be interested
in five ways your safety depends on machine learning.
And for access to the entire web series
go to TheDoctorDataShow.com.
♪ Who's your data ♪
♪ Provide me the data to improve ♪
♪ And I'll apply the computation ♪
♪ I love it when you call me big data ♪
♪ Predictive analytics can help you with decisions ♪
♪ You can call, mail, credit, or hire with precision ♪
♪ On law, love, and life, you can prognosticate ♪
♪ Whom to investigate, incarcerate ♪
♪ Set up on a date, or medicate ♪
♪ Charlie Brown never gets his kicks ♪
♪ That's why every old dog needs a brand new trick ♪
♪ If you get sick of chasing sticks ♪
♪ Or clicks with just a quick fix ♪
♪ You need to learn to predict ♪
♪ I can predict your every move ♪
♪ Just gimme all your information ♪
♪ Who's your data ♪
♪ Provide me the data to improve ♪
♪ And I'll apply the computation ♪
♪ I love it when you call me Big Data ♪
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