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Daina Bouquin: It is 1893.

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A damp dark night on a lake in
northern Michigan.

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Two men sit in a rowboat
drifting along the reedy edge.

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In the stern, John Hammer holds
a wooden paddle and keeps their

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progress quiet.

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At the bow, a wooden frame
balances two heavy box cameras

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side by side.

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Each holding a single sheet of
glass coated in light-sensitive

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emulsion.

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One exposure.

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Above them, a kerosene lamp
with a reflector throws a single

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sharp spear of light across the
water.

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Behind that beam, hidden in the
shadows, George Shiras holds a

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small pan of magnesium powder.

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They watch the shoreline until
a pair of eyes catches the

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light.

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Twin emerald points in the
brush.

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A deer stands frozen,
transfixed by the lamp.

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The boat creeps closer.

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Fifty feet, forty feet, thirty.

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Shiras squeezes the trigger.

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There is a crack, a white hot
hiss.

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For a fraction of a second, the
deer, the hills, the lake all

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stand out in dark daylight.

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Then the smoke rolls over the
water and the blackness rushes

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back in.

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When they develop the plate,
the deer is still there, stunned

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at the water's edge, alive and
fixed in glass.

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But the only creatures they can
capture this way are the ones

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that come to the shoreline.

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Shiras wants to see the rest of
the forest.

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And he is at his core a hunter.

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He knows the woods by habit, by
patience.

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He knows where the deer walk.

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So he carries the heavy camera
into the woods, stretches a

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single black thread across an
animal path, and connects it to

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the shutter and the flash.

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Then he leaves.

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Sometime after dark, a body
crosses the trail.

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The thread tightens, and the
darkness turns white.

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In the morning, the deer is
there on the glass.

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Shiras has built a camera that
waits.

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His thread has given it a sense
of touch.

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But the thread sags in the
rain.

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It
tangles. And it cannot catch a bat or a bird.

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Or anything that steps over it
or under it.

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To remove the thread, the
camera needed to feel more than

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pressure.

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It needed to feel heat.

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I'm Daina Bouquin, and this is
Found in the Machine.

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Every warm thing gives off
invisible light.

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Infrared radiation.

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A deer.

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The raccoon beside the trash
cans.

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You, right now, you are
glowing, broadcasting your

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presence wherever you are.

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But if you warm up a piece of
tourmaline, it doesn't just

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glow.

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It begins to pull at the world
around it.

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Dust, ash, and tiny pieces of
straw cling to its surface.

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Inside the stone, a change in
temperature shifts its

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electrical balance and creates a
faint charge at the surface.

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Not heat in electricity out
exactly.

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A change in temperature in.

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A fleeting electrical signal
out.

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That signal can be amplified.

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It can be used to close a
circuit.

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It can wake a camera.

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This is pyroelectricity.

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But a single detector cannot
tell what caused the temperature

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to change.

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To a stone, warmer is simply
warmer.

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The sun rising over a clearing
warms the air.

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The detector warms.

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Wire that detector to a camera
and it will confidently

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photograph the empty field.

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To use pyroelectricity, to make
the signal useful, engineers

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needed the machine to also
notice movement.

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So they paired two pyroelectric
elements together and balanced

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them against each other like two
sides of a scale.

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When a slow even shift in
temperature, like sunlight

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warming a room, reaches both
elements at once, the scale

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stays balanced.

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That broad change produces
silence.

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But if a body moves through the
room, it warms one element and

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then the other.

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For a fraction of a second, the
scale tips.

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That disagreement becomes a
trigger, telling a camera to

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wake up.

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This is what happens with the
floodlight above your driveway.

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With the lights that turn on
when you enter a room.

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It's what happens inside a
camera trap.

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The sensors in those little
machines do not see you.

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They contain pyroelectric
elements that just notice when

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the infrared patterns in front
of them briefly stop agreeing

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with each other.

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It is called a passive infrared
sensor because it sends nothing

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into the world.

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No beam, no searching pulse.

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It simply waits for the
invisible light already being

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emitted around it to move in a
certain way.

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But even with two pyroelectric
elements, the sensor still needs

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help to focus all that infrared
light.

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And it turns out the cheapest
solution came from the sea.

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From the work of an early
19th-century French physicist

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named Augustin Fresnel.

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Fresnel designed a new kind of
glass for lighthouses, made of

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step-cut concentric rings that
could gather the scattered,

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wasted light that flew towards
the land instead of the water.

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His lens bent the lighthouse's
brightness into a single intense

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beam aimed at the horizon so
the ships could see the light

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from further away.

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Now if you look at the milky
plastic window on a motion

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detecting light, or a burglar
alarm, or a trail camera, you

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will see a pattern of tiny
molded ridges.

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That is a Fresnel lens, molded
in plastic.

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Cheap and easy to make.

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But instead of throwing visible
light out into the dark to warn

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ships, it reaches out and
gathers up the scattered

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invisible light and bends it
inward toward the sensor.

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Now take that sensor with its
paired elements and its

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inside-out lighthouse lens and
strap it to a tree on the coast

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of British Columbia.

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In Heiltsuk territory, an
invasion was quietly taking hold

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under the water.

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European green crabs were
moving in.

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They threatened the native
clams, the salmon, the eelgrass.

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So the Nation's Environmental
Guardians fought back.

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They sunk traps into the deep
water, baiting them with herring

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and sea lion meat.

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But the traps started coming
back broken.

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The Guardians would find the
cages dragged out of the water,

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netting shredded, the little
orange bait cups mangled or

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completely missing.

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Sometimes a trap vanished
entirely.

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At first they guessed it was
sea lions or maybe otters, but

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the damage didn't quite make
sense.

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They needed to know what was
happening beneath the surface.

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So they pointed a camera at the
surf to watch.

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Then, one day in May 2024, the
sensor registered a change in

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temperature on the shore.

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The camera woke up.

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It recorded a female coastal
wolf.

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In this territory, the wolves
are different.

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They cross open water between
islands with their backs

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submerged, only eyes and ears
and snouts above the surface.

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They bite the heads off
spawning salmon and eat only the

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brains.

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Crack muscles with their jaws.

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But this wolf wasn't hunting in
the tide pools.

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The camera caught her wading
into the cold salt water.

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She swam out until she reached
a floating buoy attached to a

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crab trap.

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She grabbed it in her jaws and
swam back, dropping it on the

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rocks.

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Then she took the rope in her
mouth and she pulled.

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She stood on the shore, pulling
the line over and over,

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understanding that this rope
connected to something that she

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could not see, heavy and hidden
in the depth.

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She dragged the awkward
contraption all the way onto the

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beach and tore through the
mesh.

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She extracted that little
orange bait cup, licked the sea

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lion meat clean, and trotted
away.

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In less than three minutes, the
camera solved the mystery.

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It recorded a wolf executing a
deliberate multi-step heist.

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It captured the animal
understanding a cause and effect

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relationship that scientists
didn't know wild wolves were

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capable of grasping.

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But the camera that caught the
wolves hauling in crab traps

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also caught many, many, many
things that were not wolves.

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A branch warmed by the sun
swinging across the field of

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view, the camera wakes.

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A patch of light lands suddenly
on wet leaves and the camera

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wakes.

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For every photograph of a wolf
on a beach, there may be dozens

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with nothing in them at all.

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Nothing, nothing, nothing.

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Wolf.

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Nothing.

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Again.

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But a false alarm only wastes
time.

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A missed animal can rewrite the
record.

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It can say "Nothing was here."
For 30 years, engineers tried to

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make the signal itself smarter.

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They filtered waveforms and
analyzed pulse shapes.

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But the size and signal depends
on how warm the body is, how

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fast it moves, how far away it
stands.

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And none of that tells you what
it is.

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So people sit at screens.

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Students and tens of thousands
of volunteers working on citizen

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science projects from home.

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They move through images one at
a time.

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Nothing.

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00:12:00,639 --> 00:12:01,519
Nothing. Nothing.

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Maybe a bird.

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Nothing. Nothing.

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00:12:06,240 --> 00:12:07,360
Wolf.

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Each label turns the photograph
into an example.

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00:12:13,360 --> 00:12:17,600
Collect enough examples and
they can train a new machine.

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00:12:17,600 --> 00:12:21,440
The first machine answered, did
something change?

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00:12:21,440 --> 00:12:26,399
But the second one tries to
answer, what was it?

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00:12:26,399 --> 00:12:30,159
This is supervised machine
learning.

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00:12:30,159 --> 00:12:35,039
But the machine learning
classifier does not know what a

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00:12:35,039 --> 00:12:35,919
wolf is.

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00:12:35,919 --> 00:12:39,360
It learns statistical
relationships from images that

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00:12:39,360 --> 00:12:41,360
people have labeled wolf.

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00:12:41,360 --> 00:12:46,159
Textures, contours,
arrangements of pixels.

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00:12:46,159 --> 00:12:49,919
When a new photograph arrives,
it estimates which of its

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00:12:49,919 --> 00:12:51,600
categories fits best.

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00:12:51,600 --> 00:12:52,639
Nothing. Nothing.

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00:12:53,519 --> 00:13:00,159
Something to label "wolf." The
sensor detects, the camera

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00:13:00,159 --> 00:13:03,679
records, the classifier proposes
a name.

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00:13:03,679 --> 00:13:06,960
Then people decide what that
name means.

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00:13:06,960 --> 00:13:13,919
For most of a century, there
were almost no wolves left in

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00:13:13,919 --> 00:13:15,039
the American West.

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00:13:15,039 --> 00:13:18,879
We had shot and poisoned them
nearly to nothing.

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As the few survivors began to
wander back, the reports came

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00:13:23,440 --> 00:13:23,759
in.

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A coyote seen from a moving
car.

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A shape on a ridge.

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00:13:29,679 --> 00:13:32,240
A big dog at dusk.

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00:13:32,240 --> 00:13:36,799
Wildlife offices filled with
sightings of wolves that were

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00:13:36,799 --> 00:13:37,840
not wolves.

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00:13:37,840 --> 00:13:38,879
Nothing. Nothing.

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00:13:39,600 --> 00:13:40,480
Nothing.

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00:13:40,480 --> 00:13:46,240
But in 2023, Colorado began
reintroducing gray wolves to the

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state's western slope.

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00:13:47,759 --> 00:13:51,840
Animals were captured in Oregon
and British Columbia, fitted

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with GPS collars that logged
their position every four hours,

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and released into the
mountains.

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00:13:58,000 --> 00:14:01,039
Trail cameras watched den
sites.

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00:14:01,039 --> 00:14:04,399
Aerial surveys tracked pack
movements.

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00:14:04,399 --> 00:14:08,240
Biologists collected scat for
genetic analysis.

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It became one of the most
closely monitored wolf

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00:14:12,240 --> 00:14:13,840
populations in history.

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00:14:13,840 --> 00:14:18,960
In June 2025, a trail camera
outside a den in Root County

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00:14:18,960 --> 00:14:22,240
captured the first images of
pups born to the King Mountain

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00:14:22,240 --> 00:14:22,799
Pack.

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00:14:22,799 --> 00:14:26,720
No person was there to see
them, but the camera was.

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It recorded the tumbling,
clumsy little bodies before

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00:14:30,399 --> 00:14:32,799
anyone else even knew they had
arrived.

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00:14:32,799 --> 00:14:36,639
Then came January 2026.

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00:14:36,639 --> 00:14:43,600
A licensed hunter in Northwest
Colorado saw an animal.

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00:14:43,600 --> 00:14:47,360
And to him it looked like a
coyote.

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00:14:47,360 --> 00:14:52,000
Coyotes are legal to hunt
year-round in Colorado.

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00:14:52,000 --> 00:14:55,200
So he aimed.

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He fired.

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00:14:57,600 --> 00:15:05,120
She was a wolf.

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00:15:05,120 --> 00:15:09,600
She had been translocated from
British Columbia a year earlier

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00:15:09,600 --> 00:15:12,000
to support repopulation efforts.

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00:15:12,000 --> 00:15:15,759
When the hunter saw her GPS
collar, he reported the mistake

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immediately.

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That collar had been logging
her position every four hours.

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The trail cameras were
recording.

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The data was being collected.

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None of the technology failed.

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We have spent more than a
century building machines that

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wait in our place because our
own attention falters.

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We need them to sit in the
snow.

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To wait in the dark.

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To feel the heat we cannot
feel.

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We need them to do the mundane,
tireless watching.

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But when George Shiras drifted
across that lake in a rowboat in

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1893, he didn't just want data.

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He used the camera to show
other people what he had seen.

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To make them understand that
the deer at the water's edge was

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not just a shape, not just a
target, but something worth

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paying attention to.

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His images were meant to create
a sense of presence.

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To make people really look.

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Shiras gave the machine a
thread.

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Engineers gave it a way to feel
heat.

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Lenses allowed it to focus, and
people trained algorithms to

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name what it sees.

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The machine can document what
moves through the dark.

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It can show us what we missed.

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But the machine does not think.

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It does not believe.

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It does not doubt.

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It feels the heat, throws the
switch, and leaves the rest to

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us.

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I'm Daina Bouquin and this is
Found in the Machine.

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Before I go, I want to tell you
about something new.

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I've just launched a Patreon
for the show, and members will

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get access to a bonus mini
episode series called Found in

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the Margins.

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I'll be putting it out every
other week, alternating with the

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main feed.

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These are short stories about
the strange and wonderful stuff

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I stumble across while
researching the main show, and

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just I'm not quite sure how to
ever turn it into a full

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00:17:35,759 --> 00:17:36,559
episode.

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And I'll be honest with you, I
was laid off from my day job

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this week.

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So if you've ever thought about
supporting the show, now it

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would mean a lot.

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You can find the Patreon link
along with other ways to support

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00:17:52,880 --> 00:17:56,559
Found in the Machine at
foundinthemachine.com/support.

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And thank you as always for
listening.