Last February, a Tomahawk cruise missile struck an Iranian elementary school, killing more than 150 people, most of them children.
asserted
most → strike → them
President Trump initially suggested that it could have been somebody else's Tomahawk: "Whether it's Iran or somebody else, the fact that a Tomahawk – a Tomahawk is very generic, it's sold to other countries, but that's being investigated right now," he said.
uncertain
he → suggest → countries
When Iran put out video as evidence that it was a U.S. missile, Hany Farid, one of the world's foremost experts on deepfakes, was contacted by several media outlets to determine if it was real or fake.
asserted
it → put → outlets
"What was difficult about this case was you had very little to work on," he said.
asserted
he → have → little
"We had a very grainy, shaky video.
asserted
We → have → video
And we had to reason about the physical plausibility of that missile striking the ground.
asserted
we → have → ground
But he also had certain aspects that he could study scientifically, involving the speed of sound, the speed of light, and the size of the missile: "And those three things were enough for us to say that this is physically plausible," he said.
uncertain
he → have → missile
"And this is the important part: all evidence led us to the belief that the video was authentic, and there's been no refuting of that."
But that's the world we live in: Reality itself, so perfectly mimicked by artificial intelligence, that even reality is suspect.
asserted
reality → lead → intelligence
Farid said, "Here's the thing that makes me a little crazy, as a scientist in particular: I don't think you should be able to go into a court of law and say, 'Here is some evidence that is damning enough to put this person to death,' and then the judge says, 'Well, how do you know it's real?'
asserted
it → say → death
And I say, 'Well, because the computer told me so.'
asserted
computer → say → me
And if I can't explain to you, to a judge, and to a jury, what is happening here – because the lighting, because the shadows, because the geometry, whatever it is – that's what we need to be able to do."
asserted
we → explain → what
A case in point: Alex Pretti, who was killed by an ICE agent in Minnesota.
asserted
who → kill → Minnesota
If you remember the scene last January 24, it was chaotic.
asserted
it → remember → scene
Several ICE agents with guns surrounded Pretti.
asserted
agents → surround → Pretti
Farid showed us a blurry photo of Pretti that was taken from the original video.
asserted
that → show → video
"Somebody took this and 'enhanced' it," he said.
asserted
he → take → it
According to Farid, the crispness of this "enhanced" image makes the scene feel "more real, but what it also did is when it enhanced it, it seems to have put a gun in [Pretti's] hand.
uncertain
it → accord → hand
If you go back to this video, the camera's panning a little bit and what's happening is, there's a shadow behind his hand on the street.
asserted
happening → go → street
And what the AI did is that it hallucinated this."
asserted
it → hallucinate → this
Farid says artificial intelligence has learned, "When you see photos like this with three men with guns, everybody's got a gun."
asserted
everybody → say → gun
And now, when you do Google searches for events like Pretti's killing, it shows the "enhanced" image.
asserted
it → do → image
By Farid's estimate, half of the content that's widely viewed on the Internet is fake, basically a toss of the coin.
asserted
that → view → coin
Much of it has its own sneering label: "AI slop."
asserted
Much → have → label
Footage of airplanes skidding on the tarmac, toddlers dancing a two-step, Elvis.
asserted
toddlers → skid → step
"AI slop is a broad term that defines people generating content using AI just to grab eyeballs, video after video after video driving engagement," Farid said.
asserted
Farid → define → engagement
For the most part, Farid limits his examinations to the really serious stuff, the deepfakes.
asserted
Farid → limit → stuff
Even so, he's inundated.
asserted
he → inundate → ?
He said, "We're seeing deepfakes being weaponized in many, many different ways, from the creation of child sexual abuse material, to non-consensual intimate imagery, to full-blown state-sponsored disinformation campaigns, to what the North Koreans have done, [which] is to have their IT workers interview for jobs remotely.
asserted
workers → say → jobs
And they conceal their accent using AI.
asserted
they → conceal → AI
They can conceal their identity using a deepfake, where they change their identity from eyebrow to chin, cheek to cheek, in real time over a video call.
asserted
they → conceal → call
They can conceal their geolocation.
asserted
They → conceal → geolocation
And they interview for jobs.
asserted
they → interview → jobs
I will tell you, just about every single Fortune 500 company has hired one or more of these, whether they know it or not.
asserted
they → tell → it
"And now, the question you want to ask yourself is, what's next?
asserted
what → want → yourself
Well, so one CEO said, 'Okay.
asserted
CEO → say → ?
We're gonna start flying people across the country?
asserted
We → go → country
I mean it's exhausting.
asserted
it → mean → ?
I mean, if we're always looking at our shoulder over every email, every text message, every voicemail, every phone call, every video call?
asserted
we → mean → email
I'm exhausted, and I do this for a living."
asserted
I → do → living
Asked if he has nightmares over deepfakes, Farid replied, "I look at content all day long.
asserted
I → ask → content
…and 37 more, not listed.
The Aporia
