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So, artificial intelligence
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is known for disrupting
all kinds of industries.
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What about ice cream?
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What kind of mind-blowing
new flavors could we generate
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with the power of an advanced
artificial intelligence?
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So I teamed up with a group of coders
from Kealing Middle School
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to find out the answer to this question.
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They collected over 1,600
existing ice cream flavors,
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and together, we fed them to an algorithm
to see what it would generate.
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And here are some of the flavors
that the AI came up with.
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[Pumpkin Trash Break]
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(Laughter)
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[Peanut Butter Slime]
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[Strawberry Cream Disease]
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(Laughter)
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These flavors are not delicious,
as we might have hoped they would be.
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So the question is: What happened?
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What went wrong?
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Is the AI trying to kill us?
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Or is it trying to do what we asked,
and there was a problem?
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In movies, when something
goes wrong with AI,
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it's usually because the AI has decided
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that it doesn't want to obey
the humans anymore,
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and it's got its own goals,
thank you very much.
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In real life, though,
the AI that we actually have
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is not nearly smart enough for that.
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It has the approximate computing power
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of an earthworm,
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or maybe at most a single honeybee,
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and actually, probably maybe less.
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Like, we're constantly learning
new things about brains
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that make it clear how much our AIs
don't measure up to real brains.
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So today's AI can do a task
like identify a pedestrian in a picture,
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but it doesn't have a concept
of what the pedestrian is
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beyond that it's a collection
of lines and textures and things.
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It doesn't know what a human actually is.
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So will today's AI
do what we ask it to do?
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It will if it can,
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but it might not do what we actually want.
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So let's say that you
were trying to get an AI
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to take this collection of robot parts
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and assemble them into some kind of robot
to get from Point A to Point B.
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Now, if you were going to try
and solve this problem
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by writing a traditional-style
computer program,
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you would give the program
step-by-step instructions
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on how to take these parts,
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how to assemble them
into a robot with legs
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and then how to use those legs
to walk to Point B.
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But when you're using AI
to solve the problem,
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it goes differently.
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You don't tell it
how to solve the problem,
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you just give it the goal,
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and it has to figure out for itself
via trial and error
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how to reach that goal.
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And it turns out that the way AI tends
to solve this particular problem
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is by doing this:
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it assembles itself into a tower
and then falls over
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and lands at Point B.
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And technically, this solves the problem.
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Technically, it got to Point B.
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The danger of AI is not that
it's going to rebel against us,
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it's that it's going to do
exactly what we ask it to do.
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So then the trick
of working with AI becomes:
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How do we set up the problem
so that it actually does what we want?
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So this little robot here
is being controlled by an AI.
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The AI came up with a design
for the robot legs
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and then figured out how to use them
to get past all these obstacles.
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But when David Ha set up this experiment,
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he had to set it up
with very, very strict limits
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on how big the AI
was allowed to make the legs,
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because otherwise ...
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(Laughter)
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And technically, it got
to the end of that obstacle course.
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So you see how hard it is to get AI
to do something as simple as just walk.
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So seeing the AI do this,
you may say, OK, no fair,
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you can't just be
a tall tower and fall over,
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you have to actually, like,
use legs to walk.
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And it turns out,
that doesn't always work, either.
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This AI's job was to move fast.
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They didn't tell it that it had
to run facing forward
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or that it couldn't use its arms.
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So this is what you get
when you train AI to move fast,
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you get things like somersaulting
and silly walks.
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It's really common.
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So is twitching along the floor in a heap.
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(Laughter)
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So in my opinion, you know what
should have been a whole lot weirder
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is the "Terminator" robots.
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Hacking "The Matrix" is another thing
that AI will do if you give it a chance.
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So if you train an AI in a simulation,
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it will learn how to do things like
hack into the simulation's math errors
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and harvest them for energy.
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Or it will figure out how to move faster
by glitching repeatedly into the floor.
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When you're working with AI,
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it's less like working with another human
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and a lot more like working
with some kind of weird force of nature.
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And it's really easy to accidentally
give AI the wrong problem to solve,
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and often we don't realize that
until something has actually gone wrong.
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So here's an experiment I did,
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where I wanted the AI
to copy paint colors,
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to invent new paint colors,
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given the list like the ones
here on the left.
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And here's what the AI
actually came up with.
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[Sindis Poop, Turdly, Suffer, Gray Pubic]
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(Laughter)
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So technically,
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it did what I asked it to.
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I thought I was asking it for,
like, nice paint color names,
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but what I was actually asking it to do
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was just imitate the kinds
of letter combinations
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that it had seen in the original.
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And I didn't tell it anything
about what words mean,
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or that there are maybe some words
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that it should avoid using
in these paint colors.
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So its entire world
is the data that I gave it.
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Like with the ice cream flavors,
it doesn't know about anything else.
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So it is through the data
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that we often accidentally tell AI
to do the wrong thing.
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This is a fish called a tench.
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And there was a group of researchers
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who trained an AI to identify
this tench in pictures.
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But then when they asked it
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what part of the picture it was actually
using to identify the fish,
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here's what it highlighted.
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Yes, those are human fingers.
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Why would it be looking for human fingers
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if it's trying to identify a fish?
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Well, it turns out that the tench
is a trophy fish,
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and so in a lot of pictures
that the AI had seen of this fish
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during training,
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the fish looked like this.
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(Laughter)
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And it didn't know that the fingers
aren't part of the fish.
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So you see why it is so hard
to design an AI
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that actually can understand
what it's looking at.
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And this is why designing
the image recognition
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in self-driving cars is so hard,
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and why so many self-driving car failures
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are because the AI got confused.
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I want to talk about an example from 2016.
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There was a fatal accident when somebody
was using Tesla's autopilot AI,
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but instead of using it on the highway
like it was designed for,
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they used it on city streets.
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And what happened was,
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a truck drove out in front of the car
and the car failed to brake.
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Now, the AI definitely was trained
to recognize trucks in pictures.
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But what it looks like happened is
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the AI was trained to recognize
trucks on highway driving,
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where you would expect
to see trucks from behind.
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Trucks on the side is not supposed
to happen on a highway,
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and so when the AI saw this truck,
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it looks like the AI recognized it
as most likely to be a road sign
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and therefore, safe to drive underneath.
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Here's an AI misstep
from a different field.
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Amazon recently had to give up
on a résumé-sorting algorithm
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that they were working on
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when they discovered that the algorithm
had learned to discriminate against women.
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What happened is they had trained it
on example résumés
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of people who they had hired in the past.
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And from these examples, the AI learned
to avoid the résumés of people
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who had gone to women's colleges
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or who had the word "women"
somewhere in their resume,
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as in, "women's soccer team"
or "Society of Women Engineers."
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The AI didn't know that it wasn't supposed
to copy this particular thing
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that it had seen the humans do.
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And technically, it did
what they asked it to do.
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They just accidentally asked it
to do the wrong thing.
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And this happens all the time with AI.
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AI can be really destructive
and not know it.
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So the AIs that recommend
new content in Facebook, in YouTube,
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they're optimized to increase
the number of clicks and views.
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And unfortunately, one way
that they have found of doing this
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is to recommend the content
of conspiracy theories or bigotry.
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The AIs themselves don't have any concept
of what this content actually is,
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and they don't have any concept
of what the consequences might be
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of recommending this content.
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So, when we're working with AI,
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it's up to us to avoid problems.
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And avoiding things going wrong,
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that may come down to
the age-old problem of communication,
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where we as humans have to learn
how to communicate with AI.
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We have to learn what AI
is capable of doing and what it's not,
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and to understand that,
with its tiny little worm brain,
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AI doesn't really understand
what we're trying to ask it to do.
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So in other words, we have
to be prepared to work with AI
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that's not the super-competent,
all-knowing AI of science fiction.
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We have to prepared to work with an AI
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that's the one that we actually have
in the present day.
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And present-day AI is plenty weird enough.
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Thank you.
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(Applause)