Le intelligenze artificiali possono essere meno intelligenti di quanto credono e portarsi dietro degli atteggiamenti errori e manie che invece sono frutto  del retaggio dei loro programmatori e perpetuare cosi’ degli errori come il clickbite o la discriminazione sessuale e razziale sul lavoro.

Janelle Shane

wikipedia [ENG]

TED

TRASCRIZIONE

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