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Margaret Mitchell: How we can build AI to help humans, not hurt us
Margaret Mičel (Margaret Mitchell): Kako da napravimo veštačku inteligenciju tako da pomogne ljudima, a ne da nas povredi
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Kao istraživač u Guglu, Margaret Mičel pomaže razvoju računara koji mogu da komuniciraju o tome šta vide i razumeju. Ona nam priča priču kojom upozorava na pukotine, „mrtve uglove“ i predrasude koje podsvesno ubacujemo u veštačku inteligenciju, i moli nas da razmislimo o tome šta će tehnologija koju danas stvaramo značiti za sutrašnjicu. „Sve što sada vidimo je trenutna slika u evoluciji veštačke inteligencije,“ kaže Mičel. „Ukoliko želimo da se veštačka inteligencija razvije tako da pomogne ljudima, onda moramo sada da definišemo ciljeve i strategije koje će omogućiti taj put.“
Margaret Mitchell - AI research scientist
Margaret Mitchell is a senior research scientist in Google's Research & Machine Intelligence group, working on artificial intelligence. Full bio
Margaret Mitchell is a senior research scientist in Google's Research & Machine Intelligence group, working on artificial intelligence. Full bio
Double-click the English transcript below to play the video.
00:13
I work on helping computers
communicate about the world around us.
communicate about the world around us.
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Ja pomažem da računari
saopštavaju informacije o svetu oko nas.
saopštavaju informacije o svetu oko nas.
00:17
There are a lot of ways to do this,
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Postoji mnogo načina da se to uradi,
00:19
and I like to focus on helping computers
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i ja volim da se fokusiram
na pomaganje da računari
na pomaganje da računari
00:22
to talk about what they see
and understand.
and understand.
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pričaju o tome šta vide i razumeju.
00:25
Given a scene like this,
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Na slici kao što je ova,
savremeni algoritmi
za prepoznavanje oblika
za prepoznavanje oblika
00:27
a modern computer-vision algorithm
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00:29
can tell you that there's a woman
and there's a dog.
and there's a dog.
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će videti da su tu žena i pas.
00:32
It can tell you that the woman is smiling.
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Prepoznaće da se žena smeje.
00:34
It might even be able to tell you
that the dog is incredibly cute.
that the dog is incredibly cute.
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Može čak proceniti
da je pas neverovatno sladak.
da je pas neverovatno sladak.
00:38
I work on this problem
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Ja radim na ovom problemu
00:40
thinking about how humans
understand and process the world.
understand and process the world.
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razmišljajući kako ljudi
razumeju svet i razmišljaju o njemu.
razumeju svet i razmišljaju o njemu.
00:45
The thoughts, memories and stories
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Misli, sećanja i priče
00:48
that a scene like this
might evoke for humans.
might evoke for humans.
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koje slika kao što je ova
mogu izazvati kod ljudi.
mogu izazvati kod ljudi.
00:51
All the interconnections
of related situations.
of related situations.
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Sve međuzavisnosti povezanih situacija.
00:55
Maybe you've seen
a dog like this one before,
a dog like this one before,
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Možda ste već videli psa kao što je ovaj,
00:58
or you've spent time
running on a beach like this one,
running on a beach like this one,
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ili ste provodili vreme
u trčanju na plaži kao što je ova,
u trčanju na plaži kao što je ova,
01:01
and that further evokes thoughts
and memories of a past vacation,
and memories of a past vacation,
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a to dalje podstiče misli
i sećanja na odmor iz prošlosti,
i sećanja na odmor iz prošlosti,
01:06
past times to the beach,
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vreme provedeno na plaži
01:08
times spent running around
with other dogs.
with other dogs.
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i u trčanju sa drugim psima.
01:11
One of my guiding principles
is that by helping computers to understand
is that by helping computers to understand
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Jedan od mojih vodećih principa
je da pomaganjem računarima da shvate
je da pomaganjem računarima da shvate
01:16
what it's like to have these experiences,
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šta znači imati ovakva iskustva,
01:19
to understand what we share
and believe and feel,
and believe and feel,
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da shvate šta mi imamo zajedničko,
u šta verujemo i šta osećamo,
u šta verujemo i šta osećamo,
01:26
then we're in a great position
to start evolving computer technology
to start evolving computer technology
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dolazimo do odlične pozicije za početak
razvoja računarske tehnologije
razvoja računarske tehnologije
01:30
in a way that's complementary
with our own experiences.
with our own experiences.
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tako da dopunjava naša sopstvena iskustva.
01:35
So, digging more deeply into this,
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Ako uđemo malo dublje u temu,
01:38
a few years ago I began working on helping
computers to generate human-like stories
computers to generate human-like stories
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pre par godina sam počela rad
na pomaganju računarima da stvaraju priče
01:44
from sequences of images.
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na osnovu niza slika.
01:47
So, one day,
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Jednog dana
01:49
I was working with my computer to ask it
what it thought about a trip to Australia.
what it thought about a trip to Australia.
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sam radila na računaru i pitala ga
šta misli o putovanju u Australiju.
šta misli o putovanju u Australiju.
01:54
It took a look at the pictures,
and it saw a koala.
and it saw a koala.
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Pogledao je slike i video je koalu.
01:58
It didn't know what the koala was,
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Nije znao šta je koala,
01:59
but it said it thought
it was an interesting-looking creature.
it was an interesting-looking creature.
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ali je rekao da misli
da to biće izgleda zanimljivo.
da to biće izgleda zanimljivo.
02:04
Then I shared with it a sequence of images
about a house burning down.
about a house burning down.
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Zatim sam sa njim podelila
niz slika kuća u plamenu.
niz slika kuća u plamenu.
02:09
It took a look at the images and it said,
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Pogledao je slike i rekao:
02:13
"This is an amazing view!
This is spectacular!"
This is spectacular!"
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„Ovo izgleda sjajno!
Ovo je spektakularno!“
Ovo je spektakularno!“
02:17
It sent chills down my spine.
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Niz kičmu su mi prošli žmarci.
02:20
It saw a horrible, life-changing
and life-destroying event
and life-destroying event
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Video je užasan događaj
koji menja i uništava živote ljudi
koji menja i uništava živote ljudi
02:25
and thought it was something positive.
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i mislio je da je to nešto pozitivno.
02:27
I realized that it recognized
the contrast,
the contrast,
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Shvatila sam da je prepoznao kontrast,
02:31
the reds, the yellows,
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crvene i žute boje,
02:34
and thought it was something
worth remarking on positively.
worth remarking on positively.
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i da je mislio da je to nešto
što treba označiti kao pozitivno.
što treba označiti kao pozitivno.
02:37
And part of why it was doing this
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Delimično je razlog tome
02:39
was because most
of the images I had given it
of the images I had given it
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to što je većina slika koje sam mu davala
02:42
were positive images.
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bila pozitivna.
02:44
That's because people
tend to share positive images
tend to share positive images
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To je zbog toga što su ljudi
skloni da dele pozitivne slike
skloni da dele pozitivne slike
02:48
when they talk about their experiences.
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kada pričaju o svojim iskustvima.
02:51
When was the last time
you saw a selfie at a funeral?
you saw a selfie at a funeral?
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Kada ste poslednji put
videli selfi sa sahrane?
videli selfi sa sahrane?
Shvatila sam da, dok sam radila
na unapređenju veštačke inteligencije
na unapređenju veštačke inteligencije
02:55
I realized that,
as I worked on improving AI
as I worked on improving AI
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02:58
task by task, dataset by dataset,
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zadatak po zadatak, bazu po bazu,
03:02
that I was creating massive gaps,
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da sam u stvari stvarala velike pukotine,
03:05
holes and blind spots
in what it could understand.
in what it could understand.
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rupe i „mrtve uglove“
u onome što može da razume.
03:10
And while doing so,
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Time sam sam ubacivala
različite pristrasnosti.
različite pristrasnosti.
03:11
I was encoding all kinds of biases.
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03:15
Biases that reflect a limited viewpoint,
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Pristrasnosti koje odražavaju
ograničeno gledište,
ograničeno gledište,
03:18
limited to a single dataset --
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ograničeno na samo jedan skup podataka -
03:21
biases that can reflect
human biases found in the data,
human biases found in the data,
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pristrasnosti koje odražavaju
ljudske pristrasnosti pronađene u podacima
ljudske pristrasnosti pronađene u podacima
03:25
such as prejudice and stereotyping.
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kao što su predrasude i stereotipi.
03:29
I thought back to the evolution
of the technology
of the technology
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Razmislila sam o evoluciji tehnologije
03:32
that brought me to where I was that day --
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koja me je dovela tu
gde sam bila u tom trenutku -
gde sam bila u tom trenutku -
03:35
how the first color images
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kako su prve slike u boji
03:38
were calibrated against
a white woman's skin,
a white woman's skin,
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bile podešene prema boji kože belih žena,
03:41
meaning that color photography
was biased against black faces.
was biased against black faces.
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što znači da je fotografija u boji
bila naklonjena protiv crnih lica.
03:46
And that same bias, that same blind spot
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Ta ista naklonjenost, isti „mrtav ugao“
03:49
continued well into the '90s.
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se nastavio duboko u '90-im godinama.
03:51
And the same blind spot
continues even today
continues even today
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Isti „mrtav ugao“ traje čak i danas
03:54
in how well we can recognize
different people's faces
different people's faces
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u tome koliko dobro možemo
prepoznati lica različitih ljudi
prepoznati lica različitih ljudi
u tehnologiji za prepoznavanje lica.
03:58
in facial recognition technology.
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04:01
I though about the state of the art
in research today,
in research today,
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Razmišljala sam o najnaprednijim
istraživanjima danas,
istraživanjima danas,
04:04
where we tend to limit our thinking
to one dataset and one problem.
to one dataset and one problem.
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gde pokušavamo da ograničimo
svoje razmišljanje
svoje razmišljanje
na jedan skup podataka i jedan problem.
04:09
And that in doing so, we were creating
more blind spots and biases
more blind spots and biases
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A čineći to, stvaramo
još više „mrtvih uglova“ i predrasuda
još više „mrtvih uglova“ i predrasuda
04:14
that the AI could further amplify.
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koje veštačka inteligencija
može još više da pojača.
može još više da pojača.
04:17
I realized then
that we had to think deeply
that we had to think deeply
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Tada sam shvatila
da moramo da dobro razmislimo
da moramo da dobro razmislimo
04:19
about how the technology we work on today
looks in five years, in 10 years.
looks in five years, in 10 years.
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o tome kako će tehnologija
na kojoj danas radimo
na kojoj danas radimo
da izgleda za pet ili 10 godina.
04:25
Humans evolve slowly,
with time to correct for issues
with time to correct for issues
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Ljudi evoluiraju polako,
imaju vremena da isprave greške
imaju vremena da isprave greške
04:29
in the interaction of humans
and their environment.
and their environment.
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u interakciji sa ljudima i okruženjem.
04:33
In contrast, artificial intelligence
is evolving at an incredibly fast rate.
is evolving at an incredibly fast rate.
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Za razliku od njih, veštačka inteligencija
se razvija neverovatnom brzinom.
se razvija neverovatnom brzinom.
04:39
And that means that it really matters
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To znači da je zaista bitno
04:40
that we think about this
carefully right now --
carefully right now --
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da o ovome pažljivo razmislimo sada -
04:44
that we reflect on our own blind spots,
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da razmislimo o svojim „mrtvim uglovima“,
04:47
our own biases,
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o sopstvenim predrasudama,
04:49
and think about how that's informing
the technology we're creating
the technology we're creating
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i da razmislimo o tome kako to utiče
na tehnologiju koju stvaramo
na tehnologiju koju stvaramo
04:53
and discuss what the technology of today
will mean for tomorrow.
will mean for tomorrow.
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i da razmotrimo šta će značiti
tehnologija današnjice za sutrašnjicu.
tehnologija današnjice za sutrašnjicu.
04:58
CEOs and scientists have weighed in
on what they think
on what they think
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Direktori i naučnici raspravljaju
o tome kakva će biti
o tome kakva će biti
05:01
the artificial intelligence technology
of the future will be.
of the future will be.
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veštačka inteligencija u budućnosti.
05:05
Stephen Hawking warns that
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Stiven Hoking upozorava da
05:06
"Artificial intelligence
could end mankind."
could end mankind."
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„Veštačka inteligencija
može uništiti čovečanstvo.“
može uništiti čovečanstvo.“
05:10
Elon Musk warns
that it's an existential risk
that it's an existential risk
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Ilon Mask upozorava
da je to rizik za preživljavanje
da je to rizik za preživljavanje
05:13
and one of the greatest risks
that we face as a civilization.
that we face as a civilization.
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i jedan od najvećih rizika
sa kojima se susrećemo kao civilizacija.
sa kojima se susrećemo kao civilizacija.
05:17
Bill Gates has made the point,
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Bil Gejts je izneo stav:
05:19
"I don't understand
why people aren't more concerned."
why people aren't more concerned."
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„Ne razumem zašto ljudi
nisu više zabrinuti.“
nisu više zabrinuti.“
05:23
But these views --
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Ali ova mišljenja
su deo priče.
05:25
they're part of the story.
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05:28
The math, the models,
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Matematika, modeli,
05:30
the basic building blocks
of artificial intelligence
of artificial intelligence
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osnovni sastavni delovi
veštačke inteligencije
veštačke inteligencije
05:33
are something that we call access
and all work with.
and all work with.
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su nešto čemu svi možemo
da pristupimo i da radimo sa time.
da pristupimo i da radimo sa time.
05:36
We have open-source tools
for machine learning and intelligence
for machine learning and intelligence
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Imamo alate otvorenog koda
za mašinsko učenje i inteligenciju
za mašinsko učenje i inteligenciju
05:40
that we can contribute to.
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kojima možemo da doprinesemo.
05:42
And beyond that,
we can share our experience.
we can share our experience.
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Osim toga, možemo da delimo naše iskustvo.
05:46
We can share our experiences
with technology and how it concerns us
with technology and how it concerns us
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Možemo da delimo naša iskustva
sa tehnologijom, kako nas brine
sa tehnologijom, kako nas brine
05:50
and how it excites us.
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i kako nas uzbuđuje.
05:52
We can discuss what we love.
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Možemo da razmatramo šta volimo.
05:55
We can communicate with foresight
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Možemo da govorimo
05:57
about the aspects of technology
that could be more beneficial
that could be more beneficial
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o budućim aspektima tehnologije
koji bi bili delotvorniji
koji bi bili delotvorniji
06:02
or could be more problematic over time.
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ili problematičniji tokom vremena.
06:05
If we all focus on opening up
the discussion on AI
the discussion on AI
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Ako se svi fokusiramo na otvaranje
diskusije o veštačkoj inteligenciji
diskusije o veštačkoj inteligenciji
06:09
with foresight towards the future,
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sa pogledom na budućnost,
06:13
this will help create a general
conversation and awareness
conversation and awareness
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to će pomoći kreiranju
uopštenih razgovora i svesti
uopštenih razgovora i svesti
06:17
about what AI is now,
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o tome šta je veštačka inteligencija sada,
06:21
what it can become
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šta može da postane
06:23
and all the things that we need to do
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i sve stvari koje treba da uradimo
06:25
in order to enable that outcome
that best suits us.
that best suits us.
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da ostvarimo rezultat
koji bi nam najbolje služio.
koji bi nam najbolje služio.
06:29
We already see and know this
in the technology that we use today.
in the technology that we use today.
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Već vidimo i znamo to iz tehnologije
koju danas koristimo.
koju danas koristimo.
06:33
We use smart phones
and digital assistants and Roombas.
and digital assistants and Roombas.
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Koristimo pametne telefone,
digitalne pomoćnike i robotske usisivače.
digitalne pomoćnike i robotske usisivače.
06:38
Are they evil?
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Da li su zli?
06:40
Maybe sometimes.
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Možda ponekad.
06:42
Are they beneficial?
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Da li su korisni?
06:45
Yes, they're that, too.
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Jesu i to.
06:48
And they're not all the same.
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I nisu svi isti.
06:50
And there you already see
a light shining on what the future holds.
a light shining on what the future holds.
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Već možemo da vidimo
da je budućnost svetla.
da je budućnost svetla.
06:54
The future continues on
from what we build and create right now.
from what we build and create right now.
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Budućnost nastaje
iz onoga što kreiramo upravo sada.
iz onoga što kreiramo upravo sada.
06:59
We set into motion that domino effect
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Mi pokrećemo taj domino efekat
07:01
that carves out AI's evolutionary path.
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koji kreira evolucioni put
veštačke inteligencije.
veštačke inteligencije.
07:05
In our time right now,
we shape the AI of tomorrow.
we shape the AI of tomorrow.
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U svom vremenu sada, mi oblikujemo
veštačku inteligenciju sutrašnjice.
veštačku inteligenciju sutrašnjice.
07:08
Technology that immerses us
in augmented realities
in augmented realities
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Tehnologiju koja nas uvodi
u izmenjenu stvarnost
u izmenjenu stvarnost
07:12
bringing to life past worlds.
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oživljavajući prošlost.
07:15
Technology that helps people
to share their experiences
to share their experiences
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Tehnologiju koja pomaže ljudima
da dele svoja iskustva
da dele svoja iskustva
07:20
when they have difficulty communicating.
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kada imaju problem da komuniciraju.
07:23
Technology built on understanding
the streaming visual worlds
the streaming visual worlds
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Tehnologiju zasnovanu
na prenosu sadržaja vizuelnog sveta,
na prenosu sadržaja vizuelnog sveta,
07:27
used as technology for self-driving cars.
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korišćenu kao tehnologiju
za samovozeće automobile.
za samovozeće automobile.
07:32
Technology built on understanding images
and generating language,
and generating language,
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Tehnologiju zasnovanu
na razumevanju slika i generisanju jezika,
na razumevanju slika i generisanju jezika,
07:35
evolving into technology that helps people
who are visually impaired
who are visually impaired
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koja evoluira u tehnologiju
koja pomaže ljudima sa oštećenim vidom
koja pomaže ljudima sa oštećenim vidom
07:40
be better able to access the visual world.
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da imaju bolji pristup vizuelnom svetu.
07:42
And we also see how technology
can lead to problems.
can lead to problems.
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Takođe vidimo kako tehnologija
može da dovede do problema.
može da dovede do problema.
07:46
We have technology today
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Danas imamo tehnologiju
07:48
that analyzes physical
characteristics we're born with --
characteristics we're born with --
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koja analizira fizičke karakteristike
sa kojima smo rođeni -
sa kojima smo rođeni -
07:52
such as the color of our skin
or the look of our face --
or the look of our face --
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kao što je boja kože ili izgled lica -
07:55
in order to determine whether or not
we might be criminals or terrorists.
we might be criminals or terrorists.
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kako bi odredila da li smo možda
kriminalci ili teroristi.
kriminalci ili teroristi.
07:59
We have technology
that crunches through our data,
that crunches through our data,
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Imamo tehnologiju
koja pretražuje naše podatke,
koja pretražuje naše podatke,
08:02
even data relating
to our gender or our race,
to our gender or our race,
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čak i podatke o našem polu ili rasi,
08:05
in order to determine whether or not
we might get a loan.
we might get a loan.
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da bi odredila da li možemo
da dobijemo kredit.
da dobijemo kredit.
08:09
All that we see now
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Sve što sada vidimo
08:11
is a snapshot in the evolution
of artificial intelligence.
of artificial intelligence.
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je trenutna slika u evoluciji
veštačke inteligencije.
veštačke inteligencije.
08:15
Because where we are right now,
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Zato što je ovo gde smo sada
08:17
is within a moment of that evolution.
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samo trenutak u toj evoluciji.
08:20
That means that what we do now
will affect what happens down the line
will affect what happens down the line
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To znači da će ono što sada radimo
uticati na to šta se dešava
uticati na to šta se dešava
08:24
and in the future.
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u budućnosti.
Ukoliko želimo da pomognemo
da se veštačka inteligencija razvije
da se veštačka inteligencija razvije
08:26
If we want AI to evolve
in a way that helps humans,
in a way that helps humans,
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tako da pomogne ljudima,
08:30
then we need to define
the goals and strategies
the goals and strategies
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onda moramo da definišemo
ciljeve i strategije
ciljeve i strategije
08:32
that enable that path now.
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koje će omogućiti taj put sada.
08:35
What I'd like to see is something
that fits well with humans,
that fits well with humans,
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Ono što bih želela da vidim
je nešto što će se uklopiti sa ljudima,
je nešto što će se uklopiti sa ljudima,
08:39
with our culture and with the environment.
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sa našom kulturom i okruženjem.
08:43
Technology that aids and assists
those of us with neurological conditions
those of us with neurological conditions
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Tehnologiju koja pomaže onima
sa neurološkim problemima
sa neurološkim problemima
08:47
or other disabilities
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ili drugim vidovima invaliditeta
08:49
in order to make life
equally challenging for everyone.
equally challenging for everyone.
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kako bi život bio podjednako
izazovan za svakoga.
izazovan za svakoga.
08:54
Technology that works
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Tehnologiju koja radi
08:55
regardless of your demographics
or the color of your skin.
or the color of your skin.
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bez obzira na poreklo ili boju kože.
09:00
And so today, what I focus on
is the technology for tomorrow
is the technology for tomorrow
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Tako da ono na šta se danas fokusiram
je tehnologija sutrašnjice
je tehnologija sutrašnjice
09:05
and for 10 years from now.
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i 10 godina od danas.
09:08
AI can turn out in many different ways.
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Veštačka inteligencija
može da krene različitim putevima.
može da krene različitim putevima.
09:11
But in this case,
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Ali u ovom slučaju,
09:12
it isn't a self-driving car
without any destination.
without any destination.
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to nije samovozeći automobil bez cilja.
09:16
This is the car that we are driving.
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To je automobil koji mi vozimo.
09:19
We choose when to speed up
and when to slow down.
and when to slow down.
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Mi biramo kada da ubrzamo
i kada da usporimo.
i kada da usporimo.
09:23
We choose if we need to make a turn.
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Mi biramo da li treba da skrenemo.
09:26
We choose what the AI
of the future will be.
of the future will be.
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Mi biramo šta će veštačka inteligencija
budućnosti zapravo biti.
budućnosti zapravo biti.
09:31
There's a vast playing field
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Veliki je broj mogućnosti
09:32
of all the things that artificial
intelligence can become.
intelligence can become.
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šta veštačka inteligencija može postati.
09:36
It will become many things.
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I postaće dosta toga.
09:39
And it's up to us now,
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Sada je na nama
09:41
in order to figure out
what we need to put in place
what we need to put in place
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da smislimo šta treba da uradimo
09:44
to make sure the outcomes
of artificial intelligence
of artificial intelligence
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da bismo bili sigurni da rezultati
veštačke inteligencije
veštačke inteligencije
09:48
are the ones that will be
better for all of us.
better for all of us.
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budu oni koji su dobri za sve nas.
09:51
Thank you.
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Hvala.
09:52
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Margaret Mitchell - AI research scientistMargaret Mitchell is a senior research scientist in Google's Research & Machine Intelligence group, working on artificial intelligence.
Why you should listen
Margaret Mitchell's research involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. Her work combines computer vision, natural language processing, social media as well as many statistical methods and insights from cognitive science. Before Google, Mitchell was a founding member of Microsoft Research's "Cognition" group, focused on advancing artificial intelligence, and a researcher in Microsoft Research's Natural Language Processing group.
Margaret Mitchell | Speaker | TED.com