TEDWomen 2015
Rana el Kaliouby: This app knows how you feel -- from the look on your face
Rana el Kalioubi (Rana el Kaliouby): Ova aplikacija zna kako se osećate - po vašem izrazu lica
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Naše emocije utiču na svaki aspekt naših života - kako učimo, kako komuniciramo, kako donosimo odluke. Ipak, odsutne su iz naših digitalnih života; uređaji i aplikacije koje koristimo ne mogu znati kako se mi osećamo. Naučnik Rana el Kalioubi teži da promeni to. Ona predstavlja demo verziju moćne tehnologije koja čita izraze na vašem licu i povezuje ih sa odgovarajućim emocijama. Ovaj „emotivni motor” nosi sa sobom bitne implikacije, kaže ona, i mogao bi da promeni ne samo način na koji komuniciramo sa mašinama - već i jedni sa drugima.
Rana el Kaliouby - Computer scientist
What if a computer could recognize your facial expression, and react to how you feel? Rana el Kaliouby sees big possibilities in making technology emotionally aware. Full bio
What if a computer could recognize your facial expression, and react to how you feel? Rana el Kaliouby sees big possibilities in making technology emotionally aware. Full bio
Double-click the English transcript below to play the video.
00:12
Our emotions influence
every aspect of our lives,
every aspect of our lives,
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Naše emocije utiču
na svaki aspekt naših života,
na svaki aspekt naših života,
od našeg zdravlja i načina učenja
00:16
from our health and how we learn,
to how we do business and make decisions,
to how we do business and make decisions,
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do načina obavljanja posla
i donošenja odluka,
i donošenja odluka,
00:20
big ones and small.
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bile one velike ili male.
Naše emocije takođe utiču na to
kako se povezujemo jedni sa drugima.
kako se povezujemo jedni sa drugima.
00:22
Our emotions also influence
how we connect with one another.
how we connect with one another.
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00:27
We've evolved to live
in a world like this,
in a world like this,
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Razvili smo se za život
u svetu kao što je ovaj,
u svetu kao što je ovaj,
00:31
but instead, we're living
more and more of our lives like this --
more and more of our lives like this --
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ali umesto toga,
živimo sve više naše živote ovako -
živimo sve više naše živote ovako -
ovo je SMS poruka
koju mi je ćerka poslala sinoć -
koju mi je ćerka poslala sinoć -
00:35
this is the text message
from my daughter last night --
from my daughter last night --
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00:38
in a world that's devoid of emotion.
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u svetu koji je lišen emocija.
00:41
So I'm on a mission to change that.
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Tako sam na misiji to da promenim.
00:43
I want to bring emotions
back into our digital experiences.
back into our digital experiences.
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Želim da vratim emocije
u naše digitalno iskustvo.
u naše digitalno iskustvo.
00:48
I started on this path 15 years ago.
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Krenula sam ovim putem pre 15 godina.
Bila sam kompjuterski naučnik u Egiptu
00:51
I was a computer scientist in Egypt,
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00:53
and I had just gotten accepted to
a Ph.D. program at Cambridge University.
a Ph.D. program at Cambridge University.
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i samo što su me primili
na doktorski program
na univerzitetu u Kembridžu.
na univerzitetu u Kembridžu.
Tako sam uradila nešto prilično neobično
00:57
So I did something quite unusual
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00:59
for a young newlywed Muslim Egyptian wife:
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za jednu mladu, tek venčanu ženu
koja je muslimanska Egipćanka:
koja je muslimanska Egipćanka:
(Smeh)
01:05
With the support of my husband,
who had to stay in Egypt,
who had to stay in Egypt,
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uz podršku mog muža
koji je morao da ostane u Egiptu,
koji je morao da ostane u Egiptu,
01:08
I packed my bags and I moved to England.
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spakovala sam kofere
i preselila se u Englesku.
i preselila se u Englesku.
01:11
At Cambridge, thousands of miles
away from home,
away from home,
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Na Kembridžu, kilometrima daleko od kuće,
shvatila sam da više vremena
provodim sa laptopom
provodim sa laptopom
01:14
I realized I was spending
more hours with my laptop
more hours with my laptop
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nego sa bilo kojim drugim ljudskim bićem.
01:18
than I did with any other human.
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01:20
Yet despite this intimacy, my laptop
had absolutely no idea how I was feeling.
had absolutely no idea how I was feeling.
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Ipak, i pored ove prisnosti, moj laptop
nije imao pojma kako se osećam.
nije imao pojma kako se osećam.
01:25
It had no idea if I was happy,
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Nije imao pojma da li sam srećna,
01:28
having a bad day, or stressed, confused,
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da li sam imala loš dan,
da li sam pod stresom, zbunjena,
da li sam pod stresom, zbunjena,
01:31
and so that got frustrating.
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tako da je to postalo frustrirajuće.
Još gore, dok sam komunicirala
sa porodicom kod kuće preko mreže,
sa porodicom kod kuće preko mreže,
01:35
Even worse, as I communicated
online with my family back home,
online with my family back home,
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osećala sam da sve moje emocije
nestaju u sajber prostoru.
nestaju u sajber prostoru.
01:41
I felt that all my emotions
disappeared in cyberspace.
disappeared in cyberspace.
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01:44
I was homesick, I was lonely,
and on some days I was actually crying,
and on some days I was actually crying,
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Nedostajala mi je kuća, bila sam usamljena
i nekih dana sam zapravo i plakala,
i nekih dana sam zapravo i plakala,
01:49
but all I had to communicate
these emotions was this.
these emotions was this.
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ali sve što sam imala
da iskažem ove emocije bilo je ovo.
da iskažem ove emocije bilo je ovo.
01:54
(Laughter)
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(Smeh)
01:56
Today's technology
has lots of I.Q., but no E.Q.;
has lots of I.Q., but no E.Q.;
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Današnja tehnologija ima
mnogo IQ-a, ali nema EQ;
mnogo IQ-a, ali nema EQ;
mnogo kognitivne inteligencije,
ali nimalo emocionalne inteligencije,
ali nimalo emocionalne inteligencije,
02:01
lots of cognitive intelligence,
but no emotional intelligence.
but no emotional intelligence.
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02:04
So that got me thinking,
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te me je to nagnalo na razmišljanje.
02:07
what if our technology
could sense our emotions?
could sense our emotions?
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Šta ako bi naša tehnologija mogla
da oseti naše emocije?
da oseti naše emocije?
02:10
What if our devices could sense
how we felt and reacted accordingly,
how we felt and reacted accordingly,
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Šta ako bi uređaji mogli
da prepoznaju i reaguju na naša osećanja,
da prepoznaju i reaguju na naša osećanja,
baš kao što bi reagovao
i emocionalno inteligentan prijatelj?
i emocionalno inteligentan prijatelj?
02:14
just the way an emotionally
intelligent friend would?
intelligent friend would?
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02:18
Those questions led me and my team
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Ta pitanja su navela mene i moj tim
da napravimo tehnologiju
da napravimo tehnologiju
02:22
to create technologies that can read
and respond to our emotions,
and respond to our emotions,
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koja može da čita
i reaguje na naše emocije,
i reaguje na naše emocije,
02:26
and our starting point was the human face.
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a naša početna tačka bilo je ljudsko lice.
02:30
So our human face happens to be
one of the most powerful channels
one of the most powerful channels
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Dakle, ljudsko lice
je jedno od najmoćnijih kanala
je jedno od najmoćnijih kanala
02:33
that we all use to communicate
social and emotional states,
social and emotional states,
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koje koristimo da prenesemo
društvena i emocionalna stanja,
društvena i emocionalna stanja,
02:37
everything from enjoyment, surprise,
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sve od zadovoljstva, iznenađenja,
02:40
empathy and curiosity.
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empatije do radoznalosti.
02:44
In emotion science, we call each
facial muscle movement an action unit.
facial muscle movement an action unit.
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U nauci o emocijama,
sve pokrete facijalnih mišića
nazivamo akcijskim jedinicama.
nazivamo akcijskim jedinicama.
02:49
So for example, action unit 12,
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Tako, na primer, akcijska jedinica 12
nije holivudski blokbaster,
nije holivudski blokbaster,
02:52
it's not a Hollywood blockbuster,
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nego se radi o podizanju ugla usana,
što je glavna komponenta osmeha.
što je glavna komponenta osmeha.
02:54
it is actually a lip corner pull,
which is the main component of a smile.
which is the main component of a smile.
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Probajte to. Hajde da svi nabacimo osmehe.
02:58
Try it everybody. Let's get
some smiles going on.
some smiles going on.
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Još jedan primer je akcijska jedinica 4.
To je boranje obrve.
To je boranje obrve.
03:01
Another example is action unit 4.
It's the brow furrow.
It's the brow furrow.
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To je kada skupite obrve
i stvorite sve ove teksture i bore.
i stvorite sve ove teksture i bore.
03:03
It's when you draw your eyebrows together
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03:06
and you create all
these textures and wrinkles.
these textures and wrinkles.
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Ne volimo ih, ali one su jasan pokazatelj
neke negativne emocije.
neke negativne emocije.
03:08
We don't like them, but it's
a strong indicator of a negative emotion.
a strong indicator of a negative emotion.
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Tako imamo oko 45 ovih akcijskih jedinica
03:12
So we have about 45 of these action units,
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i one se kombinuju
da izraze stotine emocija.
da izraze stotine emocija.
03:14
and they combine to express
hundreds of emotions.
hundreds of emotions.
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Podučavanje kompjutera
da čita ove facijalne emocije je teško
da čita ove facijalne emocije je teško
03:18
Teaching a computer to read
these facial emotions is hard,
these facial emotions is hard,
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03:22
because these action units,
they can be fast, they're subtle,
they can be fast, they're subtle,
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zato što ove akcijske jedinice
mogu biti brze, suptilne
mogu biti brze, suptilne
03:25
and they combine in many different ways.
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i kombinuju se na mnogo različitih načina.
Uzmite tako, na primer,
osmeh i zloban osmeh.
osmeh i zloban osmeh.
03:27
So take, for example,
the smile and the smirk.
the smile and the smirk.
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03:31
They look somewhat similar,
but they mean very different things.
but they mean very different things.
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Izgledaju pomalo slično
ali imaju veoma različita značenja.
ali imaju veoma različita značenja.
(Smeh)
03:35
(Laughter)
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03:36
So the smile is positive,
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Dakle, osmeh je pozitivan,
a zloban osmeh je često negativan.
03:39
a smirk is often negative.
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Ponekad jedan zloban osmeh može
da vas napravi poznatim.
da vas napravi poznatim.
03:41
Sometimes a smirk
can make you become famous.
can make you become famous.
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03:45
But seriously, it's important
for a computer to be able
for a computer to be able
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Ozbiljno, važno je
da kompjuter bude u stanju
da kompjuter bude u stanju
03:47
to tell the difference
between the two expressions.
between the two expressions.
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da prepozna razliku između
ova dva izraza.
ova dva izraza.
Dakle, kako to postižemo?
03:50
So how do we do that?
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Obezbeđujemo svojim algoritmima
03:52
We give our algorithms
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desetine hiljada primera ljudi
za koje znamo da se osmehuju,
za koje znamo da se osmehuju,
03:54
tens of thousands of examples
of people we know to be smiling,
of people we know to be smiling,
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03:58
from different ethnicities, ages, genders,
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ljudi različitih etničkih pripadnosti,
godina, različitog pola,
godina, različitog pola,
04:01
and we do the same for smirks.
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a to isto činimo za podsmehe.
Onda, uz dubinski pristup učenju,
04:04
And then, using deep learning,
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04:05
the algorithm looks for all these
textures and wrinkles
textures and wrinkles
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algoritam traži sve ove teksture i bore
04:08
and shape changes on our face,
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i promene oblika na našim licima,
i u suštini uči da svi osmesi imaju
zajedničke osobine,
zajedničke osobine,
04:11
and basically learns that all smiles
have common characteristics,
have common characteristics,
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04:14
all smirks have subtly
different characteristics.
different characteristics.
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da se svi zlobni osmesi suptilno razlikuju
od osmeha po osobinama.
od osmeha po osobinama.
04:17
And the next time it sees a new face,
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I sledeći put kada vidi novo lice,
04:20
it essentially learns that
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u suštini uči da ovo lice ima
iste osobine osmeha
iste osobine osmeha
04:22
this face has the same
characteristics of a smile,
characteristics of a smile,
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04:25
and it says, "Aha, I recognize this.
This is a smile expression."
This is a smile expression."
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i kaže „Aha, prepoznajem ovo.
Ovo je izraz osmeha.”
Ovo je izraz osmeha.”
04:30
So the best way to demonstrate
how this technology works
how this technology works
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Najbolji način da pokažemo
kako ova tehnologija funkcioniše
kako ova tehnologija funkcioniše
04:33
is to try a live demo,
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jeste da probamo demo-verziju uživo,
tako da mi treba dobrovoljac.
tako da mi treba dobrovoljac.
04:35
so I need a volunteer,
preferably somebody with a face.
preferably somebody with a face.
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Poželjno je da taj neko ima lice.
(Smeh)
04:39
(Laughter)
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04:41
Cloe's going to be our volunteer today.
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Kloi će nam danas biti dobrovoljac.
04:45
So over the past five years, we've moved
from being a research project at MIT
from being a research project at MIT
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Dakle, u zadnjih pet godina,
od istraživačkog projekta na MIT-u
od istraživačkog projekta na MIT-u
04:49
to a company,
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postali smo kompanija,
u kojoj je moj tim vredno radio
da ova tehnologija uspe,
da ova tehnologija uspe,
04:50
where my team has worked really hard
to make this technology work,
to make this technology work,
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kako mi volimo da kažemo, u divljini.
04:54
as we like to say, in the wild.
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Takođe smo je smanjili
tako da osnovni emotivni motor
tako da osnovni emotivni motor
04:56
And we've also shrunk it so that
the core emotion engine
the core emotion engine
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radi na bilo kom mobilnom uređaju
koji ima kameru, kao što je ovaj Ajped.
koji ima kameru, kao što je ovaj Ajped.
04:59
works on any mobile device
with a camera, like this iPad.
with a camera, like this iPad.
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Dakle, hajde da probamo.
05:02
So let's give this a try.
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Kao što možete da vidite,
algoritam je pronašao Kloino lice.
algoritam je pronašao Kloino lice.
05:06
As you can see, the algorithm
has essentially found Cloe's face,
has essentially found Cloe's face,
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05:10
so it's this white bounding box,
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To je ovaj beli granični okvir,
05:12
and it's tracking the main
feature points on her face,
feature points on her face,
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koji prati glavne tačke odlika
na njenom licu,
na njenom licu,
05:14
so her eyebrows, her eyes,
her mouth and her nose.
her mouth and her nose.
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dakle, njene obrve, njene oči,
njena usta i njen nos.
njena usta i njen nos.
05:17
The question is,
can it recognize her expression?
can it recognize her expression?
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Pitanje je, da li može prepoznati
njen izraz lica?
njen izraz lica?
Dakle, testiraćemo mašinu.
05:20
So we're going to test the machine.
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Pre svega, da vidimo tvoje
pokeraško lice. Da, super.
pokeraško lice. Da, super.
05:22
So first of all, give me your poker face.
Yep, awesome. (Laughter)
Yep, awesome. (Laughter)
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(Smeh)
05:26
And then as she smiles,
this is a genuine smile, it's great.
this is a genuine smile, it's great.
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Onda, kada se osmehne,
ovo je iskren osmeh, odlično.
ovo je iskren osmeh, odlično.
Vidite da se zelena traka
puni kada se osmehuje.
puni kada se osmehuje.
05:29
So you can see the green bar
go up as she smiles.
go up as she smiles.
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To je bio širok osmeh.
05:31
Now that was a big smile.
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A jedan suptilan osmeh da vidimo
da li kompjuter ume da prepozna?
da li kompjuter ume da prepozna?
05:32
Can you try a subtle smile
to see if the computer can recognize?
to see if the computer can recognize?
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Prepoznaje i suptilne osmehe.
05:36
It does recognize subtle smiles as well.
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Vredno smo radili da ovo ostvarimo.
05:38
We've worked really hard
to make that happen.
to make that happen.
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Zatim podignute obrve,
pokazatelj iznenađenja.
pokazatelj iznenađenja.
05:40
And then eyebrow raised,
indicator of surprise.
indicator of surprise.
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Boranje obrva,
što je pokazatelj zbunjenosti.
što je pokazatelj zbunjenosti.
05:43
Brow furrow, which is
an indicator of confusion.
an indicator of confusion.
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05:47
Frown. Yes, perfect.
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Mrštenje. Da, savršeno.
05:51
So these are all the different
action units. There's many more of them.
action units. There's many more of them.
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Ovo su različite akcijske jedinice.
Postoji ih još mnogo više.
Postoji ih još mnogo više.
Ovo je samo skraćena demo verzija.
05:55
This is just a slimmed-down demo.
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Svako čitanje nazivamo
tačkom emotivnih podataka
tačkom emotivnih podataka
05:57
But we call each reading
an emotion data point,
an emotion data point,
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06:00
and then they can fire together
to portray different emotions.
to portray different emotions.
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i one mogu zajedno da rade
da iskažu različite emocije.
da iskažu različite emocije.
06:03
So on the right side of the demo --
look like you're happy.
look like you're happy.
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Dakle, na desnoj strani demo verzije -
izgledaj kao da si srećna.
izgledaj kao da si srećna.
Dakle, to je radost. Radost se povećava.
06:07
So that's joy. Joy fires up.
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06:09
And then give me a disgust face.
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Sad mi pokaži izraz gađenja.
Pokušaj da se setiš kako si se osećala
kada je Zejn napustio „One Direction".
kada je Zejn napustio „One Direction".
06:11
Try to remember what it was like
when Zayn left One Direction.
when Zayn left One Direction.
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(Smeh)
06:15
(Laughter)
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Da, naboraj nos. Super.
06:17
Yeah, wrinkle your nose. Awesome.
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06:21
And the valence is actually quite
negative, so you must have been a big fan.
negative, so you must have been a big fan.
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Valenca je stvarno krajnje negativna,
tako da mora da si bila veliki fan.
tako da mora da si bila veliki fan.
Valenca označava koliko je
iskustvo pozitivno ili negativno,
iskustvo pozitivno ili negativno,
06:25
So valence is how positive
or negative an experience is,
or negative an experience is,
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06:27
and engagement is how
expressive she is as well.
expressive she is as well.
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a angažman je taj koji označava
koliko je ona ekspresivna.
koliko je ona ekspresivna.
06:30
So imagine if Cloe had access
to this real-time emotion stream,
to this real-time emotion stream,
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Zamislite da Kloi ima pristup
ovom emotivnom prenosu u realnom vremenu
ovom emotivnom prenosu u realnom vremenu
06:34
and she could share it
with anybody she wanted to.
with anybody she wanted to.
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i da može da ga podeli sa kim god želi.
06:36
Thank you.
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Hvala ti.
(Aplauz)
06:39
(Applause)
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06:45
So, so far, we have amassed
12 billion of these emotion data points.
12 billion of these emotion data points.
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Dakle, do sada smo nagomilali 12 milijardi
ovih tačaka emotivnih podataka.
ovih tačaka emotivnih podataka.
To je najveća baza emocija u svetu.
06:51
It's the largest emotion
database in the world.
database in the world.
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Sakupili smo je
kroz 2,9 miliona klipova lica,
kroz 2,9 miliona klipova lica,
06:53
We've collected it
from 2.9 million face videos,
from 2.9 million face videos,
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06:56
people who have agreed
to share their emotions with us,
to share their emotions with us,
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ljudi koji su pristali da podele
svoje emocije sa nama,
svoje emocije sa nama,
06:59
and from 75 countries around the world.
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a to iz 75 zemalja širom sveta.
07:02
It's growing every day.
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Broj svaki dan raste.
07:04
It blows my mind away
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Raspamećuje me
to da sada možemo odrediti količinu
nečega tako ličnog kao što su emocije
nečega tako ličnog kao što su emocije
07:06
that we can now quantify something
as personal as our emotions,
as personal as our emotions,
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07:09
and we can do it at this scale.
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i da to možemo obaviti na ovom nivou.
07:12
So what have we learned to date?
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Dakle, šta smo naučili do sada?
07:15
Gender.
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Pol.
Naši podaci potvrđuju ono
što možda pretpostavljate.
što možda pretpostavljate.
07:17
Our data confirms something
that you might suspect.
that you might suspect.
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07:21
Women are more expressive than men.
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Žene su eskpresivnije od muškaraca.
07:22
Not only do they smile more,
their smiles last longer,
their smiles last longer,
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Ne samo da se više osmehuju,
njihovi osmesi traju duže.
njihovi osmesi traju duže.
07:25
and we can now really quantify
what it is that men and women
what it is that men and women
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Sada stvarno možemo odrediti
šta je to na šta muškarci i žene
šta je to na šta muškarci i žene
07:28
respond to differently.
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reaguju drugačije.
07:30
Let's do culture: So in the United States,
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Pozabavimo se kulturom.
U Sjedinjenim Državama,
U Sjedinjenim Državama,
07:32
women are 40 percent
more expressive than men,
more expressive than men,
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žene su 40% ekspresivnije od muškaraca,
07:36
but curiously, we don't see any difference
in the U.K. between men and women.
in the U.K. between men and women.
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ali neobično je to da nema razlike
u UK između muškaraca i žena.
u UK između muškaraca i žena.
07:39
(Laughter)
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(Smeh)
07:43
Age: People who are 50 years and older
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Godine. Ljudi koji imaju
50 godina ili stariji od toga
50 godina ili stariji od toga
07:47
are 25 percent more emotive
than younger people.
than younger people.
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su 25% emotivniji od mlađih ljudi.
Žene u dvadesetima osmehuju se
mnogo više od muškaraca istih godina,
mnogo više od muškaraca istih godina,
07:51
Women in their 20s smile a lot more
than men the same age,
than men the same age,
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što je možda neophodno pri zabavljanju.
07:55
perhaps a necessity for dating.
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07:59
But perhaps what surprised us
the most about this data
the most about this data
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Ipak, možda najveće iznenađenje
u vezi ovih podataka
u vezi ovih podataka
je to da smo stalno izražajni,
08:02
is that we happen
to be expressive all the time,
to be expressive all the time,
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čak i kada sedimo sami
ispred naših uređaja,
ispred naših uređaja,
08:05
even when we are sitting
in front of our devices alone,
in front of our devices alone,
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08:08
and it's not just when we're watching
cat videos on Facebook.
cat videos on Facebook.
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i to ne samo kada gledamo
klipove sa mačkama na Fejbuku.
klipove sa mačkama na Fejbuku.
08:12
We are expressive when we're emailing,
texting, shopping online,
texting, shopping online,
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Izražajni smo kada šaljemo i-mejlove,
SMS-ove, kupujemo onlajn,
SMS-ove, kupujemo onlajn,
08:15
or even doing our taxes.
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čak i kada obrađujemo porez.
08:17
Where is this data used today?
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Gde se ovi podaci koriste danas?
08:19
In understanding how we engage with media,
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Kada treba da razumemo
kako da se angažujemo na mrežama,
kako da se angažujemo na mrežama,
08:22
so understanding virality
and voting behavior;
and voting behavior;
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da razumemo viralnost
i ponašanja pri glasanju,
i ponašanja pri glasanju,
08:25
and also empowering
or emotion-enabling technology,
or emotion-enabling technology,
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kao i da razumemo tehnologije
koje osnažuju i omogućuju emocije.
koje osnažuju i omogućuju emocije.
08:27
and I want to share some examples
that are especially close to my heart.
that are especially close to my heart.
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Želim takođe da podelim
i primere koji su mi posebno dragi.
i primere koji su mi posebno dragi.
08:33
Emotion-enabled wearable glasses
can help individuals
can help individuals
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Naočare koje omogućuju emocije
mogu da pomognu pojedincima
mogu da pomognu pojedincima
08:36
who are visually impaired
read the faces of others,
read the faces of others,
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koji imaju oštećen vid
da čitaju lica drugih ljudi
da čitaju lica drugih ljudi
08:39
and it can help individuals
on the autism spectrum interpret emotion,
on the autism spectrum interpret emotion,
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i mogu da pomognu pojedincima
sa autizmom da protumače emocije,
sa autizmom da protumače emocije,
nešto sa čim stvarno imaju problema.
08:43
something that they really struggle with.
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U obrazovanju, zamislite
kada bi vaše aplikacije za učenje
kada bi vaše aplikacije za učenje
08:47
In education, imagine
if your learning apps
if your learning apps
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08:50
sense that you're confused and slow down,
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mogle da osete kada ste zbunjeni i uspore,
08:53
or that you're bored, so it's sped up,
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ili kada vam je dosadno i ubrzaju,
kao što bi dobar profesor
uradio u učionici.
uradio u učionici.
08:55
just like a great teacher
would in a classroom.
would in a classroom.
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Šta bi bilo ako bi vaš ručni sat
mogao da prati vaše raspoloženje,
mogao da prati vaše raspoloženje,
08:59
What if your wristwatch tracked your mood,
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09:01
or your car sensed that you're tired,
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ili kada bi vaš auto mogao
da oseti kada ste umorni,
da oseti kada ste umorni,
09:04
or perhaps your fridge
knows that you're stressed,
knows that you're stressed,
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ili kada bi vaš frižider
znao kada ste pod stresom,
znao kada ste pod stresom,
09:06
so it auto-locks to prevent you
from binge eating. (Laughter)
from binge eating. (Laughter)
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pa se automatski zatvori
da vas spreči da se opsesivno prejedate?
da vas spreči da se opsesivno prejedate?
Da, i ja bih to volela.
(Smeh)
09:12
I would like that, yeah.
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09:15
What if, when I was in Cambridge,
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Šta bi bilo da sam na Kembridžu
imala pristup svom emotivnom prenosu
u realnom vremenu,
u realnom vremenu,
09:17
I had access to my real-time
emotion stream,
emotion stream,
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09:19
and I could share that with my family
back home in a very natural way,
back home in a very natural way,
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da sam mogla da ga podelim to
sa porodicom kod kuće na prirodan način,
sa porodicom kod kuće na prirodan način,
09:23
just like I would've if we were all
in the same room together?
in the same room together?
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kao što bih uradila
da smo svi u istoj sobi zajedno?
da smo svi u istoj sobi zajedno?
09:27
I think five years down the line,
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Mislim da će kroz pet godina,
svi naši uređaji imati emotivni čip
09:30
all our devices are going
to have an emotion chip,
to have an emotion chip,
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09:32
and we won't remember what it was like
when we couldn't just frown at our device
when we couldn't just frown at our device
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i nećemo se sećati kako je bilo
kada nismo mogli
samo da se namrštimo uređajima,
samo da se namrštimo uređajima,
09:36
and our device would say, "Hmm,
you didn't like that, did you?"
you didn't like that, did you?"
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a da oni ne kažu:
„Hmm, to ti se nije baš svidelo, zar ne?”
„Hmm, to ti se nije baš svidelo, zar ne?”
09:41
Our biggest challenge is that there are
so many applications of this technology,
so many applications of this technology,
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Naš najveći izazov je
veliki broj korisnika ove tehnologije.
veliki broj korisnika ove tehnologije.
Moj tim i ja smo shvatili
da je ne možemo napraviti sami,
da je ne možemo napraviti sami,
09:44
my team and I realize that we can't
build them all ourselves,
build them all ourselves,
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09:47
so we've made this technology available
so that other developers
so that other developers
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pa smo ovu tehnologiju učinili dostupnom
tako da i drugi programeri
tako da i drugi programeri
mogu da počnu sa građenjem
i iskažu kreativnost.
i iskažu kreativnost.
09:51
can get building and get creative.
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09:53
We recognize that
there are potential risks
there are potential risks
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Razumemo da su mogući i rizici
09:57
and potential for abuse,
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i da je moguća zloupotreba,
09:59
but personally, having spent
many years doing this,
many years doing this,
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ali, budući da sam mnogo godina
provela u radu na ovome,
provela u radu na ovome,
verujem da su prednosti čovečanstva
10:02
I believe that the benefits to humanity
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sa emocionalno inteligentnom tehnologijom
10:05
from having emotionally
intelligent technology
intelligent technology
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važnije od moguće zloupotrebe.
10:07
far outweigh the potential for misuse.
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Pozivam vas sve da budete deo te rasprave.
10:11
And I invite you all to be
part of the conversation.
part of the conversation.
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10:13
The more people who know
about this technology,
about this technology,
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Što više ljudi zna za ovu tehnologiju,
10:16
the more we can all have a voice
in how it's being used.
in how it's being used.
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to će više nas imati pravo
da odlučuje kako će se ona koristiti.
da odlučuje kako će se ona koristiti.
10:21
So as more and more
of our lives become digital,
of our lives become digital,
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Dakle, kako naši životi postaju
sve više i više digitalni,
sve više i više digitalni,
10:25
we are fighting a losing battle
trying to curb our usage of devices
trying to curb our usage of devices
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sve više gubimo bitku u pokušaju
da smanjimo korišćenje ovih uređaja
da smanjimo korišćenje ovih uređaja
10:29
in order to reclaim our emotions.
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da bismo povratili naše emocije.
Dakle, ono što ja pokušavam umesto toga
je da uvedem emocije u našu tehnologiju
je da uvedem emocije u našu tehnologiju
10:32
So what I'm trying to do instead
is to bring emotions into our technology
is to bring emotions into our technology
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10:36
and make our technologies more responsive.
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da bi tehnologija bila prijemčivija.
10:38
So I want those devices
that have separated us
that have separated us
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Dakle, želim da nas ovi uređaji
koji su nas rastavili ponovo spoje.
koji su nas rastavili ponovo spoje.
10:41
to bring us back together.
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10:43
And by humanizing technology,
we have this golden opportunity
we have this golden opportunity
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Kada damo ljudska svojstva tehnologiji,
dobijamo zlatnu priliku
dobijamo zlatnu priliku
10:48
to reimagine how we
connect with machines,
connect with machines,
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da obnovimo način
na koji se povezujemo sa mašinama
na koji se povezujemo sa mašinama
10:51
and therefore, how we, as human beings,
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i stoga, kako se mi, kao ljudska bića,
povezujemo jedni sa drugima.
10:56
connect with one another.
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10:58
Thank you.
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Hvala vam.
(Aplauz)
11:00
(Applause)
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ABOUT THE SPEAKER
Rana el Kaliouby - Computer scientistWhat if a computer could recognize your facial expression, and react to how you feel? Rana el Kaliouby sees big possibilities in making technology emotionally aware.
Why you should listen
Rana el Kaliouby, chief science officer and co-founder of Affectiva, an MIT Media Lab spin-off, is on a mission to bring emotion intelligence to our digital experiences. She leads the company's emotion analytics team, which is responsible for developing emotion-sensing algorithms and mining the world's largest emotion data database. So far, they've collected 12 billion emotion data points from 2.9 million face videos from volunteers in 75 countries. The company’s platform is used by many Fortune Global 100 companies to measure consumer engagement, and is pioneering emotion-enabled digital apps for enterprise, entertainment, video communication and online education.
Entrepreneur magazine called el Kaliouby one of “The 7 Most Powerful Women To Watch in 2014,” and the MIT Technology Review included her in their list of the “Top 35 Innovators Under 35.”
Rana el Kaliouby | Speaker | TED.com