Eric Berlow and Sean Gourley: Mapping ideas worth spreading

Prevodilac: Mile Živković Lektor: Dejan Vicai

Eric Berlow: I'm an ecologist, and Sean's a physicist,

Erik Berlou: Ja sam ekolog, a Šon je fizičar

and we both study complex networks.

i obojica proučavamo kompleksne mreže.

And we met a couple years ago when we discovered

Sreli smo se pre nekoliko godina kada smo otkrili

that we had both given a short TED Talk

da smo obojica imali kratak TED govor

about the ecology of war,

o ekologiji rata

and we realized that we were connected

i shvatili smo da smo povezani

by the ideas we shared before we ever met.

idejama koje smo delili pre nego što smo se upoznali.

And then we thought, you know, there are thousands

I onda smo pomislili da postoji hiljade

of other talks out there, especially TEDx Talks,

govora, naročito TEDx govora

that are popping up all over the world.

koji nastaju po celom svetu.

How are they connected,

and what does that global conversation look like?

Kako su oni povezani,

i na šta liči taj globalni razgovor?

So Sean's going to tell you a little bit about how we did that.

Šon će vam reći nešto o tome kako smo to uradili.

Sean Gourley: Exactly. So we took 24,000 TEDx Talks

Šon Gurli: Upravo tako. Uzeli smo 24 000 TEDx govora

from around the world, 147 different countries,

iz celog sveta, 147 različitih zemalja,

and we took these talks and we wanted to find

uzeli smo ih i pokušali smo da pronađemo

the mathematical structures that underly

matematičke strukture

the ideas behind them.

ideja iza njih.

And we wanted to do that so we could see how

To smo hteli da uradimo kako bismo videli na koji način

they connected with each other.

su povezani jedni sa drugima.

And so, of course, if you're going to do this kind of stuff,

Naravno, ako želite da radite nešto poput ovog,

you need a lot of data.

biće vam potrebno puno podataka.

So the data that you've got is a great thing called YouTube,

Podaci koje imate su sa ove sjajne stvari, Jutjuba

and we can go down and basically pull

i u osnovi možemo uzeti

all the open information from YouTube,

sve otvorene informacije sa Jutjuba,

all the comments, all the views, who's watching it,

sve komentare, broj pregleda, ko je gledao te snimke,

where are they watching it, what are they saying in the comments.

odakle ih gledaju, šta kažu u komentarima.

But we can also pull up, using speech-to-text translation,

Ali takođe putem prevođenja govora u tekst, možemo uzeti

we can pull the entire transcript,

ceo transkript

and that works even for people with kind of funny accents like myself.

i to funkcioniše čak i za ljude sa čudnim akcentom poput mog.

So we can take their transcript

Možemo uzeti njihov transkript

and actually do some pretty cool things.

i zapravo uraditi neke prilično kul stvari.

We can take natural language processing algorithms

Možemo da uzmemo algoritme za obradu prirodnog jezika

to kind of read through with a computer, line by line,

kako bimso na neki način čitali kompjuterom, red po red,

extracting key concepts from this.

izvlačeći glavne koncepte.

And we take those key concepts and they sort of form

Uzimamo te glavne koncepte i na neki način oni formiraju

this mathematical structure of an idea.

matematičku strukturu ideje.

And we call that the meme-ome.

To zovemo mimom.

And the meme-ome, you know, quite simply,

Jednostavno govoreći, mimom je

is the mathematics that underlies an idea,

matematika koja stoji iza ideje

and we can do some pretty interesting analysis with it,

i sa njom možemo da napravimo prilično zanimljivu analizu,

which I want to share with you now.

koju želim da sada podelim sa vama.

So each idea has its own meme-ome,

Svaka ideja ima sopstveni mimom

and each idea is unique with that,

i svaka ideja je tako unikatna,

but of course, ideas, they borrow from each other,

ali ideje naravno pozajmljuju jedna od druge,

they kind of steal sometimes,

ponekad kao da kradu,

and they certainly build on each other,

i svakako se unapređuju

and we can go through mathematically

i možemo matematički da prođemo

and take the meme-ome from one talk

i uzmemo mimom iz jednog govora

and compare it to the meme-ome from every other talk,

i uporedimo ga sa mimomom iz svakog drugog govora,

and if there's a similarity between the two of them,

i ako postoji sličnost između to dvoje,

we can create a link and represent that as a graph,

možemo da napravimo vezu i prikažemo je grafikonom,

just like Eric and I are connected.

kao što smo povezani Erik i ja.

So that's theory, that's great.

To je teorija, to je sjajno.

Let's see how it works in actual practice.

Hajde da vidimo kako zaista radi u praksi.

So what we've got here now is the global footprint

Ovde imamo globalni otisak

of all the TEDx Talks over the last four years

svih TEDx govora iz protekle 4 godine

exploding out around the world

kako eksplodiraju širom sveta

from New York all the way down to little old New Zealand in the corner.

od Njujorka pa sve dole do Novog Zelanda.

And what we did on this is we analyzed the top 25 percent of these,

Ovde smo analizirali gornjih 25% govora

and we started to see where the connections occurred,

i počeli smo da vidimo gde se javljaju veze,

where they connected with each other.

gde su bili povezani jedan sa drugim.

Cameron Russell talking about image and beauty

Kameron Rasel koja priča o izgledu i lepoti

connected over into Europe.

povezana je sa Evropom.

We've got a bigger conversation about Israel and Palestine

Razgovori o Izraelu i Palestini se više šire

radiating outwards from the Middle East.

ka napolju od Srednjeg istoka.

And we've got something a little broader

Imamo nešto malo šire

like big data with a truly global footprint

poput velikih podataka sa stvarno globalnim otiskom

reminiscent of a conversation

koji podseća na razgovor

that is happening everywhere.

koji se dešava svuda.

So from this, we kind of run up against the limits

Odavde smo nekako došli do granica onoga

of what we can actually do with a geographic projection,

što možemo da uradimo sa geografskom projekcijom,

but luckily, computer technology allows us to go out

ali srećom, kompjuterska tehnologija nam dozvoljava da zađemo

into multidimensional space.

u multidimenzionalni prostor.

So we can take in our network projection

Možemo uzeti našu projekciju mreže

and apply a physics engine to this,

i na nju primeniti podlogu za fiziku

and the similar talks kind of smash together,

i slični govori se nekako sudaraju

and the different ones fly apart,

a suprotni odbijaju

and what we're left with is something quite beautiful.

i za posledicu imamo nešto zaista predivno.

EB: So I want to just point out here that every node is a talk,

EB: Hteo bih da naglasim da je svaki čvor jedan govor,

they're linked if they share similar ideas,

povezani su ako imaju slične ideje

and that comes from a machine reading

i to potiče iz mašinskog čitanja

of entire talk transcripts,

transkripta celih govora,

and then all these topics that pop out,

a sve ove teme koje iskaču

they're not from tags and keywords.

nisu ključne reči i oznake.

They come from the network structure

One potiču iz mrežne strukture

of interconnected ideas. Keep going.

povezanih ideja. Nastavi.

SG: Absolutely. So I got a little quick on that,

ŠG: Naravno. Malo sam brzo uleteo u to,

but he's going to slow me down.

ali on će me usporiti.

We've got education connected to storytelling

Imamo obrazovanje povezano sa pričanjem priča,

triangulated next to social media.

u trouglu sa društvenim medijima.

You've got, of course, the human brain right next to healthcare,

Ljudski mozak je, naravno, odmah pored zdravstva,

which you might expect,

što se dalo očekivati,

but also you've got video games, which is sort of adjacent,

ali takođe imate video igrice, koje su donkele bliske

as those two spaces interface with each other.

jer se ta dva prostora susreću jedan sa drugim.

But I want to take you into one cluster

Hoću da vas povedem do jedne grupe

that's particularly important to me, and that's the environment.

koja mi je naročito bitna, a to je okolina.

And I want to kind of zoom in on that

Hteo bih da zumiram na to

and see if we can get a little more resolution.

i vidim da li možemo dobiti malo više rezolucije.

So as we go in here, what we start to see,

Kako zalazimo tu, opet primenjujemo fizičku podlogu

apply the physics engine again,

na ono što počinjemo da vidimo,

we see what's one conversation

vidimo da se jedan razgovor

is actually composed of many smaller ones.

zapravo sastoji od mnogo manjih.

The structure starts to emerge

Počinje da se javlja struktura

where we see a kind of fractal behavior

tamo gde vidimo fraktalno ponašanje

of the words and the language that we use

reči i jezika koje koristimo

to describe the things that are important to us

da bismo opisali nama bitne stvari

all around this world.

širom sveta.

So you've got food economy and local food at the top,

Ekonomija hrane i lokalna hrana su na vrhu,

you've got greenhouse gases, solar and nuclear waste.

imate efekat staklene bašte, solarni i nuklearni otpad.

What you're getting is a range of smaller conversations,

Dobijate asortiman manjih razgovora

each connected to each other through the ideas

koji su svi povezani jedan sa drugim kroz ideje

and the language they share,

i jezik koji su im zajednički

creating a broader concept of the environment.

i tako stvaraju širi koncept čovekove okoline.

And of course, from here, we can go

Odavde naravno možemo zumirati

and zoom in and see, well, what are young people looking at?

i pogledati, u šta to gledaju mladi?

And they're looking at energy technology and nuclear fusion.

Gledaju na tehnologiju energije i nuklearnu fuziju.

This is their kind of resonance

Ovo je njihova vrsta rezonance

for the conversation around the environment.

za razgovor o čovekovoj okolini.

If we split along gender lines,

Ako podelimo ovo na polove

we can see females resonating heavily

možemo videti da žene jako odjekuju

with food economy, but also out there in hope and optimism.

sa ekonomijom hrane, ali da ima i nade i optimizma.

And so there's a lot of exciting stuff we can do here,

Ima dosta toga uzbudljivog što ovde možemo uradi

and I'll throw to Eric for the next part.

i sada će Erik nastaviti sa sledećim delom.

EB: Yeah, I mean, just to point out here,

EB: Da, samo da naglasim,

you cannot get this kind of perspective

ovakvu vrstu perspektive ne možete dobiti

from a simple tag search on YouTube.

jednostavnom pretragom oznaka na Jutjubu.

Let's now zoom back out to the entire global conversation

Hajde sada da odzumiramo na ceo globalni razgovor

out of environment, and look at all the talks together.

o okolini i pogledamo sve govore zajedno.

Now often, when we're faced with this amount of content,

Često kada smo suočeni sa ovom količinom sadržaja

we do a couple of things to simplify it.

učinićemo nekoliko stvari da to pojednostavimo.

We might just say, well,

Možda samo kažemo,

what are the most popular talks out there?

pa koji su to najpopularniji govori?

And a few rise to the surface.

Nekoliko ih isplivava na površinu.

There's a talk about gratitude.

Tu je govor o zahvalnosti.

There's another one about personal health and nutrition.

Još jedan o ličnom zdravlju i ishrani.

And of course, there's got to be one about porn, right?

Naravno, mora da postoji i jedan o pornografiji, zar ne?

And so then we might say, well, gratitude, that was last year.

Onda možemo reći, zahvalnost je stvar prošle godine.

What's trending now? What's the popular talk now?

Šta je sada popularno? Koji je govor sada popularan?

And we can see that the new, emerging, top trending topic

Možemo videti da je nova, najpopularnija tema u nastajanju -

is about digital privacy.

digitalna privatnost.

So this is great. It simplifies things.

Ovo je sjajno. Pojednostavljuje stvari.

But there's so much creative content

Ali ima toliko kreativnog sadržaja

that's just buried at the bottom.

na samom dnu gomile.

And I hate that. How do we bubble stuff up to the surface

Mrzim to. Kako da na površinu dovedemo stvari koje su

that's maybe really creative and interesting?

možda zaista kreativne i zanimljive?

Well, we can go back to the network structure of ideas

Možemo se vratiti na mrežnu strukturu ideja

to do that.

kako bismo to uradili.

Remember, it's that network structure

Setite se, ta mrežna struktura

that is creating these emergent topics,

stvara ove novonastale teme.

and let's say we could take two of them,

Recimo da uzmemo dve takve teme

like cities and genetics, and say, well, are there any talks

poput gradova i genetike i vidimo da li ima govora

that creatively bridge these two really different disciplines.

koji na kreativan način povezuju ove dve zaista različite discipline.

And that's -- Essentially, this kind of creative remix

I to je - zapravo, ova vrsta kreativnog remiksa

is one of the hallmarks of innovation.

jedna je od oznaka inovacije.

Well here's one by Jessica Green

Evo jednog sa Džesikom Grin

about the microbial ecology of buildings.

o mikrobiološkoj ekologiji zgrada.

It's literally defining a new field.

Bukvalno se definiše novo polje.

And we could go back to those topics and say, well,

Možemo da se vratimo na te teme i kažemo,

what talks are central to those conversations?

koji govori su ključni za ove razgovore?

In the cities cluster, one of the most central

U grupi o gradovima, jedan od ključnih

was one by Mitch Joachim about ecological cities,

je govor Miča Joakima o ekološkim gradovima,

and in the genetics cluster,

a u grupi o genetici

we have a talk about synthetic biology by Craig Venter.

imamo govor o sintetičkoj biologiji Grega Ventera.

These are talks that are linking many talks within their discipline.

Ovo su govori koji povezuju mnogo drugih unutar svoje discipline.

We could go the other direction and say, well,

Možemo otići u drugom pravcu i reći,

what are talks that are broadly synthesizing

koji su govori koji naširoko sintetizuju

a lot of different kinds of fields.

više različitih polja?

We used a measure of ecological diversity to get this.

Da bismo ovo dobili koristili smo merenje ekološke raznovrsnosti.

Like, a talk by Steven Pinker on the history of violence,

Na primer, govor Stivena Pinkera o istoriji nasilja

very synthetic.

je veoma sintetički.

And then, of course, there are talks that are so unique

Naravno postoje i govori koji su tako unikatni

they're kind of out in the stratosphere, in their own special place,

da su u stratosferi, na svom posebnom mestu

and we call that the Colleen Flanagan index.

i to zovemo indeksom Kolin Flenagan.

And if you don't know Colleen, she's an artist,

Ako ne znate Kolin, ona je umetnica

and I asked her, "Well, what's it like out there

i pitao sam je: "Kako je to biti

in the stratosphere of our idea space?"

u stratosferi našeg prostora ideja?"

And apparently it smells like bacon.

Izgleda da to miriše kao slanina.

I wouldn't know.

Ne bih znao.

So we're using these network motifs

Koristimo ove motive mreže

to find talks that are unique,

da nađemo unikatne govore,

ones that are creatively synthesizing a lot of different fields,

one koji na kreativan način sintetizuju mnogo različitih polja,

ones that are central to their topic,

one koji su bitni za svoju temu

and ones that are really creatively bridging disparate fields.

i one koji na kreativan način povezuju nepovezana polja.

Okay? We never would have found those with our obsession

U redu? Ovo nikada ne bismo pronašli sa našom opsesijom

with what's trending now.

o tome šta je sada popularno.

And all of this comes from the architecture of complexity,

Ovo sve kreće od arhitekture kompleksnosti,

or the patterns of how things are connected.

od šablona po kojima su stvari povezane.

SG: So that's exactly right.

ŠG: Upravo tako.

We've got ourselves in a world

Zatekli smo se u svetu

that's massively complex,

koji je neverovatno kompleksan

and we've been using algorithms to kind of filter it down

i koristimo algoritme kako bismo sve to filtrirali

so we can navigate through it.

i upravljali kroz to.

And those algorithms, whilst being kind of useful,

Ovi algoritmi, iako su nekako korisni,

are also very, very narrow, and we can do better than that,

takođe su veoma uske primene i mi možemo bolje od toga

because we can realize that their complexity is not random.

jer možemo da shvatimo da njihova kompleksnost nije nasumična.

It has mathematical structure,

Ona ima matematičku strukturu

and we can use that mathematical structure

i možemo iskoristiti tu matematičku strukturu

to go and explore things like the world of ideas

da istražimo stvari poput sveta ideja

to see what's being said, to see what's not being said,

da vidimo šta se govori, šta se ne govori

and to be a little bit more human

i da budemo malo više ljudi

and, hopefully, a little smarter.

i nadam se malo pametniji.

Thank you.

Hvala vam.

(Applause)

(Aplauz)