ABOUT THE SPEAKER
Blaise Agüera y Arcas - Software architect
Blaise Agüera y Arcas works on machine learning at Google. Previously a Distinguished Engineer at Microsoft, he has worked on augmented reality, mapping, wearable computing and natural user interfaces.

Why you should listen

Blaise Agüera y Arcas is principal scientist at Google, where he leads a team working on machine intelligence for mobile devices. His group works extensively with deep neural nets for machine perception and distributed learning, and it also investigates so-called "connectomics" research, assessing maps of connections within the brain.

Agüera y Arcas' background is as multidimensional as the visions he helps create. In the 1990s, he authored patents on both video compression and 3D visualization techniques, and in 2001, he made an influential computational discovery that cast doubt on Gutenberg's role as the father of movable type.

He also created Seadragon (acquired by Microsoft in 2006), the visualization technology that gives Photosynth its amazingly smooth digital rendering and zoom capabilities. Photosynth itself is a vastly powerful piece of software capable of taking a wide variety of images, analyzing them for similarities, and grafting them together into an interactive three-dimensional space. This seamless patchwork of images can be viewed via multiple angles and magnifications, allowing us to look around corners or “fly” in for a (much) closer look. Simply put, it could utterly transform the way we experience digital images.

He joined Microsoft when Seadragon was acquired by Live Labs in 2006. Shortly after the acquisition of Seadragon, Agüera y Arcas directed his team in a collaboration with Microsoft Research and the University of Washington, leading to the first public previews of Photosynth several months later. His TED Talk on Seadragon and Photosynth in 2007 is rated one of TED's "most jaw-dropping." He returned to TED in 2010 to demo Bing’s augmented reality maps.

Fun fact: According to the author, Agüera y Arcas is the inspiration for the character Elgin in the 2012 best-selling novel Where'd You Go, Bernadette?

More profile about the speaker
Blaise Agüera y Arcas | Speaker | TED.com
TED2007

Blaise Agüera y Arcas: How PhotoSynth can connect the world's images

Blaise Aguera y Arcas demonstrira Photosynth

Filmed:
5,831,957 views

Blaisse Aguera y Arcas vodi sjajnu demonstraciju Photosynth-a, softvera koji bi mogao promijeniti način na koji promatramo digitalne slike. Koristeći fotografije odabrane s Weba, Photosynth gradi pejzaže iz sna koji oduzimaju dah i dopušta nam da se krećemo kroz njih.
- Software architect
Blaise Agüera y Arcas works on machine learning at Google. Previously a Distinguished Engineer at Microsoft, he has worked on augmented reality, mapping, wearable computing and natural user interfaces. Full bio

Double-click the English transcript below to play the video.

00:25
What I'm going to showpokazati you first, as quicklybrzo as I can,
0
0
2000
Ono što ću vam prvo pokazati, što brže mogu,
00:27
is some foundationaltemeljni work, some newnovi technologytehnologija
1
2000
4000
jest neki osnovni rad, neka nova tehnologija
00:31
that we broughtdonio to MicrosoftMicrosoft as partdio of an acquisitionstjecanje
2
6000
3000
koju smo donijeli u Microsoft kao dio akvizicije
00:34
almostskoro exactlytočno a yeargodina agoprije. This is SeadragonSeadragon,
3
9000
3000
skoro točno prije godinu dana. Ovo je Seadragon.
00:37
and it's an environmentokolina in whichkoji you can eitherili locallylokalno or remotelydaljinski
4
12000
3000
I to je okoliš u kojem možete lokalno ili udaljeno
00:40
interactinterakcija with vastogroman amountsiznosi of visualvidni datapodaci.
5
15000
3000
biti u interakciji s ogromnom količinom vizualnih podataka.
00:43
We're looking at manymnogi, manymnogi gigabytesgigabyte-ovi of digitaldigitalni photosfotografije here
6
18000
3000
Gledamo mnoštvo i mnoštvo gigabajta digitalnih fotografija
00:46
and kindljubazan of seamlesslyneprimjetno and continuouslyneprekidno zoomingzumiranje in,
7
21000
3000
i nekako neprimjetno i kontinuirano povećavamo,
00:50
panningpomicanja throughkroz the thing, rearrangingpreraspodjela it in any way we want.
8
25000
2000
pomičemo se, organiziramo ih kako god želimo.
00:52
And it doesn't matterstvar how much informationinformacija we're looking at,
9
27000
4000
I nije važno koliku količinu informacija promatramo,
00:56
how bigvelika these collectionszbirke are or how bigvelika the imagesslika are.
10
31000
3000
nije važno kolike su te kolekcije ili kolike su slike.
00:59
MostVećina of them are ordinaryobičan digitaldigitalni camerafotoaparat photosfotografije,
11
34000
2000
Većina njih su obične fotografije s digitalne kamere,
01:01
but this one, for exampleprimjer, is a scanskenirati from the LibraryBiblioteka of CongressKongres,
12
36000
3000
ali ova na primjer, je sken iz knjižnice Kongresa,
01:05
and it's in the 300 megapixelmegapixel rangeopseg.
13
40000
2000
i nalazi se u rasponu od 300 megapiksela.
01:08
It doesn't make any differencerazlika
14
43000
1000
Nema nikakve razlike
01:09
because the only thing that oughttreba to limitograničiti the performanceizvođenje
15
44000
3000
jer jedina stvar koja bi trebala ograničavati performanse
01:12
of a systemsistem like this one is the numberbroj of pixelspiksela on your screenzaslon
16
47000
3000
sustava poput ovog je broj piksela na vašem ekranu
01:15
at any givendan momenttrenutak. It's alsotakođer very flexiblefleksibilno architecturearhitektura.
17
50000
3000
u bilo kojem datom trenutku. To je također vrlo fleksibilna arhitektura.
01:18
This is an entirečitav bookrezervirati, so this is an exampleprimjer of non-imagesobe-slike datapodaci.
18
53000
3000
Ovo je čitava knjiga, primjer podataka koji nisu slike.
01:22
This is "BleakPust Housekuća" by DickensDickens. EverySvaki columnkolona is a chapterpoglavlje.
19
57000
5000
Ovo je Hladna kuća od Dickensa. Svaki stupac je poglavlje.
01:27
To provedokazati to you that it's really texttekst, and not an imageslika,
20
62000
4000
Kako bih vam dokazao da je to stvarno tekst, a ne slika,
01:31
we can do something like so, to really showpokazati
21
66000
2000
možemo učiniti nešto poput ovoga, da zaista pokažemo
01:33
that this is a realstvaran representationprikaz of the texttekst; it's not a pictureslika.
22
68000
3000
da je ovo stvarni prikaz teksta, da nije slika.
01:37
Maybe this is a kindljubazan of an artificialUmjetna way to readčitati an e-booke-knjiga.
23
72000
2000
Možda je ovo pomalo umjetan način da se čita e-knjiga.
01:39
I wouldn'tne bi recommendPreporuči it.
24
74000
1000
Ne bih ga preporučio.
01:40
This is a more realisticrealno casespis. This is an issueizdanje of The GuardianČuvar.
25
75000
3000
Ovo je malo realističniji slučaj. Ovo je primjerak Guardian-a.
01:43
EverySvaki largeveliki imageslika is the beginningpočetak of a sectionodjeljak.
26
78000
2000
Svaka velika slika je početak odjeljka.
01:45
And this really givesdaje you the joyradost and the good experienceiskustvo
27
80000
3000
I ovo zaista daje radost i dobro iskustvo
01:48
of readingčitanje the realstvaran paperpapir versionverzija of a magazinečasopis or a newspapernovine,
28
83000
5000
čitanja prave papirnate verzije časopisa ili novina,
01:54
whichkoji is an inherentlyinherentno multi-scales više skala kindljubazan of mediumsrednji.
29
89000
1000
koji su sami po sebi medij s više razina.
01:56
We'veMoramo alsotakođer doneučinio a little something
30
91000
1000
Također smo napravili ponešto
01:57
with the cornerugao of this particularposebno issueizdanje of The GuardianČuvar.
31
92000
3000
s uglom ovog primjerka Guardian-a.
02:00
We'veMoramo madenapravljen up a fakelažan adoglas that's very highvisok resolutionrezolucija --
32
95000
3000
Osmislili smo lažni oglas koji je vrlo visoke rezolucije --
02:03
much higherviši than you'dti bi be ableu stanju to get in an ordinaryobičan adoglas --
33
98000
2000
puno više nego u običnom oglasu --
02:05
and we'veimamo embeddedugrađen extraekstra contentsadržaj.
34
100000
2000
i ugradili smo dodatne sadržaje.
02:07
If you want to see the featuresznačajke of this carautomobil, you can see it here.
35
102000
2000
Ako želite vidjeti osobine ovog auta, možete ih vidjeti ovdje.
02:10
Or other modelsmodeli, or even technicaltehnička specificationsspecifikacije.
36
105000
4000
Ili druge modele, ili čak tehničke specifikacije.
02:15
And this really getsdobiva at some of these ideasideje
37
110000
2000
I ovo stvarno pokreće neke od tih ideja
02:18
about really doing away with those limitsgranice on screenzaslon realstvaran estateimanje.
38
113000
4000
da se uklone granice oglašavanja nekretnina na ekranima.
02:22
We hopenada that this meanssredstva no more pop-upsskočni prozori
39
117000
2000
Nadamo se da će to značiti kraj prozora koji iskaču
02:24
and other kindljubazan of rubbishsmeće like that -- shouldn'tne treba be necessarypotreban.
40
119000
2000
i druge vrste smeća poput tog -- to ne bi bilo potrebno.
02:27
Of coursenaravno, mappingkartografija is one of those really obviousočigledan applicationsaplikacije
41
122000
2000
Naravno, kartografija je jedna od zaista očitih primjena
02:29
for a technologytehnologija like this.
42
124000
2000
za ovakvu tehnologiju.
02:31
And this one I really won'tnavika spendprovesti any time on,
43
126000
2000
Na ovo uopće neću trošiti vrijeme,
02:33
exceptosim to say that we have things to contributedoprinijeti to this fieldpolje as well.
44
128000
2000
samo ću reći da imamo što doprinjeti i na ovom polju.
02:37
But those are all the roadsceste in the U.S.
45
132000
2000
Ali ovo su sve ceste u Americi
02:39
superimposedpreklapaju on topvrh of a NASANASA geospatialgeoprostorne imageslika.
46
134000
4000
postavljene iznad NASA-ine geoprostorne slike.
02:44
So let's pullVuci up, now, something elsedrugo.
47
139000
2000
Podignimo, sada nešto drugo.
02:46
This is actuallyzapravo liveživjeti on the WebWeb now; you can go checkprovjeriti it out.
48
141000
3000
Ovo je zapravo živo na internetu sada, možete ga otići provjeriti.
02:49
This is a projectprojekt calledzvao PhotosynthPhotosynth,
49
144000
1000
Ovo je projekt koji se zove Photosynth,
02:51
whichkoji really marriesse ženi two differentdrugačiji technologiestehnologije.
50
146000
1000
koji združuje dvije različite tehnologije.
02:52
One of them is SeadragonSeadragon
51
147000
1000
Jedna od njih je Seadragon
02:54
and the other is some very beautifullijep computerračunalo visionvizija researchistraživanje
52
149000
2000
a druga je prekrasno istraživanje vezano uz računalni vid
02:57
doneučinio by NoahNoa SnavelySnavely, a graduatediplomirani studentstudent at the UniversitySveučilište of WashingtonWashington,
53
152000
2000
kojim se bavi Noah Snavely, apsolvent na Sveučilištu u Washingtonu,
03:00
co-advisedKo savjetuje by SteveSteve SeitzToplinom at U.W.
54
155000
2000
pod savjetovanjem od strane Steve Seitza na Sveučilištu u Washingtonu
03:02
and RickRick SzeliskiSzeliski at MicrosoftMicrosoft ResearchIstraživanja. A very nicelijepo collaborationkolaboracija.
55
157000
4000
i Ricka Szeilski pri Microsoft istraživanjima. Vrlo dobra suradnja.
03:07
And so this is liveživjeti on the WebWeb. It's poweredpogon by SeadragonSeadragon.
56
162000
2000
Dakle ovo je uživo na Internetu. Pokreće ga Seadragon.
03:09
You can see that when we kindljubazan of do these sortsvrste of viewspregleda,
57
164000
2000
Možete vidjeti to kad radimo ovakve vrste pogleda,
03:12
where we can diveronjenje throughkroz imagesslika
58
167000
1000
gdje možemo zaroniti kroz slike
03:14
and have this kindljubazan of multi-resolutionmulti-rezolucije experienceiskustvo.
59
169000
1000
i imamo ovakvo više-rezolucijsko iskustvo.
03:16
But the spatialprostorni arrangementaranžman of the imagesslika here is actuallyzapravo meaningfulznačajan.
60
171000
4000
Ali prostorni raspored slika ovdje je zaista smislen.
03:20
The computerračunalo visionvizija algorithmsalgoritmi have registeredregistrirani these imagesslika togetherzajedno
61
175000
3000
Algoritmi za računalni vid su registrirali ove slike skupa,
03:23
so that they correspondodgovaraju to the realstvaran spaceprostor in whichkoji these shotssnimke --
62
178000
4000
tako da odgovaraju stvarnom prostoru u kojem su ove slike --
03:27
all takenpoduzete nearblizu GrassiGrassi LakesJezera in the CanadianKanadski RockiesRockies --
63
182000
2000
sve slikane blizu jezera Grassi u kanadskom Stjenjaku --
03:31
all these shotssnimke were takenpoduzete. So you see elementselementi here
64
186000
2000
napravljene. Dakle ovdje vidite elemente
03:33
of stabilizedstabiliziran slide-showprikaz slajdova or panoramicpanoramski imagingobrada slike,
65
188000
4000
stabilizirane prezentacije ili panoramskog slikanja,
03:40
and these things have all been relatedpovezan spatiallyprostorno.
66
195000
2000
i ovo su sve stvari koje su prostorno povezane.
03:42
I'm not sure if I have time to showpokazati you any other environmentsokruženja.
67
197000
3000
Nisam siguran imam li vremena da vam pokažem neke druge okoliše.
03:45
There are some that are much more spatialprostorni.
68
200000
1000
Koji su puno prostraniji.
03:47
I would like to jumpskok straightravno to one of Noah'sNoina originalizvornik data-setsskupova podataka --
69
202000
3000
Želio bih odmah prijeći na jedne od Noinih originalnih setova podataka --
03:50
and this is from an earlyrano prototypeprototip of PhotosynthPhotosynth
70
205000
2000
i ovo je iz ranog prototipa Photosyntha
03:52
that we first got workingrad in the summerljeto --
71
207000
2000
koji smo prvo počeli raditi u ljeto --
03:54
to showpokazati you what I think
72
209000
1000
da vam pokažem što mislim
03:55
is really the punchudarac linecrta behindiza this technologytehnologija,
73
210000
3000
što je zapravo ponta ove tehnologije,
03:59
the PhotosynthPhotosynth technologytehnologija. And it's not necessarilyobavezno so apparentOčito
74
214000
2000
Photosynth tehnologije. I ona nije nužno tako očita
04:01
from looking at the environmentsokruženja that we'veimamo put up on the websiteweb stranica.
75
216000
3000
kao gledanje okoliša koje smo stavili na web stranicu.
04:04
We had to worrybrinuti about the lawyersodvjetnici and so on.
76
219000
2000
Morali smo se brinuti za odvjetnike i tako dalje.
04:07
This is a reconstructionrekonstrukcija of NotreNotre DameDame CathedralKatedrala
77
222000
1000
Ovo je rekonstrukcija katedrale Notre Dame
04:09
that was doneučinio entirelypotpuno computationallyRačunski
78
224000
2000
koja je napravljena potpuno računalno
04:11
from imagesslika scrapedstruganje from FlickrFlickr. You just typetip NotreNotre DameDame into FlickrFlickr,
79
226000
3000
od slika skupljenih na Flickr-u. Samo utipkate Notre Dame u Flickr,
04:14
and you get some picturesSlike of guys in t-shirtsmajice, and of the campuskampus
80
229000
3000
i dobijete slike ljudi u majicama, i sveučilišta
04:17
and so on. And eachsvaki of these orangenarančasta conesčešeri representspredstavlja an imageslika
81
232000
4000
i tako dalje. I svaki od ovih narančastih čunjeva predstavlja sliku
04:22
that was discoveredotkriven to belongpripadati to this modelmodel.
82
237000
2000
za koju se otkrilo da pripada ovom modelu.
04:26
And so these are all FlickrFlickr imagesslika,
83
241000
2000
Tako da su ovo sve slike s Flickr-a,
04:28
and they'vešto ga do all been relatedpovezan spatiallyprostorno in this way.
84
243000
3000
i one su sve povezane prostorno ovim putem.
04:31
And we can just navigateploviti in this very simplejednostavan way.
85
246000
2000
I možemo se kretati kroz njih na ovaj jednostavan način.
04:35
(ApplausePljesak)
86
250000
9000
(Pljesak)
04:44
You know, I never thought that I'd endkraj up workingrad at MicrosoftMicrosoft.
87
259000
2000
Znate, nikad nisam mislio da ću raditi u Microsoftu.
04:46
It's very gratifyingprijatan to have this kindljubazan of receptionrecepcija here.
88
261000
4000
Zaista je lijepo biti primljen na ovaj način ovdje.
04:50
(LaughterSmijeh)
89
265000
3000
(Smijeh)
04:53
I guessnagađati you can see
90
268000
3000
Pretpostavljam da možete vidjeti
04:56
this is lots of differentdrugačiji typesvrste of cameraskamere:
91
271000
2000
da je ovo mnoštvo različitih kamera:
04:58
it's everything from cellćelija phonetelefon cameraskamere to professionalprofesionalac SLRsSLR fotoaparata,
92
273000
3000
tu imamo sve od kamera s mobilnih telefona do profesionalnih fotoaparata,
05:02
quitedosta a largeveliki numberbroj of them, stitchedprošiveni
93
277000
1000
zaista velik broj njih, povezanih
05:03
togetherzajedno in this environmentokolina.
94
278000
1000
u ovom okolišu.
05:04
And if I can, I'll find some of the sortvrsta of weirdčudan onesone.
95
279000
2000
I ako uspijem, pronaći ću neke od zaista čudnih.
05:08
So manymnogi of them are occludedputovizatvaraju by faceslica, and so on.
96
283000
3000
Mnoge od njih su zaklonjene licima, i tako dalje.
05:13
SomewhereNegdje in here there are actuallyzapravo
97
288000
1000
Negdje ovdje imamo zapravo
05:15
a seriesniz of photographsfotografije -- here we go.
98
290000
1000
serije fotografija -- evo nas.
05:17
This is actuallyzapravo a posterposter of NotreNotre DameDame that registeredregistrirani correctlyispravno.
99
292000
3000
Ovo je zapravo plakat Notre Dame koji se registrirao ispravno.
05:21
We can diveronjenje in from the posterposter
100
296000
2000
Možemo zaroniti iz plakata
05:24
to a physicalfizička viewpogled of this environmentokolina.
101
299000
3000
u fizički pogled na ovaj okoliš.
05:31
What the pointtočka here really is is that we can do things
102
306000
3000
Ono što je zapravo poanta ovdje, je to da možemo činiti razne stvari
05:34
with the socialsocijalni environmentokolina. This is now takinguzimanje datapodaci from everybodysvi --
103
309000
5000
sa socijalnim okolišem. Ovdje uzimamo podatke od svih --
05:39
from the entirečitav collectivekolektivan memorymemorija
104
314000
1000
od cjelokupne kolektivne memorije
05:40
of, visuallyvizuelno, of what the EarthZemlja looksizgled like --
105
315000
2000
onoga kako Zemlja izgleda --
05:43
and linkveza all of that togetherzajedno.
106
318000
1000
i sve smo to povezali.
05:44
All of those photosfotografije becomepostati linkedpovezan togetherzajedno,
107
319000
2000
Sve ove slike su postale povezane,
05:46
and they make something emergentpojavni
108
321000
1000
da bi stvorile nešto što izranja
05:47
that's greaterviše than the sumiznos of the partsdijelovi.
109
322000
2000
što je veće od zbroja svih dijelova.
05:49
You have a modelmodel that emergesproizlazi of the entirečitav EarthZemlja.
110
324000
2000
Imate model koji izranja iz čitave Zemlje.
05:51
Think of this as the long tailrep to StephenStjepan Lawler'sLover 's VirtualVirtualni EarthZemlja work.
111
326000
5000
Razmišljajte o ovome kao o nastavku rada Stephena Lawlera na Virtualnoj zemlji.
05:56
And this is something that growsraste in complexitysloženost
112
331000
2000
I ovo je nešto čemu složenost raste
05:58
as people use it, and whosečije benefitsprednosti becomepostati greaterviše
113
333000
3000
kako ga ljudi koriste, i čije povlastice postaju veće
06:01
to the usersKorisnici as they use it.
114
336000
2000
za korisnike kako ga koriste.
06:03
TheirNjihova ownvlastiti photosfotografije are gettinguzimajući taggedOznačene with meta-datameta-podataka
115
338000
2000
Njihove slike se označavaju meta podatcima
06:05
that somebodyneko elsedrugo enteredušao.
116
340000
1000
koje je netko drugi unio.
06:07
If somebodyneko botheredsmeta to tagoznaka all of these saintssveci
117
342000
3000
Ako se netko potrudio označiti sve ove svece
06:10
and say who they all are, then my photofoto of NotreNotre DameDame CathedralKatedrala
118
345000
3000
i reći tko su oni, onda je moja slika katedrale Notre Dame
06:13
suddenlyiznenada getsdobiva enrichedObogaćen with all of that datapodaci,
119
348000
2000
iznenada postala obogaćena svim ovim podatcima,
06:15
and I can use it as an entryulaz pointtočka to diveronjenje into that spaceprostor,
120
350000
3000
i ja ju mogu koristiti kao točku ulaza kako bih zaronio u taj prostor,
06:18
into that meta-verseMeta-stih, usingkoristeći everybodysvi else'sdrugo je photosfotografije,
121
353000
2000
u taj meta svemir, koristeći fotografije svih drugih,
06:21
and do a kindljubazan of a cross-modaltransmodalne
122
356000
2000
kako bih napravio isprepleteni model
06:25
and cross-userkriž-korisnik socialsocijalni experienceiskustvo that way.
123
360000
3000
i isprepleteno korisničko iskustvo na ovaj način.
06:28
And of coursenaravno, a by-productnusproizvod of all of that
124
363000
1000
I naravno, kao nusproizvod svega toga
06:30
is immenselyneizmjerno richbogat virtualvirtualan modelsmodeli
125
365000
2000
strašno bogate virtualne modele
06:32
of everysvaki interestingzanimljiv partdio of the EarthZemlja, collectedprikupljeni
126
367000
2000
svakog zanimljivog dijela Zemlje, prikupljenih
06:35
not just from overheadnad glavom flightsletovi and from satellitesatelit imagesslika
127
370000
3000
ne samo slikama iz aviona ili satelitskim slikama
06:38
and so on, but from the collectivekolektivan memorymemorija.
128
373000
2000
i tako dalje, već i iz kolektivne memorije.
06:40
Thank you so much.
129
375000
2000
Hvala vam puno.
06:42
(ApplausePljesak)
130
377000
11000
(Pljesak)
06:53
ChrisChris AndersonAnderson: Do I understandrazumjeti this right? That what your softwaresoftver is going to allowdopustiti,
131
388000
4000
Chris Anderson: Jesam li dobro shvatio? Da će ovo što tvoj softver nudi dozvoliti,
06:58
is that at some pointtočka, really withinunutar the nextSljedeći fewnekoliko yearsgodina,
132
393000
2000
da u jednom trenutku, zapravo u sljedećih nekoliko godina,
07:01
all the picturesSlike that are sharedpodijeljen by anyonebilo tko acrosspreko the worldsvijet
133
396000
4000
sve slike koje se dijele bilo gdje u svijetu
07:05
are going to basicallyu osnovi linkveza togetherzajedno?
134
400000
2000
budu povezane?
07:07
BAABAA: Yes. What this is really doing is discoveringotkrivanja.
135
402000
2000
BAA: Da. Ono što ovo zapravo čini je otkriva.
07:09
It's creatingstvaranje hyperlinkshiperveze, if you will, betweenizmeđu imagesslika.
136
404000
3000
Kreira hiper veze, da tako kažem, između slika.
07:12
And it's doing that
137
407000
1000
I to čini
07:13
basedzasnovan on the contentsadržaj insideiznutra the imagesslika.
138
408000
1000
na osnovu sadražaja na slikama.
07:14
And that getsdobiva really excitinguzbudljiv when you think about the richnessbogatstvo
139
409000
3000
I to postaje zaista uzbudljivo kad razmišljaš o bogatstvu
07:17
of the semanticsemantički informationinformacija that a lot of those imagesslika have.
140
412000
2000
jezikoslovnih informacija koje puno ovih slika ima.
07:19
Like when you do a webmreža searchtraži for imagesslika,
141
414000
2000
Poput toga kada tražite slike na Internetu,
07:22
you typetip in phrasesfraze, and the texttekst on the webmreža pagestranica
142
417000
2000
samo upišete frazu, i tekst na web stranici
07:24
is carryingnošenje a lot of informationinformacija about what that pictureslika is of.
143
419000
3000
sadrži puno podataka o tome o čemu je slika.
07:27
Now, what if that pictureslika linkslinkovi to all of your picturesSlike?
144
422000
2000
A što ako se ta slika poveže sa svim vašim slikama?
07:29
Then the amountiznos of semanticsemantički interconnectionmeđusobnu povezanost
145
424000
2000
Onda je količina jezikoslovne povezanosti
07:31
and the amountiznos of richnessbogatstvo that comesdolazi out of that
146
426000
1000
i količina bogatstva koja proizlazi iz toga
07:32
is really hugeogroman. It's a classicklasik networkmreža effectposljedica.
147
427000
3000
zaista ogromna. To je klasičan efekt mreže.
07:35
CACA: BlaiseVlaha, that is trulyuistinu incrediblenevjerojatan. CongratulationsČestitke.
148
430000
2000
CA: Blaise, ovo je zaista nevjerojatno. Čestitam.
07:37
BAABAA: ThanksHvala so much.
149
432000
1000
BAA: Hvala vam jako puno.

▲Back to top

ABOUT THE SPEAKER
Blaise Agüera y Arcas - Software architect
Blaise Agüera y Arcas works on machine learning at Google. Previously a Distinguished Engineer at Microsoft, he has worked on augmented reality, mapping, wearable computing and natural user interfaces.

Why you should listen

Blaise Agüera y Arcas is principal scientist at Google, where he leads a team working on machine intelligence for mobile devices. His group works extensively with deep neural nets for machine perception and distributed learning, and it also investigates so-called "connectomics" research, assessing maps of connections within the brain.

Agüera y Arcas' background is as multidimensional as the visions he helps create. In the 1990s, he authored patents on both video compression and 3D visualization techniques, and in 2001, he made an influential computational discovery that cast doubt on Gutenberg's role as the father of movable type.

He also created Seadragon (acquired by Microsoft in 2006), the visualization technology that gives Photosynth its amazingly smooth digital rendering and zoom capabilities. Photosynth itself is a vastly powerful piece of software capable of taking a wide variety of images, analyzing them for similarities, and grafting them together into an interactive three-dimensional space. This seamless patchwork of images can be viewed via multiple angles and magnifications, allowing us to look around corners or “fly” in for a (much) closer look. Simply put, it could utterly transform the way we experience digital images.

He joined Microsoft when Seadragon was acquired by Live Labs in 2006. Shortly after the acquisition of Seadragon, Agüera y Arcas directed his team in a collaboration with Microsoft Research and the University of Washington, leading to the first public previews of Photosynth several months later. His TED Talk on Seadragon and Photosynth in 2007 is rated one of TED's "most jaw-dropping." He returned to TED in 2010 to demo Bing’s augmented reality maps.

Fun fact: According to the author, Agüera y Arcas is the inspiration for the character Elgin in the 2012 best-selling novel Where'd You Go, Bernadette?

More profile about the speaker
Blaise Agüera y Arcas | Speaker | TED.com