Roy Price is a man that most of you have probably never heard about, even though he may have been responsible for 22 somewhat mediocre minutes of your life on April 19, 2013. He may have also been responsible for 22 very entertaining minutes, but not very many of you. And all of that goes back to a decision that Roy had to make about three years ago.
Roj Prajs je čovek za koga većina vas verovatno nije nikad čula, iako je možda odgovoran za 22 prilično osrednja minuta vašeg života, 19. aprila 2013. Možda je takođe odgovoran za 22 veoma zabavna minuta, ali za mali broj vas. A sve se svodi na odluku koju je Roj morao da donese pre oko tri godine.
So you see, Roy Price is a senior executive with Amazon Studios. That's the TV production company of Amazon. He's 47 years old, slim, spiky hair, describes himself on Twitter as "movies, TV, technology, tacos." And Roy Price has a very responsible job, because it's his responsibility to pick the shows, the original content that Amazon is going to make. And of course that's a highly competitive space. I mean, there are so many TV shows already out there, that Roy can't just choose any show. He has to find shows that are really, really great. So in other words, he has to find shows that are on the very right end of this curve here.
Dakle, vidite, Roj Prajs je viši producent u Amazon Studios. To je Amazonova TV produkcijska firma. On ima 47 godina, vitak je, ima jež frizuru, sebe opisuje na Tviteru kao nekog ko voli "filmove, TV, tehnologiju i takose". A Roj Prajs ima veoma odgovoran posao jer je njegova odgovornost da izabere serije, originalne sadržine, koje će Amazon da snima. I naravno, to je izuzetno konkurentan prostor. Mislim, već postoji toliko TV serija te Roj ne može prosto da izabere bilo koju. Mora da pronađe serije koje su zaista, zaista sjajne. Drugim rečima, mora da pronađe serije koje su na samom desnom kraju ove krive ovde.
So this curve here is the rating distribution of about 2,500 TV shows on the website IMDB, and the rating goes from one to 10, and the height here shows you how many shows get that rating. So if your show gets a rating of nine points or higher, that's a winner. Then you have a top two percent show. That's shows like "Breaking Bad," "Game of Thrones," "The Wire," so all of these shows that are addictive, whereafter you've watched a season, your brain is basically like, "Where can I get more of these episodes?" That kind of show. On the left side, just for clarity, here on that end, you have a show called "Toddlers and Tiaras" --
Dakle, ova kriva ovde predstavlja raspodelu ocena oko 2,500 TV serija sa vebsajta IMDB, a ocene se kreću od jedan do deset, a visina krive vam pokazuje koliko je serija imalo tu ocenu. Pa, ako vaša serija dobije ocenu od devet poena ili više, to je pobednik. Onda imate seriju iz prvih dva posto. To su serije, poput: "Čiste hemije", "Igre prestola", "Žice", dakle, sve te serije koje vam uđu pod kožu, gde nakon što ste odgledali prvu sezonu, vaš mozak je u fazonu: "Gde da nađem još ovih epizoda?" Taj tip serije. Na levoj strani, čisto da bude jasno, ovde na kraju, imate seriju koja se zove "Devojčice i dijademe" -
(Laughter)
(Smeh)
-- which should tell you enough about what's going on on that end of the curve.
- to vam dovoljno govori o tome šta se dešava na tom kraju krive.
Now, Roy Price is not worried about getting on the left end of the curve, because I think you would have to have some serious brainpower to undercut "Toddlers and Tiaras." So what he's worried about is this middle bulge here, the bulge of average TV, you know, those shows that aren't really good or really bad, they don't really get you excited. So he needs to make sure that he's really on the right end of this.
E sad, Roja Prajsa ne brine da će završiti na levoj strani krive jer mislim da morate da posedujete prilično ozbiljne umne moći da podrijete "Devojčice i dijademe". Dakle, njega brine ovo središnje ispupčenje ovde, tu su prosečne TV serije, znate, one serije koje nisu naročito dobre, ni loše, zbog njih se ne uzbuđujete previše. Pa mora da se postara da zaista bude na desnom kraju ovoga.
So the pressure is on, and of course it's also the first time that Amazon is even doing something like this, so Roy Price does not want to take any chances. He wants to engineer success. He needs a guaranteed success, and so what he does is, he holds a competition.
Dakle, prisutan je pritisak i, naravno, takođe je prvi put da Amazon uopšte radi nešto slično, pa Roj Prajs ne želi da rizikuje. Želi da isplanira uspeh. Potreban mu je zagarantovan uspeh, pa on organizuje takmičenje.
So he takes a bunch of ideas for TV shows, and from those ideas, through an evaluation, they select eight candidates for TV shows, and then he just makes the first episode of each one of these shows and puts them online for free for everyone to watch. And so when Amazon is giving out free stuff, you're going to take it, right? So millions of viewers are watching those episodes.
Uzima gomilu ideja za TV serije i od tih ideja, kroz procenu, biraju osam kandidata za TV serije, a potom samo snimaju prvu epizodu svake od ovih serija i postavljaju ih besplatno na internet gde ih svako može gledati. A kada Amazon poklanja nešto, uzećete to, zar ne? Dakle, milioni gledalaca gledaju ove epizode.
What they don't realize is that, while they're watching their shows, actually, they are being watched. They are being watched by Roy Price and his team, who record everything. They record when somebody presses play, when somebody presses pause, what parts they skip, what parts they watch again. So they collect millions of data points, because they want to have those data points to then decide which show they should make. And sure enough, so they collect all the data, they do all the data crunching, and an answer emerges, and the answer is, "Amazon should do a sitcom about four Republican US Senators." They did that show.
Međutim, ne shvataju da dok gledaju svoje serije, zapravo njih gledaju. Posmatraju ih Roj Prajs i njegova ekipa, koji sve snimaju. Snimaju kad neko pritisne start, kad neko pritisne pauzu, koje delove preskaču, koje delove iznova gledaju. Tako su sakupili milione jedinica podataka jer su im potrebne te jedinice podataka kako bi potom odlučili koju će seriju snimati. I svakako, sakupili su sve podatke, usitnili su sve podatke i pojavio se odgovor, a odgovor je: "Amazon bi trebalo da snimi sitkom o četiri američka republikanska senatora." Snimili su tu seriju.
So does anyone know the name of the show? (Audience: "Alpha House.") Yes, "Alpha House," but it seems like not too many of you here remember that show, actually, because it didn't turn out that great. It's actually just an average show, actually -- literally, in fact, because the average of this curve here is at 7.4, and "Alpha House" lands at 7.5, so a slightly above average show, but certainly not what Roy Price and his team were aiming for. Meanwhile, however, at about the same time, at another company, another executive did manage to land a top show using data analysis, and his name is Ted, Ted Sarandos, who is the Chief Content Officer of Netflix, and just like Roy, he's on a constant mission to find that great TV show, and he uses data as well to do that, except he does it a little bit differently. So instead of holding a competition, what he did -- and his team of course -- was they looked at all the data they already had about Netflix viewers, you know, the ratings they give their shows, the viewing histories, what shows people like, and so on. And then they use that data to discover all of these little bits and pieces about the audience: what kinds of shows they like, what kind of producers, what kind of actors. And once they had all of these pieces together, they took a leap of faith, and they decided to license not a sitcom about four Senators but a drama series about a single Senator. You guys know the show?
Da li iko zna naziv te serije? (Publika: "Alpha House.") Da, "Alpha House", ali čini se da se nekolicina vas ovde zapravo seća te serije jer nije ispala naročito dobro. Zapravo se radi o prosečnoj seriji, zapravo - bukvalno, uistinu, jer prosečna vrednost na ovoj krivoj je 7,4, a "Alpha House" se nalazi na 7,5, dakle, samo malo iznad prosečne serije, ali svakako to nije ono što su Roj i njegova ekipa želeli. U međuvremenu, međutim, otprilike istovremeno, u drugoj firmi, drugi producent je uspeo da isporuči vrhunsku seriju, obradom podataka, a on se zove Ted, Ted Sarantos, on je glavni referent sadržaja na Netfliksu, i baš kao i Roj, on je stalno na misiji pronalaženja sjajne TV serije, i on koristi podatke da bi to postigao, samo što to on radi malčice drugačije. Pa su umesto organizovanja takmičenja, on - i naravno njegova ekipa - pogledali sve podatke koje su već imali o gledaocima Netfliksa, znate, ocene koje daju serijama, istoriju gledanja, koje serije ljudi vole i tako dalje. A onda su koristili te podatke da otkriju sve te sitnice i pojedinosti o publici: koje serije vole, koje producente, koje glumce. I kada su sklopili sve te komadiće, otisnuli su se u nepoznato i odlučili da odobre, ne sitkom o četvorici senatora, već dramu o jednom senatoru. Znate li, ljudi, tu seriju?
(Laughter)
(Smeh)
Yes, "House of Cards," and Netflix of course, nailed it with that show, at least for the first two seasons.
Da, "Kuća od karata" i Netfliks je naravno trijumfovao tom serijom, bar tokom prve dve sezone.
(Laughter) (Applause)
(Smeh) (Aplauz)
"House of Cards" gets a 9.1 rating on this curve, so it's exactly where they wanted it to be.
"Kuća od karata" ima ocenu od 9,1 na ovoj krivoj, dakle, tu su i želeli da stignu.
Now, the question of course is, what happened here? So you have two very competitive, data-savvy companies. They connect all of these millions of data points, and then it works beautifully for one of them, and it doesn't work for the other one. So why? Because logic kind of tells you that this should be working all the time. I mean, if you're collecting millions of data points on a decision you're going to make, then you should be able to make a pretty good decision. You have 200 years of statistics to rely on. You're amplifying it with very powerful computers. The least you could expect is good TV, right?
E sad, pitanje je naravno, šta se ovde desilo? Imate dve veoma konkurentne firme, spretne s podacima. One spajaju sve ove milione jedinica podataka, a onda se za jednu sve završi lepo, a za drugu ne. Zašto? Jer, logika vam govori da bi ovo trebalo stalno da funkcioniše. Mislim, ako sakupljate milione jedinica podataka za odluku koju donosite, onda bi trebalo da ste u stanju da donesete valjanu odluku. Imate da se oslonite na 200-godišnju statistiku. Nju ste pojačali izuzetno moćnim kompjuterima. Najmanje što biste očekivali je dobra televizija, zar ne?
And if data analysis does not work that way, then it actually gets a little scary, because we live in a time where we're turning to data more and more to make very serious decisions that go far beyond TV. Does anyone here know the company Multi-Health Systems? No one. OK, that's good actually. OK, so Multi-Health Systems is a software company, and I hope that nobody here in this room ever comes into contact with that software, because if you do, it means you're in prison.
A, ako obrada podataka ne funkcioniše tako, onda zapravo postaje malčice zastrašujuće jer živimo u vremenu u kom se sve više okrećemo podacima da bismo doneli veoma ozbiljne odluke koje sežu mimo televizije. Da li iko ovde zna za firmu Multi-Health Systems? Niko. U redu, to je zapravo dobro. U redu, Multi-Health Systems je softverska firma i nadam se da niko iz ove prostorije nikada neće doći u dodir s tim softverom jer ako dođete, to će značiti da ste u zatvoru.
(Laughter)
(Smeh)
If someone here in the US is in prison, and they apply for parole, then it's very likely that data analysis software from that company will be used in determining whether to grant that parole. So it's the same principle as Amazon and Netflix, but now instead of deciding whether a TV show is going to be good or bad, you're deciding whether a person is going to be good or bad. And mediocre TV, 22 minutes, that can be pretty bad, but more years in prison, I guess, even worse.
Ako je neko, ovde u SAD-u, u zatvoru i prijavi se za uslovnu, onda će verovatno softver te firme za obradu podataka da koriste kako bi utvrdili da li da vam odobre uslovnu. Dakle, isti je princip kao kod Amazona i Nefliksa, ali sad umesto odlučivanja o tome da li će serija da bude dobra ili loša, odlučuje se da li će osoba da bude dobra ili loša. A prosečna televizijska 22 minuta mogu da budu vrlo loša, ali dodatne godine u zatvoru su, valjda, još gore.
And unfortunately, there is actually some evidence that this data analysis, despite having lots of data, does not always produce optimum results. And that's not because a company like Multi-Health Systems doesn't know what to do with data. Even the most data-savvy companies get it wrong. Yes, even Google gets it wrong sometimes.
I nažalost, zapravo imamo neke dokaze da ova obrada podataka, uprkos velikom broju podataka, ne daje uvek optimalne rezultate. A to nije zato što firma, poput Multi-Health Systems ne zna šta da radi s podacima. Čak i firme koje su stručnjaci za podatke, greše. Da, čak i Gugl ponekad pogreši.
In 2009, Google announced that they were able, with data analysis, to predict outbreaks of influenza, the nasty kind of flu, by doing data analysis on their Google searches. And it worked beautifully, and it made a big splash in the news, including the pinnacle of scientific success: a publication in the journal "Nature." It worked beautifully for year after year after year, until one year it failed. And nobody could even tell exactly why. It just didn't work that year, and of course that again made big news, including now a retraction of a publication from the journal "Nature." So even the most data-savvy companies, Amazon and Google, they sometimes get it wrong. And despite all those failures, data is moving rapidly into real-life decision-making -- into the workplace, law enforcement, medicine. So we should better make sure that data is helping.
Godine 2009, Gugl je najavio da su u stanju, koristeći obradu podataka, da predvide epidemiju influence, gadnog oblika gripa, koristeći obradu podataka s Guglovih pretraga. I savršeno je funkcionisalo i dospeli su u glavne vesti, uključujući i vrhunac naučnog uspeha: objavu u magazinu "Nejčer". Savršeno je funkcionisalo godinu za godinom, sve dok jedne godine nije zatajilo. A niko nije čak mogao da tačno kaže zašto. Prosto nije funkcionisalo te godine i, naravno, to je ponovo bila glavna vest, uključujući sada i povlačenje članka iz časopisa "Nejčer". Dakle, čak i stručnjaci za podatke, Amazon i Gugl, ponekad pogreše. I uprkos svim tim neuspesima, podaci sve brže postaju deo odlučivanja u stvarnom životu - na poslu, u policiji, medicini. Dakle, trebalo bi da se postaramo da nam podaci budu korisni.
Now, personally I've seen a lot of this struggle with data myself, because I work in computational genetics, which is also a field where lots of very smart people are using unimaginable amounts of data to make pretty serious decisions like deciding on a cancer therapy or developing a drug. And over the years, I've noticed a sort of pattern or kind of rule, if you will, about the difference between successful decision-making with data and unsuccessful decision-making, and I find this a pattern worth sharing, and it goes something like this.
Sad, lično sam video mnogo ove borbe s podacima jer radim na polju računarske genetike, što je takođe oblast gde mnogo veoma pametnih ljudi koristi nezamislivu količinu podataka da bi doneli veoma ozbiljne odluke, poput odlučivanja o terapiji za rak ili o razvoju leka. I vremenom sam primetio nešto nalik obrascu ili nekakvom pravilu, ako hoćete, o razlici između uspešnog odlučivanja pomoću podataka i neuspešnog odlučivanja i smatram da ovaj obrazac vredi deliti, a radi se o sledećem.
So whenever you're solving a complex problem, you're doing essentially two things. The first one is, you take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces, and then of course you do the second part. You put all of these bits and pieces back together again to come to your conclusion. And sometimes you have to do it over again, but it's always those two things: taking apart and putting back together again.
Dakle, kad god rešavate složen problem, u suštini radite dve stvari. Prvo razlažete dati problem na sastavne komadiće i delove kako biste podrobno analizirali te komadiće i delove i potom, naravno, prelazite na drugi deo. Sastavljate ponovo sve ove komadiće i delove da biste došli do zaključka. A ponekad to morate da uradite više puta, ali uvek se radi o ove dve stvari: rastavljanju i ponovnom sastavljanju.
And now the crucial thing is that data and data analysis is only good for the first part. Data and data analysis, no matter how powerful, can only help you taking a problem apart and understanding its pieces. It's not suited to put those pieces back together again and then to come to a conclusion. There's another tool that can do that, and we all have it, and that tool is the brain. If there's one thing a brain is good at, it's taking bits and pieces back together again, even when you have incomplete information, and coming to a good conclusion, especially if it's the brain of an expert.
E sad, ključno je da su podaci i obrada podataka jedino korisni u prvom delu. Podaci i obrada podataka, ma koliko moćni bili, jedino vam mogu pomoći da razložite problem i da razumete njegove delove. Neodgovarajući su za ponovno sastavljanje tih delova i za dolaženje do zaključka. Postoji drugo oruđe koje može to da uradi i svi ga imamo, a to oruđe je mozak. Ako je mozak u nečemu dobar, to je ponovno sastavljanje komadića i delova, čak i kad imate nepotpunu informaciju, i dolaženje do dobrog zaljučka, naročito ako se radi o mozgu stručnjaka.
And that's why I believe that Netflix was so successful, because they used data and brains where they belong in the process. They use data to first understand lots of pieces about their audience that they otherwise wouldn't have been able to understand at that depth, but then the decision to take all these bits and pieces and put them back together again and make a show like "House of Cards," that was nowhere in the data. Ted Sarandos and his team made that decision to license that show, which also meant, by the way, that they were taking a pretty big personal risk with that decision. And Amazon, on the other hand, they did it the wrong way around. They used data all the way to drive their decision-making, first when they held their competition of TV ideas, then when they selected "Alpha House" to make as a show. Which of course was a very safe decision for them, because they could always point at the data, saying, "This is what the data tells us." But it didn't lead to the exceptional results that they were hoping for.
I zato verujem da je Netfliks bio uspešan jer su koristili podatke i mozak tamo gde im je i mesto u procesu. Koristili su podatke kako bi prvobitno shvatili gomile stvari o svojoj publici koje u suprotnom ne bi bili u stanju da razumeju tako podrobno, ali potom je odluka da uzmu sve te komadiće i delove i da ih ponovo sastave i naprave seriju, poput "Kuće od karata", toga nije bilo u podacima. Ted Sarandos i njegova ekipa su odlučili da odobre tu seriju, što je takođe značilo, usput, da su preuzimali prilično veliki lični rizik tom odlukom. A Amazon, s druge strane, oni su pogrešili u postupku. Vodili su se podacima sve vreme donošenja odluke, prvo kada su organizovali takmičenje u TV idejama, potom kada su odabrali da snimaju seriju "Alpha House". Što je naravno bila veoma bezbedna odluka za njih jer su uvek mogli da pokažu na podatke i kažu: "Tako su nam podaci rekli." Međutim to nije dovelo do izvanrednih rezultata kojima su se nadali.
So data is of course a massively useful tool to make better decisions, but I believe that things go wrong when data is starting to drive those decisions. No matter how powerful, data is just a tool, and to keep that in mind, I find this device here quite useful. Many of you will ...
Dakle, podaci su svakako izuzetno korisno oruđe da bolje odlučujete, ali verujem da stvari kreću po zlu kada su podaci vodeći u odlučivanju. Ma koliko moćni, podaci su samo oruđe, a da biste imali to na umu, smatram da je ovo sredstvo ovde korisno. Mnogi od vas će...
(Laughter)
(Smeh)
Before there was data, this was the decision-making device to use.
Pre podataka, ovo je bio uređaj koji ste koristili u odlučivanju.
(Laughter)
(Smeh)
Many of you will know this. This toy here is called the Magic 8 Ball, and it's really amazing, because if you have a decision to make, a yes or no question, all you have to do is you shake the ball, and then you get an answer -- "Most Likely" -- right here in this window in real time. I'll have it out later for tech demos.
Mnogima je ovo poznato. Ova igračka se zove magična osmica i zaista je izvanredna jer ako treba da odlučite o nečemu, o pitanju sa da ili ne, sve što je potrebno je da protresete kuglu i potom dobijate odgovor - "najverovatnije" - baš tu u ovom prorezu, u realnom vremenu. Kasnije ću je podvrći tehničkim probama.
(Laughter)
(Smeh)
Now, the thing is, of course -- so I've made some decisions in my life where, in hindsight, I should have just listened to the ball. But, you know, of course, if you have the data available, you want to replace this with something much more sophisticated, like data analysis to come to a better decision. But that does not change the basic setup. So the ball may get smarter and smarter and smarter, but I believe it's still on us to make the decisions if we want to achieve something extraordinary, on the right end of the curve. And I find that a very encouraging message, in fact, that even in the face of huge amounts of data, it still pays off to make decisions, to be an expert in what you're doing and take risks. Because in the end, it's not data, it's risks that will land you on the right end of the curve.
Sad, radi se, naravno - doneo sam neke odluke u svom životu za koje se kasnije ispostavilo da je trebalo da poslušam kuglu. Međutim, znate, naravno, ako su vam podaci dostupni, želećete da zamenite ovo nečim daleko prefinjenijim, poput obrade podataka, da biste doneli bolju odluku. Ali to ne menja osnovnu postavku. Dakle, kugla može da postaje sve pametnija i pametnija, ali verujem da je odlučivanje i dalje na nama, ako želimo da postignemo nešto izuzetno na desnom kraju ove krive. I za mene je to zapravo veoma ohrabrujuća poruka, da čak i kad ste suočeni s ogromnom količinom podataka, i dalje se isplati odlučivati, biti stručnjak u onome što radite i preuzimati rizike. Jer, naposletku, neće vas podaci već rizici smestiti na desni kraj ove krive.
Thank you.
Hvala vam.
(Applause)
(Aplauz)