Technology has brought us so much: the moon landing, the Internet, the ability to sequence the human genome. But it also taps into a lot of our deepest fears, and about 30 years ago, the culture critic Neil Postman wrote a book called "Amusing Ourselves to Death," which lays this out really brilliantly. And here's what he said, comparing the dystopian visions of George Orwell and Aldous Huxley. He said, Orwell feared we would become a captive culture. Huxley feared we would become a trivial culture. Orwell feared the truth would be concealed from us, and Huxley feared we would be drowned in a sea of irrelevance. In a nutshell, it's a choice between Big Brother watching you and you watching Big Brother. (Laughter)
Tehnologija nam je donela puno toga: sletanje na Mesec, internet, mogućnost da sekvenciramo ljudski genom. Ali je i dotakla mnogo naših najdubljih strahova, i pre oko 30 godina, kritičar kulture Nil Postman napisao je knjigu po imenu "Zabavljamo se na mrtvo", koja to pokazuje na briljantan način. I evo šta je on rekao, upoređujući distopijske vizije Džordža Orvela i Oldosa Hakslija. Rekao je: "Orvel se plašio da ćemo postati zatočena kultura. Haksli se plašio da ćemo postati beznačajna kultura. Orvel se plašio da će istina biti sakrivena od nas, a Haksli se plašio da ćemo se udaviti u moru nebitnih stvari". Ukratko, izbor se svodi na Velikog Brata koji posmatra tebe i tebe koji posmatraš "Velikog brata". (Smeh)
But it doesn't have to be this way. We are not passive consumers of data and technology. We shape the role it plays in our lives and the way we make meaning from it, but to do that, we have to pay as much attention to how we think as how we code. We have to ask questions, and hard questions, to move past counting things to understanding them. We're constantly bombarded with stories about how much data there is in the world, but when it comes to big data and the challenges of interpreting it, size isn't everything. There's also the speed at which it moves, and the many varieties of data types, and here are just a few examples: images, text, video, audio. And what unites this disparate types of data is that they're created by people and they require context.
Ali ne mora da bude tako. Mi nismo pasivni potrošači podataka i tehnologije. Mi oblikujemo ulogu koju oni igraju u našim životima i način na koji ih shvatamo, ali da bismo to uradili, moramo da obratimo onoliko pažnje na to kako mislimo koliko i na to kako kodiramo. Moramo da postavljamo pitanja, teška pitanja, da pređemo sa brojanja stvari na njihovo razumevanje. Konstantno smo bombardovani pričama o tome koliko podataka ima u svetu, ali kada se radi o velikoj količini podataka i izazovima njihovog tumačenja, količina nije sve. Bitna je i brzina kojom se menjaju, mnoge varijacije tipova podataka, i ovo su samo neki od primera: fotografije, tekst, video, audio. I ono što ujedinjuje ove različite tipove podataka je to da su ih stvorili ljudi, a to povlači i kontekst.
Now, there's a group of data scientists out of the University of Illinois-Chicago, and they're called the Health Media Collaboratory, and they've been working with the Centers for Disease Control to better understand how people talk about quitting smoking, how they talk about electronic cigarettes, and what they can do collectively to help them quit. The interesting thing is, if you want to understand how people talk about smoking, first you have to understand what they mean when they say "smoking." And on Twitter, there are four main categories: number one, smoking cigarettes; number two, smoking marijuana; number three, smoking ribs; and number four, smoking hot women. (Laughter)
Postoji grupa naučnika koja se bavi podacima na Univerzitetu Ilinois u Čikagu, zovu ih Health Media Collaboratory, i oni sarađuju sa Centrom za kontrolu bolesti da bi bolje razumeli kako ljudi pričaju kako će da prestanu da puše, kako pričaju o elektronskim cigaretama, i šta oni mogu da urade kao kolektiv da im pomognu da prestanu. Interesantna stvar je da, ako hoćete da razumete kako ljudi pričaju o pušenju, prvo morate da razumete na šta misle kada kažu "pušenje". Na Tviteru, postoje četiri glavne kategorije: broj jedan, pušenje cigareta; broj dva, pušenje marihuane; broj tri, dimljena rebarca, i broj četiri, zgodne žene. (Smeh)
So then you have to think about, well, how do people talk about electronic cigarettes? And there are so many different ways that people do this, and you can see from the slide it's a complex kind of a query. And what it reminds us is that language is created by people, and people are messy and we're complex and we use metaphors and slang and jargon and we do this 24/7 in many, many languages, and then as soon as we figure it out, we change it up.
Onda morate da razmislite, kako ljudi pričaju o elektronskim cigaretama? Postoji toliko različitih načina na koji ljudi to rade, i kao što vidite sa slajda, to je kompleksno pitanje. I ono nas podseća na to da jezik stvaraju ljudi, a ljudi su zbrkani i kompleksni i koristimo metafore i sleng i žargon i radimo to non-stop na mnogo, mnogo jezika, i taman kada se stvari slegnu, mi sve promenimo.
So did these ads that the CDC put on, these television ads that featured a woman with a hole in her throat and that were very graphic and very disturbing, did they actually have an impact on whether people quit? And the Health Media Collaboratory respected the limits of their data, but they were able to conclude that those advertisements — and you may have seen them — that they had the effect of jolting people into a thought process that may have an impact on future behavior. And what I admire and appreciate about this project, aside from the fact, including the fact that it's based on real human need, is that it's a fantastic example of courage in the face of a sea of irrelevance.
Dakle, da li su ove reklame koje je CDC napravio, ove televizijske reklame koje su imale ženu sa rupom u grlu i koje su bile vrlo snažne i vrlo uznemirujuće, da li su one zapravo imale uticaja da ljudi ostave pušenje? Health Media Collaboratory je bila svesna ograničenosti podataka, ali bili su u mogućnosti da zaključe da te reklame - i možda ste ih videli - da su imale efekat da trgnu ljude da uđu u proces razmišljanja koji će možda imati uticaj na buduće ponašanje. Ono čemu se divim i što poštujem kod ovog projekta, pored činjenice, uključujući činjenicu da je baziran na realnoj ljudskoj potrebi, je da je sjajan primer hrabrosti uprkos moru irelevantnih stvari.
And so it's not just big data that causes challenges of interpretation, because let's face it, we human beings have a very rich history of taking any amount of data, no matter how small, and screwing it up. So many years ago, you may remember that former President Ronald Reagan was very criticized for making a statement that facts are stupid things. And it was a slip of the tongue, let's be fair. He actually meant to quote John Adams' defense of British soldiers in the Boston Massacre trials that facts are stubborn things. But I actually think there's a bit of accidental wisdom in what he said, because facts are stubborn things, but sometimes they're stupid, too.
I nije samo velika količina podataka ta koja prouzrokuje probleme u interpretaciji jer, budimo realni, mi ljudi imamo veoma bogatu istoriju da od bilo koje količine podataka, ma koliko male, izvedemo loše zaključke. Pre mnogo godina, možda se sećate, bivšeg predsednika Ronalda Regana snažno su kritikovali zbog izjave da su činjenice glupost. Bio je to lapsus, budimo pošteni. On je zapravo želeo da citira Džona Adamsa i njegovu odbranu britanskih vojnika u suđenju za Bostonski masakr koji kaže da su činjenice neumoljive. Ono što ja zapravo mislim je da u onome što je on rekao postoji i delić slučajne mudrosti, jer činjenice jesu neumoljive, ali su nekada i glupe.
I want to tell you a personal story about why this matters a lot to me. I need to take a breath. My son Isaac, when he was two, was diagnosed with autism, and he was this happy, hilarious, loving, affectionate little guy, but the metrics on his developmental evaluations, which looked at things like the number of words — at that point, none — communicative gestures and minimal eye contact, put his developmental level at that of a nine-month-old baby. And the diagnosis was factually correct, but it didn't tell the whole story. And about a year and a half later, when he was almost four, I found him in front of the computer one day running a Google image search on women, spelled "w-i-m-e-n." And I did what any obsessed parent would do, which is immediately started hitting the "back" button to see what else he'd been searching for. And they were, in order: men, school, bus and computer. And I was stunned, because we didn't know that he could spell, much less read, and so I asked him, "Isaac, how did you do this?" And he looked at me very seriously and said, "Typed in the box."
Hoću da vam ispričam ličnu priču o tome zašto sve ovo meni mnogo znači. Moram da udahnem. Mom sinu Isaku, kada je imao dve godine, dijagnostikovan je autizam, a on je bio srećan, razdragan, pun ljubavi, srdačan mali momak, ali pokazatelji njegove razvojne evaluacije, koji su gledali na stvari kao što su broj reči - u tom trenutku, nijedna - gestikuliranje i minimalni kontakt očima, rangirali su njegov razvojni nivo na nivo devetomesečne bebe. Dijagnoza je činjenično bila tačna, ali nije pričala celu priču. Oko godinu i po dana kasnije, kada mu je bilo skoro četiri godine, zatekla sam ga za kompjuterom jednog dana kako pretražuje Gugl za slikama žena, tražeći "žne". I ja sam uradila isto što i svaki opsednuti roditelj - odmah sam počela da pritiskam dugme "nazad" kako bih videla šta je još pretraživao. Pretrage su bile: muškarci, škola, autobus i kompjuter. Bila sam zatečena, jer nismo znali da on ume da sriče, kamoli da čita, i pitala sam ga: "Isače, kako si ovo uradio?" Pogledao me je veoma ozbiljno i rekao: "Kucao sam u polje za tekst."
He was teaching himself to communicate, but we were looking in the wrong place, and this is what happens when assessments and analytics overvalue one metric — in this case, verbal communication — and undervalue others, such as creative problem-solving. Communication was hard for Isaac, and so he found a workaround to find out what he needed to know. And when you think about it, it makes a lot of sense, because forming a question is a really complex process, but he could get himself a lot of the way there by putting a word in a search box.
On je učio sebe da komunicira, ali mi smo gledali na pogrešnom mestu, i to je ono što se desi kad procene i analize daju previše značaja jednom pokazatelju - u ovom slučaju, verbalnoj komunikaciji - a premalo značaja drugim, kao što je kreativno rešavanje problema. Komunikacija je bila teška za Isaka, pa je on našao drugi put kako bi došao do onoga što ga zanima. I kada razmislite o tome, to ima smisla, jer formiranje pitanja je veoma složen proces, ali je on mogao dosta toga da postigne upisujući reč u polje za pretraživanje.
And so this little moment had a really profound impact on me and our family because it helped us change our frame of reference for what was going on with him, and worry a little bit less and appreciate his resourcefulness more.
I tako je taj momenat imao veoma jak uticaj na mene i našu porodicu jer nam je pomogao da promenimo fokus u vezi sa onim što se dešavalo sa njim i da brinemo malo manje, a da više cenimo njegovu snalažljivost.
Facts are stupid things. And they're vulnerable to misuse, willful or otherwise. I have a friend, Emily Willingham, who's a scientist, and she wrote a piece for Forbes not long ago entitled "The 10 Weirdest Things Ever Linked to Autism." It's quite a list. The Internet, blamed for everything, right? And of course mothers, because. And actually, wait, there's more, there's a whole bunch in the "mother" category here. And you can see it's a pretty rich and interesting list. I'm a big fan of being pregnant near freeways, personally. The final one is interesting, because the term "refrigerator mother" was actually the original hypothesis for the cause of autism, and that meant somebody who was cold and unloving.
Činjenice su glupost. I lako ih je pogrešno protumačiti, svesno ili ne. Ja imam prijateljicu, Emili Vilingem, koja je naučnik, i ona je nedavno napisala članak za Forbs pod imenom "10 najčudnijih stvari ikada vezanih za autizam". Jako zanimljiva lista. Internet, krivac za sve, je l' da? I naravno majke, jer eto. I zapravo, čekajte, ima još, postoji čitava gomila u kategoriji "majka". I možete da vidite da je prilično bogata i zanimljiva lista. Ja sam veliki fan stavke "trudnoća blizu autoputa". Poslednji je interesantan, jer je termin "frižider majka" zapravo bio originalna hipoteza za uzrok autizma, i opisuje nekoga ko je hladan i bez ljubavi.
And at this point, you might be thinking, "Okay, Susan, we get it, you can take data, you can make it mean anything." And this is true, it's absolutely true, but the challenge is that we have this opportunity to try to make meaning out of it ourselves, because frankly, data doesn't create meaning. We do. So as businesspeople, as consumers, as patients, as citizens, we have a responsibility, I think, to spend more time focusing on our critical thinking skills. Why? Because at this point in our history, as we've heard many times over, we can process exabytes of data at lightning speed, and we have the potential to make bad decisions far more quickly, efficiently, and with far greater impact than we did in the past. Great, right? And so what we need to do instead is spend a little bit more time on things like the humanities and sociology, and the social sciences, rhetoric, philosophy, ethics, because they give us context that is so important for big data, and because they help us become better critical thinkers. Because after all, if I can spot a problem in an argument, it doesn't much matter whether it's expressed in words or in numbers. And this means teaching ourselves to find those confirmation biases and false correlations and being able to spot a naked emotional appeal from 30 yards, because something that happens after something doesn't mean it happened because of it, necessarily, and if you'll let me geek out on you for a second, the Romans called this "post hoc ergo propter hoc," after which therefore because of which.
U ovom trenutku, možda mislite, "U redu, Suzan, razumemo, moguće je uzeti podatke i napraviti od njih bilo šta." I to je tačno, to je apsolutno tačno, ali izazov je da imamo ovu priliku da sami pokušamo da shvatimo smisao, jer iskreno, podaci ne stvaraju smisao. Mi ga stvaramo. Pa kao poslovni ljudi, kao potrošači, kao pacijenti, kao građani, mi imamo odgovornost, po mom mišljenju, da provodimo više vremena fokusirajući se na veštinu kritičkog razmišljanja. Zašto? Zato što u ovom trenutku naše istorije, kao što smo čuli mnogo puta do sad, mi možemo da obradimo eksabajte podataka brzinom svetlosti, pa imamo potencijal da donosimo loše odluke mnogo brže, efikasnije i sa većim posledicama nego ikada pre. Sjajno, zar ne? Ono što treba da uradimo umesto toga je da potrošimo malo više vremena na stvari poput humanističkih nauka i sociologije, i društvenih nauka, retorike, filozofije, etike jer one pružaju kontekst koji je tako bitan za velike količine podataka, jer nam one pomažu da budemo bolji u kritičkom mišljenju. Jer na kraju svega, ako ja mogu da primetim problem u nekom argumentu, nije mnogo bitno da li je on izražen u rečima ili brojevima. A to zahteva da naučimo sebe da prepoznajemo pristrasnost u zaključcima i lažne korelacije i da možemo da primetimo čistu emotivnu manipulaciju sa 30 metara, jer ako se nešto desilo posle nečega, ne znači da se nužno desilo baš zbog toga, i ako mi dozvolite da budem štreber na sekund, Rimljani su ovo zvali "Post hoc ergo propter hoc", "Posle toga, dakle zbog toga".
And it means questioning disciplines like demographics. Why? Because they're based on assumptions about who we all are based on our gender and our age and where we live as opposed to data on what we actually think and do. And since we have this data, we need to treat it with appropriate privacy controls and consumer opt-in, and beyond that, we need to be clear about our hypotheses, the methodologies that we use, and our confidence in the result. As my high school algebra teacher used to say, show your math, because if I don't know what steps you took, I don't know what steps you didn't take, and if I don't know what questions you asked, I don't know what questions you didn't ask. And it means asking ourselves, really, the hardest question of all: Did the data really show us this, or does the result make us feel more successful and more comfortable?
To znači i preispitivanje disciplina kao što je demografija. Zašto? Zato što su takve discipline zasnovane na pretpostavkama o tome ko smo mi na osnovu našeg pola i naših godina i gde živimo umesto na osnovu toga šta mislimo i radimo. Pošto imamo tu veliku količinu podataka, moramo da je tretiramo sa prikladnom kontrolom privatnosti i pristankom potrošača, i pored toga, moramo biti jasni kada su u pitanju naše hipoteze, metodologije koje koristimo, i naša uverenost u rezultat. Kao što je moj učitelj algebre iz srednje škole umeo da kaže: "Pokaži proces, jer ako ne znam koje si korake napravila, ne znam ni koje korake nisi napravila, i ako ne znam koja pitanja si postavila, ne znam ni koja pitanja nisi postavila". I to znači zapitati se, zapravo, najteže pitanje od svih: "Da li su nam podaci ovo pokazali, ili da li rezultat čini da se osećamo uspešnije i zadovoljnije?"
So the Health Media Collaboratory, at the end of their project, they were able to find that 87 percent of tweets about those very graphic and disturbing anti-smoking ads expressed fear, but did they conclude that they actually made people stop smoking? No. It's science, not magic.
Health Media Collaboratory, na kraju svog projekta, pokazala je da je 87 procenata tvitova baš o onim snažnim i uznemirujućim antipušačkim reklamama izražavalo strah, ali da li su zaključili da su zapravo učinili da ljudi prestanu da puše? Ne. To je nauka, a ne magija.
So if we are to unlock the power of data, we don't have to go blindly into Orwell's vision of a totalitarian future, or Huxley's vision of a trivial one, or some horrible cocktail of both. What we have to do is treat critical thinking with respect and be inspired by examples like the Health Media Collaboratory, and as they say in the superhero movies, let's use our powers for good.
Ako želimo da otključamo moć podataka, ne moramo slepo da pratimo Orvelovu viziju totalitarne budućnosti, ili Hakslijevu viziju trivijalne, ili neki užasavajući koktel obe. Ono što moramo da uradimo je da tretiramo kritičko mišljenje sa poštovanjem i da budemo inspirisani primerima kao što je Health Media Collaboratory, i kao što kažu u filmovima o superherojima, hajde da koristimo naše moći da činimo dobro.
Thank you.
Hvala vam.
(Applause)
(Aplauz)