So this is my niece. Her name is Yahli. She is nine months old. Her mum is a doctor, and her dad is a lawyer. By the time Yahli goes to college, the jobs her parents do are going to look dramatically different.
Dakle, ovo je moja nećaka. Zove se Jali. Stara je devet meseci. Njena majka je doktor, a njen otac je advokat. Kad Jali pođe na fakultet, poslovi koje njeni roditelji obavljaju izgledaće drastično drugačije.
In 2013, researchers at Oxford University did a study on the future of work. They concluded that almost one in every two jobs have a high risk of being automated by machines. Machine learning is the technology that's responsible for most of this disruption. It's the most powerful branch of artificial intelligence. It allows machines to learn from data and mimic some of the things that humans can do. My company, Kaggle, operates on the cutting edge of machine learning. We bring together hundreds of thousands of experts to solve important problems for industry and academia. This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten.
Istraživači sa Oksforda su 2013. uradili istraživanje o budućnosti poslova. Zaključili su da je gotovo jedan od svaka dva posla pod velikim rizikom da bude mašinski automatizovan. Mašinsko učenje je tehnologija koja je najodgovornija za ovaj raskol. To je najmoćnija grana veštačke inteligencije. Omogućuje mašinama da uče iz podataka i da oponašaju neke stvari koje ljudi mogu da rade. Moja firma, Kagle, se bavi najnaprednijim vidom mašinskog učenja. Spajamo na stotine hiljada eksperata kako bismo rešili važne probleme u industriji i akademiji. To nam pruža jedinstvenu perspektivu na to šta mašine mogu, šta ne mogu i koje poslove mogu da automatizuju ili ugroze.
Machine learning started making its way into industry in the early '90s. It started with relatively simple tasks. It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes. Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks. In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays. The winning algorithms were able to match the grades given by human teachers. Last year, we issued an even more difficult challenge. Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.
Mašinsko učenje se počelo probijati u industriji tokom ranih '90-ih. Počelo je relativno jednostavnim zadacima. Počelo je stvarima poput bavljenja kreditnim rizikom kod molbi za zajam, sortiranjem pošte čitanjem ručno pisanih slova zip kodova. Tokom proteklih nekoliko godina imali smo drastična dostignuća. Mašinsko učenje je sada sposobno za daleko, daleko složenije zadatke. Godine 2012. Kagle je izazvao njegovu zajednicu da naprave algoritam koji bi ocenjivao srednjoškolske eseje. Pobednički algoritmi su mogli da daju podudarne ocene kao i ljudski profesori. Prošle godine smo napravili čak i komplikovaniji izazov. Možete li da uzmete snimak oka i da dijagnostikujete očnu bolest pod nazivom dijabetička retinopatija? Opet su pobednički algoritmi mogli da daju podudarnu dijagnozu kao i ljudski oftalmolozi.
Now, given the right data, machines are going to outperform humans at tasks like this. A teacher might read 10,000 essays over a 40-year career. An ophthalmologist might see 50,000 eyes. A machine can read millions of essays or see millions of eyes within minutes. We have no chance of competing against machines on frequent, high-volume tasks.
Sad, uz odgovarajuće podatke mašine će da nadmaše ljude u sličnim zadacima. Nastavnik može da pročita 10.000 eseja tokom 40-ogodišnje karijere. Oftalmolog može da pregleda 50.000 očiju. Mašina može da pročita na milione eseja ili da pregleda na milione očiju za nekoliko minuta. Nemamo nikakve šanse u takmičenju s mašinama na učestalim zadacima velikog obima.
But there are things we can do that machines can't do. Where machines have made very little progress is in tackling novel situations. They can't handle things they haven't seen many times before. The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. Now, humans don't. We have the ability to connect seemingly disparate threads to solve problems we've never seen before.
Ali ima nešto što mi možemo, a mašine ne mogu. Mašine su postigle veoma mali napredak kod bavljenja novim situacijama. Ne mogu da savladaju nešto što nisu videle mnogo puta ranije. Temeljno ograničenje mašinskog učenja je što mašine moraju da uče iz obilja prethodnih podataka. A ljudi ne moraju. Sposobni smo da povežemo naoko nepovezane niti kako bismo rešili za nas nov problem.
Percy Spencer was a physicist working on radar during World War II, when he noticed the magnetron was melting his chocolate bar. He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent -- any guesses? -- the microwave oven.
Persi Spenser je bio fizičar koji je radio na radaru tokom II svetskog rata, kad je primetio kako magnetron topi njegovu tablu čokolade. Mogao je da poveže sopstveno razumevanje elektromagnetne radijacije sa poznavanjem kuvanja kako bi izumeo - pretpostavljate li šta? - mikrotalasnu pećnicu.
Now, this is a particularly remarkable example of creativity. But this sort of cross-pollination happens for each of us in small ways thousands of times per day. Machines cannot compete with us when it comes to tackling novel situations, and this puts a fundamental limit on the human tasks that machines will automate.
Sad, ovo je izrazito upečatljiv primer kreativnosti. Ali ovakva plodna ukrštanja nam se dešavaju na mikroplanu hiljadama puta tokom dana. Mašine ne mogu da se takmiče s nama kad je u pitanju bavljenje novim situacijama, a ovo postavlja temeljno ograničenje na ljudske zadatke koje mašine mogu da automatizuju.
So what does this mean for the future of work? The future state of any single job lies in the answer to a single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting smarter and smarter. Today they grade essays. They diagnose certain diseases. Over coming years, they're going to conduct our audits, and they're going to read boilerplate from legal contracts. Accountants and lawyers are still needed. They're going to be needed for complex tax structuring, for pathbreaking litigation. But machines will shrink their ranks and make these jobs harder to come by.
Pa, šta ovo znači za budućnost rada? Budućnost svakog posla počiva u odgovoru na samo jedno pitanje: do koje mere je taj posao svodiv na učestale zadatke velikog obima i u kojoj meri uključuje bavljenje novim situacijama? Kod učestalih zadataka velikog obima mašine postaju sve pametnije i pametnije. Danas one ocenjuju eseje. Dijagnostikuju određene bolesti. U narednim godinama radiće revizije poreza i čitaće opšta mesta u pravnim ugovorima. I dalje ćemo trebati računovođe i advokate. Trebaće nam za složeno struktuiranje poreza, za pionirske parnice. No, mašine će suziti njihovo zvanje i učiniće ove poslove težim za nalaženje.
Now, as mentioned, machines are not making progress on novel situations. The copy behind a marketing campaign needs to grab consumers' attention. It has to stand out from the crowd. Business strategy means finding gaps in the market, things that nobody else is doing. It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy.
Sad, kao što sam pomenuo mašine ne postižu napredak kod novih situacija. Poruka marketinške kampanje mora da zgrabi pažnju potrošača. Mora da se ističe u gomili. Poslovna strategija znači nalaženje rupa u tržištu, stvari koje niko drugi ne radi. Ljudi su ti koji će da stvaraju poruke marketinških kampanja, i ljudi su ti koji će razvijati naše poslovne strategije.
So Yahli, whatever you decide to do, let every day bring you a new challenge. If it does, then you will stay ahead of the machines.
Pa, Jali, čime god odlučiš da se baviš, neka ti svaki dan donese novi izazov. Ako bude tako, bićeš ispred mašina.
Thank you.
Hvala vam.
(Applause)
(Aplauz)