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.
Ovo je moja nećakinja. Zove se Yahli. Ima devet mjeseci. Njezina mama je liječnica, a tata je odvjetnik. Kada se Yahli upiše na fakultet, poslovi koje njezini roditelji rade izgledat će potpuno 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.
2013. godine, znanstvenici sveučilišta u Oxfordu istraživali su budućnost rada. Zaključili su da gotovo svaki drugi posao ima visok rizik da bude automatiziran strojevima. Strojno učenje je tehnologija koja je odgovorna za većinu tog remećenja. To je najmoćnija grana umjetne inteligencije. Strojevi mogu učiti iz podataka i oponašati neke radnje svojstvene ljudima. Moja tvrtka Kaggle bavi se najnaprednijim vidom strojnog učenja. Mi povezujemo stotine tisuća stručnjaka radi rješavanja važnih industrijskih i akademskih problema. To nam daje jedinstveni uvid u sposobnost strojeva, njihove mogućnosti i poslove koje bi mogli automatizirati ili ugroziti.
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.
Strojno učenje postalo je dio industrije početkom 90-ih godina. Počelo je relativno jednostavnim zadacima. Počelo je procjenjivanjem kreditnog rizika sa zahtjeva za kredit i razvrstavanjem pošte čitanjem ručno napisanih poštanskih brojeva. Kroz proteklih nekoliko godina, postigli smo nevjerojatne stvari. Strojno učenje sada postiže daleko, daleko naprednije rezultate. 2012. godine Kaggle je pozvao zajednicu da napravi algoritam koji će ocjenjivati srednjoškolske eseje. Najbolji algoritmi dali su istu ocjenu kao i profesori. Prošle smo godine zadali još jedan zahtjevniji zadatak. Možete li pomoću snimke oka dijagnosticirati očnu bolest zvanu dijabetička retinopatija? I ponovno, najbolji algoritmi dali su istu dijagnozu kao i 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.
Pomoću pravilnih podataka, strojevi mogu prestići ljude u zadacima poput ovih. Profesor može pročitati 10.000 eseja kroz 40-godišnju karijeru. Oftalmolog može pregledati 50.000 očiju. Stroj može pročitati milijune eseja ili pregledati milijune očiju u roku od par minuta. Jednostavno se ne možemo natjecati protiv strojeva u čestim zadacima s mnogo podataka.
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.
No, postoje stvari koje mi možemo, a koje strojevi ne mogu. Područje gdje su strojevi vrlo malo napredovali je rješavanje novonastalih situacija. Oni se ne mogu nositi sa stvarima koje nisu vidjeli puno puta u prošlosti. Osnovno ograničenje strojnog učenja je to do mora učiti iz velike količine prijašnjih podataka. Ljudi ne moraju. Mi imamo sposobnost spojiti naizgled nepovezane niti i riješiti novonastale probleme.
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.
Percy Spencer bio je fizičar koji je radio na radaru tijekom 2. svjetskog rata kada je primijetio da mu magnetron otapa čokoladu. On je povezao svoje razumijevanje elektromagnetske radijacije sa svojim znanjem o kuhanju da bi na kraju izumio -- možete pogoditi? -- mikrovalnu 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.
Ovo je jedan izvanredan primjer kreativnosti. No, ovakva se povezivanja, u malim omjerima, kod svakoga od nas događaju tisućama puta dnevno. Strojevi se ne mogu mjeriti s nama u rješavanju tih novonastalih situacija, i to uvelike ograničava broj poslova u kojima strojevi mogu zamijeniti ljude.
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.
I što to onda znači za budućnost rada? Budućnost bilo kojeg posla ovisi o odgovoru na pitanje: Do koje se mjere taj posao može svesti na ponavljajuće zadatke s mnogo podataka, a koliko uključuje rješavanje novonastalih situacija. U čestim zadacima s mnogo podataka, strojevi postaju sve pametniji. Danas oni ocjenjuju eseje. Dijagnosticiraju neke bolesti. S godinama će biti u stanju vršiti revizije i čitati standardne tekstove na ugovorima. Računovođe i odvjetnici još su potrebni. Oni će biti potrebni za složene porezne sustave i u pravnim sporovima. Međutim, strojevi će to promijeniti i smanjiti dostupnost tih poslova.
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.
Kao što sam spomenuo, strojevi ne napreduju u rješavanju novonastalih situacija. Marketinška kampanja mora privući pažnju potrošača. Mora se isticati. Poslovna strategija uključuje nalaženje rupa, stvari koje nitko drugi ne radi. Ljudi će biti ti koji će stvarati marketinške kampanje i ljudi će biti ti koji će razvijati 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.
Tako da, Yahli, što god odlučila raditi, neka ti svaki dan donese neki novi izazov. Ako tako bude, uvijek ćeš biti ispred strojeva.
Thank you.
Hvala.
(Applause)
(Pljesak)