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.
To je moja nečakinja. Ime ji je Yahli. Stara je devet mesecev. Njena mama je zdravnica in njen oče je odvetnik. Ko bo šla Yahli na fakulteto, bodo poklici, ki jih opravljata njena starša, izgledali zelo drugače.
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 so raziskovalci Oxfordske univerze naredili študijo o prihodnosti dela. Ugotovili so, da ima skoraj ena od dveh služb visoko tveganje, da jo zamenja stroj. Strojno učenje je tehnologija, ki je najbolj zaslužna za to motnjo. Je najbolj uspešna veja umetne inteligence. Strojem omogoča, da se učijo iz podatkov in posnemajo stvari, ki jih ljudje lahko počnejo. Moje podjetje, Kaggle, je najbolj napredno v strojnem učenju. Združujemo sto tisoče strokovnjakov, da rešujejo pomembne probleme za industrijo in znanost. To nam daje edinstveno perspektivo o tem, kaj stroji zmorejo, česa ne, in katere službe bodo avtomatizirali ali ogrozili.
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 je začelo prodirati v industrijo v zgodnjih 90-ih. Začelo se je z relativno preprostimi nalogami. Začelo se je z ocenjevanjem tveganja pri prosilcih za kredite, sortiranje pošte, tako da so brali na roke napisane poštne številke. V zadnjih nekaj letih smo naredili nekaj dramatičnih prebojev. Strojno učenje je sedaj sposobno veliko, veliko bolj kompleksnih nalog. Leta 2012 je Kaggle izzval svojo skupnost, naj zgradi algoritem, ki bo lahko ocenjeval srednješolske eseje. Ocene zmagovalnih algoritmov so se ujemale s tistimi, ki so jih dali učitelji. Lani smo dali še težji izziv. Lahko slikaš oko in diagnosticiraš očesno bolezen, imenovano diabetična retinopatija? Spet so se diagnoze najboljših algoritmov ujemale z diagnozami oftalmologov.
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.
S pravimi podatki bi lahko bili stroji boljši od ljudi pri takih nalogah. Učitelj, v svoji 40-letni karieri prebere morda 10.000 esejev. Oftalmolog vidi 50.000 oči. Stroj lahko prebere na milijone esejev ali vidi milijone oči v nekaj minutah. Ne moremo tekmovati s stroji na pogostih, obsežnih nalogah.
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.
A mi lahko počnemo stvari, ki jih stroji ne morejo. Področje, na katerem so stroji le malo napredovali, je obvladovanje novih situacij. Ne obvladajo stvari, ki jih niso videli že večkrat. Osnovna omejitev strojnega učenja je, da se mora učiti iz velike količine preteklih podatkov. Tega ljudem ni treba. Imamo sposobnost, da povežemo na videz različne konce, da rešimo nove 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 je bil fizik, ki je delal na radarju med drugo svetovno vojno, ko je opazil, da magnetron topi njegovo čokoladico. Zmožen je bil povezati svoje znanje o elektromagnetni radiaciji s svojim znanjem o kuhanju, da je izumil - uganete kaj? - mikrovalovno pečico.
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.
No, to je res neverjeten primer ustvarjalnosti. A tako navzkrižno opraševanje se dogaja vsakemu od nas po malem tisočkrat na dan. Stroji z nami ne morejo tekmovati, ko pride do ubadanja z novimi situacijami in to da temeljno omejitev na človeške naloge, ki jih bodo stroji avtomatizirali.
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.
Kaj torej to pomeni za prihodnost dela? Prihodnost katerekoli službe leži v odgovoru na eno vprašanje: do katere mere lahko to službo zreduciramo na pogoste, obsežne naloge in do katere mere vsebuje obvladovanje novih situacij? Na pogostih, obsežnih nalogah stroji postajajo vse pametnejši. Danes ocenjujejo eseje. Diagnosticirajo določene bolezni. Čez leta bodo delali revizije in brali šablonska besedila na pogodbah. Računovodje in odvetnike še potrebujemo. Potrebni bodo za kompleksno davčno strukturiranje, za prelomne sodne postopke. A stroji bodo zožili njihove vrste in težje bo dobiti te službe.
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.
Kot sem omenil, stroji pri novih situacijah ne napredujejo. Reklamno besedilo za marketinško kampanjo mora pritegniti potrošnika. Izstopati mora iz množice. Poslovna strategija je iskanje tržnih niš, stvari, ki jih nihče drug ne dela. Ljudje bodo ustvarjali reklamna besedila v marketinških kampanjah in ljudje bodo razvijali 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.
Yahli, karkoli se odločiš početi, naj ti vsak dan prinese nov izziv. Če ti bo, boš imela prednost pred stroji.
Thank you.
Hvala.
(Applause)
(Aplavz)