The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom

Translator: Nika Kotnik Reviewer: Tilen Pigac - EFZG

So this is my niece.

To je moja nečakinja.

Her name is Yahli.

Ime ji je Yahli.

She is nine months old.

Stara je devet mesecev.

Her mum is a doctor, and her dad is a lawyer.

Njena mama je zdravnica in njen oče je odvetnik.

By the time Yahli goes to college,

Ko bo šla Yahli na fakulteto,

the jobs her parents do are going to look dramatically different.

bodo poklici, ki jih opravljata njena starša, izgledali zelo drugače.

In 2013, researchers at Oxford University did a study on the future of work.

2013 so raziskovalci Oxfordske univerze naredili študijo o prihodnosti dela.

They concluded that almost one in every two jobs have a high risk

Ugotovili so, da ima skoraj ena od dveh služb visoko tveganje,

of being automated by machines.

da jo zamenja stroj.

Machine learning is the technology

Strojno učenje je tehnologija,

ki je najbolj zaslužna za to motnjo.

that's responsible for most of this disruption.

It's the most powerful branch of artificial intelligence.

Je najbolj uspešna veja umetne inteligence.

It allows machines to learn from data

Strojem omogoča, da se učijo iz podatkov

and mimic some of the things that humans can do.

in posnemajo stvari, ki jih ljudje lahko počnejo.

My company, Kaggle, operates on the cutting edge of machine learning.

Moje podjetje, Kaggle, je najbolj napredno v strojnem učenju.

We bring together hundreds of thousands of experts

Združujemo sto tisoče strokovnjakov,

to solve important problems for industry and academia.

da rešujejo pomembne probleme za industrijo in znanost.

This gives us a unique perspective on what machines can do,

To nam daje edinstveno perspektivo o tem, kaj stroji zmorejo,

what they can't do

česa ne,

and what jobs they might automate or threaten.

in katere službe bodo avtomatizirali ali ogrozili.

Machine learning started making its way into industry in the early '90s.

Strojno učenje je začelo prodirati v industrijo v zgodnjih 90-ih.

It started with relatively simple tasks.

Začelo se je z relativno preprostimi nalogami.

It started with things like assessing credit risk from loan applications,

Začelo se je z ocenjevanjem tveganja pri prosilcih za kredite,

sorting the mail by reading handwritten characters from zip codes.

sortiranje pošte, tako da so brali na roke napisane poštne številke.

V zadnjih nekaj letih smo naredili nekaj dramatičnih prebojev.

Over the past few years, we have made dramatic breakthroughs.

Machine learning is now capable of far, far more complex tasks.

Strojno učenje je sedaj sposobno veliko, veliko bolj kompleksnih nalog.

In 2012, Kaggle challenged its community

Leta 2012 je Kaggle izzval svojo skupnost,

naj zgradi algoritem, ki bo lahko ocenjeval srednješolske eseje.

to build an algorithm that could grade high-school essays.

The winning algorithms were able to match the grades

Ocene zmagovalnih algoritmov so se ujemale

given by human teachers.

s tistimi, ki so jih dali učitelji.

Lani smo dali še težji izziv.

Last year, we issued an even more difficult challenge.

Lahko slikaš oko in diagnosticiraš očesno bolezen,

Can you take images of the eye and diagnose an eye disease

imenovano diabetična retinopatija?

called diabetic retinopathy?

Spet so se diagnoze najboljših algoritmov ujemale

Again, the winning algorithms were able to match the diagnoses

given by human ophthalmologists.

z diagnozami oftalmologov.

Now, given the right data, machines are going to outperform humans

S pravimi podatki bi lahko bili stroji boljši od ljudi

at tasks like this.

pri takih nalogah.

A teacher might read 10,000 essays over a 40-year career.

Učitelj, v svoji 40-letni karieri prebere morda 10.000 esejev.

An ophthalmologist might see 50,000 eyes.

Oftalmolog vidi 50.000 oči.

A machine can read millions of essays or see millions of eyes

Stroj lahko prebere na milijone esejev ali vidi milijone oči

within minutes.

v nekaj minutah.

We have no chance of competing against machines

Ne moremo tekmovati s stroji

on frequent, high-volume tasks.

na pogostih, obsežnih nalogah.

But there are things we can do that machines can't do.

A mi lahko počnemo stvari, ki jih stroji ne morejo.

Where machines have made very little progress

Področje, na katerem so stroji le malo napredovali,

je obvladovanje novih situacij.

is in tackling novel situations.

They can't handle things they haven't seen many times before.

Ne obvladajo stvari, ki jih niso videli že večkrat.

The fundamental limitations of machine learning

Osnovna omejitev strojnega učenja

is that it needs to learn from large volumes of past data.

je, da se mora učiti iz velike količine preteklih podatkov.

Now, humans don't.

Tega ljudem ni treba.

Imamo sposobnost, da povežemo na videz različne konce,

We have the ability to connect seemingly disparate threads

da rešimo nove probleme.

to solve problems we've never seen before.

Percy Spencer was a physicist working on radar during World War II,

Percy Spencer je bil fizik, ki je delal na radarju med drugo svetovno vojno,

when he noticed the magnetron was melting his chocolate bar.

ko je opazil, da magnetron topi njegovo čokoladico.

He was able to connect his understanding of electromagnetic radiation

Zmožen je bil povezati svoje znanje o elektromagnetni radiaciji

with his knowledge of cooking

s svojim znanjem o kuhanju,

in order to invent -- any guesses? -- the microwave oven.

da je izumil - uganete kaj? - mikrovalovno pečico.

Now, this is a particularly remarkable example of creativity.

No, to je res neverjeten primer ustvarjalnosti.

But this sort of cross-pollination happens for each of us in small ways

A tako navzkrižno opraševanje se dogaja vsakemu od nas po malem

thousands of times per day.

tisočkrat na dan.

Machines cannot compete with us

Stroji z nami ne morejo tekmovati,

when it comes to tackling novel situations,

ko pride do ubadanja z novimi situacijami

and this puts a fundamental limit on the human tasks

in to da temeljno omejitev na človeške naloge,

that machines will automate.

ki jih bodo stroji avtomatizirali.

Kaj torej to pomeni za prihodnost dela?

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:

Prihodnost katerekoli službe leži v odgovoru na eno vprašanje:

To what extent is that job reducible to frequent, high-volume tasks,

do katere mere lahko to službo zreduciramo na pogoste, obsežne naloge

and to what extent does it involve tackling novel situations?

in do katere mere vsebuje obvladovanje novih situacij?

On frequent, high-volume tasks, machines are getting smarter and smarter.

Na pogostih, obsežnih nalogah stroji postajajo vse pametnejši.

Danes ocenjujejo eseje. Diagnosticirajo določene bolezni.

Today they grade essays. They diagnose certain diseases.

Over coming years, they're going to conduct our audits,

Čez leta bodo delali revizije

and they're going to read boilerplate from legal contracts.

in brali šablonska besedila na pogodbah.

Accountants and lawyers are still needed.

Računovodje in odvetnike še potrebujemo.

They're going to be needed for complex tax structuring,

Potrebni bodo za kompleksno davčno strukturiranje,

za prelomne sodne postopke.

for pathbreaking litigation.

A stroji bodo zožili njihove vrste in težje bo dobiti te službe.

But machines will shrink their ranks

and make these jobs harder to come by.

Now, as mentioned,

Kot sem omenil, stroji pri novih situacijah ne napredujejo.

machines are not making progress on novel situations.

The copy behind a marketing campaign needs to grab consumers' attention.

Reklamno besedilo za marketinško kampanjo mora pritegniti potrošnika.

Izstopati mora iz množice.

It has to stand out from the crowd.

Poslovna strategija je iskanje tržnih niš,

Business strategy means finding gaps in the market,

things that nobody else is doing.

stvari, ki jih nihče drug ne dela.

It will be humans that are creating the copy behind our marketing campaigns,

Ljudje bodo ustvarjali reklamna besedila v marketinških kampanjah

and it will be humans that are developing our business strategy.

in ljudje bodo razvijali naše poslovne strategije.

So Yahli, whatever you decide to do,

Yahli, karkoli se odločiš početi,

let every day bring you a new challenge.

naj ti vsak dan prinese nov izziv.

If it does, then you will stay ahead of the machines.

Če ti bo, boš imela prednost pred stroji.

Hvala.

Thank you.

(Applause)

(Aplavz)