Det her er min niece. Hun hedder Yahli. Hun er ni måneder gammel. Hendes mor er læge, og hendes far er advokat. Når Yahli studere på universitetet vil hendes forældres arbejde se meget anderledes ud.
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.
I 2013 lavede forskere fra Oxford en undersøgelse om fremtidens arbejde. De konkluderede, at næsten hver andet job har stor risiko for at blive automatiseret af maskiner. Machine learning er den teknologi der ligger til grunde for størstedelen af ændringerne. Det er den mest virkningsfulde del af kunstig intelligens. Det tillader maskiner at lære fra data og efterligne nogen af de ting, som mennesker kan gøre. Mit firma, Kaggle, arbejder på det nyeste inden for machine learning. Vi samler flere hundrede tusinder eksperter som skal løse vigtige problemer for den industrielle og akademiske verden Det giver os et indblik i hvad maskiner kan, hvad de ikke kan, og hvilke job de måske kommer til at automatisere
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.
Første gang machine learning blev brugt industrielt, var i start 90'erne. Det startede simpelt. Det startede med at vurdere kreditrisiko fra låneansøgninger, brev sortering ved at læse håndskrevne tegn fra postnumre. Gennem de sidste par år er der sket banebrydende gennembrud. Machine learning er nu i stand til langt mere komplekse opgaver. I 2012 udfordrede Kaggle sit lokalsamfund til at programmere en algoritme til at bedømme gymnasie stile. Algoritmen der vandt, gav den samme karakter som den rigtige lærer gjorde. Sidste år lavede vi en sværere udfordring. Kan man tage billeder af øjet og diagnostisere en øjensygdom kaldet diabetisk retinopati? Igen gav algoritmen der vandt den samme diagnose som var givet af en øjenlæge.
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.
Maskiner udkonkurrerer altid mennesker ved opgaver som denne, givet at den får de rigtige data. En lærer læser måske 10.000 stile over en 40-årig karriere. En øjenlæge ser måske 50.000 øjne En maskine kan læse millioner af stile eller se millioner af øjne på få minutter. Vi kan ikke hamle op med maskinerne, når det gælder mængde opgaver.
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.
Men der er ting, vi kan, som maskiner ikke kan. Maskiner er ikke blevet særlig meget bedre til at takle unikke og nye situationer De kan ikke arbejde med ting, de ikke har set en masse gange før. De grundlæggende begrænsninger for machine learning er at de skal lære fra store mængder tidligere data. Det skal mennesker ikke. Vi har evnen til at finde sammenhængen i forskellige situationer, og løse problemer vi ikke har set før.
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.
Mens fysikeren Percy Spenser arbejdede med radarer under 2. verdenskrig, opdagede han at magnetronen smeltede hans chokolade bar. Han forenede sin forståelse for elektromagnetisk stråling med sit kendskab til madlavning for at opfinde -- nogen gæt? -- mikrobølgeovnen.
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.
Det er et eksempel på særlig kreativitet. Den forenende måde at tænke på, sker for as alle på mindre stadier tusinder af gange om dagen. Maskiner kan ikke hamle op med os, når det kommer til unikke situationer, og det skaber en begrænsning for hvilke opgaver maskiner komme til at automatisere.
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.
Så hvilken betydning har det for fremtidens arbejde? Om et job er sikret for fremtiden, kan besvares med et spørgsmål: I hvor stor en grad kan jobbet nedskrives til mængde opgaver, og i hvor stor grad indebærer det unikke situationer? Når det gælder mængde opgaver, bliver maskiner klogere og klogere. I dag bedømmer de stile. De diagnosticere visse sygdomme. De følgende år, vil de foretage vores regnskaber. De kommer til at læse standard tekst fra juridiske kontrakter Revisorer og advokater skal stadig bruges. De skal bruges til kompleks skatte-strukturering, til banebrydende retstvister. Men maskiner vil mindske deres omdømme, og gøre den type jobs mindre hyppige.
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.
Som jeg har nævnt, laver maskiner ingen fremskridt når det gælder unikke situationer. Rammen for en marketing kampagne skal fange folks opmærksomhed, den skal skille sig ud. Erhvervs strategi går ud på at finde mangler i markedet, noget som ingen andre gør. Det kommer til at være mennesker som skaber rammen for en marketing kampagne, og det vil være mennesker, som vil udvikle vores erhvervs strategi.
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.
Så hvad end du beslutter dig for at lave, Yahli, lad hver dag bringe nye udfordringer. For så vil du forblive foran maskinerne
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.
Tak.
Thank you.
(Klapsalve)
(Applause)