Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't
Anthony Goldbloom: Ktoré pracovné miesta prevezmú stroje a ktoré nie?
Anthony Goldbloom crowdsources solutions to difficult problems using machine learning. Full bio
Double-click the English transcript below to play the video.
and her dad is a lawyer.
are going to look dramatically different.
did a study on the future of work.
Oxfordskej univerzite budúcnosť povolaní.
in every two jobs have a high risk
povolaniu hrozí,
of this disruption.
of artificial intelligence.
v umelej inteligencii.
that humans can do.
ktoré dokážu robiť ľudia.
on the cutting edge of machine learning.
s najšpičkovejším strojovým učením.
hundreds of thousands of experts
for industry and academia.
priemyslu a výskumu.
on what machines can do,
čo stroje môžu robiť,
automate or threaten.
automatizovať alebo ohroziť.
into industry in the early '90s.
priemyslu na začiatku 90. rokov.
credit risk from loan applications,
rizika žiadateľov o pôžičku,
handwritten characters from zip codes.
rukou písaných znakov v PSČ.
dramatic breakthroughs.
k veľkému prelomu.
of far, far more complex tasks.
oveľa náročnejších úloh.
slohových prác stredoškolákov.
that could grade high-school essays.
were able to match the grades
rovnaké hodnotenie
an even more difficult challenge.
s ešte náročnejšou úlohou.
and diagnose an eye disease
a diagnostikovať očné ochorenie
were able to match the diagnoses
diagnostikovali rovnako
machines are going to outperform humans
over a 40-year career.
počas svojej 40-ročnej kariéry.
or see millions of eyes
alebo prezrieť milióny očí
against machines
úlohách šancu.
that machines can't do.
a stroje nie.
very little progress
they haven't seen many times before.
ktoré nevideli mnohokrát predtým.
of machine learning
from large volumes of past data.
seemingly disparate threads
zdanlivo nezlúčiteľné súvislosti
s ktorými sme sa predtým nestretli.
working on radar during World War II,
svetovej vojny pracoval na radare,
was melting his chocolate bar.
mu topil čokoládu.
of electromagnetic radiation
o elektromagnetickej radiácii
the microwave oven.
mikrovlnnú rúru.
example of creativity.
happens for each of us in small ways
sa stáva každému z nás pri drobnostiach
novel situations,
on the human tasks
for the future of work?
pre budúcnosť povolaní?
in the answer to a single question:
miesta závisí od odpovede na otázku:
to frequent, high-volume tasks,
na opakovanú, veľkoobjemovú úlohu
tackling novel situations?
na nové situácie?
machines are getting smarter and smarter.
sú stroje čoraz lepšie.
They diagnose certain diseases.
Diagnostikovať určité ochorenia.
they're going to conduct our audits,
from legal contracts.
za komplexné daňové štrukturovanie
for complex tax structuring,
on novel situations.
riešenia nových situácií.
needs to grab consumers' attention.
zaujať zákazníka.
finding gaps in the market,
hľadajú diery na trhu,
the copy behind our marketing campaigns,
budú tvoriť ľudia,
our business strategy.
prinesie novú výzvu.
ahead of the machines.
budeš vždy o krok pred strojmi.
ABOUT THE SPEAKER
Anthony Goldbloom - Machine learning expertAnthony Goldbloom crowdsources solutions to difficult problems using machine learning.
Why you should listen
Anthony Goldbloom is the co-founder and CEO of Kaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists and has worked with companies ranging Facebook to GE on problems ranging from predicting friendships to flight arrival times.
Before Kaggle, Anthony worked as an econometrician at the Reserve Bank of Australia, and before that the Australian Treasury. In 2011 and 2012, Forbes named Anthony one of the 30 under 30 in technology; in 2013 the MIT Tech Review named him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award. He holds a first call honors degree in Econometrics from the University of Melbourne.
Anthony Goldbloom | Speaker | TED.com