Sebastian Thrun and Chris Anderson: What AI is -- and isn't
Sebastian Thrun és Chris Anderson: A számítógépek új nemzedéke önmagát programozza
Sebastian Thrun is a passionate technologist who is constantly looking for new opportunities to make the world better for all of us. Full bioChris Anderson - TED Curator
After a long career in journalism and publishing, Chris Anderson became the curator of the TED Conference in 2002 and has developed it as a platform for identifying and disseminating ideas worth spreading. Full bio
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what machine learning is,
mi a gépi tanulás,
nagy lelkesedésünknek,
and also of the concern
intelligencia és a gépi tanulás
intelligence and machine learning
in its past until recently.
of computing and datasets
okos gépek létrehozását.
pl. mobiltelefont kell programozni,
say, your phone,
very long kitchen recipe,
turn down the temperature.
the temperature."
has 12 million lines of code.
12 millió sorból áll.
can cause your computer to crash.
összeomolhat a számítógépünk.
makes so much money.
a szoftvermérnökök.
can find their own rules.
megtalálják maguknak a szabályaikat.
deciphering, step by step,
minden előadódó esetre
the computer examples
which recently was won by Google.
amelyet nemrég a Google fejlesztett ki.
you would really write down all the rules,
a szabályokat lejegyezzük,
residing Go champion.
the software engineer
where this has become really possible --
ahol ez lehetségessé válik, zavarba ejtő;
was about machine learning.
insignificant, don't read it,
annyit tudtak, mint a csótány agya.
were as big as a cockroach brain.
to really emulate
az emberi gondolkozással.
take advantage of the fact
kezelhet, mint az ember.
much more data than people can.
egymillió játszmát nézett át.
more than a million games.
aki ennyit áttanulmányozna.
study a million games.
a hundred billion web pages.
weboldalt nézett át.
a hundred billion web pages.
a gép találhat olyan szabályokat,
the computer can find rules
to, "If he does that, I will do that,"
arra én ezt lépem" helyett
looks like a winning pattern,
a winning pattern."
how you raise children.
hogy szabályt adjunk minden esetre;
giving kids a rule for every contingency
s kialakítja a szabályait.
and they have this big program.
they get slapped or spanked,
a good grade in school,
hirtelen megkönnyítették.
so much easier all of a sudden.
Csak rengeteg adatot adunk nekik.
We just give them lots of data.
to the spectacular improvement
tovább Voyage néven.
into a spin-off called Voyage.
called deep learning
a kaliforniai Mountain Viewból
from Mountain View, California,
az úton 133 közlekedési lámpával.
and 133 traffic lights.
a Google önvezetőautó-csoportjának,
the Google self-driving car team.
the world's best software engineers
szoftverfejlesztő mérnökeit,
into the computer brain,
that often surpasses human agility.
amely gyakran felülmúlja az emberét.
másfél óra alatt 53 km-t tesz meg.
about 33 miles, an hour and a half.
of this program on the left,
program nagy részében
the computer sees as trucks and cars
kamionokat, autókat,
itt ez fő betáplált adat;
image, which is the main input here,
s jelzőlámpák fölismerésére kell.
other cars, traffic lights.
szolgáló radar van.
to do distance estimation.
in these kind of systems.
pl. fák, melyeket a lézer rajzol ki.
and so on depicted by the laser.
a legtöbb figyelemreméltó dolog.
is centering on the camera image now.
a pontos radar- és lézerérzékelőkről
sensors like radars and lasers
on the left thing, what is that?
az adaptív sebességtartó automatához.
for your adaptive cruise control,
how to regulate velocity
távolságának függvényében.
the cars in front of you are.
got an example, I think,
learning part takes place.
tűztünk ki az Udacity-diákok elé:
a challenge to Udacity students
Nanodegree programját.
a self-driving car Nanodegree.
how to steer this car?"
ezt az autót vezetni!"
rájönni a helyes vezetési módra.
to get the steering right.
"It's a deep learning competition,
"Ez mély tanulás verseny,
mint a Google vagy a Facebook,
like Google or Facebook,
at least six months of work.
100 submissions from students,
nálam jobban vezeti magát
drive on this imagery,
to a computer now,
to comprehend the data,
of powerful applications
the other day about cancer.
CA: This is cool.
CA: Elképesztő.
into what's happening
400,000 dollars a year,
ahhoz legalább tízéves képzés kell.
to be a good dermatologist.
the machine learning version of it.
változatát látjuk,
for these machine learning algorithms.
a gépi tanulás algoritmusaira.
Facebook-munkatárs,
by a Facebook Fellow called Yann LeCun,
as the human brain.
but it emulates the same thing.
és kiválasztja a kontúrokat,
the visual input and extracts edges
more complicated edges
bonyolult fogalmakat alkotni.
really complicated concepts.
cat faces and dog faces
ki tudja választani
at Stanford has shown is that
ábrázoló fotón tanul,
of skin conditions,
that this is the case,
tártunk rendszerünk elé
that we presented to our network
Stanford-level dermatologists,
25 bőrgyógyász elé,
the performance classification accuracy
egy szinten állt, vagy jobb volt,
Megrendítő példa.
That's a moving piece.
és 2017-ben publikáltuk a Nature-ben:
in "Nature" earlier this year
dermatologists images
hogy meggyőződjünk a helyes diagnózisról.
we had the correct classification.
by one of our collaborators.
one of the three best, apparently,
"Ez nem bőrrák."
"This is not skin cancer."
a second moment, where he said,
and ran our piece of software,
jobban hiszek, mint magamnak",
the iPhone a little bit more than myself,"
to get it biopsied.
that we actually found,
találtuk meg valaki melanomáját,
would have gone unclassified,
efféle alkalmazásokra,
for an app like this right now,
végzésére való alkalmazásokat?
making an app that allows self-checking?
rákalkalmazásokról szóló ímélekkel,
about cancer apps,
10, 15, 20 melanomas removed,
melanomát távolítottak el,
might be overlooked, like this one,
fölkérések konferenciákra.
these days, I guess.
and impress a TED audience.
lenyűgözni a TED közönségét.
etikus dologhoz jutni.
something out that's ethical.
használva úgy döntenek,
the assistance of a doctor
s megerősítik adatainkat,
and our data holds up,
to take this kind of technology
orvosai nem jutnak el.
doctors never, ever set foot.
with this army of Udacity students,
a different form of machine learning
a csoportbölcsességgel.
with a form of crowd wisdom.
that could actually outperform
even a vast company?
amelyektől leesik az állam,
instances that blow my mind,
is these competitions that we run.
a self-driving car
to San Francisco on surface streets.
utcákon haladva San Franciscóba.
hét évig fejlesztett autójához,
after seven years of Google work,
és háromhavi munka kellett hozzá.
and three months to do this.
an army of students
egyedül crowdsourcingot.
who use crowdsourcing.
használja a vezetéshez.
where people do bug-finding crowdsourcing
crowdsourcingban.
in crowdsourcing.
három hónap alatt megépíteni az autót,
this car in three months,
akiket nem toboroztunk,
who are never hired,
and I don't even know.
maybe 9,000 answers.
lehet, hogy ez nem jó.
which is maybe not the best thing to do.
tekintik, ami viszont jó.
of their education, too, which is nice.
értek el a mély tanulással.
to produce amazing deep learning results.
gépi tanulás szintézise elképesztő.
and great machine learning is amazing.
a TED2017 első napján,
the first day [of TED2017]
turned out to be two amateur chess players
győztese két amatőr sakkozó lett,
mediocre-to-good, computer programs,
egy nagymesterét s egy jó sakkozóét,
with one great chess player,
you're talking about a much richer version
the fantastic panels yesterday morning,
a tegnap reggeli előadásokat,
that we sometimes confuse
összekeverjük az MI-vel történteket
with this kind of overlord threat,
consciousness, right?
hogy az MI-mnek tudata legyen.
is for my AI to have consciousness.
with the dishwasher
a mosogatógépbe,
and I don't want them.
nem akarok ilyeneket.
an augmentation of people.
és a gépi okosság ötvözése
of human smarts and machine smarts
is as old as machines are.
témája egyidős a gépekkel.
a gőzgép föltalálásával kezdődött
place because it made steam engines
önállóan földet művelni,
that couldn't farm by itself,
de erősebbé tettek.
it made us stronger.
új hulláma minket mint emberi fajt
will make us much, much stronger
of this for some people,
scary for people is when you have
rewrite its own code,
multiple copies of itself,
értek-e és fejlődtek-e.
if a goal is achieved and improved.
az intelligenciatesztben.
on an intelligence test.
that's moderately good at that,
hogy elszabadulnak a dolgok:
some sort of runaway effect
on Thursday evening,
on Friday morning,
meg miegymás miatt
of computers and so forth,
hogy fönnáll a lehetősége,
what I heard you say.
we had exactly this thing:
the game against itself
is a rewriting of the rules.
absolutely no concern
nagyon korlátozott területről származnak.
these are all very single-domain things.
that seemed nearly capable
olvasni és érteni, ahogy mi,
and understand in the sense that we can,
magasabb fokú mintázatát észlelte.
patterns of meaning.
hogy amint ez kiszélesedik,
as this broadens out,
kind of runaway effect?
I draw the line, honestly.
és nem akarom kisebbíteni,
I don't want to downplay it --
the thing that's on my mind these days,
is something else.
to the present date
gojátszma miatt működik olyan jól,
is because of massive numbers of Go plays,
nem képes vezetni.
or fly a plane.
vagy az Udacity önvezető autója
or the Udacity self-driving car
de semmi máshoz sem ért.
and it can't do anything else.
domain-specific function,
az ún. generális MI terén,
on this thing called "general AI,"
"Hey, invent for me special relativity
föl a speciális relativitáselméletet,
és tudomásul veszem.
and I want to acknowledge them.
"What if we can take anything repetitive
munkában százszor hatékonyabbak lennénk?"
100 times as efficient?"
we all worked in agriculture
monoton munkát végeztünk.
doing repetitive things,
of being able to take an AI,
valamely monoton tevékenységben.
as effective in these repetitive things.
a little terrifying to some people,
egyeseknek egy kissé rémisztő,
a gép sokkal jobban boldogul,
can do this repetitive thing
is the thing that's talked about
glorious aspects of what's possible.
vonatkozásai föltárulhatnának.
and it's a big issue,
by several guest speakers.
optimistic person,
back 300 years ago.
a 140 éves háborús időszakot,
of continuous war,
or software engineer or TV anchor.
szoftvermérnök vagy tévébemondó.
egy kis gőzgéppel, és azt mondja:
with a little steam engine in his pocket,
magukat, hogy mást csinálhassanak."
as strong, so you can do something else."
there was no real stage,
with the cows in the stable,
concerned about it,
mi lesz, ha majd gép helyettesít?"
and what if the machine does this for me?"
mert mindig készséggel elismerjük
past progress and the benefit of it,
és a belőle származó előnyöket,
villamosságot, orvosi ellátást.
or electricity or medical supply.
which was impossible 300 years ago.
de 300 éve ez lehetetlen volt.
the same rules to the future.
of my work is repetitive,
on stupid, repetitive email.
idétlen, rutin ímélek megválaszolásával.
that helps me get rid of this.
segítsen megszabadulni tőlük.
őrülten kreatívak vagyunk;
are insanely creative;
more than anybody else.
I think you can go to your hotel maid
ha a szállóban a szobalánnyal
you find a creative idea.
valami kreatív ötlet.
is to turn this creativity into action.
hogy tettekben öltsön testet.
build Google in a day?
megcsinálhatnánk a Google-t?
and invent the next Snapchat,
föltalálnánk egy új Snapchatot,
az elképesztő kreativitást,
a mezőgazdaság igáját,
in my opinion.
great side effects.
and education and shelter
az oktatás, a lakhatás
affordable to all of us,
megfizethető lesz,
that this time it's different
hogy kivételes időket élünk,
that we've used in the past
is that, not completely,
different from the kind of creativity
belief as an AI person --
szilárd meggyőződésem,
any real progress on creativity
nem tapasztaltam kreativitásban
really important for people to realize,
hogy ez mindenkiben tudatosuljon,
kifejezés olyannyira fenyegető,
intelligence" is so threatening,
tossing a movie in,
egyszer csak urunkká válik,
the computer is our overlord,
van a monoton munkában.
do repetitive things.
entirely on the repetitive end.
a monoton oldalt jellemzi.
we've become superhuman.
the Atlantic in 11 hours.
az Atlanti óceánon.
a másik fél visszaszól nekünk.
shouting back to us.
Megsértjük a fizikai törvényeket.
We're breaking the rules of physics.
we're going to remember everything
in my early stages of Alzheimer's.
an IQ of 1,000 or more.
spelling classes for our kids,
több helyesírás-órájuk,
is that we can be super creative.
it's going to be painful,
of more than those jobs.
to just a new level of empowerment
60-100,000 years old, give or take --
in terms of invention,
és becsben tartunk,
it's a little bit older.
manufacturing, penicillin --
a modern gyártás, penicillin,
nem pedig lassult.
has gone up, not gone down, in my opinion.
things have been invented yet. Right?
dolgok 1%-a van fölfedezve.
Hopefully, I'll change this.
Remélem, ezen változtatok.
people laughed about. (Laughs)
nevettek ezen. (Nevetés)
Working secretly on flying cars.
Titokban dolgozni a repülő autón.
implant in our brain
once you have it, you'll love it.
megkapják, tetszeni fog.
we haven't invented yet
from one location to another.
that flight wouldn't exist,
than you could run,
that you can't beam a person
and your brilliance.
s ragyogó előadását.
ABOUT THE SPEAKERS
Sebastian Thrun - Educator, entrepreneurSebastian Thrun is a passionate technologist who is constantly looking for new opportunities to make the world better for all of us.
Why you should listen
Sebastian Thrun is an educator, entrepreneur and troublemaker. After a long life as a professor at Stanford University, Thrun resigned from tenure to join Google. At Google, he founded Google X, home to self-driving cars and many other moonshot technologies. Thrun also founded Udacity, an online university with worldwide reach, and Kitty Hawk, a "flying car" company. He has authored 11 books, 400 papers, holds 3 doctorates and has won numerous awards.
Sebastian Thrun | Speaker | TED.com
Chris Anderson - TED Curator
After a long career in journalism and publishing, Chris Anderson became the curator of the TED Conference in 2002 and has developed it as a platform for identifying and disseminating ideas worth spreading.
Why you should listen
Chris Anderson is the Curator of TED, a nonprofit devoted to sharing valuable ideas, primarily through the medium of 'TED Talks' -- short talks that are offered free online to a global audience.
Chris was born in a remote village in Pakistan in 1957. He spent his early years in India, Pakistan and Afghanistan, where his parents worked as medical missionaries, and he attended an American school in the Himalayas for his early education. After boarding school in Bath, England, he went on to Oxford University, graduating in 1978 with a degree in philosophy, politics and economics.
Chris then trained as a journalist, working in newspapers and radio, including two years producing a world news service in the Seychelles Islands.
Back in the UK in 1984, Chris was captivated by the personal computer revolution and became an editor at one of the UK's early computer magazines. A year later he founded Future Publishing with a $25,000 bank loan. The new company initially focused on specialist computer publications but eventually expanded into other areas such as cycling, music, video games, technology and design, doubling in size every year for seven years. In 1994, Chris moved to the United States where he built Imagine Media, publisher of Business 2.0 magazine and creator of the popular video game users website IGN. Chris eventually merged Imagine and Future, taking the combined entity public in London in 1999, under the Future name. At its peak, it published 150 magazines and websites and employed 2,000 people.
This success allowed Chris to create a private nonprofit organization, the Sapling Foundation, with the hope of finding new ways to tackle tough global issues through media, technology, entrepreneurship and, most of all, ideas. In 2001, the foundation acquired the TED Conference, then an annual meeting of luminaries in the fields of Technology, Entertainment and Design held in Monterey, California, and Chris left Future to work full time on TED.
He expanded the conference's remit to cover all topics, including science, business and key global issues, while adding a Fellows program, which now has some 300 alumni, and the TED Prize, which grants its recipients "one wish to change the world." The TED stage has become a place for thinkers and doers from all fields to share their ideas and their work, capturing imaginations, sparking conversation and encouraging discovery along the way.
In 2006, TED experimented with posting some of its talks on the Internet. Their viral success encouraged Chris to begin positioning the organization as a global media initiative devoted to 'ideas worth spreading,' part of a new era of information dissemination using the power of online video. In June 2015, the organization posted its 2,000th talk online. The talks are free to view, and they have been translated into more than 100 languages with the help of volunteers from around the world. Viewership has grown to approximately one billion views per year.
Continuing a strategy of 'radical openness,' in 2009 Chris introduced the TEDx initiative, allowing free licenses to local organizers who wished to organize their own TED-like events. More than 8,000 such events have been held, generating an archive of 60,000 TEDx talks. And three years later, the TED-Ed program was launched, offering free educational videos and tools to students and teachers.
Chris Anderson | Speaker | TED.com