TED2017
Noriko Arai: Can a robot pass a university entrance exam?
Noriko Arai: Kan en robot bestå en universitetsoptagelsesprøve?
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Vi hilser på Todai Robot, en kunstig intelligens projekt som endte i den øverste 20 procent af kandidaterne til optagelsesprøven på Tokyo Universitet - uden at forstå noget som helst. Selv om at robotten nok ikke skal på universitet snart, fremkalder dens succes vigtig spørgsmål om fremtiden af undervisning. Hvordan kan man hjælpe børn til at blive mestre i de ting, som mennesker altid vil kunne gøre bedre end kunstig intelligens?
Noriko Arai - AI expert
Could an AI pass the entrance exam for the University of Tokyo? Noriko Arai oversees a project that wants to find out. Full bio
Could an AI pass the entrance exam for the University of Tokyo? Noriko Arai oversees a project that wants to find out. Full bio
Double-click the English transcript below to play the video.
00:13
Today, I'm going to talk about AI and us.
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I dag, skal jeg tale om
kunstig intelligens og os.
kunstig intelligens og os.
00:18
AI researchers have always said
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Forskere har altid sagt
00:20
that we humans do not need to worry,
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at vi mennesker ikke skal bekymrer os,
00:22
because only menial jobs
will be taken over by machines.
will be taken over by machines.
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fordi kun det kedeligste arbejde
bliver overtaget af maskiner.
bliver overtaget af maskiner.
00:27
Is that really true?
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Er det virkelig sandt?
00:30
They have also said
that AI will create new jobs,
that AI will create new jobs,
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De siger også, at kunstig intelligens
skaber nye arbejdspladser,
skaber nye arbejdspladser,
00:34
so those who lose their jobs
will find a new one.
will find a new one.
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så de der mister jobbet kan finde et nyt.
00:38
Of course.
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Selvfølgelig.
00:39
But the real question is:
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Men, det rigtige spørgsmål er:
00:41
How many of those
who may lose their jobs to AI
who may lose their jobs to AI
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Hvor mange af dem som mister deres
arbejde til kunstig intelligens
arbejde til kunstig intelligens
00:45
will be able to land a new one,
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vil kunne finde nyt
00:48
especially when AI is smart enough
to learn better than most of us?
to learn better than most of us?
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når kunstig intelligens er bedre til
at lære, end de fleste mennesker?
at lære, end de fleste mennesker?
00:55
Let me ask you a question:
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Lade mig spørge jer om noget:
00:58
How many of you think
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Hvor mange af jer tror
01:00
that AI will pass the entrance examination
of a top university by 2020?
of a top university by 2020?
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at kunstig intelligens kan bestå en
optagelsesprøve til universitetet inden 2020?
optagelsesprøve til universitetet inden 2020?
01:07
Oh, so many. OK.
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Nå, så mange! Ok.
01:10
So some of you may say, "Of course, yes!"
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Nogle af jer siger, "Ja, selvfølgelig!"
01:15
Now singularity is the issue.
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og tænker singularitet.
01:18
And some others may say, "Maybe,
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Og andre siger, "Måske,
01:21
because AI already won
against a top Go player."
against a top Go player."
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fordi kunstig intelligens
har slået en top-Go spiller."
har slået en top-Go spiller."
01:27
And others may say, "No, never. Uh-uh."
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Stadig andre siger,
"Nej, aldrig. Slet ikke."
"Nej, aldrig. Slet ikke."
01:32
That means we do not know
the answer yet, right?
the answer yet, right?
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Det må jo betyde
at vi endnu ikke kender svaret?
at vi endnu ikke kender svaret?
01:36
So that was the reason why
I started Todai Robot Project,
I started Todai Robot Project,
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Det var grunden til at jeg
startede Todai Robot projektet
startede Todai Robot projektet
01:41
making an AI which passes
the entrance examination
the entrance examination
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og skabte en kunstig intelligens
som består optagelsesprøven
som består optagelsesprøven
01:45
of the University of Tokyo,
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på Tokyo Universitet,
01:47
the top university in Japan.
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Japans top-universitet.
01:51
This is our Todai Robot.
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Det er vores Todai Robot.
01:56
And, of course, the brain of the robot
is working in the remote server.
is working in the remote server.
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Og selvfølgelig arbejder robottens
hjerne på en selvstændig server.
hjerne på en selvstændig server.
02:02
It is now writing a 600-word essay
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Lige nu skriver den en 600-ords stil,
02:06
on maritime trade in the 17th century.
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om maritimhandel i det 17. århundrede.
02:11
How does that sound?
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Vildt, ikke?
02:14
Why did I take the entrance exam
as its benchmark?
as its benchmark?
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Hvorfor valgte jeg prøven som grundlag?
02:19
Because I thought we had to study
the performance of AI
the performance of AI
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Fordi vi bør studere hvad
kunstig intelligens kan præstere
kunstig intelligens kan præstere
02:23
in comparison to humans,
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sammenlignet med mennesker,
02:26
especially on the scales and expertise
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specielt de evner og ekspertise
02:28
which are believed
to be acquired only by humans
to be acquired only by humans
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man tror at kun mennesker mestrer
02:32
and only through education.
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og kun igennem undervisning.
02:35
To enter Todai, the University of Tokyo,
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For at blive optaget på Todai,
som er Tokyo Universitet
som er Tokyo Universitet
02:39
you have to pass
two different types of exams.
two different types of exams.
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skal man bestå to
forskellige prøver.
forskellige prøver.
02:44
The first one is
a national standardized test
a national standardized test
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Først en statslig nationalprøve
02:48
in multiple-choice style.
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i 'multiple-choice' stil.
02:50
You have to take seven subjects
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Man skal op i syv fag
02:52
and achieve a high score --
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og opnå en høj score -
02:54
I would say like an 84 percent
or more accuracy rate --
or more accuracy rate --
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nok mere end 84 procent -
02:59
to be allowed to take
the second stage written test
the second stage written test
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for at kom videre til den anden prøve,
03:03
prepared by Todai.
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som Todai står for.
03:06
So let me first explain
how modern AI works,
how modern AI works,
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Lade mig først forklare hvordan
moderne kunstig intelligens virker,
moderne kunstig intelligens virker,
03:12
taking the "Jeopardy!" challenge
as an example.
as an example.
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ved at bruge "Jeopardy!" som et eksempel.
03:17
Here is a typical "Jeopardy!" question:
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Her er et typisk "Jeopardy!" spørgsmål:
03:20
"Mozart's last symphony
shares its name with this planet."
shares its name with this planet."
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"Mozarts sidste symfoni
deler navn med denne planet."
deler navn med denne planet."
03:26
Interestingly, a "Jeopardy!"
question always asks,
question always asks,
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Interessant nok, så spørger en
"Jeopardy!" spørgsmål altid,
"Jeopardy!" spørgsmål altid,
03:30
always ends with "this" something:
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ender altid med "denne" noget,
03:33
"this" planet, "this" country,
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"denne" planet, "dette" land,
03:36
"this" rock musician, and so on.
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"denne" rockmusiker, og så videre.
03:39
In other words, "Jeopardy!" doesn't ask
many different types of questions,
many different types of questions,
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Sagt med andre ord, har "Jeopardy!"
ikke mange forskellige typer spørgsmål,
ikke mange forskellige typer spørgsmål,
03:43
but a single type,
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men kun en type,
03:45
which we call "factoid questions."
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der kaldes "faktuelt spørgsmål".
03:48
By the way, do you know the answer?
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Forresten, kender du svaret?
03:53
If you do not know the answer
and if you want to know the answer,
and if you want to know the answer,
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Hvis man ikke kender svaret,
og vil vide det,
og vil vide det,
03:58
what would you do?
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hvad gøre man så?
04:00
You Google, right? Of course.
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Man googler det, ikke? Selvfølgelig.
04:03
Why not?
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Hvorfor ikke?
04:04
But you have to pick appropriate keywords
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Men man skal vælge relaterede ord,
04:08
like "Mozart," "last"
and "symphony" to search.
and "symphony" to search.
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som "Mozart", "sidste", og "symfoni"
til at søge med.
til at søge med.
04:13
The machine basically does the same.
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Maskinen gør nogenlunde det samme.
04:16
Then this Wikipedia page
will be ranked top.
will be ranked top.
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Denne Wikipedia rangerer højt.
04:21
Then the machine reads the page.
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Så læser maskinen siden.
04:23
No, uh-uh.
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Nej, desværre ikke.
04:25
Unfortunately, none of the modern AIs,
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Ingen af de moderne kunstig intelligenser,
04:28
including Watson, Siri and Todai Robot,
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herunder Watson, Siri og Todai Robot,
04:32
is able to read.
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kan læse.
04:35
But they are very good
at searching and optimizing.
at searching and optimizing.
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Men, de er gode til at søge og optimere.
04:40
It will recognize
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Den ville genkende
04:42
that the keywords "Mozart,"
"last" and "symphony"
"last" and "symphony"
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at "Mozart", "sidste", og "symfoni"
04:45
are appearing heavily around here.
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dukker op mange gange her.
04:49
So if it can find a word which is a planet
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Så hvis den finder et ord som er en planet
04:54
and which is co-occurring
with these keywords,
with these keywords,
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og som dukker op sammen med de andre ord,
04:57
that must be the answer.
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så må det være svaret.
05:00
This is how Watson finds
the answer "Jupiter," in this case.
the answer "Jupiter," in this case.
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Det er sådan at Watson finder
svaret "Jupiter" i dette tilfælde.
svaret "Jupiter" i dette tilfælde.
05:08
Our Todai Robot works similarly,
but a bit smarter
but a bit smarter
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Vores Todai Robot fungerer på
en lignende måde, men lidt bedre,
en lignende måde, men lidt bedre,
05:12
in answering history yes-no questions,
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når den svarer historiske ja-nej spørgsmål
05:16
like, "'Charlemagne repelled the Magyars.'
Is this sentence true or false?"
Is this sentence true or false?"
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som "Charlemagne udstødte magyarerne".
05:23
Our robot starts producing
a factoid question,
a factoid question,
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Robotten skaber selv et faktum spørgsmål:
05:27
like: "Charlemagne repelled
[this person type]" by itself.
[this person type]" by itself.
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"Charlemagne udstødte [persontype]".
05:32
Then, "Avars" but not
"Magyars" is ranked top.
"Magyars" is ranked top.
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Her er det "avarerne", ikke "magyarerne",
som rangerer øverst.
som rangerer øverst.
05:38
This sentence is likely to be false.
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Sætningen er sandsynligvis falsk.
05:42
Our robot does not read,
does not understand,
does not understand,
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Vores robot læser ikke, forstår ikke,
05:48
but it is statistically
correct in many cases.
correct in many cases.
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men er statistisk set
korrekt i mange tilfælde.
korrekt i mange tilfælde.
05:54
For the second stage written test,
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I anden skriftlige test,
05:56
it is required to write
a 600-word essay like this one:
a 600-word essay like this one:
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skal man skrive en 600-ords stil som denne:
06:01
[Discuss the rise and fall
of the maritime trade
of the maritime trade
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[Diskuter vækst og fald for
maritimhandel
maritimhandel
06:04
in East and Southeast Asia
in the 17th century ...]
in the 17th century ...]
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i Øst- og Sydøstasien i det 17. århundrede]
06:06
and as I have shown earlier,
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og som jeg viste tidligere,
06:07
our robot took the sentences
from the textbooks and Wikipedia,
from the textbooks and Wikipedia,
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tog vores robot sætninger fra
fagbøger og Wikipedia,
fagbøger og Wikipedia,
06:12
combined them together,
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kombinerede dem,
06:14
and optimized it to produce an essay
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og formede en stil
06:17
without understanding a thing.
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uden at forstå noget som helst.
06:20
(Laughter)
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(latter)
06:21
But surprisingly, it wrote a better essay
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Men overraskende nok
skrev den en bedre stil
skrev den en bedre stil
06:26
than most of the students.
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en de fleste af de studerende.
06:28
(Laughter)
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(latter)
06:30
How about mathematics?
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Hvad med matematik?
06:33
A fully automatic math-solving machine
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En fuldautomatisk matematikmaskine
06:36
has been a dream
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har været en drøm
06:38
since the birth of the word
"artificial intelligence,"
"artificial intelligence,"
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siden fødslen af ordet
"kunstig intelligens",
"kunstig intelligens",
06:43
but it has stayed at the level
of arithmetic for a long, long time.
of arithmetic for a long, long time.
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men har holdt sig på niveauet
af regning i langt, langt tid.
af regning i langt, langt tid.
06:51
Last year, we finally succeeded
in developing a system
in developing a system
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Sidste år, lykkes det os til sidst
at lave et system
at lave et system
06:56
which solved pre-university-level
problems from end to end,
problems from end to end,
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som løste komplette opgaver
på pre-universitets-niveau,
på pre-universitets-niveau,
07:02
like this one.
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som denne her.
07:05
This is the original problem
written in Japanese,
written in Japanese,
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Dette er den originale
problemformulering på Japansk
problemformulering på Japansk
07:09
and we had to teach it
2,000 mathematical axioms
2,000 mathematical axioms
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og vi måtte lære den
2.000 matematiske aksiomer
2.000 matematiske aksiomer
07:14
and 8,000 Japanese words
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og 8.000 Japanske ord
07:16
to make it accept the problems
written in natural language.
written in natural language.
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før den forstod opgaven på
på naturligt sprog.
på naturligt sprog.
07:22
And it is now translating
the original problems
the original problems
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Nu oversætter den de oprindelige opgaver
07:25
into machine-readable formulas.
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til maskinsprog.
07:30
Weird, but it is now ready
to solve it, I think.
to solve it, I think.
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Underligt, men nu tror jeg at
den er klar til at løse den.
den er klar til at løse den.
07:36
Go and solve it.
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Løse den så.
07:38
Yes! It is now executing
symbolic computation.
symbolic computation.
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Ja! Nu udfører den symbolske udregninger.
07:44
Even more weird,
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Endnu mere underligt,
07:45
but probably this is the most
fun part for the machine.
fun part for the machine.
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men det her er nok
den sjoveste del for maskinen.
den sjoveste del for maskinen.
07:50
(Laughter)
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(latter)
07:52
Now it outputs a perfect answer,
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Nu udskriver den et perfekt svar,
07:55
though its proof is impossible to read,
even for mathematicians.
even for mathematicians.
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men beviset er umuligt at læse,
selv for matematikere.
selv for matematikere.
08:02
Anyway, last year our robot
was among the top one percent
was among the top one percent
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Sidste år var vores robot
blandt de 1% bedste
blandt de 1% bedste
08:10
in the second stage written
exam in mathematics.
exam in mathematics.
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i den skriftlige prøve på andet
niveau matematik.
niveau matematik.
08:14
(Applause)
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(publikum klapper)
08:18
Thank you.
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Tak.
08:19
So, did it enter Todai?
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Kom den ind på Todai?
08:22
No, not as I expected.
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Nej, ikke som jeg forventede.
08:26
Why?
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Hvorfor?
08:28
Because it doesn't understand any meaning.
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Fordi den forstår slet ikke mening.
08:32
Let me show you a typical error
it made in the English test.
it made in the English test.
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Lade mig vise jer en typisk fejl,
som den lavede i engelsk prøven.
som den lavede i engelsk prøven.
08:36
[Nate: We're almost at the bookstore.
Just a few more minutes.
Just a few more minutes.
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[Nate: Vi er næsten ved boghandleren.
Bare et par minutter mere.
Bare et par minutter mere.
08:39
Sunil: Wait. ______ .
Nate: Thank you! That always happens ...]
Nate: Thank you! That always happens ...]
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Sunil: Vent. _____.
Nate: Tak! Det sker altid...]
Nate: Tak! Det sker altid...]
08:42
Two people are talking.
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To mennesker taler.
08:43
For us, who can understand
the situation --
the situation --
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For os, som kan forstå situationen -
08:45
[1. "We walked for a long time."
2. "We're almost there."
2. "We're almost there."
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[1. "Vi gik i lang tid."
2. "Vi er der næsten."
2. "Vi er der næsten."
08:48
3. "Your shoes look expensive."
4. "Your shoelace is untied."]
4. "Your shoelace is untied."]
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3. "Dine sko ser dyre ud."
4. "Dit snørebånd er gået op."]
4. "Dit snørebånd er gået op."]
08:51
it is obvious number four
is the correct answer, right?
is the correct answer, right?
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- er det klart at nummer fire
er det rigtig svar, ikke?
er det rigtig svar, ikke?
08:54
But Todai Robot chose number two,
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Men Todai Robot valgte nummer to,
08:56
even after learning 15 billion
English sentences
English sentences
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selv efter at havde lært
15 milliarder engelske sætninger
15 milliarder engelske sætninger
09:02
using deep learning technologies.
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ved brug af deep-learning teknologier.
09:07
OK, so now you might
understand what I said:
understand what I said:
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Ok, så nu forstår i måske hvad jeg sagde,
09:12
modern AIs do not read,
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moderne kunstig intelligenser læser ikke,
09:15
do not understand.
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forstår ikke.
09:17
They only disguise as if they do.
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De lader kun som om at de gør.
09:24
This is the distribution graph
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Det er fordelingsgrafen
09:27
of half a million students
who took the same exam as Todai Robot.
who took the same exam as Todai Robot.
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af en halv million studerende
som tog den samme prøve som Todai Robot.
som tog den samme prøve som Todai Robot.
09:34
Now our Todai Robot
is among the top 20 percent,
is among the top 20 percent,
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Vores Todai Robot er blandt de 20% bedste,
09:40
and it was capable to pass
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og den kunne havde bestået
09:43
more than 60 percent
of the universities in Japan --
of the universities in Japan --
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på mere end 60%
af universiteterne i Japan
af universiteterne i Japan
09:47
but not Todai.
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men ikke på Todai.
09:50
But see how it is beyond the volume zone
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Bemærk at den overgår flokken
09:54
of to-be white-collar workers.
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af dem som bliver kontorarbejdere.
10:00
You might think I was delighted.
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I tror måske at jeg var rigtig glad.
10:03
After all, my robot was surpassing
students everywhere.
students everywhere.
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Min robot vandt jo over studerende overalt.
10:09
Instead, I was alarmed.
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Men i sted for, var jeg bekymret.
10:13
How on earth could this unintelligent
machine outperform students --
machine outperform students --
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Hvordan i alverden kunne den her dumme
maskine præstere bedre end studerende -
maskine præstere bedre end studerende -
10:18
our children?
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vores børn?
10:20
Right?
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Ikke?
10:22
I decided to investigate
what was going on in the human world.
what was going on in the human world.
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Jeg bestemte mig for at undersøge det
der skete i den menneskelige verden.
der skete i den menneskelige verden.
10:28
I took hundreds of sentences
from high school textbooks
from high school textbooks
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Jeg tog hundredevis af sætninger
fra gymnasiebøger
fra gymnasiebøger
10:33
and made easy multiple-choice quizzes,
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og skabte nemme 'multiple-choice' opgaver
10:37
and asked thousands
of high school students to answer.
of high school students to answer.
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og fik tusindvis af gymnasiestuderende
til at svare.
til at svare.
10:42
Here is an example:
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Her er et eksempel:
10:43
[Buddhism spread to ... ,
Christianity to ... and Oceania,
Christianity to ... and Oceania,
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[Buddhisme udbredt til ...,
kristendom til ... og Oceanien,
kristendom til ... og Oceanien,
10:46
and Islam to ...]
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og islam til ...]
10:47
Of course, the original problems
are written in Japanese,
are written in Japanese,
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De originale opgaver er på Japansk,
10:50
their mother tongue.
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deres modersmål.
10:51
[ ______ has spread to Oceania.
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[ _____ er udbredt til Oceanien.
10:53
1. Hinduism 2. Christianity
3. Islam 4. Buddhism ]
3. Islam 4. Buddhism ]
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1. Hinduisme 2. Kristendom
3. Islam 4. Buddhisme ]
3. Islam 4. Buddhisme ]
10:55
Obviously, Christianity
is the answer, isn't it?
is the answer, isn't it?
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Det giver jo sig selv
at svaret er kristendom?
at svaret er kristendom?
10:58
It's written!
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Det står der jo!
11:01
And Todai Robot chose
the correct answer, too.
the correct answer, too.
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Og Todai Robot valgte også rigtig.
11:06
But one-third of junior
high school students
high school students
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Men en 1/3 af eleverne i 1.g
11:11
failed to answer this question.
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kunne ikke svare på spørgsmålet.
11:16
Do you think it is only the case in Japan?
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Tror i at det kun er tilfældet
i Japan?
i Japan?
11:19
I do not think so,
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Det tror jeg ikke,
11:21
because Japan is always ranked
among the top in OECD PISA tests,
among the top in OECD PISA tests,
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for Japan ligger altid i toppen
i OECD PISA testen,
i OECD PISA testen,
11:28
measuring 15-year-old
students' performance in mathematics,
students' performance in mathematics,
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som vurderer 15-åriges evner i matematik,
11:31
science and reading
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naturvidenskab og læsning
11:33
every three years.
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hver tredje år.
11:39
We have been believing
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Vi har haft opfattelsen
11:41
that everybody can learn
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at alle kan lære
11:43
and learn well,
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og lære godt,
11:45
as long as we provide
good learning materials
good learning materials
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så længe det rigtige
materiale er til rådighed
materiale er til rådighed
11:49
free on the web
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gratis online
11:50
so that they can access
through the internet.
through the internet.
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så de kan tilgå det via internet.
11:53
But such wonderful materials
may benefit only those who can read well,
may benefit only those who can read well,
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Men disse gode redskaber
hjælper måske kun dem der læser godt,
hjælper måske kun dem der læser godt,
12:00
and the percentage
of those who can read well
of those who can read well
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og andelen af dygtige læsere
12:04
may be much less than we expected.
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er måske meget lavere end vi tror.
12:10
How we humans will coexist with AI
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Hvordan vi kan sameksistere
med kunstig intelligens
med kunstig intelligens
12:14
is something we have
to think about carefully,
to think about carefully,
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er noget vi må tænke nøje over,
12:17
based on solid evidence.
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baseret på solid information.
12:21
At the same time,
we have to think in a hurry
we have to think in a hurry
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Men samtidig må vi tænke hurtigt,
12:25
because time is running out.
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for tiden er kort.
12:28
Thank you.
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Tak.
12:29
(Applause)
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(publikum klapper)
12:34
Chris Anderson: Noriko, thank you.
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Chris Anderson: Noriko, mange tak.
12:36
Noriko Arai: Thank you.
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Noriko Arai: Tak.
12:38
CA: In your talk, you so beautifully
give us a sense of how AIs think,
give us a sense of how AIs think,
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CA: Din tale giver os et smukt indblik i
hvordan kunstig intelligens tænker,
hvordan kunstig intelligens tænker,
12:43
what they can do amazingly
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hvad de kan gøre super godt
12:45
and what they can't do.
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og hvad de ikke kan.
12:46
But -- do I read you right,
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Men - mener du virkelig,
12:48
that you think we really need
quite an urgent revolution in education
quite an urgent revolution in education
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at vi har brug for en hurtig
revolution inden for undervisning,
revolution inden for undervisning,
12:53
to help kids do the things
that humans can do better than AIs?
that humans can do better than AIs?
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for at hjælpe børn til at gøre det
vi allerede gør bedre end kunstig intelligens?
vi allerede gør bedre end kunstig intelligens?
12:57
NA: Yes, yes, yes.
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NA: Ja, ja, ja.
12:59
Because we humans
can understand the meaning.
can understand the meaning.
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Fordi vi mennesker forstår meningen.
13:03
That is something
which is very, very lacking in AI.
which is very, very lacking in AI.
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Det er noget som virkelig mangler
i kunstig intelligens.
i kunstig intelligens.
13:08
But most of the students
just pack the knowledge
just pack the knowledge
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Men de fleste elever suger bare viden,
13:12
without understanding
the meaning of the knowledge,
the meaning of the knowledge,
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uden at forstå betydningen af den viden,
13:16
so that is not knowledge,
that is just memorizing,
that is just memorizing,
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så det er ikke at lære noget,
det er bare at huske,
det er bare at huske,
13:19
and AI can do the same thing.
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og kunstig intelligens kan gøre det samme.
13:21
So we have to think about
a new type of education.
a new type of education.
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Så vi må opfinde en ny type undervisning.
13:25
CA: A shift from knowledge,
rote knowledge, to meaning.
rote knowledge, to meaning.
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CA: En ændring fra viden,
tom viden, til mening.
tom viden, til mening.
13:28
NA: Mm-hmm.
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NA: Ja.
13:29
CA: Well, there's a challenge
for the educators. Thank you so much.
for the educators. Thank you so much.
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CA: Nå, der er da en udfordring
for lærerne. Mange tak.
for lærerne. Mange tak.
13:33
NA: Thank you very much. Thank you.
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NA: Tusind tak. Tak.
13:34
(Applause)
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(publikum klapper)
ABOUT THE SPEAKER
Noriko Arai - AI expertCould an AI pass the entrance exam for the University of Tokyo? Noriko Arai oversees a project that wants to find out.
Why you should listen
Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan.
The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks. Arai founded the Research Institute of Science for Education to elucidate why so many students fail to read and how she can support them.
Noriko Arai | Speaker | TED.com