BOX SET: 6 Minute English - 'Technology 2' English mega-class! Thirty minutes of new vocabulary!

168,169 views ใƒป 2022-10-16

BBC Learning English


ืื ื ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ืœืžื˜ื” ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ. ื›ืชื•ื‘ื™ื•ืช ืžืชื•ืจื’ืžื•ืช ืžืชื•ืจื’ืžื•ืช ื‘ืžื›ื•ื ื”.

00:05
Hello. This is 6 Minute English
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ืฉืœื•ื. ื–ื•ื”ื™ 6 ื“ืงื•ืช ืื ื’ืœื™ืช
00:07
from BBC Learning English.
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ืžื‘ื™ืช BBC Learning English.
00:09
Iโ€™m Sam.
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00:09
And Iโ€™m Neil.
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ืื ื™ ืกืื.
ื•ืื ื™ ื ื™ืœ.
00:10
On Saturday mornings I love going
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ื‘ืฉื‘ืช ื‘ื‘ื•ืงืจ ืื ื™ ืื•ื”ื‘ ืœืœื›ืช
00:12
to watch football in the park.
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ืœืจืื•ืช ื›ื“ื•ืจื’ืœ ื‘ืคืืจืง.
00:14
The problem is when itโ€™s cold and
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ื”ื‘ืขื™ื” ื”ื™ื ื›ืฉืงืจ
00:16
rainy - I look out the bedroom window
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ื•ื’ืฉื•ื - ืื ื™ ืžืกืชื›ืœ ืžื—ืœื•ืŸ ื—ื“ืจ ื”ืฉื™ื ื”
00:18
and go straight back to bed!
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ื•ื—ื•ื–ืจ ื™ืฉืจ ืœืžื™ื˜ื”!
00:20
Well, instead of going to the park, why
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ื•ื‘ื›ืŸ, ื‘ืžืงื•ื ืœืœื›ืช ืœืคืืจืง, ืœืžื”
00:22
not bring the park to you? Imagine
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ืฉืœื ืชื‘ื™ื ืืช ื”ืคืืจืง ืืœื™ืš? ืชืืจื• ืœืขืฆืžื›ื
00:24
watching a live version of the
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ืœืจืื•ืช ื’ืจืกื” ื—ื™ื” ืฉืœ
00:26
football match at home in the warm,
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ืžืฉื—ืง ื”ื›ื“ื•ืจื’ืœ ื‘ื‘ื™ืช ื‘ื—ืžื™ืžื•ืช,
00:28
with friends. Sound good, Sam?
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ืขื ื—ื‘ืจื™ื. ื ืฉืžืข ื˜ื•ื‘, ืกืื?
00:30
Sounds great! โ€“ but how can I be in
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ื ืฉืžืข ื ืคืœื! โ€“ ืื‘ืœ ืื™ืš ืื ื™ ื™ื›ื•ืœ ืœื”ื™ื•ืช
00:32
two places at once? Is there some
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ื‘ืฉื ื™ ืžืงื•ืžื•ืช ื‘ื• ื–ืžื ื™ืช? ื™ืฉ ืื™ื–ื•
00:34
amazing invention to do that?
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ื”ืžืฆืื” ืžื“ื”ื™ืžื” ืœืขืฉื•ืช ืืช ื–ื”?
00:36
There might be, Sam - and it could
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ื™ื›ื•ืœ ืœื”ื™ื•ืช ืฉื™ืฉ, ืกื - ื•ื–ื” ื™ื›ื•ืœ
00:38
be happening sooner than you think,
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ืœืงืจื•ืช ืžื•ืงื“ื ืžืžื” ืฉืืชื” ื—ื•ืฉื‘,
00:40
thanks to developments in VR, or
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ื”ื•ื“ื•ืช ืœื”ืชืคืชื—ื•ื™ื•ืช ื‘-VR, ืื•
00:42
virtual reality. According to Facebook
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ื‘ืžืฆื™ืื•ืช ืžื“ื•ืžื”. ืœื“ื‘ืจื™
00:44
boss, Mark Zuckerberg, in the future
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ื”ื‘ื•ืก ืฉืœ ืคื™ื™ืกื‘ื•ืง, ืžืืจืง ืฆื•ืงืจื‘ืจื’, ื‘ืขืชื™ื“
00:46
weโ€™ll all spend much of our time
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ื›ื•ืœื ื• ื ื‘ืœื” ื—ืœืง ื ื™ื›ืจ ืžื–ืžื ื ื•
00:48
living and working in the โ€˜metaverseโ€™ โ€“ a
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ื‘ื—ื™ื™ื ื•ืขื‘ื•ื“ื” ื‘-'metaverse' -
00:51
series of virtual worlds.
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ืกื“ืจื” ืฉืœ ืขื•ืœืžื•ืช ื•ื™ืจื˜ื•ืืœื™ื™ื.
00:53
Virtual reality is a topic weโ€™ve discussed
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ืžืฆื™ืื•ืช ืžื“ื•ืžื” ื”ื™ื ื ื•ืฉื ืฉื“ื™ื‘ืจื ื• ืขืœื™ื•
00:56
before at 6 Minute English. But when
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ื‘ืขื‘ืจ ื‘-6 ื“ืงื•ืช ืื ื’ืœื™ืช. ืื‘ืœ ื›ืฉืคื™ื™ืกื‘ื•ืง
00:59
Facebook announced that it was
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ื”ื•ื“ื™ืขื” ืฉื”ื™ื
01:00
hiring ten thousand new workers
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ืžื’ื™ื™ืกืช ืขืฉืจืช ืืœืคื™ื ืขื•ื‘ื“ื™ื ื—ื“ืฉื™ื
01:02
to develop VR for the โ€˜metaverseโ€™, we
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ื›ื“ื™ ืœืคืชื— VR ืขื‘ื•ืจ ื”-'metaverse',
01:05
thought it was time for another look.
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ื—ืฉื‘ื ื• ืฉื”ื’ื™ืข ื”ื–ืžืŸ ืœื‘ื“ื™ืงื” ื ื•ืกืคืช.
01:06
Is this programme, weโ€™ll be hearing two
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ื”ืื ื–ื• ืชื•ื›ื ื™ืช, ืื ื—ื ื• ื ืฉืžืข ืฉืชื™
01:08
different opinions on the โ€˜metaverseโ€™
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ื“ืขื•ืช ืฉื•ื ื•ืช ืขืœ ื”"ืžื˜ืื•ื•ืจืก"
01:10
and how it might shape the future.
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ื•ื›ื™ืฆื“ ื”ื•ื ืขืฉื•ื™ ืœืขืฆื‘ ืืช ื”ืขืชื™ื“.
01:12
But first I have a question for you, Neil.
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ืื‘ืœ ืงื•ื“ื ื™ืฉ ืœื™ ืฉืืœื” ืืœื™ืš, ื ื™ืœ.
01:15
According to a 2021 survey by
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ืœืคื™ ืกืงืจ ืžืฉื ืช 2021 ืฉืœ
01:17
gaming company, Thrive Analytics, what
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ื—ื‘ืจืช ื”ืžืฉื—ืงื™ื Thrive Analytics, ืื™ื–ื”
01:20
percentage of people who try virtual
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ืื—ื•ื– ืžื”ืื ืฉื™ื ืฉืžื ืกื™ื
01:22
reality once want to try it again? Is it:
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ืžืฆื™ืื•ืช ืžื“ื•ืžื” ืคืขื ืื—ืช ืจื•ืฆื™ื ืœื ืกื•ืช ืื•ืชื” ืฉื•ื‘? ื”ืื ื–ื”:
01:26
a) 9 percent?
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ื) 9 ืื—ื•ื–?
01:28
b) 49 percent? or,
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ื‘) 49 ืื—ื•ื–? ืื•,
01:31
c) 79 percent?
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ื’) 79 ืื—ื•ื–?
01:33
I guess with VR you either love it
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ืื ื™ ืžื ื™ื— ืฉืขื VR ืืชื” ืื• ืื•ื”ื‘ ืืช ื–ื”
01:35
or hate it, so Iโ€™ll say b) 49 percent of
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ืื• ืฉื•ื ื ืืช ื–ื”, ืื– ืื ื™ ืื’ื™ื“ ื‘) 49 ืื—ื•ื–
01:38
people want to try it again.
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ืžื”ืื ืฉื™ื ืจื•ืฆื™ื ืœื ืกื•ืช ืืช ื–ื” ืฉื•ื‘.
01:40
OK, Iโ€™ll reveal the correct answer
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ื‘ืกื“ืจ, ืื ื™ ืื’ืœื” ืืช ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื”
01:41
later in the programme. But what
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ื‘ื”ืžืฉืš ื”ืชื•ื›ื ื™ืช. ืื‘ืœ ืžื”
01:43
Neil said is true: people tend to either
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ืฉื ื™ืœ ืืžืจ ื ื›ื•ืŸ: ืื ืฉื™ื ื ื•ื˜ื™ื
01:46
love virtual reality or hate it.
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ืœืื”ื•ื‘ ืžืฆื™ืื•ืช ืžื“ื•ืžื” ืื• ืœืฉื ื•ื ืื•ืชื”.
01:48
Somebody who loves it is
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ืžื™ ืฉืื•ื”ื‘ืช ืืช ื–ื” ื”ื™ื
01:50
Emma Ridderstad, CEO of Warpinโ€™, a
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ืืžื” ืจื™ื“ืจืกื˜ืื“, ืžื ื›"ืœื™ืช Warpin',
01:52
company which develops
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ื—ื‘ืจื” ื”ืžืคืชื—ืช
01:54
VR technology.
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ื˜ื›ื ื•ืœื•ื’ื™ื™ืช VR.
01:55
Here she is telling BBC World
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ื”ื ื” ื”ื™ื ืžืกืคืจืช ืœืชื•ื›ื ื™ืช BBC World
01:57
Service programme, Tech Tent, her
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Service, Tech Tent, ืืช
01:59
vision of the future:
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ื—ื–ื•ืŸ ื”ืขืชื™ื“ ืฉืœื”:
02:00
In ten years, everything that you
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ื‘ืขื•ื“ ืขืฉืจ ืฉื ื™ื, ื›ืœ ืžื” ืฉืืชื”
02:02
do on your phone today, you will
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ืขื•ืฉื” ื‘ื˜ืœืคื•ืŸ ืฉืœืš ื”ื™ื•ื, ืืชื”
02:04
do in 3-D, through your classes
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ืชืขืฉื” ื‘ืชืœืช ืžื™ืžื“, ื“ืจืš ื”ืฉื™ืขื•ืจื™ื ืฉืœืš
02:06
for example. You will be able to do
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ืœืžืฉืœ. ืชื•ื›ืœ ืœืขืฉื•ืช ืืช
02:09
your shopping, you will be able to
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ื”ืงื ื™ื•ืช ืฉืœืš, ืชื•ื›ืœ
02:10
meet your friends, you will be able
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ืœืคื’ื•ืฉ ืืช ื”ื—ื‘ืจื™ื ืฉืœืš, ืชื•ื›ืœ
02:12
to work remotely with whomever
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ืœืขื‘ื•ื“ ืžืจื—ื•ืง ืขื ืžื™
02:15
you want, you will be able to share
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ืฉืชืจืฆื”, ืชื•ื›ืœ ืœืฉืชืฃ
02:17
digital spaces, share music, share
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ื—ืœืœื™ื ื“ื™ื’ื™ื˜ืœื™ื™ื, ืœืฉืชืฃ ืžื•ื–ื™ืงื”, ืœืฉืชืฃ
02:21
art, share projects in digital spaces
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ืืžื ื•ืช, ืœืฉืชืฃ ืคืจื•ื™ืงื˜ื™ื ื‘ืžืจื—ื‘ื™ื ื“ื™ื’ื™ื˜ืœื™ื™ื
02:24
between each other. And you will also
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ื‘ื™ืŸ ืื—ื“ ืœืฉื ื™. ื•ื’ื
02:26
be able to integrate the digital objects
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ืชื•ื›ืœ ืœืฉืœื‘ ืืช ื”ืื•ื‘ื™ื™ืงื˜ื™ื ื”ื“ื™ื’ื™ื˜ืœื™ื™ื
02:28
in your physical world, making the
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ื‘ืขื•ืœื ื”ืคื™ื–ื™ ืฉืœืš, ืžื” ืฉื”ื•ืคืš ืืช
02:31
world much more phygital than
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ื”ืขื•ืœื ืœื”ืจื‘ื” ื™ื•ืชืจ ืคื™ื’ื™ื˜ืœื™ ืžืžื”
02:33
is it today.
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ืฉื”ื•ื ื”ื™ื•ื.
02:35
Virtual reality creates 3-D, or
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ืžืฆื™ืื•ืช ืžื“ื•ืžื” ื™ื•ืฆืจืช ื—ื•ื•ื™ื•ืช ืชืœืช ืžืžื“ื™ื•ืช, ืื•
02:37
three-dimensional experiences where
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ืชืœืช ืžื™ืžื“ื™ื•ืช ืฉื‘ื”ืŸ
02:39
objects have the three dimensions of
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ืœืื•ื‘ื™ื™ืงื˜ื™ื ื™ืฉ ืืช ืฉืœื•ืฉืช ื”ืžื™ืžื“ื™ื ืฉืœ
02:42
length, width and height. This makes
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ืื•ืจืš, ืจื•ื—ื‘ ื•ื’ื•ื‘ื”. ื–ื” ื’ื•ืจื
02:44
them look lifelike and solid, not
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ืœื”ื ืœื”ื™ืจืื•ืช ืืžื™ืชื™ื™ื ื•ืžื•ืฆืงื™ื, ืœื
02:47
two-dimensional and flat.
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ื“ื• ืžื™ืžื“ื™ื™ื ื•ืฉื˜ื•ื—ื™ื.
02:49
Emma says that in the future VR will
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ืืžื” ืื•ืžืจืช ืฉื‘ืขืชื™ื“ VR
02:52
mix digital objects and physical
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ืชืขืจื‘ื‘ ืื•ื‘ื™ื™ืงื˜ื™ื ื“ื™ื’ื™ื˜ืœื™ื™ื ื•ืื•ื‘ื™ื™ืงื˜ื™ื ืคื™ื–ื™ื™ื
02:54
objects to create exciting new
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ื›ื“ื™ ืœื™ืฆื•ืจ ื—ื•ื•ื™ื•ืช ื—ื“ืฉื•ืช ื•ืžืจื’ืฉื•ืช
02:55
experiences โ€“ like staying home to
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- ื›ืžื• ืœื”ื™ืฉืืจ ื‘ื‘ื™ืช
02:58
watch the same football match
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ืœืฆืคื•ืช ื‘ืื•ืชื• ืžืฉื—ืง ื›ื“ื•ืจื’ืœ
02:59
that is simultaneously happening in
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ืฉืžืชืจื—ืฉ ื‘ื• ื–ืžื ื™ืช
03:01
the park. She blends the words
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ื‘ืคืืจืง. ื”ื™ื ืžืฉืœื‘ืช ืืช ื”ืžื™ืœื™ื
03:04
โ€˜physicalโ€™ and โ€˜digitalโ€™ to make a new
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'ืคื™ื–ื™' ื•'ื“ื™ื’ื™ื˜ืœื™' ื›ื“ื™ ืœื™ืฆื•ืจ
03:06
word describing this
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ืžื™ืœื” ื—ื“ืฉื” ื”ืžืชืืจืช โ€‹โ€‹ืืช
03:07
combination: phygital.
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ื”ืฉื™ืœื•ื‘ ื”ื–ื”: phygital.
03:09
But while a โ€˜phygitalโ€™ future sounds
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ืื‘ืœ ื‘ืขื•ื“ ืฉืขืชื™ื“ 'ืคื™ื’ื™ื˜ืœื™' ื ืฉืžืข
03:11
like paradise to some, others are
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ืœื—ืœืงื ื›ืžื• ื’ืŸ ืขื“ืŸ, ืื—ืจื™ื
03:13
more sceptical โ€“ they doubt that
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ืกืคืงื ื™ื ื™ื•ืชืจ - ื”ื ื‘ืกืคืง ืื
03:15
VR will come true or be useful.
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VR ื™ืชื’ืฉื ืื• ื™ื”ื™ื” ืฉื™ืžื•ืฉื™.
03:18
One such sceptic is technology
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ืกืคืงืŸ ืื—ื“ ื›ื–ื” ื”ื•ื
03:20
innovator, Dr Nicola Millard. For one
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ื—ื“ืฉืŸ ื”ื˜ื›ื ื•ืœื•ื’ื™ื”, ื“"ืจ ื ื™ืงื•ืœื” ืžื™ืœืืจื“.
03:23
thing, she doesnโ€™t like wearing a
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ื“ื‘ืจ ืื—ื“, ื”ื™ื ืœื ืื•ื”ื‘ืช ืœืœื‘ื•ืฉ
03:24
VR headset โ€“ the heavy helmet and
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ืื•ื–ื ื™ื•ืช VR - ื”ืงืกื“ื” ื”ื›ื‘ื“ื”
03:26
glasses that create virtual reality
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ื•ื”ืžืฉืงืคื™ื™ื ืฉื™ื•ืฆืจื•ืช ืžืฆื™ืื•ืช ืžื“ื•ืžื”
03:29
for the wearer โ€“ something she
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ืขื‘ื•ืจ ื”ืœื•ื‘ืฉ ืื•ืชื” - ืžืฉื”ื• ืฉื”ื™ื
03:30
explained to BBC World Serviceโ€™s,
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ื”ืกื‘ื™ืจื” ืœ-BBC World Service,
03:32
Tech Tent:
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Tech Tent:
03:34
There are some basic things to
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ื™ืฉ ื›ืžื” ื“ื‘ืจื™ื ื‘ืกื™ืกื™ื™ื ืฉืฆืจื™ืš
03:36
think about. So, how do we
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ืœื—ืฉื•ื‘ ืขืœื™ื”ื. ืื– ืื™ืš ืื ื—ื ื•
03:37
access it? So, the reason, sort of,
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ื ื™ื’ืฉื™ื ืœื–ื”? ืื– ื”ืกื™ื‘ื”, ืกื•ื’ ืฉืœ,
03:40
social networks took off was, weโ€™ve
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ื”ืจืฉืชื•ืช ื”ื—ื‘ืจืชื™ื•ืช ื”ืžืจื™ืื• ื”ื™ื™ืชื”
03:42
got mobile technologies that let
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ืฉื™ืฉ ืœื ื• ื˜ื›ื ื•ืœื•ื’ื™ื•ืช ื ื™ื™ื“ื•ืช ืฉืžืืคืฉืจื•ืช
03:44
us use it. Now, obviously one of
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ืœื ื• ืœื”ืฉืชืžืฉ ื‘ื”ืŸ. ืขื›ืฉื™ื•, ื‘ืจื•ืจ ืฉืื—ื“
03:46
the barriers can be that VR or AR
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ื”ื—ืกืžื™ื ื™ื›ื•ืœ ืœื”ื™ื•ืช ืื•ื–ื ื™ื•ืช VR ืื• AR
03:48
headsets - so VR, Iโ€™ve always been
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- ืื– VR, ืชืžื™ื“ ื”ื™ื™ืชื™
03:51
slightly sceptical about. Iโ€™ve called
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ืžืขื˜ ืกืงืคื˜ื™ ืœื’ื‘ื™ื•. ืงืจืืชื™
03:53
it โ€˜vomity realityโ€™ for a while because,
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ืœื–ื” 'ืžืฆื™ืื•ืช ืงื™ื' ื‘ืžืฉืš ื–ืžืŸ ืžื”, ื›ื™
03:55
frankly, I usually need a bucket
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ืœืžืขืŸ ื”ืืžืช, ืื ื™ ื‘ื“ืจืš ื›ืœืœ ืฆืจื™ืš ื“ืœื™
03:58
somewhere close if youโ€™ve got a
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ืื™ืคืฉื”ื• ืงืจื•ื‘ ืื ื™ืฉ ืœืš
04:00
headset on meโ€ฆ and also, do I want
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ืื•ื–ื ื™ื•ืช ืขืœื™ื™... ื•ื’ื, ื”ืื ืื ื™ ืจื•ืฆื”
04:01
to spend vast amounts of time in
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ืœื‘ืœื•ืช ื›ืžื•ื™ื•ืช ืขืฆื•ืžื•ืช ืฉืœ ื–ืžืŸ ื‘ืื•ื–ื ื™ื•ืช
04:03
those rather unwieldy headsets?
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ื”ืžืกื•ืจื‘ืœื•ืช ื”ืืœื”. ?
04:05
Now, I know theyโ€™re talking AR as
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ืขื›ืฉื™ื•, ืื ื™ ื™ื•ื“ืข ืฉื”ื ืžื“ื‘ืจื™ื ื’ื ืขืœ AR
04:07
well and obviously that does not
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ื•ื‘ืจื•ืจ ืฉื–ื” ืœื
04:08
necessarily need a headset, but I
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ื‘ื”ื›ืจื— ืฆืจื™ืš ืื•ื–ื ื™ื•ืช, ืื‘ืœ ืื ื™
04:10
think weโ€™re seeing some quite
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ื—ื•ืฉื‘ ืฉืื ื—ื ื• ืจื•ืื™ื ื›ืžื”
04:12
immersive environments coming
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ืกื‘ื™ื‘ื•ืช ื“ื™ ืกื•ื—ืคื•ืช
04:13
out at the moment as well.
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ื™ื•ืฆืื•ืช ื›ืจื’ืข.
04:15
Nicola called VR โ€˜vomity realityโ€™
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ื ื™ืงื•ืœื” ื›ื™ื ืชื” ืืช ื”-VR 'ืžืฆื™ืื•ืช ืงื™ื'
04:18
because wearing a headset makes
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ืžื›ื™ื•ื•ืŸ ืฉื—ื‘ื™ืฉื” ืฉืœ ืื•ื–ื ื™ื•ืช ื’ื•ืจืžืช
04:20
her feel sick, maybe because itโ€™s
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ืœื” ืœื”ืจื’ื™ืฉ ื‘ื—ื™ืœื”, ืื•ืœื™ ื‘ื’ืœืœ ืฉื–ื”
04:22
so unwieldy โ€“ difficult to move or
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ื›ืœ ื›ืš ืœื ืžื ื•ื”ืœ - ืงืฉื” ืœื–ื•ื– ืื•
04:25
wear because itโ€™s big and heavy.
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ืœืœื‘ื•ืฉ ื›ื™ ื–ื” ื’ื“ื•ืœ ื•ื›ื‘ื“.
04:27
She also makes a difference
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ื”ื™ื ื’ื ืขื•ืฉื” ื”ื‘ื“ืœ
04:28
between VR - virtual reality- and AR,
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ื‘ื™ืŸ VR - ืžืฆื™ืื•ืช ืžื“ื•ืžื” - ืœื‘ื™ืŸ AR,
04:32
which stands for augmented
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ื”ืžื™ื™ืฆื’
04:33
reality โ€“ tech which adds to the
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ืžืฆื™ืื•ืช ืจื‘ื•ื“ื” - ื˜ื›ื ื•ืœื•ื’ื™ื” ืฉืžื•ืกื™ืคื” ืœืขื•ืœื
04:36
ordinary physical world by
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ื”ืคื™ื–ื™ ื”ืจื’ื™ืœ ืขืœ ื™ื“ื™
04:37
projecting virtual words, pictures
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ื”ืงืจื ืช ืžื™ืœื™ื, ืชืžื•ื ื•ืช
04:40
and characters, usually by wearing
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ื•ื“ืžื•ื™ื•ืช ื•ื™ืจื˜ื•ืืœื™ื•ืช, ื‘ื“ืจืš ื›ืœืœ ื‘ืืžืฆืขื•ืช
04:42
glasses or with a mobile phone.
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ืžืฉืงืคื™ื™ื ืื• ืขื ื˜ืœืคื•ืŸ ื ื™ื™ื“.
04:44
While virtual reality replaces what
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ื‘ืขื•ื“ ืฉืžืฆื™ืื•ืช ืžื“ื•ืžื” ืžื—ืœื™ืคื” ืืช ืžื”
04:46
you hear and see, augmented
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ืฉืืชื” ืฉื•ืžืข ื•ืจื•ืื”,
04:48
reality adds to it. Both VR and AR
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ืžืฆื™ืื•ืช ืจื‘ื•ื“ื” ืžื•ืกื™ืคื” ืœื”. ื’ื VR ื•ื’ื AR
04:52
are immersive experiences โ€“ they
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ื”ื ื—ื•ื•ื™ื•ืช ืกื•ื—ืคื•ืช - ื”ื
04:54
stimulate your senses and surround
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ืžื’ืจื™ื ืืช ื”ื—ื•ืฉื™ื ืฉืœืš ื•ืžืงื™ืคื™ื
04:56
you so that you feel completely
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ืื•ืชืš ื›ืš ืฉืืชื” ืžืจื’ื™ืฉ
04:58
involved in the experience.
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ืžืขื•ืจื‘ ืœื—ืœื•ื˜ื™ืŸ ื‘ื—ื•ื•ื™ื”.
04:59
In fact, the experience feels so real
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ืœืžืขืฉื”, ื”ื—ื•ื•ื™ื” ืžืจื’ื™ืฉื” ื›ืœ ื›ืš ืืžื™ืชื™ืช
05:02
that people keep coming back
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ืฉืื ืฉื™ื ืžืžืฉื™ื›ื™ื ืœื—ื–ื•ืจ
05:03
for more.
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ื‘ืฉื‘ื™ืœ ืขื•ื“.
05:04
Right! In my question I asked
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ื™ืžื™ืŸ! ื‘ืฉืืœืชื™ ืฉืืœืชื™ ืืช
05:06
Neil how many people who try
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ื ื™ืœ ื›ืžื” ืื ืฉื™ื ืฉืžื ืกื™ื
05:08
VR for the first time want to try
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VR ื‘ืคืขื ื”ืจืืฉื•ื ื” ืจื•ืฆื™ื ืœื ืกื•ืช
05:10
it again.
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ืืช ื–ื” ืฉื•ื‘.
05:11
I guessed it was about half โ€“
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ืฉื™ืขืจืชื™ ืฉื–ื” ื‘ืขืจืš ื—ืฆื™ -
05:12
49 percent. Was I right?
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49 ืื—ื•ื–. ืฆื“ืงืชื™?
05:14
You wereโ€ฆ wrong, Iโ€™m afraid.
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ื˜ืขื™ืช... ืื ื™ ื—ื•ืฉืฉ.
05:17
The correct answer is much
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ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื” ื”ื™ื ื”ืจื‘ื”
05:18
higher - 79 percent of people
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ื™ื•ืชืจ ื’ื‘ื•ื”ื” - 79 ืื—ื•ื– ืžื”ืื ืฉื™ื
05:21
would give VR another try.
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ื”ื™ื• ืžื ืกื™ื VR ื ื•ืกืฃ.
05:23
I suppose because the experience
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ืื ื™ ืžื ื™ื— ืฉื‘ื’ืœืœ ืฉื”ื—ื•ื•ื™ื”
05:24
was so immersive โ€“ stimulating,
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ื”ื™ื™ืชื” ื›ืœ ื›ืš ืกื•ื—ืคืช - ืžืขื•ืจืจืช,
05:27
surrounding and realistic.
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ืžืงื™ืคื” ื•ืžืฆื™ืื•ืชื™ืช.
05:29
Ok, A, letโ€™s recap the other
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ืื•ืงื™, ื', ื‘ื•ืื• ื ืกื›ื ืืช ืื•ืฆืจ
05:30
vocabulary from this programme
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ื”ืžื™ืœื™ื ื”ืื—ืจ ืžื”ืชื•ื›ื ื™ืช ื”ื–ื•
05:32
on the โ€˜metaverseโ€™, a kind of
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ืขืœ ื”-'metaverse', ืกื•ื’ ืฉืœ
05:34
augmented reality โ€“ reality which
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ืžืฆื™ืื•ืช ืžื•ื’ื‘ืจืช - ืžืฆื™ืื•ืช
05:37
is enhanced or added to
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ืฉืžืฉื•ืคืจืช ืื• ืžืชื•ื•ืกืคืช
05:38
by technology.
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ืขืœ ื™ื“ื™ ื”ื˜ื›ื ื•ืœื•ื’ื™ื”.
05:40
3-D objects have three
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ืœืื•ื‘ื™ื™ืงื˜ื™ื ืชืœืช ืžื™ืžื“ื™ื™ื ื™ืฉ ืฉืœื•ืฉื”
05:41
dimensions, making them
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ืžื™ืžื“ื™ื, ืžื” ืฉื’ื•ืจื ืœื”ื
05:42
appear real and solid.
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ืœื”ื™ืจืื•ืช ืืžื™ืชื™ื™ื ื•ืžื•ืฆืงื™ื.
05:44
Phygital is an invented word
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ืคื™ื’ื™ื˜ืœ ื”ื™ื ืžื™ืœื” ืžื•ืžืฆืืช
05:46
which combines the features of
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ื”ืžืฉืœื‘ืช ืืช ื”ืชื›ื•ื ื•ืช ืฉืœ
05:47
โ€˜physicalโ€™ and โ€˜digitalโ€™ worlds.
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ืขื•ืœืžื•ืช "ืคื™ื–ื™ื™ื" ื•"ื“ื™ื’ื™ื˜ืœื™ื™ื".
05:50
A sceptical person is doubtful
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ืื“ื ืกืงืคื˜ื™ ืžื˜ื™ืœ ืกืคืง
05:52
about something.
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ื‘ืžืฉื”ื•.
05:53
And finally, unwieldy means
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ื•ืœื‘ืกื•ืฃ, ืžืกื•ืจื‘ืœ ืคื™ืจื•ืฉื•
05:55
difficult to move or carry because
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ืฉืงืฉื” ืœื”ื–ื™ื– ืื•ืชื• ืื• ืœืกื—ื•ื‘ ืื•ืชื• ืžื›ื™ื•ื•ืŸ
05:56
itโ€™s so big and heavy.
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ืฉื”ื•ื ื›ืœ ื›ืš ื’ื“ื•ืœ ื•ื›ื‘ื“.
05:58
Thatโ€™s our six minutes up, in this
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ื–ื” ืฉืฉ ื”ื“ืงื•ืช ืฉืœื ื•,
06:00
reality anyway. See you in the
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ื‘ื›ืœ ืžืงืจื” ื‘ืžืฆื™ืื•ืช ื”ื–ื•. ื ืชืจืื” ื‘-
06:02
โ€˜metaverseโ€™ soon!
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'metaverse' ื‘ืงืจื•ื‘!
06:03
Goodbye!
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ื”ึฑื™ื” ืฉืœื•ื!
06:10
Hello. This is 6 Minute English
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ืฉืœื•ื. ื–ื•ื”ื™ 6 ื“ืงื•ืช ืื ื’ืœื™ืช
06:12
from BBC Learning English.
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ืžื‘ื™ืช BBC Learning English.
06:13
Iโ€™m Neil.
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ืื ื™ ื ื™ืœ.
06:14
And Iโ€™m Sam.
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ื•ืื ื™ ืกื.
06:15
What do shopping with a credit
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ืžื” ืžืฉื•ืชืฃ ืœืงื ื™ื•ืช ื‘ื›ืจื˜ื™ืก ืืฉืจืื™
06:17
card, finding love through
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, ืœืžืฆื•ื ืื”ื‘ื” ื‘ืืžืฆืขื•ืช
06:18
internet dating and waiting for
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ื”ื™ื›ืจื•ื™ื•ืช ื‘ืื™ื ื˜ืจื ื˜ ื•ื”ืžืชื ื”
06:20
the traffic lights to change
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ืœืฉื™ื ื•ื™ ื”ืจืžื–ื•ืจื™ื
06:22
have in common?
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?
06:23
Hmmm, they all involve
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ื”ืžืž, ื›ื•ืœื ืžืขืจื‘ื™ื
06:25
computers?
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06:25
Good guess, Sam! But how
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ืžื—ืฉื‘ื™ื?
ื ื™ื—ื•ืฉ ื˜ื•ื‘, ืกื! ืื‘ืœ ืื™ืš
06:27
exactly do those computers work?
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ื‘ื“ื™ื•ืง ืขื•ื‘ื“ื™ื ืื•ืชื ืžื—ืฉื‘ื™ื?
06:29
The answer is that they all use
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ื”ืชืฉื•ื‘ื” ื”ื™ื ืฉื›ื•ืœื ืžืฉืชืžืฉื™ื
06:32
algorithms โ€“ sets of mathematical
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ื‘ืืœื’ื•ืจื™ืชืžื™ื - ืงื‘ื•ืฆื•ืช ืฉืœ
06:34
instructions which find solutions
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ื”ื•ืจืื•ืช ืžืชืžื˜ื™ื•ืช ืฉืžื•ืฆืื•ืช ืคืชืจื•ื ื•ืช
06:36
to problems.
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ืœื‘ืขื™ื•ืช.
06:37
Although they are often hidden,
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ืœืžืจื•ืช ืฉืœืขืชื™ื ืงืจื•ื‘ื•ืช ื”ื ืžื•ืกืชืจื™ื,
06:39
algorithms are all around us.
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ืืœื’ื•ืจื™ืชืžื™ื ื ืžืฆืื™ื ืžืกื‘ื™ื‘ื ื•.
06:41
From mobile phone maps to
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ืžืžืคื•ืช ื˜ืœืคื•ื ื™ื ื ื™ื™ื“ื™ื ื•ืขื“
06:43
home delivery pizza, they play a
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ืคื™ืฆื” ืžืฉืœื•ื—ื™ื ืขื“ ื”ื‘ื™ืช, ื”ื ืžืžืœืื™ื
06:45
big part of modern life. And
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ื—ืœืง ื’ื“ื•ืœ ืžื”ื—ื™ื™ื ื”ืžื•ื“ืจื ื™ื™ื. ื•ื”ื
06:47
theyโ€™re the topic of this programme.
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ื”ื ื•ืฉื ืฉืœ ื”ืชื•ื›ื ื™ืช ื”ื–ื•.
06:49
A simple way to think of algorithms
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ื“ืจืš ืคืฉื•ื˜ื” ืœื—ืฉื•ื‘ ืขืœ ืืœื’ื•ืจื™ืชืžื™ื
06:51
is as recipes. To make pancakes
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ื”ื™ื ื›ืžืชื›ื•ื ื™ื. ืœื”ื›ื ืช ืคื ืงื™ื™ืง
06:54
you mix flour, eggs and milk, then
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ืžืขืจื‘ื‘ื™ื ืงืžื—, ื‘ื™ืฆื™ื ื•ื—ืœื‘, ื•ืื–
06:56
melt butter in a frying pan and
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ืžืžื™ืกื™ื ื—ืžืื” ื‘ืžื—ื‘ืช ื•ื›ืŸ
06:58
so on. Computers do this in more
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ื”ืœืื”. ืžื—ืฉื‘ื™ื ืขื•ืฉื™ื ื–ืืช ื‘ืฆื•ืจื”
07:00
a complicated way by repeating
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ืžืกื•ื‘ื›ืช ื™ื•ืชืจ ืขืœ ื™ื“ื™ ื—ื–ืจื” ืขืœ
07:02
mathematical equations over
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ืžืฉื•ื•ืื•ืช ืžืชืžื˜ื™ื•ืช ืฉื•ื‘
07:04
and over again.
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ื•ืฉื•ื‘.
07:06
Equations are mathematical
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ืžืฉื•ื•ืื•ืช ื”ืŸ
07:07
sentences showing how two
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ืžืฉืคื˜ื™ื ืžืชืžื˜ื™ื™ื ื”ืžืจืื™ื ื›ื™ืฆื“ ืฉื ื™
07:08
things are equal. Theyโ€™re similar
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ื“ื‘ืจื™ื ืฉื•ื•ื™ื. ื”ื ื“ื•ืžื™ื
07:11
to algorithms and the most famous
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ืœืืœื’ื•ืจื™ืชืžื™ื ื•ื ื™ืชืŸ ืœื—ืฉื•ื‘ ืขืœ
07:13
scientific equation of all, Einstein's
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ื”ืžืฉื•ื•ืื” ื”ืžื“ืขื™ืช ื”ืžืคื•ืจืกืžืช ืžื›ื•ืœื,
07:15
E=MC2, can be thought of as a
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E=MC2 ืฉืœ ืื™ื™ื ืฉื˜ื™ื™ืŸ
07:19
three-part algorithm.
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ื›ืืœื’ื•ืจื™ืชื ืฉืœ ืฉืœื•ืฉื” ื—ืœืงื™ื.
07:21
But before my brain gets squashed
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ืื‘ืœ ืœืคื ื™ ืฉื”ืžื•ื— ืฉืœื™ ื™ื™ืžืขืš
07:23
by all this maths, I have a quiz
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ืžื›ืœ ื”ืžืชืžื˜ื™ืงื” ื”ื–ื•, ื™ืฉ ืœื™
07:25
question for you, Sam. As you know,
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ืฉืืœืช ื—ื™ื“ื•ืŸ ื‘ืฉื‘ื™ืœืš, ืกืื. ื›ืคื™ ืฉืืชื” ื™ื•ื“ืข,
07:27
Einsteinโ€™s famous equation is
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ื”ืžืฉื•ื•ืื” ื”ืžืคื•ืจืกืžืช ืฉืœ ืื™ื™ื ืฉื˜ื™ื™ืŸ ื”ื™ื
07:29
E=MC2 - but what does the
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E=MC2 - ืื‘ืœ ืžื”
07:32
โ€˜Eโ€™ stand for? Is it:
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ืžืกืžืœ ื”-E? ื”ืื ื–ื”:
07:33
a) electricity?
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ื) ื—ืฉืžืœ?
07:35
b) energy? or
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ื‘) ืื ืจื’ื™ื”? ืื•
07:37
c) everything?
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ื’) ื”ื›ืœ?
07:38
Iโ€™m tempted to say โ€˜Eโ€™ is for
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ืื ื™ ืžืชืคืชื” ืœื•ืžืจ ืฉ'E' ื”ื•ื ืขื‘ื•ืจ
07:40
โ€˜everythingโ€™ but I reckon I know
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'ื”ื›ืœ', ืื‘ืœ ืื ื™ ื—ื•ืฉื‘ ืฉืื ื™ ื™ื•ื“ืข
07:42
the answer: b โ€“ โ€˜Eโ€™ stands
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ืืช ื”ืชืฉื•ื‘ื”: ื‘ - 'E'
07:44
for โ€˜energyโ€™.
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ืžื™ื™ืฆื’ 'ืื ืจื’ื™ื”'.
07:45
OK, Sam, weโ€™ll find out if youโ€™re
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ื‘ืกื“ืจ, ืกื, ื ื’ืœื” ืื ืืชื”
07:46
right later in the programme.
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ืฆื•ื“ืง ื‘ื”ืžืฉืš ื”ืชื•ื›ื ื™ืช.
07:48
With all this talk of computers, you
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ืขื ื›ืœ ื”ื“ื™ื‘ื•ืจื™ื ื”ืืœื” ืขืœ ืžื—ืฉื‘ื™ื, ืืชื”
07:50
might think algorithms are a
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ืขืฉื•ื™ ืœื—ืฉื•ื‘ ืฉืืœื’ื•ืจื™ืชืžื™ื ื”ื
07:51
new idea. In fact, theyโ€™ve been
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ืจืขื™ื•ืŸ ื—ื“ืฉ. ืœืžืขืฉื”, ื”ื
07:54
around since Babylonian times,
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ืงื™ื™ืžื™ื ืžืื– ื™ืžื™ ื‘ื‘ืœ,
07:56
around 4,000 years ago.
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ืœืคื ื™ ื›-4,000 ืฉื ื”.
07:58
And their use today can be
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ื•ื”ืฉื™ืžื•ืฉ ื‘ื”ื ื›ื™ื•ื ื™ื›ื•ืœ ืœื”ื™ื•ืช
07:59
controversial. Some algorithms
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ืฉื ื•ื™ ื‘ืžื—ืœื•ืงืช. ื›ืžื” ืืœื’ื•ืจื™ืชืžื™ื
08:01
used in internet search engines
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ื”ืžืฉืžืฉื™ื ื‘ืžื ื•ืขื™ ื—ื™ืคื•ืฉ ื‘ืื™ื ื˜ืจื ื˜
08:03
have been accused of
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ื”ื•ืืฉืžื•
08:04
racial prejudice.
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ื‘ื“ืขื•ืช ืงื“ื•ืžื•ืช ื’ื–ืขื™ื•ืช.
08:06
Ramesh Srinivasan is Professor
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ืจืืžืฉ ืกืจื™ื ื™ื•ื•ืืกืŸ ื”ื•ื ืคืจื•ืคืกื•ืจ
08:08
of Information Studies at the
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ืœืœื™ืžื•ื“ื™ ืžื™ื“ืข ื‘ืื•ื ื™ื‘ืจืกื™ื˜ืช
08:09
University of California. Hereโ€™s what
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ืงืœื™ืคื•ืจื ื™ื”. ื”ื ื” ืžื”
08:12
he said when asked what the word
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ืฉื”ื•ื ืืžืจ ื›ืฉื ืฉืืœ ืžื”
08:13
โ€˜algorithmโ€™ actually means by
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ื‘ืขืฆื ืžืฉืžืขื•ืช ื”ืžื™ืœื” 'ืืœื’ื•ืจื™ืชื'
08:15
BBC World Serviceโ€™s programme,
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ื‘ืชื•ื›ื ื™ืช ืฉืœ BBC World Service,
08:17
The Forum:
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The Forum:
08:20
My understanding of the term
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ื”ื”ื‘ื ื” ืฉืœื™ ืฉืœ ื”ืžื•ื ื— '
08:22
โ€˜algorithmโ€™ is that itโ€™s not necessarily
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ืืœื’ื•ืจื™ืชื' ื”ื™ื ืฉื–ื” ืœื ื‘ื”ื›ืจื—
08:24
the bogyman, or its not necessarily
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ื”ื‘ื•ื’ืžืŸ, ืื• ืฉื–ื” ืœื ื‘ื”ื›ืจื—
08:27
something that is, you know, inscrutable
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ืžืฉื”ื• ืฉื”ื•ื, ืืชื” ื™ื•ื“ืข, ื‘ืœืชื™ ื ื™ืชื ืช ืœื‘ื“ื™ืงื”
08:29
or mysterious to all people โ€“ itโ€™s the
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ืื• ืžืกืชื•ืจื™ืช ืœื›ืœ ื”ืื ืฉื™ื โ€“ ื–ื•ื”ื™
08:31
set of instructions that you write in
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ืงื‘ื•ืฆืช ื”ื”ื•ืจืื•ืช ืฉืืชื” ื›ื•ืชื‘
08:35
some mathematical form or in
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ื‘ืฆื•ืจื” ืžืชืžื˜ื™ืช ื›ืœืฉื”ื™ ืื•
08:37
some software code โ€“ so itโ€™s the
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ื‘ืงื•ื“ ืชื•ื›ื ื” ื›ืœืฉื”ื• โ€“ ื›ืš ืฉื–ื• ืงื‘ื•ืฆืช
08:39
repeated set of instructions that
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ื”ื”ื•ืจืื•ืช ื”ื—ื•ื–ืจืช ื•ื ืฉื ื™ืช ืืฉืจ
08:41
are sequenced, that are used and
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ืžืจืฆืคื•ืช, ื”ืžืฉืžืฉื•ืช ื•ืžื™ื•ืฉืžื•ืช
08:44
applied to answer a question or
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ื›ื“ื™ ืœืขื ื•ืช ืขืœ ืฉืืœื” ืื•
08:46
resolve a problem โ€“ itโ€™s a simple
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ืœืคืชื•ืจ ื‘ืขื™ื” โ€“ ื–ื” ืคืฉื•ื˜
08:48
as that, actually.
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ื›ืžื• ื–ื”, ืœืžืขืฉื”.
08:51
Some think that algorithms have
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ื™ืฉ ื”ืกื‘ื•ืจื™ื ืฉืืœื’ื•ืจื™ืชืžื™ื
08:52
been controversial, but Professor
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ื”ื™ื• ืฉื ื•ื™ื™ื ื‘ืžื—ืœื•ืงืช, ืื‘ืœ ืคืจื•ืคืกื•ืจ
08:54
Srinivasan says they are not
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ืกืจื™ื ื™ื•ื•ืืกืŸ ืื•ืžืจ ืฉื”ื ืœื
08:56
necessarily the bogyman. The
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ื‘ื”ื›ืจื— ื”ื‘ื•ื’ืžืŸ.
08:58
bogyman refers to something
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ื”ื‘ื•ื’ืžืŸ ืžืชื™ื™ื—ืก ืœืžืฉื”ื•
09:00
people call โ€˜badโ€™ or โ€˜evilโ€™ to make
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ืฉืื ืฉื™ื ืžื›ื ื™ื 'ืจืข' ืื• 'ืจืฉืข' ื›ื“ื™ ืœื’ืจื•ื
09:03
other people afraid.
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ืœืื ืฉื™ื ืื—ืจื™ื ืœืคื—ื“.
09:04
Professor Srinivasan thinks
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ืคืจื•ืคืกื•ืจ Srinivasan ื—ื•ืฉื‘
09:06
algorithms are neither evil nor
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ืฉืืœื’ื•ืจื™ืชืžื™ื ืื™ื ื ืžืจื•ืฉืขื™ื ื•ืื™ื ื ื ื™ืชื ื™ื
09:08
inscrutable โ€“ not showing emotions
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ืœื‘ื™ืจื•ืจ - ืื™ื ื ืžืจืื™ื ืจื’ืฉื•ืช
09:11
or thoughts and therefore very
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ืื• ืžื—ืฉื‘ื•ืช ื•ืœื›ืŸ
09:13
difficult to understand.
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ืงืฉื” ืžืื•ื“ ืœื”ื‘ื™ืŸ ืื•ืชื.
09:14
Still, it can be difficult to understand
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ื•ื‘ื›ืœ ื–ืืช, ื–ื” ื™ื›ื•ืœ ืœื”ื™ื•ืช ืงืฉื” ืœื”ื‘ื™ืŸ
09:16
exactly what algorithms are,
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ื‘ื“ื™ื•ืง ืžื” ื”ื ืืœื’ื•ืจื™ืชืžื™ื,
09:18
especially when there are many
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ื‘ืžื™ื•ื—ื“ ื›ืืฉืจ ื™ืฉื ื
09:20
different types of them. So, letโ€™s
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ืกื•ื’ื™ื ืจื‘ื™ื ื•ืฉื•ื ื™ื ืฉืœื”ื. ืื– ื‘ื•ืื•
09:22
take an example.
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ื ื™ืงื— ื“ื•ื’ืžื”.
09:23
Itโ€™s autumn and we want to
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ืกืชื™ื• ื•ืื ื—ื ื• ืจื•ืฆื™ื
09:24
collect all the apples from our
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ืœืืกื•ืฃ ืืช ื›ืœ ื”ืชืคื•ื—ื™ื ืžื”ืžื˜ืข ืฉืœื ื•
09:26
orchard and divide them into
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ื•ืœื—ืœืง ืื•ืชื ืœืฉืœื•ืฉ
09:27
three groups โ€“ big, medium
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ืงื‘ื•ืฆื•ืช - ื’ื“ื•ืœื•ืช, ื‘ื™ื ื•ื ื™ื•ืช
09:30
and small. One method is to
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ื•ืงื˜ื ื•ืช. ืฉื™ื˜ื” ืื—ืช ื”ื™ื
09:32
collect all the apples together
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ืœืืกื•ืฃ ืืช ื›ืœ ื”ืชืคื•ื—ื™ื ื™ื—ื“
09:33
and compare their sizes.
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ื•ืœื”ืฉื•ื•ืช ืืช ื”ื’ื“ืœื™ื ืฉืœื”ื.
09:35
But doing this would take hours!
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ืื‘ืœ ืœืขืฉื•ืช ืืช ื–ื” ื™ื™ืงื— ืฉืขื•ืช!
09:37
Itโ€™s much easier to first collect
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ื”ืจื‘ื” ื™ื•ืชืจ ืงืœ ืœืืกื•ืฃ
09:39
the apples from only one tree -
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ืืช ื”ืชืคื•ื—ื™ื ืžืขืฅ ืื—ื“ ื‘ืœื‘ื“ -
09:41
divide those into big, medium
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ืœื—ืœืง ืื•ืชื ืœื’ื“ื•ืœื™ื, ื‘ื™ื ื•ื ื™ื™ื
09:43
or small โ€“ and then repeat the
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ืื• ืงื˜ื ื™ื - ื•ืœืื—ืจ ืžื›ืŸ ืœื—ื–ื•ืจ ืขืœ
09:45
process for the other trees,
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ื”ืชื”ืœื™ืš ืขื‘ื•ืจ ื”ืขืฆื™ื ื”ืื—ืจื™ื,
09:47
one by one.
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ืื—ื“ ืื—ื“.
09:48
Thatโ€™s basically what algorithms
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ื–ื” ื‘ืขืฆื ืžื” ืฉื”ืืœื’ื•ืจื™ืชืžื™ื
09:50
do โ€“ they find the most efficient
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ืขื•ืฉื™ื - ื”ื ืžื•ืฆืื™ื ืืช ื”ื“ืจืš ื”ื™ืขื™ืœื” ื‘ื™ื•ืชืจ
09:52
way to get things done, or in other
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ืœื‘ืฆืข ื“ื‘ืจื™ื, ืื• ื‘ืžื™ืœื™ื ืื—ืจื•ืช
09:54
words, get the best results in the
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, ืœื”ืฉื™ื’ ืืช ื”ืชื•ืฆืื•ืช ื”ื˜ื•ื‘ื•ืช ื‘ื™ื•ืชืจ
09:56
quickest time.
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ื‘ื–ืžืŸ ื”ืžื”ื™ืจ ื‘ื™ื•ืชืจ.
09:57
Mathematics professor Ian
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ืคืจื•ืคืกื•ืจ ืœืžืชืžื˜ื™ืงื” ืื™ืืŸ
09:59
Stewart agrees. Listen as he
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ืกื˜ื™ื•ืืจื˜ ืžืกื›ื™ื. ื”ืงืฉื™ื‘ื• ื›ืฉื”ื•ื
10:01
explains how the algorithm called
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ืžืกื‘ื™ืจ ื›ื™ืฆื“ ื”ืืœื’ื•ืจื™ืชื ืฉื ืงืจื
10:03
โ€˜bubble sortโ€™ works to BBC World
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'ืžื™ื•ืŸ ื‘ื•ืขื•ืช' ืขื•ื‘ื“
10:06
Serviceโ€™s programme, The Forum:
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ืœืชื•ื›ื ื™ืช ืฉืœ BBC World Service, The Forum:
10:10
Think of when your computer is
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ืชื—ืฉื•ื‘ ืขืœ ื›ืฉื”ืžื—ืฉื‘ ืฉืœืš
10:11
sorting emails by date and maybe
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ืžืžื™ื™ืŸ ืžื™ื™ืœื™ื ืœืคื™ ืชืืจื™ืš ื•ืื•ืœื™
10:13
youโ€™ve got 500 emails and it sorts
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ื™ืฉ ืœืš 500 ืžื™ื™ืœื™ื ื•ื”ื•ื ืžืžื™ื™ืŸ
10:15
them by date in a flash.
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ืื•ืชื ืœืคื™ ืชืืจื™ืš ื‘ืžื”ื™ืจื•ืช ื”ื‘ื–ืง.
10:16
Now it doesnโ€™t use bubble sort,
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ืขื›ืฉื™ื• ื”ื•ื ืœื ืžืฉืชืžืฉ ื‘ืžื™ื•ืŸ ื‘ื•ืขื•ืช,
10:18
but it does use a sorting method
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ืื‘ืœ ื”ื•ื ื›ืŸ ืžืฉืชืžืฉ ื‘ืฉื™ื˜ืช ืžื™ื•ืŸ
10:20
and if you tried to do that by hand
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ื•ืื ืชื ืกื” ืœืขืฉื•ืช ื–ืืช ื‘ื™ื“
10:22
it would take you a very long time,
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ื–ื” ื™ื™ืงื— ืœืš ื”ืจื‘ื” ืžืื•ื“ ื–ืžืŸ,
10:23
whatever method you used.
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ื‘ื›ืœ ืฉื™ื˜ื” ืฉื‘ื” ื”ืฉืชืžืฉืช.
10:27
Professor Stewart describes how
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ืคืจื•ืคืกื•ืจ ืกื˜ื™ื•ืืจื˜ ืžืชืืจ ื›ื™ืฆื“
10:29
algorithms sort emails. To sort is a
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ืืœื’ื•ืจื™ืชืžื™ื ืžืžื™ื™ื ื™ื ืžื™ื™ืœื™ื. ืžื™ื•ืŸ ื”ื•ื
10:32
verb meaning to group together
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ืคื•ืขืœ ืฉืžืฉืžืขื•ืชื• ืœืงื‘ืฅ ื™ื—ื“
10:33
things which share similarities.
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ื“ื‘ืจื™ื ื‘ืขืœื™ ื“ืžื™ื•ืŸ.
10:35
Just like grouping the apples by
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ื‘ื“ื™ื•ืง ื›ืžื• ืœืงื‘ืฅ ืืช ื”ืชืคื•ื—ื™ื ืœืคื™
10:37
size, sorting hundreds of emails
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ื’ื•ื“ืœ, ืžื™ื•ืŸ ืฉืœ ืžืื•ืช ืžื™ื™ืœื™ื
10:39
by hand would take a long time.
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ื‘ื™ื“ ื™ื™ืงื— ื”ืจื‘ื” ื–ืžืŸ.
10:42
But using algorithms, computers
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ืื‘ืœ ื‘ืืžืฆืขื•ืช ืืœื’ื•ืจื™ืชืžื™ื, ืžื—ืฉื‘ื™ื
10:44
do it in a flash โ€“ very quickly or
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ืขื•ืฉื™ื ื–ืืช ื‘ืžื”ื™ืจื•ืช ื”ื‘ื–ืง - ืžื”ืจ ืžืื•ื“ ืื•
10:46
suddenly.
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ืคืชืื•ื.
10:47
That phrase โ€“ in a flash โ€“ reminds
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ื”ื‘ื™ื˜ื•ื™ ื”ื–ื” - ื‘ื”ื‘ื–ืง - ืžื–ื›ื™ืจ
10:49
me of how Albert Einstein came up
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ืœื™ ืื™ืš ืืœื‘ืจื˜ ืื™ื™ื ืฉื˜ื™ื™ืŸ ื”ื’ื” ืืช
10:51
with his famous equation, E=MC2.
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ื”ืžืฉื•ื•ืื” ื”ืžืคื•ืจืกืžืช ืฉืœื•, E=MC2.
10:55
And that reminds me of your quiz
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ื•ื–ื” ืžื–ื›ื™ืจ ืœื™ ืืช ืฉืืœืช ื”ื—ื™ื“ื•ืŸ ืฉืœืš
10:57
question. You asked about the โ€˜Eโ€™
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. ืฉืืœืช ืœื’ื‘ื™ ื”-E
11:00
in E=MC2. I said it stands for โ€˜energyโ€™.
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ื‘-E=MC2. ืืžืจืชื™ ืฉื–ื” ืžื™ื™ืฆื’ 'ืื ืจื’ื™ื”'.
11:04
So, was I right?
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ืื–, ืฆื“ืงืชื™?
11:05
โ€˜Energyโ€™ is the correct answer.
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'ืื ืจื’ื™ื”' ื”ื™ื ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื”.
11:08
Energy equals โ€˜Mโ€™ for mass,
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ืื ืจื’ื™ื” ืฉื•ื•ื” ืœ-'M' ืขื‘ื•ืจ ืžืกื”,
11:10
multiplied by the Constant โ€˜Cโ€™ which
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ื›ืคื•ืœ ื”ืงื‘ื•ืข 'C'
11:12
is the speed of light, squared.
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ืฉื”ื•ื ืžื”ื™ืจื•ืช ื”ืื•ืจ ื‘ืจื™ื‘ื•ืข.
11:15
OK, letโ€™s recap the vocabulary from
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ื‘ืกื“ืจ, ื‘ื•ืื• ื ืกื›ื ืืช ืื•ืฆืจ ื”ืžื™ืœื™ื ืžื”ืชื•ื›ื ื™ืช
11:17
this programme, starting with
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ื”ื–ื•, ื”ื—ืœ
11:19
equation โ€“ a mathematical statement
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ื‘ืžืฉื•ื•ืื” - ื”ืฆื”ืจื” ืžืชืžื˜ื™ืช
11:21
using symbols to show two
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ื”ืžืฉืชืžืฉืช ื‘ืกืžืœื™ื ื›ื“ื™ ืœื”ืจืื•ืช ืฉื ื™
11:23
equal things.
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ื“ื‘ืจื™ื ืฉื•ื•ื™ื.
11:24
If something is called a bogyman,
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ืื ืœืžืฉื”ื• ืงื•ืจืื™ื ื‘ื•ื’ื™ืžืŸ,
11:26
itโ€™s something considered bad
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ื–ื” ืžืฉื”ื• ืฉื ื—ืฉื‘ ืจืข
11:28
and to be feared.
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ื•ื™ืฉ ืœืคื—ื“ ืžืžื ื•.
11:29
Inscrutable people donโ€™t show
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ืื ืฉื™ื ื‘ืœืชื™ ื ื™ืชื ื™ื ืœื‘ื™ืจื•ืจ ืื™ื ื ืžืจืื™ื ืืช
11:31
their emotions so are very difficult
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ืจื’ืฉื•ืชื™ื”ื ื•ืœื›ืŸ ืงืฉื” ืžืื•ื“
11:33
to get to know.
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ืœื”ื›ื™ืจ ืื•ืชื.
11:34
Efficient means working quickly
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ื™ืขื™ืœ ืคื™ืจื•ืฉื” ืขื‘ื•ื“ื” ืžื”ื™ืจื”
11:36
and effectively in an
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ื•ื™ืขื™ืœื”
11:37
organised way.
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ื‘ืฆื•ืจื” ืžืื•ืจื’ื ืช.
11:38
The verb to sort means to group
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ื”ืคื•ืขืœ ืœืžื™ื™ืŸ ืคื™ืจื•ืฉื• ืœืงื‘ืฅ
11:40
together things which
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ื™ื—ื“ ื“ื‘ืจื™ื
11:41
share similarities.
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ื‘ืขืœื™ ืงื•ื•ื™ ื“ืžื™ื•ืŸ.
11:43
And finally, if something happens
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ื•ืœื‘ืกื•ืฃ, ืื ืžืฉื”ื• ืงื•ืจื”
11:44
in a flash, it happens quickly
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ื‘ืžื”ื™ืจื•ืช ื”ื‘ื–ืง, ื–ื” ืงื•ืจื” ื‘ืžื”ื™ืจื•ืช
11:47
or suddenly.
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ืื• ื‘ืคืชืื•ืžื™ื•ืช.
11:48
Thatโ€™s all the time we have to
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ื–ื” ื›ืœ ื”ื–ืžืŸ ืฉื™ืฉ ืœื ื•
11:49
discuss algorithms. And if
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ืœื“ื•ืŸ ื‘ืืœื’ื•ืจื™ืชืžื™ื. ื•ืื
11:51
youโ€™re still not 100% sure about
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ืืชื” ืขื“ื™ื™ืŸ ืœื ื‘ื˜ื•ื— ื‘-100%
11:53
exactly what they are, we hope
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ื‘ื“ื™ื•ืง ืžื” ื”ื, ืื ื• ืžืงื•ื•ื™ื
11:55
at least youโ€™ve learned some
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ืฉืœืคื—ื•ืช ืœืžื“ืช ืื•ืฆืจ
11:56
useful vocabulary!
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ืžื™ืœื™ื ืฉื™ืžื•ืฉื™!
11:57
Join us again soon for more
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ื”ืฆื˜ืจืฃ ืืœื™ื ื• ืฉื•ื‘ ื‘ืงืจื•ื‘
11:58
trending topics, sensational
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ืœื ื•ืฉืื™ื ืžื’ืžืชื™ื™ื ื ื•ืกืคื™ื,
12:00
science and useful vocabulary
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ืžื“ืข ืกื ืกืฆื™ื•ื ื™ ื•ืื•ืฆืจ ืžื™ืœื™ื ืฉื™ืžื•ืฉื™
12:02
here at 6 Minute English from
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ื›ืืŸ ื‘-6 ื“ืงื•ืช ืื ื’ืœื™ืช ืžื‘ื™ืช
12:04
BBC Learning English.
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BBC Learning English.
12:05
Bye for now!
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ืœื”ืชืจืื•ืช ื‘ื™ื ืชื™ื™ื!
12:06
Goodbye!
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ื”ึฑื™ื” ืฉืœื•ื!
12:13
Hello. This is 6 Minute English
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ืฉืœื•ื. ื–ื•ื”ื™ 6 ื“ืงื•ืช ืื ื’ืœื™ืช
12:14
from BBC Learning English.
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ืžื‘ื™ืช BBC Learning English.
12:16
Iโ€™m Neil.
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ืื ื™ ื ื™ืœ.
12:17
And Iโ€™m Sam.
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ื•ืื ื™ ืกื.
12:19
In recent years, many people
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ื‘ืฉื ื™ื ื”ืื—ืจื•ื ื•ืช ืื ืฉื™ื ืจื‘ื™ื
12:20
have wanted to find out more
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ืจืฆื• ืœื‘ืจืจ ื™ื•ืชืจ
12:22
about where they come from.
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ืžื”ื™ื›ืŸ ื”ื ืžื’ื™ืขื™ื.
12:24
Millions have tried to trace
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ืžื™ืœื™ื•ื ื™ื ื ื™ืกื• ืœื”ืชื—ืงื•ืช ืื—ืจ
12:25
their family history and discover
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ื”ื”ื™ืกื˜ื•ืจื™ื” ื”ืžืฉืคื—ืชื™ืช ืฉืœื”ื ื•ืœื’ืœื•ืช
12:27
how their ancestors lived
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ื›ื™ืฆื“ ื—ื™ื• ืื‘ื•ืชื™ื”ื
12:28
hundreds of years ago.
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ืœืคื ื™ ืžืื•ืช ืฉื ื™ื.
12:30
The internet has made it much
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ื”ืื™ื ื˜ืจื ื˜ ืขืฉื” ืืช ื–ื” ื”ืจื‘ื”
12:32
easier to find historical
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ื™ื•ืชืจ ืงืœ ืœืžืฆื•ื
12:33
documents and records about
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ืžืกืžื›ื™ื ื”ื™ืกื˜ื•ืจื™ื™ื ื•ืจืฉื•ืžื•ืช ืขืœ
12:35
your family history - and one of
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ื”ื”ื™ืกื˜ื•ืจื™ื” ื”ืžืฉืคื—ืชื™ืช ืฉืœืš - ื•ืื—ื“
12:37
the most useful documents for
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ื”ืžืกืžื›ื™ื ื”ืฉื™ืžื•ืฉื™ื™ื ื‘ื™ื•ืชืจ
12:39
doing this is the census.
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ืœืขืฉื•ืช ื–ืืช ื”ื•ื ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ.
12:42
A census is an official count of all
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ืžืคืงื“ ืื•ื›ืœื•ืกื™ืŸ ื”ื•ื ืกืคื™ืจื” ืจืฉืžื™ืช ืฉืœ ื›ืœ
12:45
the people living in a country.
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ื”ืื ืฉื™ื ื”ื—ื™ื™ื ื‘ืžื“ื™ื ื”.
12:46
It collects information about a
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ื–ื” ืื•ืกืฃ ืžื™ื“ืข ืขืœ
12:48
countryโ€™s population and is usually
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ืื•ื›ืœื•ืกื™ื™ืช ืžื“ื™ื ื” ื•ืžืชื‘ืฆืข ื‘ื“ืจืš ื›ืœืœ
12:50
carried out by the government.
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ืขืœ ื™ื“ื™ ื”ืžืžืฉืœื”.
12:52
In Britain, a census has been
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ื‘ื‘ืจื™ื˜ื ื™ื” ื ืขืจืš ืžืคืงื“ ืื•ื›ืœื•ืกื™ืŸ
12:54
carried out every ten years
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ื›ืœ ืขืฉืจ ืฉื ื™ื
12:56
since 1801. In 2002, when
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ืžืื– 1801. ื‘ืฉื ืช 2002, ื›ืืฉืจ
13:00
census records from a hundred
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ืจืฉื•ืžื•ืช ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ ืžืžืื”
13:02
years before became available
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ืฉื ื™ื ืœืคื ื™ ื›ืŸ ื”ืคื›ื• ืœื–ืžื™ื ื™ื
13:04
online, so many people rushed
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ื‘ืื™ื ื˜ืจื ื˜, ื›ืœ ื›ืš ื”ืจื‘ื” ืื ืฉื™ื ืžื™ื”ืจื•
13:06
to their computers to access
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ืœืžื—ืฉื‘ื™ื”ื ื›ื“ื™ ืœื’ืฉืช
13:07
them that the website crashed!
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ืืœื™ื”ื ืขื“ ืฉื”ืืชืจ ืงืจืก!
13:10
But before we find out more
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ืื‘ืœ ืœืคื ื™ ืฉื ื’ืœื” ื™ื•ืชืจ
13:12
about the census and its related
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ืขืœ ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ ื•ืื•ืฆืจ ื”ืžื™ืœื™ื ื”ืงืฉื•ืจ ืืœื™ื•
13:13
vocabulary itโ€™s time for a quiz
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ื”ื’ื™ืข ื”ื–ืžืŸ
13:15
question, Sam. Someone who
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ืœืฉืืœืช ื—ื™ื“ื•ืŸ, ืกื. ืžื™ ืฉื™ื•ื“ืข
13:18
knows a lot about his family
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ื”ืจื‘ื” ืขืœ ื”ื”ื™ืกื˜ื•ืจื™ื” ื”ืžืฉืคื—ืชื™ืช ืฉืœื•
13:19
history is British actor, Danny
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ื”ื•ื ื”ืฉื—ืงืŸ ื”ื‘ืจื™ื˜ื™, ื“ื ื™
13:21
Dyer. When BBC television
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ื“ื™ื™ืจ. ื›ืฉืชื•ื›ื ื™ืช ื”ื˜ืœื•ื•ื™ื–ื™ื” ืฉืœ ื”-BBC
13:24
programme, Who Do You
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, ืžื™ ืืชื”
13:25
Think You Are? researched
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ื—ื•ืฉื‘ ืฉืืชื”? ื—ืงืจื• ืืช
13:26
his family history they discovered
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ื”ื”ื™ืกื˜ื•ืจื™ื” ื”ืžืฉืคื—ืชื™ืช ืฉืœื• ื”ื ื’ื™ืœื•
13:28
that the actor was related to
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ืฉื”ืฉื—ืงืŸ ืงืฉื•ืจ ืœืžื™ืฉื”ื•
13:30
someone very famous โ€“ but
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ืžืคื•ืจืกื ืžืื•ื“ - ืื‘ืœ
13:32
who was it?
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ืžื™ ื–ื” ื”ื™ื”?
13:33
A) King Edward III,
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ื) ื”ืžืœืš ืื“ื•ืืจื“ ื”ืฉืœื™ืฉื™,
13:35
B) William Shakespeare, or,
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ื‘) ื•ื•ื™ืœื™ืื ืฉื™ื™ืงืกืคื™ืจ, ืื•,
13:37
C) Winston Churchill?
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ื’) ื•ื•ื™ื ืกื˜ื•ืŸ ืฆ'ืจืฆ'ื™ืœ?
13:39
Well, I know Danny Dyer usually
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ื•ื‘ื›ืŸ, ืื ื™ ื™ื•ื“ืข ืฉื“ื ื™ ื“ื™ื™ืจ ื‘ื“ืจืš ื›ืœืœ
13:42
plays tough-guy characters so
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ืžื’ืœื ื“ืžื•ื™ื•ืช ืฉืœ ื‘ื—ื•ืจื™ื ืงืฉื•ื—ื™ื ืื–
13:44
maybe itโ€™s
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ืื•ืœื™ ื–ื”
13:45
C), war hero Winston Churchill?
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C), ื’ื™ื‘ื•ืจ ื”ืžืœื—ืžื” ื•ื™ื ืกื˜ื•ืŸ ืฆ'ืจืฆ'ื™ืœ?
13:48
OK, Sam, weโ€™ll find out later if
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ื‘ืกื“ืจ, ืกืื, ื ื’ืœื” ืžืื•ื—ืจ ื™ื•ืชืจ ืื
13:50
thatโ€™s correct. Now, although
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ื–ื” ื ื›ื•ืŸ. ื›ืขืช, ืœืžืจื•ืช
13:52
the first British census took
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ืฉื”ืžืคืงื“ ื”ื‘ืจื™ื˜ื™ ื”ืจืืฉื•ืŸ
13:54
place in 1801, other censuses
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ื”ืชืงื™ื™ื ื‘-1801, ืœืžืคืงื“ื™ื ืื—ืจื™ื
13:57
have a much longer history.
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ื™ืฉ ื”ื™ืกื˜ื•ืจื™ื” ืืจื•ื›ื” ื‘ื”ืจื‘ื”.
13:59
In fact, the bible story of Mary
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ืœืžืขืฉื”, ืกื™ืคื•ืจ ื”ืชื "ืš ืขืœ ื ืกื™ืขื” ืฉืœ ืžืจื™ื
14:01
and Joseph travelling to
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ื•ื™ื•ืกืฃ
14:02
Bethlehem is linked to a
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ืœื‘ื™ืช ืœื—ื ืงืฉื•ืจ ืœืžืคืงื“
14:04
Roman census.
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ืจื•ืžื™.
14:06
So, what was the original
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ืื– ืžื” ื”ื™ื™ืชื”
14:08
reason for counting people
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ื”ืกื™ื‘ื” ื”ืžืงื•ืจื™ืช ืœืกืคื™ืจืช ืื ืฉื™ื
14:10
and what did governments
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ื•ืžื”
14:11
hope to achieve by doing so?
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ืงื™ื•ื• ื”ืžืžืฉืœื•ืช ืœื”ืฉื™ื’ ื‘ื›ืš?
14:13
Hereโ€™s Dr Kathrin Levitan, author
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ื”ื ื” ื“"ืจ ืงืชืจื™ืŸ ืœื•ื™ืชืŸ, ืžื—ื‘ืจืช
14:16
of a book on the cultural history
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ืกืคืจ ืขืœ ื”ื”ื™ืกื˜ื•ืจื™ื” ื”ืชืจื‘ื•ืชื™ืช
14:18
of the census, speaking to
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ืฉืœ ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ, ืžื“ื‘ืจืช ืขื
14:20
BBC World Service programme,
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ื”ืชื•ื›ื ื™ืช ืฉืœ BBC World Service,
14:21
The Forum:
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The Forum:
14:24
I think there were probably
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ืื ื™ ื—ื•ืฉื‘ ืฉื”ื™ื• ื›ื ืจืื”
14:25
two most common reasons.
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ืฉืชื™ ืกื™ื‘ื•ืช ื ืคื•ืฆื•ืช ื‘ื™ื•ืชืจ.
14:27
One was in order to figure out
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ื”ืื—ื“ ื”ื™ื” ืขืœ ืžื ืช ืœื”ื‘ื™ืŸ
14:29
who could fight in wars, so basically
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ืžื™ ื™ื›ื•ืœ ืœื”ื™ืœื—ื ื‘ืžืœื—ืžื•ืช, ืื– ื‘ืขืฆื
14:31
military conscription and in order
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ื’ื™ื•ืก ืฆื‘ืื™ ื•ื›ื“ื™
14:33
to find out who could fight in wars
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ืœื’ืœื•ืช ืžื™ ื™ื›ื•ืœ ืœื”ื™ืœื—ื ื‘ืžืœื—ืžื•ืช
14:35
ancient governments like the
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ืžืžืฉืœื•ืช ืขืชื™ืงื•ืช ื›ืžื•
14:36
Roman Empire had to find out how
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ื”ืื™ืžืคืจื™ื” ื”ืจื•ืžื™ืช ื”ื™ื• ืฆืจื™ื›ื™ื ืœื‘ืจืจ
14:38
many men of a certain age there were.
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ื›ืžื” ื’ื‘ืจื™ื ื‘ื’ื™ืœ ืžืกื•ื™ื ื”ื™ื•.
14:41
And I would say that the other thing
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ื•ื”ื™ื™ืชื™ ืื•ืžืจ ืฉื”ื“ื‘ืจ ื”ืฉื ื™
14:43
that censuses were most commonly
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ืฉืžืคืงื“ื™ ื”ืื•ื›ืœื•ืกื™ืŸ
14:45
used for was for purposes of taxation.
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ืฉื™ืžืฉื• ืœื• ื”ื›ื™ ื”ืจื‘ื” ื”ื™ื” ืœืฆื•ืจื›ื™ ืžื™ืกื•ื™.
14:48
According to Kathrin Levitan, ancient
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ืœืคื™ ืงืชืจื™ืŸ ืœื•ื™ืชืŸ,
14:51
censuses were used to figure out โ€“ or
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ืžืคืงื“ื™ื ืขืชื™ืงื™ื ืฉื™ืžืฉื• ื›ื“ื™ ืœื”ื‘ื™ืŸ - ืื•
14:53
understand, how many men were
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ืœื”ื‘ื™ืŸ, ื›ืžื” ื’ื‘ืจื™ื ื”ื™ื•
14:55
available to fight wars.
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ื–ืžื™ื ื™ื ืœื”ื™ืœื—ื ื‘ืžืœื—ืžื•ืช.
14:57
The Roman Empire needed a strong
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ื”ืื™ืžืคืจื™ื” ื”ืจื•ืžื™ืช ื”ื™ื™ืชื” ื–ืงื•ืงื”
15:00
army, and this depended on
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ืœืฆื‘ื ื—ื–ืง, ื•ื–ื” ื”ื™ื” ืชืœื•ื™
15:01
conscription โ€“ forcing people to
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ื‘ื’ื™ื•ืก - ืื™ืœืฅ ืื ืฉื™ื
15:04
become soldiers and join the army.
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ืœื”ืคื•ืš ืœื—ื™ื™ืœื™ื ื•ืœื”ืฆื˜ืจืฃ ืœืฆื‘ื.
15:06
The other main reason for taking
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ื”ืกื™ื‘ื” ื”ืขื™ืงืจื™ืช ื”ื ื•ืกืคืช ืœืขืจื™ื›ืช
15:08
a census was taxation โ€“ the
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ืžืคืงื“ ื”ื™ื™ืชื” ืžื™ืกื•ื™ -
15:10
system of taxing people a certain
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ืฉื™ื˜ืช ื”ืžื™ืกื•ื™ ืฉืœ ืื ืฉื™ื ืขืœ
15:12
amount of money to be paid to
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ืกื›ื•ื ื›ืกืฃ ืžืกื•ื™ื ืฉื™ืฉื•ืœื
15:14
the government for public services.
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ืœืžืžืฉืœื” ืขื‘ื•ืจ ืฉื™ืจื•ืชื™ื ืฆื™ื‘ื•ืจื™ื™ื.
15:16
Ancient and early modern censuses
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ืžืคืงื“ื™ื ืขืชื™ืงื™ื ื•ืžื•ื“ืจื ื™ื™ื ืžื•ืงื“ืžื™ื
15:18
were large and difficult-to-organise
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ื”ื™ื• ืคืจื•ื™ืงื˜ื™ื ื’ื“ื•ืœื™ื ื•ืงืฉื™ื ืœืืจื’ื•ืŸ
15:21
projects. They often involved
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. ืœืขืชื™ื ืงืจื•ื‘ื•ืช ื”ื ื”ื™ื• ืžืขื•ืจื‘ื™ื
15:23
government officials going from
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ื‘ืคืงื™ื“ื™ ืžืžืฉืœ ืฉืขื•ื‘ืจื™ื ืžื‘ื™ืช
15:25
house to house, asking questions
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ืœื‘ื™ืช, ื•ืฉืืœื• ืฉืืœื•ืช
15:27
about the people who lived there.
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ืขืœ ื”ืื ืฉื™ื ืฉื—ื™ื• ืฉื.
15:30
But over time governmentsโ€™ desire
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ืื‘ืœ ืขื ื”ื–ืžืŸ ื”ืจืฆื•ืŸ ืฉืœ ืžืžืฉืœื•ืช
15:32
to know about, and control, its
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ืœื“ืขืช ืขืœ ืื–ืจื—ื™ื” ื•ืœืฉืœื•ื˜ ื‘ื”ืŸ
15:34
citizens gave rise to new
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ื”ื•ืœื™ื“
15:35
technologies for counting people.
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ื˜ื›ื ื•ืœื•ื’ื™ื•ืช ื—ื“ืฉื•ืช ืœืกืคื™ืจืช ืื ืฉื™ื.
15:38
Hereโ€™s statistician and economist
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ื”ื ื” ื”ืกื˜ื˜ื™ืกื˜ื™ืงืื™ ื•ื”ื›ืœื›ืœืŸ
15:40
Andrew Whitby explaining how
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ืื ื“ืจื• ื•ื™ื˜ื‘ื™ ืžืกื‘ื™ืจ ืื™ืš
15:42
this happened in the US to BBC
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ื–ื” ืงืจื” ื‘ืืจื”"ื‘
15:44
World Service programme,
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ืœืชื•ื›ื ื™ืช ื”ืฉื™ืจื•ืช ื”ืขื•ืœืžื™ ืฉืœ BBC,
15:45
The Forum:
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The Forum:
15:47
The 1890 census of the United
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ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ ืฉืœ
15:49
States was the first in which some
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ืืจืฆื•ืช ื”ื‘ืจื™ืช ื‘-1890 ื”ื™ื” ื”ืจืืฉื•ืŸ ืฉื‘ื• ื ืขืฉื” ืฉื™ืžื•ืฉ
15:51
kind of electro-mechanical process
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ื‘ืกื•ื’ ืฉืœ ืชื”ืœื™ืš ืืœืงื˜ืจื•-ืžื›ื ื™
15:52
was used to count peopleโ€ฆ so
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ื›ื“ื™ ืœืกืคื•ืจ ืื ืฉื™ื... ืื– ื‘ืžืงื•ื
15:54
instead of armies of clerks reading
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ืฆื‘ืื•ืช ืฉืœ ืคืงื™ื“ื™ื ืฉืงื•ืจืื™ื ืืช
15:57
off census schedules and tabulating
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ืœื•ื—ื•ืช ื”ื–ืžื ื™ื ืฉืœ ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ ื•ืžืคืจื˜ื™ื ืืช
16:00
these things by hand, for the first
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ื”ื“ื‘ืจื™ื ื”ืืœื” ื‘ื™ื“, ื‘ืคืขื ื”ืจืืฉื•ื ื” ืชื™ืจืฉื
16:01
time an individual census record
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ืžืคืงื“ ืคืจื˜ื ื™
16:03
would be punched onto a cardโ€ฆ so
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ืขืœ ื›ืจื˜ื™ืก... ื›ืš
16:05
that there were holes in this card
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ืฉื”ื™ื• ื—ื•ืจื™ื ื‘ื›ืจื˜ื™ืก ื”ื–ื”
16:06
representing different characteristics
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ื”ืžื™ื™ืฆื’ื™ื ืžืืคื™ื™ื ื™ื ืฉื•ื ื™ื
16:08
of the person and then those cards
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ืฉืœ ื”ืื“ื ื•ืื–
16:09
could be fed through a machine.
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ื ื™ืชืŸ ื”ื™ื” ืœื”ื–ื™ืŸ ืืช ื”ืงืœืคื™ื ื”ืืœื”. ื“ืจืš ืžื›ื•ื ื”.
16:12
Old-fashioned censuses were managed
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ืžืคืงื“ื™ื ืžื™ื•ืฉื ื™ื ื ื•ื”ืœื•
16:14
by clerks โ€“ office workers whose job
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ืขืœ ื™ื“ื™ ืคืงื™ื“ื™ื - ืขื•ื‘ื“ื™ ืžืฉืจื“ ืฉืชืคืงื™ื“ื
16:16
involved keeping records.
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ื›ืœืœ ื ื™ื”ื•ืœ ืจื™ืฉื•ืžื™ื.
16:18
Thousands of clerks would record
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ืืœืคื™ ืคืงื™ื“ื™ื ื”ื™ื• ืžืชืขื“ื™ื
16:20
the information gathered in the
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ืืช ื”ืžื™ื“ืข ืฉื ืืกืฃ
16:22
census and tabulate it, in other words,
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ื‘ืžืคืงื“ ื•ืžืฆื™ื’ื™ื ืื•ืชื• ื‘ื˜ื‘ืœืื•ืช, ื‘ืžื™ืœื™ื ืื—ืจื•ืช,
16:25
show the information in the form of
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ืžืฆื™ื’ื™ื ืืช ื”ืžื™ื“ืข ื‘ืฆื•ืจื” ืฉืœ
16:27
a table with rows and columns.
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ื˜ื‘ืœื” ืขื ืฉื•ืจื•ืช ื•ืขืžื•ื“ื•ืช. ืžืคืงื“
16:30
The US census of 1890 was the first
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ื”ืื•ื›ืœื•ืกื™ืŸ ื‘ืืจื”"ื‘ ืฉืœ 1890 ื”ื™ื” ื”ืจืืฉื•ืŸ
16:33
to use machines, and many censuses
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ืฉื”ืฉืชืžืฉ ื‘ืžื›ื•ื ื•ืช, ื•ืžืคืงื“ื™ื ืจื‘ื™ื
16:36
today are electronically updated to
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ื›ื™ื•ื ืžืชืขื“ื›ื ื™ื ืืœืงื˜ืจื•ื ื™ืช ื›ื“ื™
16:38
record new trends and shifts in
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ืœืชืขื“ ืžื’ืžื•ืช ื—ื“ืฉื•ืช ื•ืฉื™ื ื•ื™ื™ื
16:40
populations as they happen.
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ื‘ืื•ื›ืœื•ืกื™ื•ืช ื‘ื–ืžืŸ ืฉื”ื ืžืชืจื—ืฉื™ื.
16:42
In fact, so much personal
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ืœืžืขืฉื”, ื›ืœ ื›ืš ื”ืจื‘ื”
16:44
information is now freely available
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ืžื™ื“ืข ืื™ืฉื™ ื–ืžื™ืŸ ื›ืขืช ื‘ืื•ืคืŸ ื—ื•ืคืฉื™
16:46
through social media and the
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ื“ืจืš ื”ืžื“ื™ื” ื”ื—ื‘ืจืชื™ืช ื•ื”ืื™ื ื˜ืจื ื˜,
16:48
internet that some people have
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ืขื“ ืฉื—ืœืง ืžื”ืื ืฉื™ื
16:50
questioned the need for having
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ื”ื˜ื™ืœื• ืกืคืง ื‘ืฆื•ืจืš ื‘ืงื™ื•ื
16:51
a census at all.
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ืžืคืงื“ ืื•ื›ืœื•ืกื™ืŸ.
16:53
Yes, it isnโ€™t hard to find out about
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ื›ืŸ, ื–ื” ืœื ืงืฉื” ืœื’ืœื•ืช ืขืœ
16:55
someone famous, like a TV star.
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ืžื™ืฉื”ื• ืžืคื•ืจืกื, ื›ืžื• ื›ื•ื›ื‘ ื˜ืœื•ื•ื™ื–ื™ื”.
16:58
Someone like Danny Dyer, you mean?
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ืžื™ืฉื”ื• ื›ืžื• ื“ื ื™ ื“ื™ื™ืจ, ืืชื” ืžืชื›ื•ื•ืŸ?
17:00
Right. In my quiz question I asked
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ื™ืžื™ืŸ. ื‘ืฉืืœืช ื”ื—ื™ื“ื•ืŸ ืฉืœื™ ืฉืืœืชื™ ืืช
17:02
Sam which historical figure TV
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ืกื ืœืื™ื–ื• ื“ืžื•ืช ื”ื™ืกื˜ื•ืจื™ืช
17:05
actor, Danny Dyer, was related to.
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ืฉื—ืงืŸ ื”ื˜ืœื•ื•ื™ื–ื™ื”, ื“ื ื™ ื“ื™ื™ืจ, ืงืฉื•ืจ.
17:07
And I said it was
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ื•ืื ื™ ืืžืจืชื™ ืฉื–ื”
17:08
C) Winston Churchill. Was I right?
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ื’) ื•ื•ื™ื ืกื˜ื•ืŸ ืฆ'ืจืฆ'ื™ืœ. ืฆื“ืงืชื™?
17:12
It was a good guess, Sam, but
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ื–ื” ื”ื™ื” ื ื™ื—ื•ืฉ ื˜ื•ื‘, ืกื, ืื‘ืœ
17:13
the actual answer was
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ื”ืชืฉื•ื‘ื” ื”ืืžื™ืชื™ืช ื”ื™ื™ืชื”
17:14
A) King Edward III. And no-one
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ื) ื”ืžืœืš ืื“ื•ืืจื“ ื”ืฉืœื™ืฉื™. ื•ืืฃ ืื—ื“ ืœื
17:17
was more surprised that he was
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ื”ื™ื” ืžื•ืคืชืข ื™ื•ืชืจ ืžื›ืš ืฉื”ื•ื
17:18
related to royalty than the
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ืงืฉื•ืจ ืœื‘ื ื™ ืžืœื•ื›ื” ืžืืฉืจ
17:20
EastEnders actor himself!
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ืฉื—ืงืŸ ื”ืื™ืกื˜ืื ื“ืจืก ืขืฆืžื•!
17:22
OK, Neil, letโ€™s recap the
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ื‘ืกื“ืจ, ื ื™ืœ, ื‘ื•ืื• ื ืกื›ื ืืช ืื•ืฆืจ
17:24
vocabulary from this programme
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ื”ืžื™ืœื™ื ืžื”ืชื•ื›ื ื™ืช ื”ื–ื•
17:26
about the census - the official
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ืขืœ ืžืคืงื“ ื”ืื•ื›ืœื•ืกื™ืŸ -
17:28
counting of a nationโ€™s population.
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ื”ืกืคื™ืจื” ื”ืจืฉืžื™ืช ืฉืœ ืื•ื›ืœื•ืกื™ื™ืช ื”ืžื“ื™ื ื”.
17:30
To figure something out means
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ืœื”ื‘ื™ืŸ ืžืฉื”ื• ืคื™ืจื•ืฉื•
17:32
to understand it.
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ืœื”ื‘ื™ืŸ ืื•ืชื•.
17:34
The Romans used conscription
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ื”ืจื•ืžืื™ื ื”ืฉืชืžืฉื• ื‘ื’ื™ื•ืก
17:36
to force men to join the army by law.
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ื›ื“ื™ ืœืืœืฅ ื’ื‘ืจื™ื ืœื”ืฆื˜ืจืฃ ืœืฆื‘ื ืขืœ ืคื™ ื—ื•ืง.
17:39
Taxation is the governmentโ€™s
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ืžื™ืกื•ื™ ื”ื•ื
17:40
system of taxing people to pay
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ื”ืฉื™ื˜ื” ืฉืœ โ€‹โ€‹ื”ืžืžืฉืœื” ืœื”ื˜ื™ืœ ืžืก ืขืœ ืื ืฉื™ื ืœืฉืœื
17:42
for public services.
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ืขื‘ื•ืจ ืฉื™ืจื•ืชื™ื ืฆื™ื‘ื•ืจื™ื™ื.
17:44
A clerk is an office worker whose
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ืคืงื™ื“ ื”ื•ื ืขื•ื‘ื“ ืžืฉืจื“ ืฉืชืคืงื™ื“ื•
17:46
job involves keeping records.
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ื›ืจื•ืš ื‘ื ื™ื”ื•ืœ ืจื™ืฉื•ืžื™ื.
17:50
And tabulate means show
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ื•ืืžืฆืขื™ ื˜ื‘ืœื” ืžืฆื™ื’ื™ื
17:51
information in the form of a table
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ืžื™ื“ืข ื‘ืฆื•ืจื” ืฉืœ ื˜ื‘ืœื”
17:53
with rows and columns.
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ืขื ืฉื•ืจื•ืช ื•ืขืžื•ื“ื•ืช.
17:55
Thatโ€™s all for our six-minute look
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ื–ื” ื”ื›ืœ ื‘ืฉื‘ื™ืœ ื”ื”ืกืชื›ืœื•ืช ืฉืœื ื• ื‘ืช ืฉืฉ ื“ืงื•ืช ื‘ืžืคืงื“
17:57
at the census, but if weโ€™ve whetted
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ื”ืื•ื›ืœื•ืกื™ืŸ, ืื‘ืœ ืื ืคืชื—ื ื• ืืช
17:59
your appetite for more why not
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ื”ืชื™ืื‘ื•ืŸ ืฉืœืš ืœืขื•ื“ ืœืžื” ืฉืœื
18:01
check out the whole episode โ€“ itโ€™s
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ืชื‘ื“ื•ืง ืืช ื”ืคืจืง ื›ื•ืœื• - ื”ื•ื
18:03
available now on the website of
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ื–ืžื™ืŸ ืขื›ืฉื™ื• ื‘ืืชืจ ืฉืœ
18:05
BBC World Service programme,
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ืชื•ื›ื ื™ืช ื”ืฉื™ืจื•ืช ื”ืขื•ืœืžื™ ืฉืœ BBC,
18:07
The Forum.
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ื”ืคื•ืจื•ื.
18:09
Bye for now!
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ืœื”ืชืจืื•ืช ื‘ื™ื ืชื™ื™ื!
18:10
Bye bye.
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ื‘ื™ื™ ื‘ื™ื™.
18:17
Hello. This is 6 Minute English
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ืฉืœื•ื. ื–ื•ื”ื™ 6 ื“ืงื•ืช ืื ื’ืœื™ืช
18:18
from BBC Learning English.
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ืžื‘ื™ืช BBC Learning English.
18:20
Iโ€™m Neil.
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18:20
And Iโ€™m Georgina.
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ืื ื™ ื ื™ืœ.
ื•ืื ื™ ื’'ื•ืจื’'ื™ื ื”.
18:22
What do Homer, Ray Charles
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ืžื” ืžืฉื•ืชืฃ ืœื”ื•ืžืจ, ืจื™ื™ ืฆ'ืืจืœืก
18:23
and Jorge Borges all have in
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ื•ื—ื•ืจื—ื” ื‘ื•ืจื—ืก
18:25
common, Georgina?
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, ื’'ื•ืจื’'ื™ื ื”?
18:26
Hmm, so thatโ€™s the ancient Greek
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ื”ืžืž, ืื– ื–ื” ื”ืžืฉื•ืจืจ ื”ื™ื•ื•ื ื™ ื”ืขืชื™ืง
18:29
poet, Homer; American singer,
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, ื”ื•ืžืจื•ืก; ื”ื–ืžืจ ื”ืืžืจื™ืงืื™,
18:31
Ray Charles; and Argentine writer,
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ืจื™ื™ ืฆ'ืืจืœืก; ื•ื”ืกื•ืคืจ ื”ืืจื’ื ื˜ื™ื ืื™,
18:33
Jorge Luis Borgesโ€ฆ I canโ€™t see
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ื—ื•ืจื—ื” ืœื•ืื™ืก ื‘ื•ืจื—ืก... ืื ื™ ืœื ื™ื›ื•ืœ ืœืจืื•ืช
18:36
much in common there, Neil.
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ื”ืจื‘ื” ื‘ืžืฉื•ืชืฃ ืฉื, ื ื™ืœ.
18:37
Well, the answer is that they
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ื•ื‘ื›ืŸ, ื”ืชืฉื•ื‘ื” ื”ื™ื ืฉื›ื•ืœื
18:38
were all blind.
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ื”ื™ื• ืขื™ื•ื•ืจื™ื.
18:40
Ah! But that obviously didnโ€™t hold
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ืื”! ืื‘ืœ ื–ื” ื›ืžื•ื‘ืŸ ืœื
18:42
them back - I mean, they were
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ื”ืคืจื™ืข ืœื”ื - ื›ืœื•ืžืจ, ื”ื ื”ื™ื•
18:43
some of the greatest artists ever!
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ื›ืžื” ืžื”ืืžื ื™ื ื”ื’ื“ื•ืœื™ื ื‘ื™ื•ืชืจ ืื™ ืคืขื!
18:45
Right, but I wonder how easy they
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ื ื›ื•ืŸ, ืื‘ืœ ืื ื™ ืชื•ื”ื” ื›ืžื” ืงืœ ื”ื™ื” ืœื”ื
18:47
would find it living and working in
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ืœื—ื™ื•ืช ื•ืœืขื‘ื•ื“
18:48
the modern world.
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ื‘ืขื•ืœื ื”ืžื•ื“ืจื ื™.
18:49
Blind people can use a guide dog
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ืื ืฉื™ื ืขื™ื•ื•ืจื™ื ื™ื›ื•ืœื™ื ืœื”ืฉืชืžืฉ ื‘ื›ืœื‘ ื ื—ื™ื™ื”
18:51
or a white cane to help them
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ืื• ื‘ืžืงืœ ืœื‘ืŸ ื›ื“ื™ ืœืขื–ื•ืจ ืœื”ื
18:52
move around.
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ืœื”ืชื ื™ื™ื“.
18:53
Yes, but a white cane is hardly
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ื›ืŸ, ืื‘ืœ ืžืงืœ ืœื‘ืŸ ื”ื•ื ื‘ืงื•ืฉื™
18:55
advanced technology! Recently,
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ื˜ื›ื ื•ืœื•ื’ื™ื” ืžืชืงื“ืžืช! ืœืื—ืจื•ื ื”
18:58
smartphone apps have been
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ื”ื•ืžืฆืื• ืืคืœื™ืงืฆื™ื•ืช ืœืกืžืืจื˜ืคื•ืŸ
18:59
invented which dramatically
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ืืฉืจ ืžืฉืคืจื•ืช ื‘ืื•ืคืŸ ื“ืจืžื˜ื™
19:01
improve the lives of blind people
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ืืช ื—ื™ื™ื”ื ืฉืœ ืขื™ื•ื•ืจื™ื
19:02
around the world.
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ื‘ืจื—ื‘ื™ ื”ืขื•ืœื.
19:04
In this programme on blindness
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ื‘ืชื•ื›ื ื™ืช ื–ื• ืขืœ ืขื™ื•ื•ืจื•ืŸ
19:05
in the digital age weโ€™ll be looking
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ื‘ืขื™ื“ืŸ ื”ื“ื™ื’ื™ื˜ืœื™, ื ืกืชื›ืœ
19:07
at some of these inventions, known
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ืขืœ ื›ืžื” ืžื”ื”ืžืฆืื•ืช ื”ืœืœื•, ื”ื™ื“ื•ืขื•ืช
19:09
collectively as assistive technology โ€“
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ื‘ื™ื—ื“ ื›ื˜ื›ื ื•ืœื•ื’ื™ื” ืžืกื™ื™ืขืช -
19:12
thatโ€™s any software or equipment
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ื–ื• ื›ืœ ืชื•ื›ื ื” ืื• ืฆื™ื•ื“
19:14
that helps people work around their
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ืฉืขื•ื–ืจื™ื ืœืื ืฉื™ื ืœืขืงื•ืฃ ืืช
19:16
disabilities or challenges.
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ื”ืžื•ื’ื‘ืœื•ืช ืื• ื”ืืชื’ืจื™ื ืฉืœื”ื.
19:18
But first itโ€™s time for my quiz
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ืื‘ืœ ืงื•ื“ื ื›ืœ ื”ื’ื™ืข ื”ื–ืžืŸ ืœืฉืืœืช ื”ื—ื™ื“ื•ืŸ ืฉืœื™
19:20
question, Georgina. In 1842 a
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, ื’'ื•ืจื’'ื™ื ื”. ื‘ืฉื ืช 1842 ื”ื•ืžืฆืื”
19:23
technique of using fingers to feel
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ื˜ื›ื ื™ืงื” ืฉืœ ืฉื™ืžื•ืฉ ื‘ืืฆื‘ืขื•ืช ื›ื“ื™ ืœื”ืจื’ื™ืฉ
19:25
printed raised dots was invented
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ื ืงื•ื“ื•ืช ืžื•ืจืžื•ืช ืžื•ื“ืคืกื•ืช
19:27
which allowed blind people to read.
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ืฉืืคืฉืจื” ืœืขื™ื•ื•ืจื™ื ืœืงืจื•ื.
19:29
But who invented it? Was it:
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ืื‘ืœ ืžื™ ื”ืžืฆื™ื ืืช ื–ื”? ื”ืื ื–ื” ื”ื™ื”:
19:31
a) Margaret Walker?,
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ื) ืžืจื’ืจื˜ ื•ื•ืงืจ?,
19:33
b) Louis Braille?, or
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ื‘) ืœื•ืื™ ื‘ืจื™ื™ืœ?, ืื•
19:35
c) Samuel Morse?
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ื’) ืกืžื•ืืœ ืžื•ืจืก?
19:36
Hmm, Iโ€™ve heard of Morse code but
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ื”ืžืž, ืฉืžืขืชื™ ืขืœ ืงื•ื“ ืžื•ืจืก ืื‘ืœ
19:39
that wouldnโ€™t help blind people
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ื–ื” ืœื ื™ืขื–ื•ืจ ืœืขื™ื•ื•ืจื™ื
19:40
read, so I think itโ€™s, b) Louis Braille.
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ืœืงืจื•ื, ืื– ืื ื™ ื—ื•ืฉื‘ ืฉื–ื”, ื‘) ืœื•ืื™ ื‘ืจื™ื™ืœ.
19:43
OK, Georgina, weโ€™ll find out the
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ื‘ืกื“ืจ, ื’'ื•ืจื’'ื™ื ื”, ืื ื—ื ื• ื ื’ืœื” ืืช
19:45
answer at the end of the programme.
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ื”ืชืฉื•ื‘ื” ื‘ืกื•ืฃ ื”ืชื•ื›ื ื™ืช.
19:47
One remarkable feature of the latest
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ืชื›ื•ื ื” ื™ื•ืฆืืช ื“ื•ืคืŸ ืฉืœ
19:49
assistive technology is its practicality.
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ื”ื˜ื›ื ื•ืœื•ื’ื™ื” ื”ืžืกื™ื™ืขืช ื”ืขื“ื›ื ื™ืช ื‘ื™ื•ืชืจ ื”ื™ื ื”ืžืขืฉื™ื•ืช ืฉืœื”.
19:52
Smartphone apps like โ€˜BeMyEyesโ€™
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ืืคืœื™ืงืฆื™ื•ืช ืœืกืžืืจื˜ืคื•ืŸ ื›ืžื• 'BeMyEyes'
19:55
allow blind users to find lost keys,
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ืžืืคืฉืจื•ืช ืœืžืฉืชืžืฉื™ื ืขื™ื•ื•ืจื™ื ืœืžืฆื•ื ืžืคืชื—ื•ืช ืฉืื‘ื“ื•,
19:57
cross busy roads and even colour
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ืœื—ืฆื•ืช ื›ื‘ื™ืฉื™ื ืกื•ืื ื™ื ื•ืืคื™ืœื•
19:59
match their clothes.
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ืœื”ืชืื™ื ืฆื‘ืข ืœื‘ื’ื“ื™ื”ื.
20:01
Brian Mwenda is CEO of a Kenyan
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ื‘ืจื™ืืŸ ืžื•ื•ื ื“ื” ื”ื•ื ืžื ื›"ืœ
20:03
company developing this kind of
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ื—ื‘ืจื” ืงื ื™ื™ืชื™ืช ื”ืžืคืชื—ืช ื˜ื›ื ื•ืœื•ื’ื™ื” ืžืกื•ื’ ื–ื”
20:05
technology. Here he explains to
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. ื›ืืŸ ื”ื•ื ืžืกื‘ื™ืจ
20:07
BBC World Service programme,
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ืœืชื•ื›ื ื™ืช BBC World Service,
20:09
Digital Planet, how his devices seek
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Digital Planet, ื›ื™ืฆื“ ื”ืžื›ืฉื™ืจื™ื ืฉืœื• ืžื‘ืงืฉื™ื
20:12
to enhance, not replace, the
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ืœืฉืคืจ, ืœื ืœื”ื—ืœื™ืฃ, ืืช
20:14
traditional white cane:
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ื”ืžืงืœ ื”ืœื‘ืŸ ื”ืžืกื•ืจืชื™:
20:16
The device is very compatible with
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ื”ืžื›ืฉื™ืจ ืชื•ืื ืžืื•ื“
20:18
any kind of white cane. So, once you
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ืœื›ืœ ืกื•ื’ ืฉืœ ืžืงืœ ืœื‘ืŸ. ืื– ื‘ืจื’ืข ืฉืืชื”
20:20
clip it on to any white cane it
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ืžืฆืžื™ื“ ืื•ืชื• ืœื›ืœ ืžืงืœ ืœื‘ืŸ ื–ื”
20:22
works perfectly to detect the
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ืขื•ื‘ื“ ื‘ืฆื•ืจื” ืžื•ืฉืœืžืช ื›ื“ื™ ืœื–ื”ื•ืช ืืช
20:24
obstacles in front of you, and it
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ื”ืžื›ืฉื•ืœื™ื ืžื•ืœืš, ื•ื”ื•ื
20:26
relies on echo-location. So,
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ืžืกืชืžืš ืขืœ ืžื™ืงื•ื ื”ื“. ืื–,
20:28
echo-location is the same technology
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ืืงื•-ืžื™ืงื•ื ื”ื™ื ืื•ืชื” ื˜ื›ื ื•ืœื•ื’ื™ื”
20:30
used by bats and dolphins to detect
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ื”ืžืฉืžืฉืช ืขื˜ืœืคื™ื ื•ื“ื•ืœืคื™ื ื™ื ื›ื“ื™ ืœื–ื”ื•ืช
20:33
prey and obstacles and all that. You
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ื˜ืจืฃ ื•ืžื›ืฉื•ืœื™ื ื•ื›ืœ ื–ื”. ืืชื”
20:36
send out a sound pulse and then
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ืฉื•ืœื— ื“ื•ืคืง ืงื•ืœ ื•ืื–
20:38
once it bounces off an obstacle, you
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ื‘ืจื’ืข ืฉื”ื•ื ืงื•ืคืฅ ืžืžื›ืฉื•ืœ, ืืชื”
20:40
can tell how far the obstacle is.
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ื™ื›ื•ืœ ืœื“ืขืช ื›ืžื” ืจื—ื•ืง ื”ืžื›ืฉื•ืœ.
20:42
When attached to a white cane, the
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ื›ืฉื”ื•ื ืžื—ื•ื‘ืจ ืœืžืงืœ ืœื‘ืŸ,
20:44
digital device - called โ€˜Sixth Senseโ€™ -
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ื”ืžื›ืฉื™ืจ ื”ื“ื™ื’ื™ื˜ืœื™ - ื”ื ืงืจื 'ื—ื•ืฉ ืฉื™ืฉื™' -
20:46
can detect obstacles โ€“ objects which
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ื™ื›ื•ืœ ืœื–ื”ื•ืช ืžื›ืฉื•ืœื™ื - ืขืฆืžื™ื ืฉื—ื•ืกืžื™ื ืืช
20:49
block your way, making it difficult for
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ื“ืจื›ืš ื•ืžืงืฉื™ื
20:51
you to move forward.
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ืขืœื™ืš ืœื”ืชืงื“ื.
20:52
โ€˜Sixth Senseโ€™ works using echo-location,
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'ื”ื—ื•ืฉ ื”ืฉื™ืฉื™' ืคื•ืขืœ ื‘ืืžืฆืขื•ืช ืืงื•-ืžื™ืงื•ื,
20:55
a kind of ultrasound like that used by
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ืžืขื™ืŸ ืื•ืœื˜ืจืกืื•ื ื“ ื›ืžื• ื–ื” ื”ืžืฉืžืฉ
20:58
bats who send out sound waves
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ืขื˜ืœืคื™ื ืฉืฉื•ืœื—ื™ื ื’ืœื™ ืงื•ืœ
21:00
which bounce off surrounding objects.
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ืฉืžืงืคื™ืฆื™ื ืืช ื”ืขืฆืžื™ื ื”ืกื•ื‘ื‘ื™ื.
21:03
The returning echoes show where these
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ื”ื”ื“ื™ื ื”ื—ื•ื–ืจื™ื ืžืจืื™ื ื”ื™ื›ืŸ
21:05
objects are located.
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ื ืžืฆืื™ื ื”ื—ืคืฆื™ื ื”ืœืœื•.
21:07
Some of the assistive apps are so
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ื—ืœืง ืžื”ืืคืœื™ืงืฆื™ื•ืช ื”ืžืกื™ื™ืขื•ืช ื›ืœ ื›ืš
21:09
smart they can even tell what kind of
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ื—ื›ืžื•ืช ืฉื”ืŸ ืžืกื•ื’ืœื•ืช ืืคื™ืœื• ืœื“ืขืช ืื™ื–ื” ืกื•ื’ ืฉืœ
21:11
object is coming up ahead โ€“ be it a
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ื—ืคืฅ ืžื’ื™ืข ืงื“ื™ืžื” - ื‘ื™ืŸ ืื ื–ื”
21:13
friend, a shop door or a speeding car.
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ื—ื‘ืจ, ื“ืœืช ื—ื ื•ืช ืื• ืžื›ื•ื ื™ืช ื“ื•ื”ืจืช.
21:16
I guess being able to move around
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ืื ื™ ืžื ื™ื— ืฉื”ื™ื›ื•ืœืช ืœื”ืกืชื•ื‘ื‘
21:18
confidently really boosts peopleโ€™s
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ื‘ื‘ื™ื˜ื—ื•ืŸ ืžืžืฉ ืžื—ื–ืงืช ืืช ื”ืขืฆืžืื•ืช ืฉืœ ืื ืฉื™ื
21:20
independence.
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.
21:21
Absolutely. And itโ€™s challenging
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ื‘ื”ื—ืœื˜. ื•ื–ื”
21:22
stereotypes around blindness too.
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ื’ื ืžืืชื’ืจ ืกื˜ืจื™ืื•ื˜ื™ืคื™ื ืกื‘ื™ื‘ ืขื™ื•ื•ืจื•ืŸ.
21:25
Blogger, Fern Lulham, who is blind
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ื”ื‘ืœื•ื’ืจื™ืช, ืคืจืŸ ืœื•ืœื”ื, ืฉืขื™ื•ื•ืจืช
21:27
herself, uses assistive apps every day.
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ื‘ืขืฆืžื”, ืžืฉืชืžืฉืช ื‘ืืคืœื™ืงืฆื™ื•ืช ืžืกื™ื™ืขื•ืช ืžื“ื™ ื™ื•ื.
21:30
Here she is talking to
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ื›ืืŸ ื”ื™ื ืžื“ื‘ืจืช ืขื
21:32
BBC World Serviceโ€™s, Digital Planet:
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ืฉื™ืจื•ืช ื”-BBC World Service, Digital Planet:
21:35
I think the more that society sees
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ืื ื™ ื—ื•ืฉื‘ ืฉื›ื›ืœ ืฉื”ื—ื‘ืจื” ืจื•ืื”
21:37
blind people in the community, at work,
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ืื ืฉื™ื ืขื™ื•ื•ืจื™ื ื‘ืงื”ื™ืœื”, ื‘ืขื‘ื•ื“ื”,
21:40
in relationships it does help to tackle
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ื‘ืžืขืจื›ื•ืช ื™ื—ืกื™ื, ื–ื” ืขื•ื–ืจ ืœื”ืชืžื•ื“ื“ ืขื
21:43
all of these stereotypes, it helps
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ื›ืœ ื”ืกื˜ืจื™ืื•ื˜ื™ืคื™ื ื”ืืœื”, ื–ื” ืขื•ื–ืจ
21:44
people to see blind and
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ืœืื ืฉื™ื ืœืจืื•ืช ืขื™ื•ื•ืจื™ื ื•ืœืงื•ื™ื™
21:46
visually-impaired people in a whole
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ืจืื™ื™ื” ืื ืฉื™ื
21:47
new way and it just normalises
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ื‘ื“ืจืš ื—ื“ืฉื” ืœื’ืžืจื™ ื•ื–ื” ืคืฉื•ื˜ ืžื ืจืžืœ
21:49
disability โ€“ thatโ€™s what we need, we
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ืžื•ื’ื‘ืœื•ืช - ื–ื” ืžื” ืฉืื ื—ื ื• ืฆืจื™ื›ื™ื, ืื ื—ื ื•
21:51
need to see people just getting on
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ืฆืจื™ื›ื™ื ืœืจืื•ืช ืื ืฉื™ื ืคืฉื•ื˜ ืžืžืฉื™ื›ื™ื
21:53
with their life and doing it and then
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ื‘ื—ื™ื™ื ืฉืœื”ื ื•ืขื•ืฉื™ื ืืช ื–ื” ื•ืื–
21:54
people wonโ€™t see it as such a big
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ืื ืฉื™ื ืœื ื™ืจืื• ื‘ื–ื”
21:56
deal anymore, itโ€™ll just be the ordinary.
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ืขื ื™ื™ืŸ ื›ื–ื” ื™ื•ืชืจ, ื–ื” ืคืฉื•ื˜ ืœื”ื™ื•ืช ื”ืจื’ื™ืœ.
22:00
Fern distinguishes between people
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ืคืจืŸ ืžื‘ื—ื™ืŸ ื‘ื™ืŸ ืื ืฉื™ื
22:02
who are blind, or unable to see, and
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ืขื™ื•ื•ืจื™ื, ืื• ืฉืื™ื ื ืžืกื•ื’ืœื™ื ืœืจืื•ืช, ืœื‘ื™ืŸ
22:04
those who are visually impaired โ€“
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ืืœื• ื”ืกื•ื‘ืœื™ื ืžืœืงื•ื™ื•ืช ืจืื™ื™ื” -
22:06
experience a decreased ability to see.
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ื—ื•ื•ื™ื ื™ืจื™ื“ื” ื‘ื™ื›ื•ืœืช ืœืจืื•ืช.
22:09
Assistive tech helps blind people
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ื˜ื›ื ื•ืœื•ื’ื™ื” ืžืกื™ื™ืขืช ืขื•ื–ืจืช ืœืขื™ื•ื•ืจื™ื
22:11
lead normal, independent lives within
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ืœื ื”ืœ ื—ื™ื™ื ื ื•ืจืžืœื™ื™ื ื•ืขืฆืžืื™ื™ื ื‘ืชื•ืš
22:14
their local communities. Fern hopes
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ื”ืงื”ื™ืœื•ืช ื”ืžืงื•ืžื™ื•ืช ืฉืœื”ื. ืคืจืŸ ืžืงื•ื•ื”
22:16
that this will help normalise disability โ€“
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ืฉื–ื” ื™ืขื–ื•ืจ ืœื ืจืžืœ ื ื›ื•ืช -
22:19
treat something as normal which has
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ื”ืชื™ื™ื—ืก ืœืžืฉื”ื• ื›ื ื•ืจืžืœื™ ืฉืœื
22:21
not been accepted as normal beforeโ€ฆ
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ื”ืชืงื‘ืœ ื›ืจื’ื™ืœ ืงื•ื“ื ืœื›ืŸ...
22:23
โ€ฆso being blind doesnโ€™t have to be a
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...ืื– ืœื”ื™ื•ืช ืขื™ื•ื•ืจ ืœื ื—ื™ื™ื‘ ืœื”ื™ื•ืช
22:26
big deal โ€“ an informal way to say
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ืขื ื™ื™ืŸ ื’ื“ื•ืœ - ื“ืจืš ืœื ืจืฉืžื™ืช ืœื•ืžืจ
22:28
something is not a serious problem.
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ืžืฉื”ื• ื”ื™ื ืœื ื‘ืขื™ื” ืจืฆื™ื ื™ืช.
22:31
Just keep your eyes closed for a
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ืคืฉื•ื˜ ืขืฆื•ื ืืช ื”ืขื™ื ื™ื™ื ืœืžืฉืš
22:32
minute and try moving around the
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ื“ืงื” ื•ื ืกื” ืœื”ืกืชื•ื‘ื‘
22:33
room. Youโ€™ll soon see how difficult
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ื‘ื—ื“ืจ. ื‘ืงืจื•ื‘ ืชืจืื” ื›ืžื”
22:36
it isโ€ฆ and how life changing this
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ื–ื” ืงืฉื”... ื•ืื™ืš
22:37
technology can be.
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ื”ื˜ื›ื ื•ืœื•ื’ื™ื” ื”ื–ื• ื™ื›ื•ืœื” ืœื”ื™ื•ืช ืžืฉื ื” ื—ื™ื™ื.
22:39
Being able to read books must also
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ื”ื™ื›ื•ืœืช ืœืงืจื•ื ืกืคืจื™ื ื—ื™ื™ื‘ืช ื’ื
22:41
open up a world of imagination.
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ืœืคืชื•ื— ืขื•ืœื ืฉืœ ื“ืžื™ื•ืŸ.
22:44
So what was the answer to your
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ืื– ืžื” ื”ื™ื™ืชื” ื”ืชืฉื•ื‘ื”
22:45
quiz question, Neil?
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ืœืฉืืœืช ื”ื—ื™ื“ื•ืŸ ืฉืœืš, ื ื™ืœ?
22:46
Ah yes. I asked Georgina who
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ืื” ื›ืŸ. ืฉืืœืชื™ ืืช ื’'ื•ืจื’'ื™ื ื”
22:48
invented the system of reading
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ืฉื”ืžืฆื™ืื” ืืช ืžืขืจื›ืช ื”ืงืจื™ืื”
22:50
where fingertips are used to feel
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ืฉื‘ื” ืžืฉืชืžืฉื™ื ื‘ืงืฆื•ืช ื”ืืฆื‘ืขื•ืช ื›ื“ื™ ืœื”ืจื’ื™ืฉ
22:52
patterns of printed raised dots.
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ื“ืคื•ืกื™ื ืฉืœ ื ืงื•ื“ื•ืช ืžื•ืจืžื•ืช ืžื•ื“ืคืกื•ืช.
22:54
What did you say, Georgina?
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ืžื” ืืžืจืช, ื’'ื•ืจื’'ื™ื ื”?
22:55
I thought it was, b) Louis Braille.
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ื—ืฉื‘ืชื™ ืฉื–ื”, ื‘) ืœื•ืื™ ื‘ืจื™ื™ืœ.
22:58
Which wasโ€ฆof course the correct
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ืžื” ืฉื”ื™ื”... ื›ืžื•ื‘ืŸ
23:00
answer! Well done, Georgina โ€“ Louise
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ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื”! ื›ืœ ื”ื›ื‘ื•ื“, ื’'ื•ืจื’'ื™ื ื” - ืœื•ืื™ื–
23:02
Braille the inventor of a reading
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ื‘ืจื™ื™ืœ ื”ืžืžืฆื™ืื”
23:04
system which is known worldwide
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ืžืขืจื›ืช ืงืจื™ืื” ื”ืžื•ื›ืจืช ื‘ืจื—ื‘ื™ ื”ืขื•ืœื
23:06
simply as braille.
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ืคืฉื•ื˜ ื›ื‘ืจื™ื™ืœ.
23:07
I suppose braille is an early example
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ืื ื™ ืžื ื™ื— ืฉื‘ืจื™ื™ืœ ื”ื•ื ื“ื•ื’ืžื” ืžื•ืงื“ืžืช
23:10
of assistive technology โ€“ systems
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ืœื˜ื›ื ื•ืœื•ื’ื™ื” ืžืกื™ื™ืขืช - ืžืขืจื›ื•ืช
23:12
and equipment that assist people
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ื•ืฆื™ื•ื“ ื”ืžืกื™ื™ืขื™ื ืœืื ืฉื™ื
23:14
with disabilities to perform everyday
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ืขื ืžื•ื’ื‘ืœื•ื™ื•ืช ืœื‘ืฆืข
23:16
functions. Letโ€™s recap the rest of
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ืคืขื•ืœื•ืช ื™ื•ืžื™ื•ืžื™ื•ืช. ื‘ื•ืื• ื ืกื›ื ืืช ืฉืืจ
23:18
the vocabulary, Neil.
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ืื•ืฆืจ ื”ืžื™ืœื™ื, ื ื™ืœ.
23:20
OK. An obstacle is an object that
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ื‘ืกื“ืจ. ืžื›ืฉื•ืœ ื”ื•ื ื—ืคืฅ
23:22
is in your way and blocks your
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ืฉืขื•ืžื“ ื‘ื“ืจื›ืš ื•ื—ื•ืกื ืืช
23:24
movement.
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ืชื ื•ืขืชืš.
23:25
Some assisted technology works
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ื—ืœืง ืžื”ื˜ื›ื ื•ืœื•ื’ื™ื” ื”ืžืกื™ื™ืขืช ืขื•ื‘ื“ืช
23:27
using echo-location โ€“ a system of
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ื‘ืืžืฆืขื•ืช ืืงื•-ืžื™ืงื•ื - ืžืขืจื›ืช
23:30
ultrasound detection used by bats.
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ื–ื™ื”ื•ื™ ืื•ืœื˜ืจืกืื•ื ื“ ื”ืžืฉืžืฉืช ืขื˜ืœืคื™ื.
23:33
Being blind is different from being
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ืœื”ื™ื•ืช ืขื™ื•ื•ืจ ืฉื•ื ื” ืžืœื”ื™ื•ืช
23:34
visually impaired - having a
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ืœืงื•ื™ ืจืื™ื™ื” - ื‘ืขืœ
23:36
decreased ability to see, whether
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ื™ืจื™ื“ื” ื‘ื™ื›ื•ืœืช ื”ืจืื™ื™ื”, ื‘ื™ืŸ ืื
23:38
disabling or not.
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ื ื›ื” ืื• ืœื.
23:40
And finally, the hope is that
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ื•ืœื‘ืกื•ืฃ, ื”ืชืงื•ื•ื” ื”ื™ื ืฉืืคืœื™ืงืฆื™ื•ืช
23:42
assistive phone apps can help
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ื˜ืœืคื•ืŸ ืžืกื™ื™ืขื•ืช ื™ื›ื•ืœื•ืช ืœืขื–ื•ืจ
23:43
normalise disability โ€“ change the
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ืœื ืจืžืœ ืžื•ื’ื‘ืœื•ืช - ืœืฉื ื•ืช ืืช
23:45
perception of something into
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ื”ืชืคื™ืกื” ืฉืœ ืžืฉื”ื•
23:47
being accepted as normalโ€ฆ
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ืœื”ื™ื•ืช ืžืงื•ื‘ืœ ื›ืจื’ื™ืœ...
23:49
..so that disability is no longer a
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..ื›ืš ืฉื ื›ื•ืช ื”ื™ื ื›ื‘ืจ ืœื
23:51
big deal โ€“ not a big problem.
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ืขื ื™ื™ืŸ ื’ื“ื•ืœ - ืœื ื‘ืขื™ื” ื’ื“ื•ืœื”.
23:53
Thatโ€™s all for this programme but
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ื–ื” ื”ื›ืœ ืขื‘ื•ืจ ื”ืชื•ื›ื ื™ืช ื”ื–ื•, ืื‘ืœ
23:55
join us again soon at 6 Minute Englishโ€ฆ
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ื”ืฆื˜ืจืฃ ืืœื™ื ื• ืฉื•ื‘ ื‘ืงืจื•ื‘ ื‘-6 ื“ืงื•ืช ืื ื’ืœื™ืช...
23:57
โ€ฆand remember you can find many
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...ื•ื–ื›ืจื• ืฉืชื•ื›ืœื• ืœืžืฆื•ื
23:59
more 6 Minute topics and useful
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ืขื•ื“ ื”ืจื‘ื” ื ื•ืฉืื™ื ืฉืœ 6 ื“ืงื•ืช ื•ืื•ืฆืจ
24:01
vocabulary archived on
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ืžื™ืœื™ื ืฉื™ืžื•ืฉื™ ื‘ืืจื›ื™ื•ืŸ ื‘-
24:02
bbclearningenglish.com.
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bbclearningenglish.com.
24:04
Donโ€™t forget we also have an app
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ืืœ ืชืฉื›ื— ืฉื™ืฉ ืœื ื• ื’ื ืืคืœื™ืงืฆื™ื”
24:06
you can download for free from
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ืฉืชื•ื›ืœ ืœื”ื•ืจื™ื“ ื‘ื—ื™ื ื
24:08
the app stores. And of course we
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ืžื—ื ื•ื™ื•ืช ื”ืืคืœื™ืงืฆื™ื•ืช. ื•ื›ืžื•ื‘ืŸ
24:10
are all over social media, so come
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ืฉืื ื—ื ื• ื‘ื›ืœ ื”ืžื“ื™ื” ื”ื—ื‘ืจืชื™ืช, ืื–
24:12
on over and say hi.
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ื‘ื•ื ื•ืชื’ื™ื“ ืฉืœื•ื.
24:13
Bye for now!
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ืœื”ืชืจืื•ืช ื‘ื™ื ืชื™ื™ื!
24:14
Bye!
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ื‘ื™ื™!
24:21
Welcome to 6 Minute English, where
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ื‘ืจื•ื›ื™ื ื”ื‘ืื™ื ืœืื ื’ืœื™ืช ืฉืœ 6 ื“ืงื•ืช, ืฉื
24:22
we bring you an intelligent topic
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ืื ื• ืžื‘ื™ืื™ื ืœื›ื ื ื•ืฉื ืื™ื ื˜ืœื™ื’ื ื˜ื™
24:24
and six related items of vocabulary.
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ื•ืฉื™ืฉื” ืคืจื™ื˜ื™ื ืงืฉื•ืจื™ื ืฉืœ ืื•ืฆืจ ืžื™ืœื™ื.
24:26
Iโ€™m Neil.
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ืื ื™ ื ื™ืœ.
24:27
And Iโ€™m Tim. And today weโ€™re talking
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ื•ืื ื™ ื˜ื™ื. ื•ื”ื™ื•ื ืื ื—ื ื• ืžื“ื‘ืจื™ื
24:30
about AI โ€“ or Artificial Intelligence.
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ืขืœ AI - ืื• ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช.
24:33
Artificial Intelligence is the ability of
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ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ื”ื™ื ื”ื™ื›ื•ืœืช ืฉืœ
24:36
machines to copy human intelligent
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ืžื›ื•ื ื•ืช ืœื”ืขืชื™ืง ื”ืชื ื”ื’ื•ืช ืื™ื ื˜ืœื™ื’ื ื˜ื™ืช ืื ื•ืฉื™ืช
24:38
behaviour โ€“ for example, an
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- ืœืžืฉืœ,
24:40
intelligent machine can learn
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ืžื›ื•ื ื” ืื™ื ื˜ืœื™ื’ื ื˜ื™ืช ื™ื›ื•ืœื” ืœืœืžื•ื“
24:42
from its own mistakes, and make
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ืžื”ื˜ืขื•ื™ื•ืช ืฉืœื”, ื•ืœืงื‘ืœ
24:43
decisions based on whatโ€™s happened
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ื”ื—ืœื˜ื•ืช ืขืœ ืกืžืš ืžื” ืฉืงืจื”
24:45
in the past.
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ื‘ืขื‘ืจ.
24:46
Thereโ€™s a lot of talk about AI these
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ื™ืฉ ื”ืจื‘ื” ื“ื™ื‘ื•ืจื™ื ืขืœ AI ื‘ื™ืžื™ื ืืœื”
24:48
days, Neil, but itโ€™s still just science
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, ื ื™ืœ, ืื‘ืœ ื–ื” ืขื“ื™ื™ืŸ ืจืง ืžื“ืข ื‘ื“ื™ื•ื ื™
24:50
fiction, isnโ€™t it?
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, ืœื?
24:52
Thatโ€™s not true โ€“ AI is everywhere.
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ื–ื” ืœื ื ื›ื•ืŸ - AI ื ืžืฆื ื‘ื›ืœ ืžืงื•ื.
24:54
Machine thinking is in our homes,
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ื—ืฉื™ื‘ื” ืžื›ื•ื ื” ื ืžืฆืืช ื‘ื‘ืชื™ื,
24:57
offices, schools and hospitals.
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ื‘ืžืฉืจื“ื™ื, ื‘ื‘ืชื™ ื”ืกืคืจ ื•ื‘ื‘ืชื™ ื”ื—ื•ืœื™ื ืฉืœื ื•.
24:59
Computer algorithms are helping
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ืืœื’ื•ืจื™ืชืžื™ ืžื—ืฉื‘ ืขื•ื–ืจื™ื
25:01
us drive our cars. Theyโ€™re diagnosing
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ืœื ื• ืœื ื”ื•ื’ ื‘ืžื›ื•ื ื™ื•ืช ืฉืœื ื•. ื”ื ืžืื‘ื—ื ื™ื
25:03
whatโ€™s wrong with us in hospitals.
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ืžื” ืœื ื‘ืกื“ืจ ืื™ืชื ื• ื‘ื‘ืชื™ ื—ื•ืœื™ื.
25:06
Theyโ€™re marking student essaysโ€ฆ
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ื”ื ืžืกืžื ื™ื ืžืืžืจื™ื ืฉืœ ืชืœืžื™ื“ื™ื...
25:07
Theyโ€™re telling us what to read on
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ื”ื ืื•ืžืจื™ื ืœื ื• ืžื” ืœืงืจื•ื
25:09
our smartphonesโ€ฆ
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ื‘ืกืžืืจื˜ืคื•ื ื™ื ืฉืœื ื•...
25:10
Well, that really does sound like
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ื•ื‘ื›ืŸ, ื–ื” ื‘ืืžืช ื ืฉืžืข ื›ืžื•
25:12
science fiction โ€“ but itโ€™s
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ืžื“ืข ื‘ื“ื™ื•ื ื™ - ืื‘ืœ ื–ื”
25:13
happening already, you say, Neil?
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ื›ื‘ืจ ืงื•ืจื”, ืืชื” ืื•ืžืจ, ื ื™ืœ?
25:15
Itโ€™s definitely happening, Tim.
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ื–ื” ื‘ื”ื—ืœื˜ ืงื•ืจื”, ื˜ื™ื.
25:17
And an algorithm, by the way, is
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ื•ืืœื’ื•ืจื™ืชื, ืื’ื‘, ื”ื•ื
25:19
a set of steps a computer follows
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ืกื˜ ืฉืœ ืฉืœื‘ื™ื ืฉืžื—ืฉื‘ ืขื•ืงื‘ ืื—ืจื™ื”ื
25:21
in order to solve a problem.
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ื›ื“ื™ ืœืคืชื•ืจ ื‘ืขื™ื”.
25:23
So can you tell me what was the
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ืื– ืืชื” ื™ื›ื•ืœ ืœื”ื’ื™ื“ ืœื™ ืžื” ื”ื™ื”
25:25
name of the computer which
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ืฉืžื• ืฉืœ ื”ืžื—ืฉื‘
25:27
famously beat world chess
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ืฉื”ื‘ื™ืก ืืช ืืœื•ืฃ ื”ืขื•ืœื ื‘ืฉื—ืžื˜,
25:28
champion Garry Kasparov
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ื’ืืจื™ ืงืกืคืจื•ื‘,
25:30
using algorithms in 1997?
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ื‘ืืžืฆืขื•ืช ืืœื’ื•ืจื™ืชืžื™ื ื‘-1997? ื”ืื
25:33
Was itโ€ฆ
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25:33
a) Hal, b) Alpha 60,
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ื–ื” ื”ื™ื”...
ื) ื”ืืœ, ื‘) ืืœืคื 60,
25:36
or, c) Deep Blue?
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ืื•, ื’) ื›ื—ื•ืœ ืขืžื•ืง?
25:38
Iโ€™ll say Deep Blue.
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ืื ื™ ืื’ื™ื“ ื›ื—ื•ืœ ืขืžื•ืง.
25:41
Although Iโ€™m just guessing.
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ืœืžืจื•ืช ืฉืื ื™ ืจืง ืžื ื—ืฉ.
25:42
Was it an educated guess, Tim?
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ื”ืื ื–ื” ื”ื™ื” ื ื™ื—ื•ืฉ ืžื•ืฉื›ืœ, ื˜ื™ื?
25:44
I know a bit about chessโ€ฆ
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ืื ื™ ื™ื•ื“ืข ืงืฆืช ืขืœ ืฉื—ืžื˜...
25:46
An educated guess is based
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ื ื™ื—ื•ืฉ ืžื•ืฉื›ืœ ืžื‘ื•ืกืก
25:48
on knowledge and experience
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ืขืœ ื™ื“ืข ื•ื ื™ืกื™ื•ืŸ
25:49
and is therefore likely to be correct.
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ื•ืœื›ืŸ ืกื‘ื™ืจ ืœื”ื ื™ื— ืฉื”ื•ื ื ื›ื•ืŸ.
25:51
Well, weโ€™ll find out later on how
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ื•ื‘ื›ืŸ, ื ื’ืœื” ืžืื•ื—ืจ ื™ื•ืชืจ ืขื“ ื›ืžื”
25:53
educated your guess was in
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ื”ื ื™ื—ื•ืฉ ืฉืœืš ื”ื™ื” ืžืฉื›ื™ืœ
25:54
this case, Tim!
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ื‘ืžืงืจื” ื”ื–ื”, ื˜ื™ื!
25:55
Indeed. But getting back to AI
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ืื›ืŸ. ืื‘ืœ ืœื—ื–ื•ืจ ืœ-AI
25:58
and what machines can do โ€“ are
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ื•ืžื” ื”ืžื›ื•ื ื•ืช ื™ื›ื•ืœื•ืช ืœืขืฉื•ืช - ื”ืื
26:00
they any good at solving real-life
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ื”ื ื˜ื•ื‘ื™ื ื‘ืคืชืจื•ืŸ ื‘ืขื™ื•ืช ืžื”ื—ื™ื™ื ื”ืืžื™ืชื™ื™ื
26:03
problems? Computers think in zeros
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? ืžื—ืฉื‘ื™ื ื—ื•ืฉื‘ื™ื ื‘ืืคืกื™ื
26:06
and ones donโ€™t they? That sounds
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ื•ืื—ื“ื™ื ืœื? ื–ื” ื ืฉืžืข
26:07
like a pretty limited language when
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ื›ืžื• ืฉืคื” ื“ื™ ืžื•ื’ื‘ืœืช ื›ืฉื–ื”
26:09
it comes to life experience!
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ืžื’ื™ืข ืœื ื™ืกื™ื•ืŸ ื—ื™ื™ื!
26:11
You would be surprised to what
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ืชื•ืคืชืข ืžื”
26:12
those zeroes and ones can do, Tim.
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ื”ืืคืกื™ื ื•ื”ืื—ื“ื™ื ื”ืืœื” ื™ื›ื•ืœื™ื ืœืขืฉื•ืช, ื˜ื™ื.
26:14
Although youโ€™re right that AI does
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ืœืžืจื•ืช ืฉืืชื” ืฆื•ื“ืง ืฉืœ-AI
26:16
have its limitations at the moment.
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ื™ืฉ ืืช ื”ืžื’ื‘ืœื•ืช ืฉืœื• ื›ืจื’ืข.
26:18
And if something has limitations
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ื•ืื ืœืžืฉื”ื• ื™ืฉ ืžื’ื‘ืœื•ืช,
26:20
thereโ€™s a limit on what it can do or
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ื™ืฉ ื’ื‘ื•ืœ ืœืžื” ืฉื”ื•ื ื™ื›ื•ืœ ืœืขืฉื•ืช ืื•
26:22
how good it can be.
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ื›ืžื” ื˜ื•ื‘ ื”ื•ื ื™ื›ื•ืœ ืœื”ื™ื•ืช.
26:23
OK โ€“ well now might be a good time
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ื‘ืกื“ืจ - ื•ื‘ื›ืŸ, ืขื›ืฉื™ื• ืื•ืœื™ ื–ื” ื–ืžืŸ ื˜ื•ื‘
26:26
to listen to Zoubin Bharhramani,
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ืœื”ืงืฉื™ื‘ ืœื–ื•ื‘ื™ืŸ ื‘ื”ื”ืจืžื ื™,
26:28
Professor of Information Engineering
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ืคืจื•ืคืกื•ืจ ืœื”ื ื“ืกืช ืžื™ื“ืข
26:30
at the University of Cambridge and
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ื‘ืื•ื ื™ื‘ืจืกื™ื˜ืช ืงื™ื™ืžื‘ืจื™ื“ื’' ื•ืกื’ืŸ
26:32
deputy director of the Leverhulme Centre
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ืžื ื”ืœ ืžืจื›ื– ืœื‘ื•ืจื”ื•ืœืžื”
26:35
for the Future of Intelligence.
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ืœืขืชื™ื“ ื”ืžื•ื“ื™ืขื™ืŸ.
26:37
Heโ€™s talking about what limitations
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ื”ื•ื ืžื“ื‘ืจ ืขืœ ื”ืžื’ื‘ืœื•ืช
26:39
AI has at the moment.
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ืฉื™ืฉ ืœ-AI ื›ืจื’ืข.
26:43
I think itโ€™s very interesting how many
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ืื ื™ ื—ื•ืฉื‘ ืฉื–ื” ืžืื•ื“ ืžืขื ื™ื™ืŸ ื›ืžื”
26:46
of the things that we take for granted โ€“
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ืžื”ื“ื‘ืจื™ื ืฉืื ื—ื ื• ืœื•ืงื—ื™ื ื›ืžื•ื‘ื ื™ื ืžืืœื™ื”ื -
26:48
we humans take for granted โ€“ as being
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ืื ื—ื ื• ื‘ื ื™ ื”ืื“ื ืœื•ืงื—ื™ื ื›ืžื•ื‘ืŸ ืžืืœื™ื• - ื›ืžื•
26:50
sort of things we donโ€™t even think about
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ื“ื‘ืจื™ื ืฉืื ื—ื ื• ืืคื™ืœื• ืœื ื—ื•ืฉื‘ื™ื ืขืœื™ื”ื
26:51
like how do we walk, how do we reach,
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ื›ืžื• ืื™ืš ืื ื—ื ื• ื”ื•ืœื›ื™ื, ืื™ืš ืื ื—ื ื• ืžื’ื™ืขื™ื,
26:54
how do we recognize our mother. You
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ืื™ืš ืื ื—ื ื• ืžื–ื”ื™ื ืืช ืฉืœื ื• ืึดืžึธื. ืืชื”
26:57
know, all these things. When you start
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ื™ื•ื“ืข, ื›ืœ ื”ื“ื‘ืจื™ื ื”ืืœื”. ื›ืฉืืชื” ืžืชื—ื™ืœ
26:59
to think how to implement them on a
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ืœื—ืฉื•ื‘ ืื™ืš ืœื™ื™ืฉื ืื•ืชื
27:01
computer, you realize that itโ€™s those
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ื‘ืžื—ืฉื‘, ืืชื” ืžื‘ื™ืŸ ืฉื–ื”
27:04
things that are incredibly difficult to get
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ื”ื“ื‘ืจื™ื ืฉืงืฉื” ืœื”ืคืœื™ื ืœื’ืจื•ื
27:09
computers to do, and thatโ€™s where the
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ืœืžื—ืฉื‘ื™ื ืœืขืฉื•ืช, ื•ืฉื
27:12
current cutting edge of research is.
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ื ืžืฆื ื—ื•ื“ ื”ื—ื ื™ืช ื”ื ื•ื›ื—ื™ ืฉืœ ื”ืžื—ืงืจ.
27:16
If we take something for granted we
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ืื ืื ื—ื ื• ืœื•ืงื—ื™ื ืžืฉื”ื• ื›ืžื•ื‘ืŸ ืžืืœื™ื• ืื ื—ื ื•
27:17
donโ€™t realise how important something is.
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ืœื ืžื‘ื™ื ื™ื ื›ืžื” ืžืฉื”ื• ื—ืฉื•ื‘.
27:20
You sometimes take me for granted, I
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ืœืคืขืžื™ื ืืชื” ืœื•ืงื— ืื•ืชื™ ื›ืžื•ื‘ืŸ ืžืืœื™ื•, ืื ื™
27:22
think, Neil.
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ื—ื•ืฉื‘, ื ื™ืœ.
27:23
No โ€“ I never take you for granted, Tim!
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ืœื - ืื ื™ ืืฃ ืคืขื ืœื ืœื•ืงื— ืื•ืชืš ื›ืžื•ื‘ืŸ ืžืืœื™ื•, ื˜ื™ื!
27:25
Youโ€™re far too important for that!
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ืืชื” ื™ื•ืชืจ ืžื“ื™ ื—ืฉื•ื‘ ื‘ืฉื‘ื™ืœ ื–ื”!
27:27
Good to hear! So things we take for
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ื˜ื•ื‘ ืœืฉืžื•ืข! ืื– ื“ื‘ืจื™ื ืฉืื ื—ื ื• ืœื•ืงื—ื™ื ื›ืžื•ื‘ื ื™ื
27:30
granted are doing every day tasks like
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ืžืืœื™ื”ื ื”ื ื‘ื™ืฆื•ืข ืžืฉื™ืžื•ืช ื™ื•ืžื™ื•ืžื™ื•ืช ื›ืžื•
27:33
walking, picking something up, or
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ื”ืœื™ื›ื”, ืœื”ืจื™ื ืžืฉื”ื• ืื•
27:35
recognizing somebody. We implement โ€“
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ืœื–ื”ื•ืช ืžื™ืฉื”ื•. ืื ื—ื ื• ืžื™ื™ืฉืžื™ื -
27:38
or perform โ€“ these things without
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ืื• ืžื‘ืฆืขื™ื - ืืช ื”ื“ื‘ืจื™ื ื”ืืœื” ื‘ืœื™
27:41
thinking โ€“ Whereas itโ€™s cutting edge
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ืœื—ืฉื•ื‘ - ื‘ืขื•ื“ ืฉื–ื”
27:43
research to try and program a
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ืžื—ืงืจ ื—ื“ืฉื ื™ ืœื ืกื•ืช ื•ืœืชื›ื ืช
27:45
machine to do them.
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ืžื›ื•ื ื” ืœืขืฉื•ืช ืื•ืชื.
27:46
Cutting edge means very new and
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ื—ื“ืฉื ื™ ืคื™ืจื•ืฉื• ื—ื“ืฉ
27:48
advanced. Itโ€™s interesting isn't it, that
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ื•ืžืชืงื“ื ืžืื•ื“. ื–ื” ืœื ืžืขื ื™ื™ืŸ, ืฉืœืคื ื™
27:50
over ten years ago a computer beat
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ื™ื•ืชืจ ืžืขืฉืจ ืฉื ื™ื ืžื—ืฉื‘ ื ื™ืฆื—
27:52
a chess grand master โ€“ but the
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ืืžืŸ ื’ื“ื•ืœ ื‘ืฉื—ืžื˜ - ืื‘ืœ
27:54
same computer would find it incredibly
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ืื•ืชื• ืžื—ืฉื‘ ื”ื™ื”
27:56
difficult to pick up a chess piece.
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ืžืชืงืฉื” ืœื”ืคืœื™ื ืœื”ืจื™ื ื›ืœื™ ืฉื—ืžื˜.
27:58
I know. Itโ€™s very strange. But now
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ืื ื™ ื™ื•ื“ืข. ื–ื” ืžืื•ื“ ืžื•ื–ืจ. ืื‘ืœ ืขื›ืฉื™ื•
28:01
youโ€™ve reminded me that we need
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ื”ื–ื›ืจืช ืœื™ ืฉืื ื—ื ื• ืฆืจื™ื›ื™ื
28:02
the answer to todayโ€™s question.
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ืืช ื”ืชืฉื•ื‘ื” ืœืฉืืœื” ืฉืœ ื”ื™ื•ื.
28:04
Which was: What was the name
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ืžื” ื”ื™ื”: ืžื” ื”ื™ื” ืฉืžื•
28:06
of the computer which famously
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ืฉืœ ื”ืžื—ืฉื‘
28:08
beat world chess champion
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ืฉื”ื‘ื™ืก ืืช ืืœื•ืฃ ื”ืขื•ืœื ื‘ืฉื—ืžื˜,
28:10
Garry Kasparov in 1997? Now, you
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ื’ืืจื™ ืงืกืคืจื•ื‘, ื‘-1997? ืขื›ืฉื™ื•,
28:12
said Deep Blue, Tim, and โ€ฆ that was
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ืืžืจืช ื›ื—ื•ืœ ืขืžื•ืง, ื˜ื™ื, ื•... ื–ื• ื”ื™ื™ืชื”
28:15
the right answer!
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ื”ืชืฉื•ื‘ื” ื”ื ื›ื•ื ื”!
28:16
You see, my educated guess was
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ืืชื” ืžื‘ื™ืŸ, ื”ื ื™ื—ื•ืฉ ื”ืžื•ืฉื›ืœ ืฉืœื™
28:18
based on knowledge and experience!
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ื”ืชื‘ืกืก ืขืœ ื™ื“ืข ื•ื ื™ืกื™ื•ืŸ!
28:20
Or maybe you were just lucky. So, the
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ืื• ืฉืื•ืœื™ ืกืชื ื”ื™ื” ืœืš ืžื–ืœ. ืื–,
28:24
IBM supercomputer Deep Blue played
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ืžื—ืฉื‘ ื”ืขืœ ืฉืœ IBM Deep Blue ืฉื™ื—ืง
28:26
against US world chess champion
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ื ื’ื“ ืืœื•ืฃ ื”ืขื•ืœื ื‘ืืจื”"ื‘,
28:28
Garry Kasparov in two chess matches.
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ื’ืืจื™ ืงืกืคืจื•ื‘, ื‘ืฉื ื™ ืžืฉื—ืงื™ ืฉื—.
28:31
The first match was played in
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ื”ืžืฉื—ืง ื”ืจืืฉื•ืŸ ื ืขืจืš
28:32
Philadelphia in 1996 and was
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ื‘ืคื™ืœื“ืœืคื™ื” ื‘-1996 ื•ื ื™ืฆื— ืขืœ
28:34
won by Kasparov. The second was
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ื™ื“ื™ ืงืกืคืจื•ื‘. ื”ืฉื ื™
28:36
played in New York City in 1997
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ืฉื™ื—ืง ื‘ื ื™ื• ื™ื•ืจืง ื‘ืฉื ืช 1997
28:39
and won by Deep Blue. The 1997
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ื•ื ื™ืฆื— ืขืœ ื™ื“ื™ Deep Blue.
28:42
match was the first defeat of a
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ื”ืžืฉื—ืง ื‘-1997 ื”ื™ื” ื”ืชื‘ื•ืกื” ื”ืจืืฉื•ื ื” ืฉืœ
28:43
reigning world chess champion
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ืืœื•ืฃ ืขื•ืœื ื‘ืฉื—ืžื˜
28:45
by a computer under
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ืขืœ ื™ื“ื™ ืžื—ืฉื‘
28:46
tournament conditions.
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ื‘ืชื ืื™ ื˜ื•ืจื ื™ืจ.
28:48
Letโ€™s go through the words we
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ื‘ื•ืื• ื ืขื‘ื•ืจ ืขืœ ื”ืžื™ืœื™ื
28:50
learned today. First up was
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ืฉืœืžื“ื ื• ื”ื™ื•ื. ืจืืฉื™ืช ื”ื™ื™ืชื”
28:52
โ€˜artificial intelligenceโ€™ or AI โ€“ the
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'ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช' ืื• AI -
28:55
ability of machines to copy human
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ื”ื™ื›ื•ืœืช ืฉืœ ืžื›ื•ื ื•ืช ืœื”ืขืชื™ืง
28:58
intelligent behaviour.
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ื”ืชื ื”ื’ื•ืช ืื™ื ื˜ืœื™ื’ื ื˜ื™ืช ืื ื•ืฉื™ืช.
28:59
โ€œThere are AI programs that can
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"ื™ืฉื ืŸ ืชื•ื›ื ื™ื•ืช ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ืฉื™ื›ื•ืœื•ืช
29:01
write poetry.โ€
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ืœื›ืชื•ื‘ ืฉื™ืจื”."
29:02
Do you have any examples you
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ื™ืฉ ืœืš ื“ื•ื’ืžืื•ืช ืฉืืชื”
29:03
can recite?
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ื™ื›ื•ืœ ืœื“ืงืœื?
29:04
Afraid I donโ€™t! Number two โ€“ an
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ืžืคื—ื“ืช ืฉืื ื™ ืœื! ืžืกืคืจ ืฉืชื™ื™ื -
29:07
algorithm is a set of steps a
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ืืœื’ื•ืจื™ืชื ื”ื•ื ืงื‘ื•ืฆื” ืฉืœ ืฉืœื‘ื™ื ืฉืžื—ืฉื‘
29:08
computer follows in order to
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ืžื‘ืฆืข ืขืœ ืžื ืช
29:10
solve a problem. For example,
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ืœืคืชื•ืจ ื‘ืขื™ื”. ืœื“ื•ื’ืžื”,
29:12
โ€œGoogle changes its search
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"ื’ื•ื’ืœ ืžืฉื ื” ืืช ืืœื’ื•ืจื™ืชื ื”ื—ื™ืคื•ืฉ ืฉืœื”
29:13
algorithm hundreds of times
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ืžืื•ืช ืคืขืžื™ื
29:15
every year.โ€
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ื‘ื›ืœ ืฉื ื”."
29:16
The adjective is algorithmic โ€“ for
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ืฉื ื”ืชื•ืืจ ื”ื•ื ืืœื’ื•ืจื™ืชืžื™ -
29:19
example, โ€œGoogle has made many
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ืœื“ื•ื’ืžื”, "ื’ื•ื’ืœ ื‘ื™ืฆืขื”
29:21
algorithmic changes.โ€
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ืฉื™ื ื•ื™ื™ื ืืœื’ื•ืจื™ืชืžื™ื™ื ืจื‘ื™ื."
29:23
Number three โ€“ if something has
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ืžืกืคืจ ืฉืœื•ืฉ - ืื ืœืžืฉื”ื• ื™ืฉ
29:25
โ€˜limitationsโ€™ โ€“ thereโ€™s a limit on
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'ืžื’ื‘ืœื•ืช' - ื™ืฉ ื’ื‘ื•ืœ
29:26
what it can do or how good it
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ืœืžื” ืฉื”ื•ื ื™ื›ื•ืœ ืœืขืฉื•ืช ืื• ื›ืžื” ื˜ื•ื‘ ื”ื•ื
29:28
can be. โ€œOur show has certain
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ื™ื›ื•ืœ ืœื”ื™ื•ืช. "ืœืชื•ื›ื ื™ืช ืฉืœื ื• ื™ืฉ
29:30
limitations โ€“ for example, itโ€™s only
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ืžื’ื‘ืœื•ืช ืžืกื•ื™ืžื•ืช - ืœืžืฉืœ, ื”ื™ื
29:32
six minutes long!โ€
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ืื•ืจื›ืช ืจืง ืฉืฉ ื“ืงื•ืช!"
29:34
Thatโ€™s right โ€“ thereโ€™s only time to
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ื–ื” ื ื›ื•ืŸ - ื™ืฉ ืจืง ื–ืžืŸ
29:35
present six vocabulary items.
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ืœื”ืฆื™ื’ ืฉื™ืฉื” ืคืจื™ื˜ื™ ืื•ืฆืจ ืžื™ืœื™ื.
29:38
Short but sweet!
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ืงืฆืจ ืื‘ืœ ืžืชื•ืง!
29:39
And very intelligent, too. OK, the
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ื•ื’ื ืžืื•ื“ ืื™ื ื˜ืœื™ื’ื ื˜ื™. ื‘ืกื“ืจ,
29:41
next item is โ€˜take something for
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ื”ืคืจื™ื˜ ื”ื‘ื ื”ื•ื 'ืœืงื—ืช ืžืฉื”ื•
29:43
grantedโ€™ โ€“ which is when we donโ€™t
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ื›ืžื•ื‘ืŸ ืžืืœื™ื•' - ื•ื–ื” ื›ืืฉืจ ืื ื—ื ื• ืœื
29:45
realise how important something is.
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ืžื‘ื™ื ื™ื ื›ืžื” ืžืฉื”ื• ื—ืฉื•ื‘.
29:47
โ€œWe take our smart phones for granted
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"ืื ื—ื ื• ืœื•ืงื—ื™ื ืืช ื”ื˜ืœืคื•ื ื™ื ื”ื—ื›ืžื™ื ืฉืœื ื• ื›ืžื•ื‘ื ื™ื ืžืืœื™ื”ื
29:49
these days โ€“ but before 1995 hardly
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ื‘ื™ืžื™ื ื• - ืื‘ืœ ืœืคื ื™ 1995 ื›ืžืขื˜
29:52
anyone owned one.โ€
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ืืฃ ืื—ื“ ืœื ื”ื™ื” ื‘ื‘ืขืœื•ืชื•".
29:54
Number five โ€“ โ€˜to implementโ€™ โ€“ means
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ืžืกืคืจ ื—ืžืฉ - 'ืœื™ื™ืฉื' - ืคื™ืจื•ืฉื•
29:56
to perform a task, or take action.
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ืœื‘ืฆืข ืžืฉื™ืžื”, ืื• ืœื ืงื•ื˜ ื‘ืคืขื•ืœื”.
29:58
โ€œNeil implemented some changes
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"ื ื™ืœ ื™ื™ืฉื ื›ืžื” ืฉื™ื ื•ื™ื™ื
30:00
to the show.โ€
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ื‘ืชื•ื›ื ื™ืช."
30:01
The final item is โ€˜cutting edgeโ€™ โ€“ new
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ื”ืคืจื™ื˜ ื”ืื—ืจื•ืŸ ื”ื•ื 'ืงื“ืžืช ื”ืงืฆื”' - ื—ื“ืฉ
30:03
and advanced โ€“ โ€œThis software is
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ื•ืžืชืงื“ื - "ื”ืชื•ื›ื ื” ื”ื–ื• ื”ื™ื
30:05
cutting edge.โ€
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ื‘ืงื“ืžืช ื”ืงืฆื”."
30:06
โ€œThe software uses cutting edge
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"ื”ืชื•ื›ื ื” ืžืฉืชืžืฉืช
30:08
technology.โ€
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ื‘ื˜ื›ื ื•ืœื•ื’ื™ื” ืžืชืงื“ืžืช."
30:10
OK โ€“ thatโ€™s all we have time for on
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ืื•ืงื™ื™ - ื–ื” ื›ืœ ืžื” ืฉื™ืฉ ืœื ื• ื–ืžืŸ ืขื‘ื•ืจ
30:11
todayโ€™s cutting edge show. But please
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ื”ืžื•ืคืข ื”ื—ื“ืฉื ื™ ืฉืœ ื”ื™ื•ื. ืื‘ืœ ืื ื
30:14
check out our Instagram, Twitter,
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ื‘ื“ื•ืง ืืช ื“ืคื™ ื”ืื™ื ืกื˜ื’ืจื, ื”ื˜ื•ื•ื™ื˜ืจ,
30:16
Facebook and YouTube pages.
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ื”ืคื™ื™ืกื‘ื•ืง ื•ื”-YouTube ืฉืœื ื•.
30:18
Bye-bye!
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30:18
Goodbye!
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ื‘ื™ื™ ื‘ื™ื™!
ื”ึฑื™ื” ืฉืœื•ื!
ืขืœ ืืชืจ ื–ื”

ืืชืจ ื–ื” ื™ืฆื™ื’ ื‘ืคื ื™ื›ื ืกืจื˜ื•ื ื™ YouTube ื”ืžื•ืขื™ืœื™ื ืœืœื™ืžื•ื“ ืื ื’ืœื™ืช. ืชื•ื›ืœื• ืœืจืื•ืช ืฉื™ืขื•ืจื™ ืื ื’ืœื™ืช ื”ืžื•ืขื‘ืจื™ื ืขืœ ื™ื“ื™ ืžื•ืจื™ื ืžื”ืฉื•ืจื” ื”ืจืืฉื•ื ื” ืžืจื—ื‘ื™ ื”ืขื•ืœื. ืœื—ืฅ ืคืขืžื™ื™ื ืขืœ ื”ื›ืชื•ื‘ื™ื•ืช ื‘ืื ื’ืœื™ืช ื”ืžื•ืฆื’ื•ืช ื‘ื›ืœ ื“ืฃ ื•ื™ื“ืื• ื›ื“ื™ ืœื”ืคืขื™ืœ ืืช ื”ืกืจื˜ื•ืŸ ืžืฉื. ื”ื›ืชื•ื‘ื™ื•ืช ื’ื•ืœืœื•ืช ื‘ืกื ื›ืจื•ืŸ ืขื ื”ืคืขืœืช ื”ื•ื•ื™ื“ืื•. ืื ื™ืฉ ืœืš ื”ืขืจื•ืช ืื• ื‘ืงืฉื•ืช, ืื ื ืฆื•ืจ ืื™ืชื ื• ืงืฉืจ ื‘ืืžืฆืขื•ืช ื˜ื•ืคืก ื™ืฆื™ืจืช ืงืฉืจ ื–ื”.

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