00:14 - 00:18
District in Hong Kong and this is the
00:16 - 00:21
view that I wake up to every morning as
00:18 - 00:23
I prepare to go to work every day I see
00:21 - 00:26
thousands of students walking around and
00:23 - 00:28
running around in the schoolyard and I
00:26 - 00:30
cannot help but wonder how the recent
00:28 - 00:32
technological developments particularly
00:30 - 00:34
in artificial intelligence are going to
00:32 - 00:37
impact the lives of thousands of
00:34 - 00:39
students every day this is because
00:37 - 00:41
recently we are going through an
00:39 - 00:44
artificial intelligence revolution in
00:41 - 00:47
education we have gpt4 we have Palm 2
00:44 - 00:50
Microsoft co-pilots Dolly to Adobe
00:47 - 00:52
Firefly and mid-journey and these can do
00:50 - 00:55
incredible things with text Generation
00:52 - 00:58
image generation audio video and
00:55 - 01:00
synthetic data generation using gpt4 for
00:58 - 01:03
example you can write a book for example
01:00 - 01:04
in a matter of days and you can create
01:04 - 01:08
how many of you have a fear that in the
01:06 - 01:11
future what you are doing now may be
01:08 - 01:14
replaced by artificial intelligence
01:11 - 01:16
and I see some hands so all those
01:14 - 01:19
artificial intelligence models what they
01:16 - 01:22
can do is to regurgitate whatever there
01:19 - 01:24
is already on the internet they cannot
01:22 - 01:26
be creative they cannot use common sense
01:24 - 01:28
let me give you an example from Hong
01:26 - 01:30
Kong suppose you're a tourist you are
01:28 - 01:31
just visiting Hong Kong and you are in
01:30 - 01:33
the Jordan station which is on the red
01:31 - 01:34
line over here and you want to go to
01:33 - 01:38
mongkok East which is on the blue line
01:34 - 01:40
there right you ask gpt4 which MTR line
01:38 - 01:42
should I take from Jordan to mongkok
01:40 - 01:44
East it will probably give you two
01:42 - 01:45
options one option would be to take the
01:44 - 01:48
South Route and the other option would
01:45 - 01:50
be to take the north round right
01:48 - 01:53
but if you just use your common sense
01:50 - 01:55
common sense and creativity you would
01:53 - 01:57
know that actually you know what I don't
01:55 - 01:59
have to take the train there I can just
01:57 - 02:02
walk there nevertheless these language
01:59 - 02:03
models can do amazing and incredible
02:05 - 02:12
some content that I created uh using
02:09 - 02:15
um an image generator AI right I just
02:12 - 02:18
asked it to create a drawing of an Asian
02:15 - 02:20
woman in the style of Van Gogh and this
02:18 - 02:23
is what I got and I asked it to create
02:20 - 02:26
another image of an Asian woman in the
02:23 - 02:28
style of Salvador Dali and this is what
02:26 - 02:31
I got and you can see some resemblance
02:28 - 02:34
here right to Salvador Dali paintings
02:31 - 02:37
the things in the sky what the woman is
02:34 - 02:39
holding the bird so
02:37 - 02:41
this is incredible but it's still an
02:39 - 02:44
amalgamation of whatever we can find
02:41 - 02:46
already whatever already exists on the
02:46 - 02:49
recently United Nations educational
02:47 - 02:52
scientific and cultural organization
02:49 - 02:55
UNESCO released a document outlining how
02:52 - 02:57
gpt4 particularly can be used in
02:55 - 03:00
education Sal Khan recently gave a TED
02:57 - 03:02
Talk actually outlining how gpt4 and
03:00 - 03:05
other large language models can be used
03:02 - 03:08
there are so many uses actually but the
03:05 - 03:11
two main uses are
03:08 - 03:14
um gpt4 being used as a personal tutor
03:11 - 03:16
by each student and it's being used by a
03:14 - 03:20
teaching assistant by each teacher right
03:16 - 03:23
so teachers can use gpt4 they can input
03:20 - 03:25
student work and they can get personal
03:23 - 03:26
feedback for each student work so you
03:25 - 03:28
don't have to read things anymore if
03:26 - 03:31
you're a teacher but we still have to
03:28 - 03:32
actually there are so many other users
03:31 - 03:35
you can use it as a guide you can use it
03:32 - 03:38
as a motivator or a study buddy right
03:35 - 03:39
and because gpt4 and other large
03:38 - 03:42
language models can be used in education
03:39 - 03:45
and they have so many uses this also has
03:42 - 03:47
implications for work because there are
03:45 - 03:50
so many examples of AI exposed work
03:47 - 03:52
activity what it means is that there are
03:50 - 03:53
so many examples of work that can be
03:52 - 03:56
completed by artificial intelligence
03:53 - 03:58
tools and that obviates a human being
03:56 - 04:01
there so we don't need a human to do
03:58 - 04:03
those repetitive tasks for us anymore
04:01 - 04:07
and because of this reason Goldman Sachs
04:03 - 04:10
recently released a report in 2023 and
04:07 - 04:11
they are argue that globally 18 of work
04:10 - 04:14
could be automated by artificial
04:11 - 04:16
intelligence and very interestingly the
04:14 - 04:18
percentage for Automation in Hong Kong
04:16 - 04:20
is about 30 percent it's the highest in
04:18 - 04:22
the world so it means that a lot of
04:20 - 04:24
people will be losing their jobs
04:22 - 04:27
to automation
04:24 - 04:30
so then what do we do as Educators
04:27 - 04:32
because I can see that some students
04:30 - 04:35
don't see the value of Education anymore
04:32 - 04:37
they are questioning why do they even
04:35 - 04:40
have to attend classes anymore right
04:37 - 04:41
in this talk I want to argue that there
04:40 - 04:43
is one and only one thing that we need
04:41 - 04:46
to do as Educators and that is to make
04:43 - 04:49
sure that our students are AI proof
04:46 - 04:51
what do I mean by being AI proof it's an
04:49 - 04:54
adjective that means
04:51 - 04:57
being resilient being immune so that we
04:54 - 04:59
will not be replaced by AI in the future
04:57 - 05:02
we will not be negatively impacted by
04:59 - 05:05
artificial intelligence in the future
05:02 - 05:08
but being AI proof also requires us to
05:05 - 05:11
be human what do I mean by being human I
05:08 - 05:12
mean using and capitalizing on our human
05:12 - 05:18
that cannot be easily replicated by
05:14 - 05:20
artificial intelligence right
05:18 - 05:23
now I want to talk about those specific
05:20 - 05:25
skills according to the organization for
05:23 - 05:26
economic and cooperation developments
05:26 - 05:30
the most crucial skills that we need to
05:28 - 05:33
pay attention to in education are
05:30 - 05:35
cognitive and metacognitive social and
05:33 - 05:37
practical skills
05:35 - 05:39
the world economic Forum has a similar
05:37 - 05:42
take on the skills required for students
05:39 - 05:43
and these are cognitive social and
05:42 - 05:45
physical skills
05:43 - 05:47
and very recently the world economic
05:45 - 05:49
Forum released their future of jobs
05:47 - 05:52
report they also argue that in the
05:49 - 05:54
workplace these are the five skills that
05:52 - 05:56
the employers um
05:54 - 05:58
seek for the most and these are
05:56 - 06:01
analytical thinking creative thinking
05:58 - 06:03
resilience flexibility and Agility
06:01 - 06:06
motivation and self-awareness curiosity
06:03 - 06:08
and lifelong learning and I argue that
06:06 - 06:10
these are the five skills that we also
06:08 - 06:12
have to try to Foster in our classes
06:10 - 06:15
irrespective of what we are teaching
06:12 - 06:17
what content we are teaching let's look
06:15 - 06:19
at a definition for each of those what
06:17 - 06:21
is analytical thinking for example it's
06:19 - 06:23
the ability to systematically and
06:21 - 06:25
logically work through an issue
06:23 - 06:27
what about creative thinking it's
06:25 - 06:29
finding novel and practical ways to
06:27 - 06:31
address challenges
06:29 - 06:34
what about resilience it's the ability
06:31 - 06:36
to recover from setbacks and I have
06:34 - 06:38
something special to say about Brazilian
06:36 - 06:39
sometimes I see among my students that
06:38 - 06:41
they do not have that much resilience
06:39 - 06:43
they don't have that much confidence
06:41 - 06:45
because they think that their English is
06:43 - 06:46
not that good enough well let me tell
06:45 - 06:49
you one thing
06:46 - 06:51
fluency in English is not an indicator
06:49 - 06:54
of intelligence
06:51 - 06:56
and pronunciation in English is not an
06:54 - 06:58
indicator of intelligence what about
06:56 - 07:00
flexibility it's the ability to change
06:58 - 07:02
to suit new conditions now we have large
07:00 - 07:04
language models and artificial
07:02 - 07:07
intelligence what is next month what is
07:04 - 07:09
next year what awaits us right so we
07:07 - 07:11
have to be ready agility is the ability
07:09 - 07:14
to think and draw conclusions quickly
07:11 - 07:17
motivation is being able to initiate and
07:14 - 07:19
maintain goal-oriented behaviors
07:17 - 07:21
self-awareness having conscious
07:19 - 07:24
knowledge of one's own character and
07:21 - 07:26
abilities strengths and weaknesses
07:24 - 07:29
curiosity we should be
07:26 - 07:31
we should be eager to learn we should
07:29 - 07:33
have an eager desire to learn and
07:31 - 07:35
lifelong learning finally it's an
07:33 - 07:37
ongoing voluntary and self-motivated
07:35 - 07:40
pursuit of knowledge we have to let our
07:37 - 07:42
students know that learning is not just
07:40 - 07:46
limited to classroom environments
07:42 - 07:49
learning is a lifelong process right
07:46 - 07:52
so how can we improve all those skills
07:49 - 07:53
that make us truly human that cannot be
07:52 - 07:56
easily replicated by artificial
07:53 - 07:57
intelligence well language learning is
07:57 - 08:02
for example I'm Turkish and I can assure
08:00 - 08:04
you that if you study Turkish and if you
08:02 - 08:07
learn Turkish you'll be more analytical
08:04 - 08:10
why because you you have to analyze such
08:07 - 08:13
sentences all the time
08:10 - 08:16
this is just one word but in English
08:13 - 08:19
it's a full sentence why because it
08:16 - 08:21
means meet meh is the negation yeah is
08:19 - 08:24
the future marker Larry is the third
08:21 - 08:25
person plural me is the question and D
08:24 - 08:28
is the past tense morphine
08:25 - 08:30
so if you're doing this whole time while
08:28 - 08:32
you're learning a language be Turkish or
08:30 - 08:34
another language of course you'll be
08:32 - 08:35
more analytical
08:34 - 08:38
let me give you an example from my own
08:35 - 08:39
Cantonese learning experience I'm really
08:38 - 08:41
interested in learning more and more
08:39 - 08:43
expressions in Cantonese and recently I
08:41 - 08:45
came across the saying
08:43 - 08:47
I hope you don't mind mine my horrible
08:47 - 08:50
intonation and tones
08:52 - 08:57
literally means
08:54 - 09:00
thank you so much literally means you're
08:57 - 09:02
finally getting married today right so I
09:00 - 09:04
was thinking about this expression
09:02 - 09:07
because literally the expression in red
09:04 - 09:10
means shutting the Skylight so why
09:07 - 09:12
should it mean to get married this
09:10 - 09:13
opened up New Horizons for me because we
09:12 - 09:15
don't have such an expression in English
09:13 - 09:18
or in Turkish let me ask you a question
09:15 - 09:20
what is the sound that a cat makes in
09:20 - 09:25
meow what about the sound that a dog
09:23 - 09:26
makes in Cantonese
09:26 - 09:32
so let's let's look cross-linguistically
09:29 - 09:35
the variation of those nature sounds we
09:32 - 09:38
call onomatopoeia in linguistics in
09:35 - 09:40
English a dog barking is Bow Wow
09:38 - 09:44
in German it's
09:40 - 09:46
wow wow French is Spanish is wow wow
09:44 - 09:48
Hebrew is how how
09:46 - 09:51
Hindi is Bobo
09:48 - 09:54
Mandarin is Wang Wang Japanese is one
09:51 - 09:57
one and Greek Greek is
09:54 - 10:00
and in a cat meowing it's kind of
09:57 - 10:03
similar across the board it's meow but
10:00 - 10:06
interestingly it's Japanese it's meow
10:03 - 10:09
and in crickets meow
10:06 - 10:11
right it's more nasal so learning new
10:11 - 10:15
is a way to understand how individuals
10:14 - 10:17
with different languages and cultural
10:15 - 10:19
backgrounds perceive the world
10:17 - 10:20
differently and of course learning
10:19 - 10:23
different languages make us more
10:20 - 10:25
analytical and creative because it opens
10:23 - 10:27
up New Horizons for us
10:25 - 10:29
let me tell you another way how we can
10:27 - 10:31
be more analytical and creative let me
10:29 - 10:35
give you an example from my own teaching
10:31 - 10:36
experience in 2019 I was teaching at
10:35 - 10:38
Case Western Reserve University in
10:36 - 10:40
Cleveland Ohio and I was supposed to
10:38 - 10:42
teach a Linguistics class
10:40 - 10:44
my students didn't seem too interested
10:42 - 10:47
in linguistics
10:44 - 10:48
so I had to motivate them right what I
10:47 - 10:50
did I said okay what we are going to do
10:48 - 10:52
is we are going to make this class a
10:50 - 10:54
project-based class first I'm going to
10:52 - 10:56
teach you more about Linguistics
10:54 - 10:57
subfields of linguistics like phonetics
10:56 - 11:00
phonology morphology syntax and
10:57 - 11:02
semantics and pragmatics and then I'm
11:00 - 11:04
going to have you create a language from
11:02 - 11:06
scratch students were really puzzled at
11:04 - 11:08
the idea of creating a language from
11:06 - 11:10
scratch they're like how on Earth am I
11:08 - 11:12
going to create a language you are crazy
11:12 - 11:16
but I gave them an example and I said
11:14 - 11:18
you know what Suppose there is a movie
11:16 - 11:20
and here is a movie poster a movie is
11:18 - 11:22
called case Clause because it was case
11:20 - 11:24
vessel Reserve University and I said
11:22 - 11:26
okay you're supposed to create a
11:24 - 11:30
language an alien language for this
11:26 - 11:33
movie just like the navi language in
11:30 - 11:38
Avatar or Klingon in Star Trek or Elvish
11:33 - 11:40
language in um Lord of the Rings right
11:38 - 11:42
and students did amazing work they use
11:40 - 11:44
their creativity and they did amazing
11:42 - 11:47
work look at this one so this student
11:44 - 11:48
created an alphabet they create a
11:47 - 11:51
language and they named it eagle and
11:48 - 11:52
they created an alphabets so these are
11:51 - 11:54
the International Phonetic symbols and
11:52 - 11:56
this these are the symbols that the
11:54 - 11:59
student created for their writing
11:56 - 11:59
systems so this is
12:04 - 12:08
you get the idea
12:06 - 12:10
and this student came up with a system
12:10 - 12:15
words appeared in peripheral shapes
12:13 - 12:18
based on the parts of the speech for
12:15 - 12:19
example verbs appeared in squares like
12:19 - 12:23
it appears in a square
12:21 - 12:26
adjectives and adverbs appeared in
12:27 - 12:34
nouns and pronouns appeared in circles
12:32 - 12:36
look at the word and because it's a
12:34 - 12:39
connector conjunction it doesn't have a
12:36 - 12:42
peripheral shape right so this was kind
12:39 - 12:43
of fascinating and mind-blowing and this
12:42 - 12:46
student came up with some vocabulary
12:43 - 12:50
items that were really interesting like
12:46 - 12:55
pretty but fragile it's EU Maisha
12:50 - 12:55
and only in daydreaming or Out Of Reach
12:55 - 12:59
and these are the symbols that the
12:57 - 13:01
student came up with
12:59 - 13:03
yet another student created a language
13:01 - 13:05
and they called it quat
13:03 - 13:07
and this student told me that they were
13:05 - 13:09
studying chemistry and they were really
13:07 - 13:11
fascinated by chemical structures and
13:09 - 13:13
they used those chemical structures to
13:11 - 13:14
create an orthography a writing system
13:13 - 13:16
for their language
13:14 - 13:19
and this is what they created
13:16 - 13:24
and look at the consonants that they
13:19 - 13:24
created look at per for example and
13:25 - 13:30
they look very similar but they are
13:28 - 13:32
distinct right yet another student
13:30 - 13:33
create a language and they named it
13:33 - 13:37
because it's a logogram a logogram is a
13:36 - 13:39
language where the symbols represent
13:37 - 13:41
real life objects and the student said
13:39 - 13:43
these morphemes are examples of a
13:41 - 13:46
logogram and resemble the process of
13:43 - 13:48
writing for the verb form of rain all
13:46 - 13:50
here represents the Sun and this curvy
13:48 - 13:54
line and the straight line under that
13:50 - 13:57
represents the cloud and these vertical
13:54 - 14:00
lines represent the raindrops you see
13:57 - 14:02
how rain as a verb is more Dynamic than
14:00 - 14:05
rain as a noun
14:02 - 14:07
that was also very interesting
14:05 - 14:09
and this student created some vocabulary
14:07 - 14:13
items I want you to look at the word
14:09 - 14:14
animal and animals animal as a singular
14:13 - 14:17
it doesn't have a full circle at the
14:14 - 14:19
bottom animals as a plural it has a full
14:17 - 14:20
circle at the bottom so you see the
14:19 - 14:22
creativity in the language system that
14:20 - 14:24
the student created right and I want you
14:22 - 14:27
to look at the word animals and flowers
14:24 - 14:30
and I want you to look at the word bees
14:27 - 14:33
because animals and flowers you combine
14:30 - 14:36
them you get bees so in the language
14:33 - 14:40
that this student created flowers sorry
14:36 - 14:41
bees are basically animal flowers right
14:40 - 14:43
another student came up with the
14:41 - 14:45
language they named it cantarin actually
14:43 - 14:48
they didn't create a language they
14:45 - 14:50
created a writing system for Cantonese
14:48 - 14:52
they were a Cantonese speaker they told
14:50 - 14:54
me that they found Cantonese orthography
14:52 - 14:56
very difficult traditional Chinese
14:54 - 14:58
orthography and they create a new
14:56 - 15:01
writing system for Cantonese look at
14:58 - 15:03
this so they said okay this could be and
15:01 - 15:06
this could be per and this could be um
15:03 - 15:09
and it goes on like that
15:06 - 15:11
and look at these words for example Lei
15:09 - 15:15
ho this is the traditional Chinese
15:11 - 15:19
orthography and the student created this
15:15 - 15:22
a simplified orthography for Cantonese
15:19 - 15:24
and look at joygin the second one so at
15:22 - 15:25
the end of the course I gave students a
15:24 - 15:27
questionnaire to see whether they like
15:25 - 15:29
the course and whether they were able to
15:27 - 15:32
master the contents
15:29 - 15:34
the results show that not only were the
15:32 - 15:37
students able to master the content of
15:34 - 15:39
linguistics but they were also having a
15:37 - 15:43
lot of fun in the cree in the creation
15:39 - 15:45
of their languages and the questionnaire
15:43 - 15:47
also showed that students were able to
15:45 - 15:49
improve their analytical thinking skills
15:47 - 15:51
because a lot of students either
15:49 - 15:54
strongly agreed or agreed with the
15:54 - 15:59
so why am I talking about
15:57 - 16:01
learning languages and creating a
15:59 - 16:03
language as a way to improve our
16:01 - 16:04
analytical and creative thinking skills
16:03 - 16:06
and motivation why
16:04 - 16:08
it's because we are losing linguistic
16:06 - 16:11
diversity in the world that's why and
16:08 - 16:13
that's a major major Global issue that
16:11 - 16:16
we are facing
16:13 - 16:17
according to UNESCO there are about 6
16:16 - 16:19
000 languages in the world and we are
16:17 - 16:22
going to lose about 90 of all those
16:19 - 16:24
languages in the next 75 years
16:22 - 16:26
but languages are a window into human
16:24 - 16:28
cognition history and culture
16:26 - 16:30
when we lose a language we're not just
16:28 - 16:32
losing a language we are losing the
16:30 - 16:36
cognition culture and all the Traditions
16:32 - 16:36
that come along with a language
16:38 - 16:43
now I want to ask you are you AI proof
16:40 - 16:46
because tomorrow is too late to AI prove
16:46 - 16:51
to be AI proof as Educators we have to
16:48 - 16:54
make sure that we help our students to
16:51 - 16:56
be AI literate but we also need to make
16:54 - 16:59
sure that we help them to capitalize on
16:56 - 17:01
their core human skills that cannot be
16:59 - 17:03
replicated by AI
17:01 - 17:05
ending my talk I want you to complete
17:03 - 17:08
the sentence I won't be replaced by
17:05 - 17:11
technology or AI because
17:08 - 17:14
and in completing this sentence I want
17:11 - 17:14
you to think of what truly makes you
17:14 - 17:20
because only by harnessing those skills
17:18 - 17:22
that truly make us human can we address
17:20 - 17:25
major Global challenges
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