May 24, 2021 • 36 mins
Gender balance in the world of Artificial Intelligence is work in progress. But female role models do exist. In this episode, YS Chi speaks with one of them. Min Chen is a computer scientist leading a team of around 250 technologists and software engineers focused on applying machine learning, natural language processing and other advanced technologies in the legal industry.
Min was one of the few female students who studied computer science in Shanghai in the 90s, and the only one from her class to pursue a career in the industry. She tells us where she found the inspiration to ‘stick with it’ and overcome adversity. She talks about the profound technological transformation taking place in the legal industry and gives us a flavour of how technology is changing the way lawyers work. Here are the reflections of a female technologist rocking the fascinating world of AI and deep learning.
Min Chen is chief technology officer of LexisNexis Legal & Professional, part of RELX, for Asia Pacific and Global Search.
This podcast is brought to you by RELX.
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Episode Transcript
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YS Chi (00:00):
The Unique Contributions podcast is brought to you by
RELX. Find out more about us byvisiting RELX.com.
Min Chen (00:09):
I want to make it very short and simple as you have
heard my story earlier. I wantto quote my math teachers
remark. Find things you'rereally passionate about and
stick to it, and don't give up.
YS Chi (00:40):
Hello, and welcome to our second series of unique
contributions, a RELX podcastwhere we bring you closer to
some of the most interestingpeople from around our business.
I'm YS Chi and I'll be exploringwith my guests some of the big
issues that matter to society,how they are making a
difference, and what broughtthem to where they are today.
(01:01):
Hello, and welcome back to ourlisteners. Our guest today is
Min Chen, Chief TechnologyOfficer of LexisNexis Legal &
Professional for Asia Pacificand Global Search. Based in
Shanghai, Min runs large teamsof technologists and software
engineers to deliver solutionsto clients in the legal
industry. The legal industry isgoing through a profound
(01:24):
technological transformation.
I'll be asking Min what thismeans for lawyers, and how the
new generation called Gen Z isshaping the future of law. Along
the way, we'll also hear aboutwhat it's like to be a woman
working in the field oftechnology and data science. So
hello Min, thank you for joiningus even though it's a night time
(01:44):
in Shanghai and the weekend.
Min Chen (01:47):
Hi YS, I'm honoured to be the guest.
YS Chi (01:51):
I would like to kick off by talking about your actual day
to day work at LexisNexis. Overa decade ago, you joined a team
with less than 10 people tosupport the China business. Then
today as CTO of LexisNexis, AsiaPacific and Global Search, you
oversee a team of about 250technologists and software
(02:15):
engineers spread acrosseverywhere from Shanghai to
Australia, US and India, etc.
The projects you work on arescattered all around the world.
This reflects a profoundtechnological transformation
that is taking place in thelegal industry it sounds like.
So please tell us a little moreabout that.
Min Chen (02:36):
Right. I think the legal industry has been
relatively conservative toadopting new technologies in the
past century because legalsystems are always jurisdiction
specific, and the evolvement oflaw is also relatively slow.
Having said that, I do noticethat rising demand of embracing
(02:56):
and seeking analytics andautomation by our customers has
been pushing changes on legalindustry through advanced
technology, particularly oncloud based solution and
artificial intelligence. Thepandemic accelerates this change
a bit because it pushes new waysof gathering, sharing and
(03:17):
dealing with data. For example,law firms want to automate those
repeatable manual tasks to keeptheir rates competitive, so they
can continue to win business.
While in-house counsel wantsautomation to lower the amount
of money they have to spend onusing outside legal counsels.
They will all need a betterdecision making tool to help
(03:39):
them and descriptive orpredictive analytics, will play
a big part of it. LexisNexis'mission is to help our customers
to improve their workefficiency. We do have a big
advantage to deliver thatmission because we have both
data and advanced technologythat includes natural language
(04:00):
processing, natural languageunderstanding, generation,
machine learning, deep learning,computer vision, and so on.
YS Chi (04:10):
So Min, at some point in time mindset began to change.
Were you there to observe thatchange in mindset and how did
LNLP decide to jump onto that?
Min Chen (04:23):
Yeah, I was there.
Pretty much we are seeing therecent three to four years. I
see those changes and that iswhy the team has been growing
from 10 to 200 at the sameperiod. Legal information is
mainly tax based and that's whyartificial intelligence plays a
critical role to address theissues of natural language
(04:45):
understanding and naturallanguage generation.
YS Chi (04:51):
This so called 'latecomer' to technology
transformation is suddenlyhaving a real fast pace. Is it
pandemic or is there somethingelse?
Min Chen (05:01):
I think it's driven by the customer problem. The
pandemic definitely is part ofthat, as already mentioned. The
pandemic actually generates thenew ways of thinking, getting
the information. But I think itis driven by the customer
problem. We have a toughcustomer problem in the legal
(05:24):
domain. Legal information isvery often complex, professional
and lengthy text baseddocuments. Given that specialty,
we need expertise and focusparticularly on natural language
understanding. I earliermentioned about that, I'm just
very obsessed with that. Sonatural language understanding
(05:44):
as well as machine readingcomprehension, having the
machine to understand thecontext of the body of the text.
Natural language generation isabout competition producing,
having the machine to producethe text. Deep learning
technology that is evolvingfaster than ever in the world of
AI, will clearly be the majorsolution to tackle these areas.
(06:06):
So when I say it is driven bycustomer problem, I give you one
example here, like auto summaryin legal domain. We have this
case summary which is criticalfor legal researchers. So
without case summary, a legalresearcher may need to read
through the whole case beforethey can be sure whether the
(06:26):
case is applicable to their caseor not. But generating summary
is not free. It highly relies onhuman efforts, and only well
trained legal practitioners canunderstand the legal domain
language. However, there aremillions of the case there and
getting labour efforts on suchwork is very challenging,
(06:47):
because it's both time andresource consuming. In order to
improve the coverage of highquality case summary to our
customers, we delivered anautomation solution to
accelerate the case law documentsummary process through multiple
state of art, deep learningtechnologies. Given the length
(07:07):
of the case law documents andcomplexity of legal domain, this
target task is even morechallenging than other
summarization tasks in otherindustries you've seen. That's
why deep learning can make a bigdifference.
YS Chi (07:21):
I suppose that the domain expertise of our folks in
LexisNexis Legal andProfessional really allows us to
work with technologists like youand your team to jump that queue
quite quickly, doesn't it?
Min Chen (07:40):
Yeah, they actually love to work with us because
they have the domain knowledge.
Every domain knowledge, if youwant to improve the coverage,
improve the speed, they need towork with a technologist to do
all the automations. Sobasically, they have their
insights. Combined with ourstrong advanced on tech
(08:01):
knowledge, these two thingscombined together can make the
magic.
YS Chi (08:07):
Now, you've talked very passionately about natural
language processing and deeplearning. Are there any other
technological breakthroughs orchanges that you have noticed
that has really jumped andaccelerated these
transformations?
Min Chen (08:25):
I would come back to deep learning, because deep
learning is a very broad area.
It is like artificialintelligence. You have lots of
things, but the traditional wayto tackle the problem is the
traditional machine learning.
There's a limitation there. Butdeep learning actually is a big
(08:46):
job to resolve the naturallanguage understanding and
natural language generationissue. In recent developments,
we launched a new product calledAsia Legal Analytics. That
product is using the deeplearning to do the knowledge
extraction. The traditional waywe use the natural language
(09:07):
processing and traditionalmachine learning, we can do
entity extraction. You canextract lawyers name, law firms
name and court names, and justjudges name. So that's the
traditional entity extraction.
Now we have a big job we callthe knowledge extraction.
Basically, you have this casewhere we're able to extract a
legal issue. What is the legalissue for this case? What is the
(09:30):
legal argument for this case?
What is the legal principle forthis case? That we call
knowledge and insightextraction. If you only use the
traditional machine learning,you're not able to get the
insight extraction. But byhaving deep learning
(09:50):
technologies really helped us tomake the difference. We just
launched that in February in theMalaysia market. We're going to
learn launch that in April forthe Hong Kong market. After our
initial launch, the customeralready feels amazed because in
the past they see LexisNexis, wehave Lex Machina, we have Ravel
(10:12):
contacts. In the US market, theyalready have this kind of
analytics. But now we have thisanalytics focused on insight
extraction. So the customerfeels amazed, it really is very
successful in the Asia market.
YS Chi (10:26):
I'm am equally intrigued. I have a dumb
question. Does this work on alllanguages? Or does it only work
on certain global, widely spokenlanguage that are single byte?
Min Chen (10:40):
Yeah, I think the overall technology is suitable
for all languages. But there's adifferent approach and
techniques we have to take. It'sbecause when we deal with
different language, it'sbasically about token. How do
you extract the token? Thedouble byte language is not like
(11:02):
English, the sentence isseparated by meaningful tokens.
So the way you extract thetoken, in a sentence is
different. You just have to getthrough that part and the rest
of the techniques can beleveraged across different
languages.
YS Chi (11:19):
Is this very expensive to do Min?
Min Chen (11:22):
It really depends on the problem you're talking
about. So I can't...
YS Chi (11:27):
It just sounds so fancy.
It sounds so complex, andtherefore you have to think it's
expensive. But obviously, you doit in a very efficient way.
Min Chen (11:36):
Yeah, well once you build a foundation there,
because we already have millionsand billions of data there.
LexisNexis has the data so wealready build the foundation.
Maybe when you build somethingfrom scratch, it takes time. It
takes multiple experiments toget there. But once you set up,
build up the foundation, youalready have that algorithm
(11:58):
there, it will be easier toleverage to other regions. To
build up your first concept, itmight be not very cheap. But if
we want to expand a similar ideato other regions, it becomes
cheaper and cheaper.
YS Chi (12:13):
That is really one of our unique advantage and unique
contributions because we haveboth the content and now the
technology.
Well, as unique as that is, Ifind you to be a very unique
(12:34):
person Min. Since we have metseveral years ago, I've been
fascinated about your growth andI'd like to turn to that for now
for a few minutes. But before wedo that, tell us a little bit
about how you're handling this,you know, unexpected and very
prolonged pandemic.
Min Chen (12:50):
Well, I want to show off a little bit YS. We have
been completely back to theoffice for a year now. I'm
definitely not, I'm not a bigfan of working from home, even
before the pandemic. So I doacknowledge we have advanced to
technologies to help people meetvirtually. For example, a few
(13:11):
weeks ago, I was demoed by theteam for an AR and VR app to
allow engineers to do pairprogramming and code review
remotely. By the way, pairprogramming is where two
engineers work together on thesame piece of code. Code review
is a group of engineers wherethey get together to review the
same piece of code. So you canimagine these kind of activities
(13:31):
require a heavy in personexperience. Now, there are
different types of tools whichcan support engineers to do the
same work virtually. But I thinkthe dynamic and the inspiration
of being able to have face toface conversation cannot be
completely replicated. There arejust a few exquisite nuances
(13:56):
that you might not be able totell them explicitly, but you
just know the difference. It'sthat difference that can make a
difference to be creative, todrive innovation in a more
efficient and productive way. Soonce the situation became better
in Shanghai, it was only likethree to four weeks, there was
(14:16):
locked down. So we were allworking from home. But when the
restrictions started to ease, Ibrought the entire team back to
the office and everyone wassuper excited to meet everyone
in 3D again. Having said that,it seems only the China gets
100% back to office, and therest of the world still majorly
work from home, including ourcustomers that we're serving in
(14:39):
Asia Pacific and globally. So tome, the ways of interaction has
changed. I think I've grown myremote research skills to the
new heights. During thepandemic, I got more chances to
talk with customers remotely.
That really helps me to learn afew innovative techniques to
capture the precise feedbackfrom customers for product
(15:02):
validation or discovery throughphone calls. That I consider as
the most valuable new skills Ideveloped during the special
period.
YS Chi (15:14):
Always looking at the positive side Min. So, you know,
some years ago, it would nothave been at all conceivable
that I would be doing this kindof conversation with a women
technologist. It's been a littleover 15 years since you joined
LexisNexis. Then you've spent alittle more time in the broader
(15:38):
tech world before that. You evenstudied computer science at
Shanghai University. Right?
Min Chen (15:42):
Right.
YS Chi (15:43):
You were one of the very few girls in that area. There
must have been a lot ofcompetitive and demanding
environment that you had to copewith at the time. Would you like
to kind of share some storiesabout that?
Min Chen (15:56):
Yeah, I can try. I think at different stage of my
life, I am lucky to always havethe unique driver that
strengthens my face to stick totechnology industry. Back to
high school where each studentwas at the moment to decide what
is the future of their majorsubject in college. My parents
(16:17):
want me to follow either oftheir pasts. My mother is a
professor of Chinese literatureand my father is the professor
of biology. I didn't know what Iwanted to be. It was my math
teacher who encouraged me topick computer science. He said,
I think that will be a perfectfit for you. Honestly, at that
(16:38):
time, I didn't know whatcomputer science meant to me,
until I followed hisinstructions and then went to
Shanghai University to major incomputer science. Very soon, I
found myself obsessed withprogramming. I could spend hours
and hours in the library towrite a code just for a better
version to beat myself. So itwas not the homework that I have
(17:01):
to finish. I really like thefeeling that I can just write a
few lines of code to resolveissues automatically. That's
amazing. I remember my scoreswere always top three in a
class, but there was one thingthat bothered me. Regardless of
my high scores, my professornever picked me to join an
(17:23):
external science programme withtop schools in Shanghai. At that
time, we had quite a fewlearning and competition
programmes with leadinguniversities, like Fudan
University, Jiao TongUniversity, and usually top
students in the class gotpicked. Those were great
opportunity to learn from othertop students of different
schools. So I felt a bit upsetthat I was not elected. So I
(17:48):
called my high school mathteacher, I always kept contact
with him. I asked him, I said,you told me computer science
would be perfect for me, andlook, I don't even get the
chance to join these programmes.
Do you still think I'm suitableto learn the subject? He
responded with two things whichI'll never forget. He first
asked me, do you love computerscience? I replied, yes, I love
(18:11):
it. So I talked to him about allthe stories that I spent a long
time to study and to learn, allout of my own interest. Then he
said, don't worry about now. Nowyou don't get chances to join
all these programmes in school,but I have faith, you will get
lots of chances when you get ajob, when you start a career. As
(18:32):
long as you love what you'redoing and you don't give up. So
that really motivated me. Irecall in my class, there were
50 students, only eight werefemale, and out of eight girl
students I was the only one topursue a career in the
technology industry aftergraduation.
YS Chi (18:53):
Oh no.
Min Chen (18:56):
I was the only one. So my first job was a programmer in
China Daily. Then three yearslater I joined Lenovo being a
tech lead for another threeyears, and then LexisNexis. So
10th of October of this year,will be my 16 years working
anniversary. A lot of people askme why you stay in this company
(19:18):
for that long. Obviously thereare multiple reasons. In
LexisNexis there are enoughgreat challenges for me to
tackle by using advancedtechnology. You heard the story,
deep learning, machine learning.
My teams and colleagues,they're all great. I'm growing
together with them. But I thinkthere's one critical reason I
never ignore. Is that in thiscompany I come across a few of
my math teacher type of people.
(19:43):
They share very similar valueand vision which is doing things
that you love and don't give upbefore you give up. So that
keeps me motivated in the longrun.
YS Chi (19:54):
Well, can I ask what the name of your math teacher was,
because I'm inspired by hischaracter.
Min Chen (20:04):
His name is Xu Quanxiang. He's just like me, a
normal person. I don't know alot about his personal life, but
he is very disciplined, veryorganised and a very fair
gentleman. In class he alwaysencouraged me to talk, a lot. So
(20:28):
he asked the question, and somestudents raise their hand. I'm
not the student who raised theirhand. He will always call out my
name, then I can always answerthe question. So once I did ask
him, why you always call out myname? He replied, he was
smiling. He said, because I knowyou know the answer. You just
(20:49):
need to stand up.
YS Chi (20:50):
Oh, wow.
Min Chen (20:52):
Yeah. So unfortunately he passed away 20 years ago, but
the legacy he leaves to me willforever remain.
YS Chi (21:00):
Wow, we should all be lucky enough to have teachers
like that through our lives.
Min Chen (21:05):
Yeah.
YS Chi (21:06):
Well, obviously, you've done very well for yourself, for
your teams and of course for ourcompany since joining. We're
very appreciative of your workand passions. You were named in
2019 as our distinguishedtechnologist. Acompany wide
award that only one person winsevery year for exemplary
(21:28):
leadership, and fundamentallychallenging and changing the way
we do business. What is moreimportant than that is that you
were the first woman to receivethat award. What did that mean
when you found out that you werethe first woman to win that
prestigious award?
Min Chen (21:47):
I'll will tell you the story. So, when I initially
learned this news from mymanager, Jeff Reihl, who is the
global CTO of LexisNexis. Ofcourse, initially, it was like a
surprise. It was announced byErik Engstrom and Kumsal. They
organised a surprise call andthey announced it to me. But it
was a very short call. Then,when we get off the call, I
(22:08):
talked with Jeff. He alsohighlighted, he said, you're the
first woman who won this award.
But my first, my instinctreaction was, is that the reason
I got selected. Well, to me it'sjust like the two sides of the
coin. I don't want to beoverlooked for opportunity
because I am a woman. But on theother hand, I also don't want to
be recognised because I am awoman. Of course, Jeff said it
(22:30):
has nothing to do with yourgender identity. I was
recognised because of mycontribution and delivery to our
company. That I have beenbuilding up the culture of
customer driven innovationamongst the engineering team and
developing high quality productsthat differentiate us in the
Asia Pacific market, and beingpart of the solutions to improve
(22:53):
customers NPS, the net promoterscore. So I consider this award
as a recognition of me beingable to make an impact on our
customers and business. I willstrive to extend that impact to
much broader customers so it'snot just Asia Pacific but also
(23:14):
globally. I think I'm movingtoward that goal closer and
closer every day.
YS Chi (23:21):
You sure are. I remember sending you a congratulatory
note after the award.
Min Chen (23:27):
I remember that. Thank you so much for the
encouragement YS.
YS Chi (23:32):
You are encouraging a lot of girls through your own
journey.
I'm curious to know, what do youthink RELX can do more to
(23:53):
improve the pipeline of womencoming through to all fields,
including STEM where they seemto be not yet in abundance? What
advice would you give femaletechnologists who look up to
recognise leaders like you?
Min Chen (24:11):
Yeah, I think I have already seen lots of forums and
panels organised with RELX tomotivate and promote women. For
example, in general I joined thepanel session of RELX Thrive
global launch in Asia Pacific.
Thrive is a grassrootorganisation to bring all female
together and provide a portalfor them to share thoughts and
(24:32):
stories. I know there are manyother communities like this
within RELX. I think RELX can domore on gender balance, because
that could push a real force fordriving innovation. To me gender
balance doesn't necessarily meanwe have to favour a particular
gender. It means we have toconsciously check whether we
(24:55):
have enough difference in theteam. When we have different
ways of thinking, personalityand culture, it can stimulate
innovation. That's why it'simportant. I think we're also
changing the way we make hiringand promotion decisions, and to
ensure that eligible women aregiven serious consideration.
(25:18):
Last but not least, I do want tohighlight, let's not forget how
critical it is that our maleleaders support all these
initiatives to me. Thetechnology industry is still a
male dominant area, and thatsituation I don't foresee will
be changed drasticallyovernight. So our male leaders
(25:40):
who are very supportive onconsciously improving gender
balance in organisation can makea big difference. For example, I
know YS you are a great sponsorand advocate on driving
diversity and inclusion. In myorganisation LexisNexis, Mike
Walsh our CEO, and Jeff Reihlthe global CTO, and Jamie
(26:02):
Buckley global CPO, they're allstrong supporters. I often think
how great the D&I we have in ourorganisation. It is majorly
depending on how open and howfar our male leaders are
embracing these ideas and reallytaking to actions. Finally, in
terms of what advice I couldgive to other female
(26:25):
technologists. I want to make itvery short and simple, as you
have heard my story earlier,because I want to quote my math
teachers remark. Find thingsyou're really passionate about,
and stick to it and don't giveup.
YS Chi (26:42):
Yeah, I think that is clearly the theme. I think the
dimension of allies, male allieswas a key topic when we
discussed during theInternational Women's Day panel.
We do seek women to do the workbut men ally, I think have the
(27:04):
responsibility to provide theair cover so that they can fight
on the ground as effectively asthey can.
Min Chen (27:12):
Yeah, exactly.
YS Chi (27:14):
Well, let's jump back to the business world that we left
earlier in our conversation.
LexisNexis has a number of techhubs. One is in North Carolina,
one in London, and here you arein one in Shanghai. How do you
keep such a big team of datascientists, engineers and
product managers spread all overdifferent locations? Yet, you're
working so well, so motivated,especially through out the
(27:37):
pandemic? What are the secretsauce?
Min Chen (27:42):
Yeah well, pandemic for me was very short. Early I
mentioned I only worked fromhome for only three to four
weeks, and then we were back. Ithink I talk from a general
perspective. There are a lot ofinitiatives we have been doing
to keep the team motivated. Forexample, we do hacks once or
twice a year to bring engineersand product managers together,
(28:05):
in order to incubate creativeideas and solutions through
non-stop programming for 24 to48 hours. Eventually each
region's winner will join a funcompetition, we call the global
Shark Tank to present our workto our global CEO, Mike Walsh,
and his MCM. Mike and his seniorleaders will pick a winner of
(28:30):
the winners, and my team arealways winning and we have won
top prize globally three yearsin a row. Besides, I want to
mention another initiative thatI ran for a few years in Asia
Pacific, to allow people to workon innovation on regular basis.
We call it 'grab the post.' Sothe idea is we pop problem
(28:51):
statements in one page, and postit at the door of my office.
These problems are verypreliminary ideas from either
internal or external customers,which are not yet put into
official business case orproduct development roadmap.
Therefore they're treated as aside project. Whoever has
(29:14):
interest to take the challengeduring their free time can just
grab the post and commit to thePOC, proof of concept delivery.
YS Chi (29:24):
Aha.
Min Chen (29:24):
Yeah. That's why we call it grab the post. Since
this is not mandatory, and it'sa side job, you can imagine in
the end, whoever grabbed thepost is truly passionate in
building great customerexperience through leadi ng edge
tech knowledge. We doacknowledge the situation, that
some of the posts have neverbeen grabbed by anyone. But
(29:47):
averagely I did amass, everythree to five months we will get
one or two challenges rejectedby the team with proof of
concept delivered and we gavethe incentive to the team who
grabbed the post. So if youdeliver the POC and that idea
got invested by our businesspartner, you will be the person
(30:10):
to lead final delivery ontoproduction. You have no idea
what being able to bring theidea solution live to customer
means to engineers, because it'sa great motivation for
engineers.
YS Chi (30:26):
Oh, yes. That's like being selected in the starting
lineup. Well, time is flying soI'm going ask one last question
before we close. You have beenvery good at sticking to what
you're passionate about.
Obviously because you probablysaw things down the road. So
(30:47):
what do you see down the road interms of transfer tech that you
expect to see over the mediumterm? And how are you going to
make sure that your team doesright on that journey?
Min Chen (31:04):
That's a good question. I think our focus and
investment priority will alwaysbe on those specific
technologies that could addresscustomer pain points. So from
mapping customer problem totechnology perspective. I want
to summarise two major trends,two major areas. One is
(31:27):
customer's expectation, andquality of service we deliver to
them will continue to increase.
So that requires more efforts ondata science work. The other
observation is that a customerwould expect technology to
create more personalised andtailor made service to meet
their specific individual needs.
(31:49):
So those technologies that couldmove sophistication of
personalization into the nextlevel, will be hard. Of course,
you have to make sure personaldata privacy is not violated. So
getting understanding and deeperunderstanding with customer
problem as the guideline, as themajor driver for us to
(32:14):
prioritise our technology,strategy and focus. Basically,
we stay close with a lot ofcommunities and channels, both
internally and externally. Thatwe could constantly share the
most current advancedtechnologies of different kinds.
For example, we always keep aclose eye on global external top
(32:38):
conference and top papers out ofthose conference. Particularly
in AI industry, of course, inother technology industrys as
well. That helps a team to staycurrent. So it's just not just
me, I force people to learn thisand that. You have to build a
culture there, that cultureshould be everyone is eager to
(33:00):
learn. But that culture isalways driven. So in terms of
what we want to learn, thatshould be always driven by what
customer problem we want toresolve. So with that, because
there's so many technologies,it's very broad, right. There's
so many different layers,different levels of the things
you have to learn. You cannotlearn all of them. So, you we
(33:22):
use the customer problem as theguideline for us to decide which
area we want to do the deepdive.
YS Chi (33:29):
Well, you had a programme called getting outside
the building and getting yourtech people to listen to
customer firsthand?
Min Chen (33:36):
Yeah.
YS Chi (33:36):
Yeah. That's awesome.
Now, I cannot close withoutasking a follow up question,
because you know, tailor-madesolution is obviously something
that we all want to do. Buttailor-made has always been
associated with high cost, high,high price. So this technology
adoption, is meant to try tomake it tailor-made without high
(33:59):
costs. Is that right?
Min Chen (34:03):
Yeah. Put it this way, I would love to rephrase that
into a cost effective way. Butactually, it's the same thing.
You're right. It's the samething. We're going to want to do
the tailor-made solution in aefficient way, in a cost
efficient, effective approach.
YS Chi (34:23):
Yeah, I think that we can do what someone has coined
mass customization, right?
Min Chen (34:30):
Yeah.
YS Chi (34:31):
Well, I'm fascinated and I could stay on for three hours
talking to you Min. It's alwaysbeen fun to talk with you. Every
time I meet you, you are fivesteps ahead.
Min Chen (34:41):
Thank you
YS Chi (34:42):
Thank you so much Min.
Min Chen (34:43):
It's also an honour and I also feel pleasure talking
with you YS.
YS Chi (34:48):
Well, thank you for sharing your stories and
insights with us today. I thinkMin has shared with us not only
about the technology of naturallanguage processing or deep
learning. But really just abouther subtle leadership that is
not about just being a pioneerand bringing the entire team
(35:11):
along, and making this uniquecontribution to the legal
community. Thank you again.
Thank you to our listeners fortuning in. Please don't forget
to hit the subscribe button onyour podcast app to get the new
episodes as soon as they'rereleased. Once again, thanks for
listening and please stay welland healthy.
The Unique Contributions podcast is brought to you by
RELX. Find out more about us byvisiting RELX.com.
Min Chen (00:09):
I want to make it very short and simple as you have
heard my story earlier. I wantto quote my math teachers
remark. Find things you'rereally passionate about and
stick to it, and don't give up.
YS Chi (00:40):
Hello, and welcome to our second series of unique
contributions, a RELX podcastwhere we bring you closer to
some of the most interestingpeople from around our business.
I'm YS Chi and I'll be exploringwith my guests some of the big
issues that matter to society,how they are making a
difference, and what broughtthem to where they are today.
(01:01):
Hello, and welcome back to ourlisteners. Our guest today is
Min Chen, Chief TechnologyOfficer of LexisNexis Legal &
Professional for Asia Pacificand Global Search. Based in
Shanghai, Min runs large teamsof technologists and software
engineers to deliver solutionsto clients in the legal
industry. The legal industry isgoing through a profound
(01:24):
technological transformation.
I'll be asking Min what thismeans for lawyers, and how the
new generation called Gen Z isshaping the future of law. Along
the way, we'll also hear aboutwhat it's like to be a woman
working in the field oftechnology and data science. So
hello Min, thank you for joiningus even though it's a night time
(01:44):
in Shanghai and the weekend.
Min Chen (01:47):
Hi YS, I'm honoured to be the guest.
YS Chi (01:51):
I would like to kick off by talking about your actual day
to day work at LexisNexis. Overa decade ago, you joined a team
with less than 10 people tosupport the China business. Then
today as CTO of LexisNexis, AsiaPacific and Global Search, you
oversee a team of about 250technologists and software
(02:15):
engineers spread acrosseverywhere from Shanghai to
Australia, US and India, etc.
The projects you work on arescattered all around the world.
This reflects a profoundtechnological transformation
that is taking place in thelegal industry it sounds like.
So please tell us a little moreabout that.
Min Chen (02:36):
Right. I think the legal industry has been
relatively conservative toadopting new technologies in the
past century because legalsystems are always jurisdiction
specific, and the evolvement oflaw is also relatively slow.
Having said that, I do noticethat rising demand of embracing
(02:56):
and seeking analytics andautomation by our customers has
been pushing changes on legalindustry through advanced
technology, particularly oncloud based solution and
artificial intelligence. Thepandemic accelerates this change
a bit because it pushes new waysof gathering, sharing and
(03:17):
dealing with data. For example,law firms want to automate those
repeatable manual tasks to keeptheir rates competitive, so they
can continue to win business.
While in-house counsel wantsautomation to lower the amount
of money they have to spend onusing outside legal counsels.
They will all need a betterdecision making tool to help
(03:39):
them and descriptive orpredictive analytics, will play
a big part of it. LexisNexis'mission is to help our customers
to improve their workefficiency. We do have a big
advantage to deliver thatmission because we have both
data and advanced technologythat includes natural language
(04:00):
processing, natural languageunderstanding, generation,
machine learning, deep learning,computer vision, and so on.
YS Chi (04:10):
So Min, at some point in time mindset began to change.
Were you there to observe thatchange in mindset and how did
LNLP decide to jump onto that?
Min Chen (04:23):
Yeah, I was there.
Pretty much we are seeing therecent three to four years. I
see those changes and that iswhy the team has been growing
from 10 to 200 at the sameperiod. Legal information is
mainly tax based and that's whyartificial intelligence plays a
critical role to address theissues of natural language
(04:45):
understanding and naturallanguage generation.
YS Chi (04:51):
This so called 'latecomer' to technology
transformation is suddenlyhaving a real fast pace. Is it
pandemic or is there somethingelse?
Min Chen (05:01):
I think it's driven by the customer problem. The
pandemic definitely is part ofthat, as already mentioned. The
pandemic actually generates thenew ways of thinking, getting
the information. But I think itis driven by the customer
problem. We have a toughcustomer problem in the legal
(05:24):
domain. Legal information isvery often complex, professional
and lengthy text baseddocuments. Given that specialty,
we need expertise and focusparticularly on natural language
understanding. I earliermentioned about that, I'm just
very obsessed with that. Sonatural language understanding
(05:44):
as well as machine readingcomprehension, having the
machine to understand thecontext of the body of the text.
Natural language generation isabout competition producing,
having the machine to producethe text. Deep learning
technology that is evolvingfaster than ever in the world of
AI, will clearly be the majorsolution to tackle these areas.
(06:06):
So when I say it is driven bycustomer problem, I give you one
example here, like auto summaryin legal domain. We have this
case summary which is criticalfor legal researchers. So
without case summary, a legalresearcher may need to read
through the whole case beforethey can be sure whether the
(06:26):
case is applicable to their caseor not. But generating summary
is not free. It highly relies onhuman efforts, and only well
trained legal practitioners canunderstand the legal domain
language. However, there aremillions of the case there and
getting labour efforts on suchwork is very challenging,
(06:47):
because it's both time andresource consuming. In order to
improve the coverage of highquality case summary to our
customers, we delivered anautomation solution to
accelerate the case law documentsummary process through multiple
state of art, deep learningtechnologies. Given the length
(07:07):
of the case law documents andcomplexity of legal domain, this
target task is even morechallenging than other
summarization tasks in otherindustries you've seen. That's
why deep learning can make a bigdifference.
YS Chi (07:21):
I suppose that the domain expertise of our folks in
LexisNexis Legal andProfessional really allows us to
work with technologists like youand your team to jump that queue
quite quickly, doesn't it?
Min Chen (07:40):
Yeah, they actually love to work with us because
they have the domain knowledge.
Every domain knowledge, if youwant to improve the coverage,
improve the speed, they need towork with a technologist to do
all the automations. Sobasically, they have their
insights. Combined with ourstrong advanced on tech
(08:01):
knowledge, these two thingscombined together can make the
magic.
YS Chi (08:07):
Now, you've talked very passionately about natural
language processing and deeplearning. Are there any other
technological breakthroughs orchanges that you have noticed
that has really jumped andaccelerated these
transformations?
Min Chen (08:25):
I would come back to deep learning, because deep
learning is a very broad area.
It is like artificialintelligence. You have lots of
things, but the traditional wayto tackle the problem is the
traditional machine learning.
There's a limitation there. Butdeep learning actually is a big
(08:46):
job to resolve the naturallanguage understanding and
natural language generationissue. In recent developments,
we launched a new product calledAsia Legal Analytics. That
product is using the deeplearning to do the knowledge
extraction. The traditional waywe use the natural language
(09:07):
processing and traditionalmachine learning, we can do
entity extraction. You canextract lawyers name, law firms
name and court names, and justjudges name. So that's the
traditional entity extraction.
Now we have a big job we callthe knowledge extraction.
Basically, you have this casewhere we're able to extract a
legal issue. What is the legalissue for this case? What is the
(09:30):
legal argument for this case?
What is the legal principle forthis case? That we call
knowledge and insightextraction. If you only use the
traditional machine learning,you're not able to get the
insight extraction. But byhaving deep learning
(09:50):
technologies really helped us tomake the difference. We just
launched that in February in theMalaysia market. We're going to
learn launch that in April forthe Hong Kong market. After our
initial launch, the customeralready feels amazed because in
the past they see LexisNexis, wehave Lex Machina, we have Ravel
(10:12):
contacts. In the US market, theyalready have this kind of
analytics. But now we have thisanalytics focused on insight
extraction. So the customerfeels amazed, it really is very
successful in the Asia market.
YS Chi (10:26):
I'm am equally intrigued. I have a dumb
question. Does this work on alllanguages? Or does it only work
on certain global, widely spokenlanguage that are single byte?
Min Chen (10:40):
Yeah, I think the overall technology is suitable
for all languages. But there's adifferent approach and
techniques we have to take. It'sbecause when we deal with
different language, it'sbasically about token. How do
you extract the token? Thedouble byte language is not like
(11:02):
English, the sentence isseparated by meaningful tokens.
So the way you extract thetoken, in a sentence is
different. You just have to getthrough that part and the rest
of the techniques can beleveraged across different
languages.
YS Chi (11:19):
Is this very expensive to do Min?
Min Chen (11:22):
It really depends on the problem you're talking
about. So I can't...
YS Chi (11:27):
It just sounds so fancy.
It sounds so complex, andtherefore you have to think it's
expensive. But obviously, you doit in a very efficient way.
Min Chen (11:36):
Yeah, well once you build a foundation there,
because we already have millionsand billions of data there.
LexisNexis has the data so wealready build the foundation.
Maybe when you build somethingfrom scratch, it takes time. It
takes multiple experiments toget there. But once you set up,
build up the foundation, youalready have that algorithm
(11:58):
there, it will be easier toleverage to other regions. To
build up your first concept, itmight be not very cheap. But if
we want to expand a similar ideato other regions, it becomes
cheaper and cheaper.
YS Chi (12:13):
That is really one of our unique advantage and unique
contributions because we haveboth the content and now the
technology.
Well, as unique as that is, Ifind you to be a very unique
(12:34):
person Min. Since we have metseveral years ago, I've been
fascinated about your growth andI'd like to turn to that for now
for a few minutes. But before wedo that, tell us a little bit
about how you're handling this,you know, unexpected and very
prolonged pandemic.
Min Chen (12:50):
Well, I want to show off a little bit YS. We have
been completely back to theoffice for a year now. I'm
definitely not, I'm not a bigfan of working from home, even
before the pandemic. So I doacknowledge we have advanced to
technologies to help people meetvirtually. For example, a few
(13:11):
weeks ago, I was demoed by theteam for an AR and VR app to
allow engineers to do pairprogramming and code review
remotely. By the way, pairprogramming is where two
engineers work together on thesame piece of code. Code review
is a group of engineers wherethey get together to review the
same piece of code. So you canimagine these kind of activities
(13:31):
require a heavy in personexperience. Now, there are
different types of tools whichcan support engineers to do the
same work virtually. But I thinkthe dynamic and the inspiration
of being able to have face toface conversation cannot be
completely replicated. There arejust a few exquisite nuances
(13:56):
that you might not be able totell them explicitly, but you
just know the difference. It'sthat difference that can make a
difference to be creative, todrive innovation in a more
efficient and productive way. Soonce the situation became better
in Shanghai, it was only likethree to four weeks, there was
(14:16):
locked down. So we were allworking from home. But when the
restrictions started to ease, Ibrought the entire team back to
the office and everyone wassuper excited to meet everyone
in 3D again. Having said that,it seems only the China gets
100% back to office, and therest of the world still majorly
work from home, including ourcustomers that we're serving in
(14:39):
Asia Pacific and globally. So tome, the ways of interaction has
changed. I think I've grown myremote research skills to the
new heights. During thepandemic, I got more chances to
talk with customers remotely.
That really helps me to learn afew innovative techniques to
capture the precise feedbackfrom customers for product
(15:02):
validation or discovery throughphone calls. That I consider as
the most valuable new skills Ideveloped during the special
period.
YS Chi (15:14):
Always looking at the positive side Min. So, you know,
some years ago, it would nothave been at all conceivable
that I would be doing this kindof conversation with a women
technologist. It's been a littleover 15 years since you joined
LexisNexis. Then you've spent alittle more time in the broader
(15:38):
tech world before that. You evenstudied computer science at
Shanghai University. Right?
Min Chen (15:42):
Right.
YS Chi (15:43):
You were one of the very few girls in that area. There
must have been a lot ofcompetitive and demanding
environment that you had to copewith at the time. Would you like
to kind of share some storiesabout that?
Min Chen (15:56):
Yeah, I can try. I think at different stage of my
life, I am lucky to always havethe unique driver that
strengthens my face to stick totechnology industry. Back to
high school where each studentwas at the moment to decide what
is the future of their majorsubject in college. My parents
(16:17):
want me to follow either oftheir pasts. My mother is a
professor of Chinese literatureand my father is the professor
of biology. I didn't know what Iwanted to be. It was my math
teacher who encouraged me topick computer science. He said,
I think that will be a perfectfit for you. Honestly, at that
(16:38):
time, I didn't know whatcomputer science meant to me,
until I followed hisinstructions and then went to
Shanghai University to major incomputer science. Very soon, I
found myself obsessed withprogramming. I could spend hours
and hours in the library towrite a code just for a better
version to beat myself. So itwas not the homework that I have
(17:01):
to finish. I really like thefeeling that I can just write a
few lines of code to resolveissues automatically. That's
amazing. I remember my scoreswere always top three in a
class, but there was one thingthat bothered me. Regardless of
my high scores, my professornever picked me to join an
(17:23):
external science programme withtop schools in Shanghai. At that
time, we had quite a fewlearning and competition
programmes with leadinguniversities, like Fudan
University, Jiao TongUniversity, and usually top
students in the class gotpicked. Those were great
opportunity to learn from othertop students of different
schools. So I felt a bit upsetthat I was not elected. So I
(17:48):
called my high school mathteacher, I always kept contact
with him. I asked him, I said,you told me computer science
would be perfect for me, andlook, I don't even get the
chance to join these programmes.
Do you still think I'm suitableto learn the subject? He
responded with two things whichI'll never forget. He first
asked me, do you love computerscience? I replied, yes, I love
(18:11):
it. So I talked to him about allthe stories that I spent a long
time to study and to learn, allout of my own interest. Then he
said, don't worry about now. Nowyou don't get chances to join
all these programmes in school,but I have faith, you will get
lots of chances when you get ajob, when you start a career. As
(18:32):
long as you love what you'redoing and you don't give up. So
that really motivated me. Irecall in my class, there were
50 students, only eight werefemale, and out of eight girl
students I was the only one topursue a career in the
technology industry aftergraduation.
YS Chi (18:53):
Oh no.
Min Chen (18:56):
I was the only one. So my first job was a programmer in
China Daily. Then three yearslater I joined Lenovo being a
tech lead for another threeyears, and then LexisNexis. So
10th of October of this year,will be my 16 years working
anniversary. A lot of people askme why you stay in this company
(19:18):
for that long. Obviously thereare multiple reasons. In
LexisNexis there are enoughgreat challenges for me to
tackle by using advancedtechnology. You heard the story,
deep learning, machine learning.
My teams and colleagues,they're all great. I'm growing
together with them. But I thinkthere's one critical reason I
never ignore. Is that in thiscompany I come across a few of
my math teacher type of people.
(19:43):
They share very similar valueand vision which is doing things
that you love and don't give upbefore you give up. So that
keeps me motivated in the longrun.
YS Chi (19:54):
Well, can I ask what the name of your math teacher was,
because I'm inspired by hischaracter.
Min Chen (20:04):
His name is Xu Quanxiang. He's just like me, a
normal person. I don't know alot about his personal life, but
he is very disciplined, veryorganised and a very fair
gentleman. In class he alwaysencouraged me to talk, a lot. So
(20:28):
he asked the question, and somestudents raise their hand. I'm
not the student who raised theirhand. He will always call out my
name, then I can always answerthe question. So once I did ask
him, why you always call out myname? He replied, he was
smiling. He said, because I knowyou know the answer. You just
(20:49):
need to stand up.
YS Chi (20:50):
Oh, wow.
Min Chen (20:52):
Yeah. So unfortunately he passed away 20 years ago, but
the legacy he leaves to me willforever remain.
YS Chi (21:00):
Wow, we should all be lucky enough to have teachers
like that through our lives.
Min Chen (21:05):
Yeah.
YS Chi (21:06):
Well, obviously, you've done very well for yourself, for
your teams and of course for ourcompany since joining. We're
very appreciative of your workand passions. You were named in
2019 as our distinguishedtechnologist. Acompany wide
award that only one person winsevery year for exemplary
(21:28):
leadership, and fundamentallychallenging and changing the way
we do business. What is moreimportant than that is that you
were the first woman to receivethat award. What did that mean
when you found out that you werethe first woman to win that
prestigious award?
Min Chen (21:47):
I'll will tell you the story. So, when I initially
learned this news from mymanager, Jeff Reihl, who is the
global CTO of LexisNexis. Ofcourse, initially, it was like a
surprise. It was announced byErik Engstrom and Kumsal. They
organised a surprise call andthey announced it to me. But it
was a very short call. Then,when we get off the call, I
(22:08):
talked with Jeff. He alsohighlighted, he said, you're the
first woman who won this award.
But my first, my instinctreaction was, is that the reason
I got selected. Well, to me it'sjust like the two sides of the
coin. I don't want to beoverlooked for opportunity
because I am a woman. But on theother hand, I also don't want to
be recognised because I am awoman. Of course, Jeff said it
(22:30):
has nothing to do with yourgender identity. I was
recognised because of mycontribution and delivery to our
company. That I have beenbuilding up the culture of
customer driven innovationamongst the engineering team and
developing high quality productsthat differentiate us in the
Asia Pacific market, and beingpart of the solutions to improve
(22:53):
customers NPS, the net promoterscore. So I consider this award
as a recognition of me beingable to make an impact on our
customers and business. I willstrive to extend that impact to
much broader customers so it'snot just Asia Pacific but also
(23:14):
globally. I think I'm movingtoward that goal closer and
closer every day.
YS Chi (23:21):
You sure are. I remember sending you a congratulatory
note after the award.
Min Chen (23:27):
I remember that. Thank you so much for the
encouragement YS.
YS Chi (23:32):
You are encouraging a lot of girls through your own
journey.
I'm curious to know, what do youthink RELX can do more to
(23:53):
improve the pipeline of womencoming through to all fields,
including STEM where they seemto be not yet in abundance? What
advice would you give femaletechnologists who look up to
recognise leaders like you?
Min Chen (24:11):
Yeah, I think I have already seen lots of forums and
panels organised with RELX tomotivate and promote women. For
example, in general I joined thepanel session of RELX Thrive
global launch in Asia Pacific.
Thrive is a grassrootorganisation to bring all female
together and provide a portalfor them to share thoughts and
(24:32):
stories. I know there are manyother communities like this
within RELX. I think RELX can domore on gender balance, because
that could push a real force fordriving innovation. To me gender
balance doesn't necessarily meanwe have to favour a particular
gender. It means we have toconsciously check whether we
(24:55):
have enough difference in theteam. When we have different
ways of thinking, personalityand culture, it can stimulate
innovation. That's why it'simportant. I think we're also
changing the way we make hiringand promotion decisions, and to
ensure that eligible women aregiven serious consideration.
(25:18):
Last but not least, I do want tohighlight, let's not forget how
critical it is that our maleleaders support all these
initiatives to me. Thetechnology industry is still a
male dominant area, and thatsituation I don't foresee will
be changed drasticallyovernight. So our male leaders
(25:40):
who are very supportive onconsciously improving gender
balance in organisation can makea big difference. For example, I
know YS you are a great sponsorand advocate on driving
diversity and inclusion. In myorganisation LexisNexis, Mike
Walsh our CEO, and Jeff Reihlthe global CTO, and Jamie
(26:02):
Buckley global CPO, they're allstrong supporters. I often think
how great the D&I we have in ourorganisation. It is majorly
depending on how open and howfar our male leaders are
embracing these ideas and reallytaking to actions. Finally, in
terms of what advice I couldgive to other female
(26:25):
technologists. I want to make itvery short and simple, as you
have heard my story earlier,because I want to quote my math
teachers remark. Find thingsyou're really passionate about,
and stick to it and don't giveup.
YS Chi (26:42):
Yeah, I think that is clearly the theme. I think the
dimension of allies, male allieswas a key topic when we
discussed during theInternational Women's Day panel.
We do seek women to do the workbut men ally, I think have the
(27:04):
responsibility to provide theair cover so that they can fight
on the ground as effectively asthey can.
Min Chen (27:12):
Yeah, exactly.
YS Chi (27:14):
Well, let's jump back to the business world that we left
earlier in our conversation.
LexisNexis has a number of techhubs. One is in North Carolina,
one in London, and here you arein one in Shanghai. How do you
keep such a big team of datascientists, engineers and
product managers spread all overdifferent locations? Yet, you're
working so well, so motivated,especially through out the
(27:37):
pandemic? What are the secretsauce?
Min Chen (27:42):
Yeah well, pandemic for me was very short. Early I
mentioned I only worked fromhome for only three to four
weeks, and then we were back. Ithink I talk from a general
perspective. There are a lot ofinitiatives we have been doing
to keep the team motivated. Forexample, we do hacks once or
twice a year to bring engineersand product managers together,
(28:05):
in order to incubate creativeideas and solutions through
non-stop programming for 24 to48 hours. Eventually each
region's winner will join a funcompetition, we call the global
Shark Tank to present our workto our global CEO, Mike Walsh,
and his MCM. Mike and his seniorleaders will pick a winner of
(28:30):
the winners, and my team arealways winning and we have won
top prize globally three yearsin a row. Besides, I want to
mention another initiative thatI ran for a few years in Asia
Pacific, to allow people to workon innovation on regular basis.
We call it 'grab the post.' Sothe idea is we pop problem
(28:51):
statements in one page, and postit at the door of my office.
These problems are verypreliminary ideas from either
internal or external customers,which are not yet put into
official business case orproduct development roadmap.
Therefore they're treated as aside project. Whoever has
(29:14):
interest to take the challengeduring their free time can just
grab the post and commit to thePOC, proof of concept delivery.
YS Chi (29:24):
Aha.
Min Chen (29:24):
Yeah. That's why we call it grab the post. Since
this is not mandatory, and it'sa side job, you can imagine in
the end, whoever grabbed thepost is truly passionate in
building great customerexperience through leadi ng edge
tech knowledge. We doacknowledge the situation, that
some of the posts have neverbeen grabbed by anyone. But
(29:47):
averagely I did amass, everythree to five months we will get
one or two challenges rejectedby the team with proof of
concept delivered and we gavethe incentive to the team who
grabbed the post. So if youdeliver the POC and that idea
got invested by our businesspartner, you will be the person
(30:10):
to lead final delivery ontoproduction. You have no idea
what being able to bring theidea solution live to customer
means to engineers, because it'sa great motivation for
engineers.
YS Chi (30:26):
Oh, yes. That's like being selected in the starting
lineup. Well, time is flying soI'm going ask one last question
before we close. You have beenvery good at sticking to what
you're passionate about.
Obviously because you probablysaw things down the road. So
(30:47):
what do you see down the road interms of transfer tech that you
expect to see over the mediumterm? And how are you going to
make sure that your team doesright on that journey?
Min Chen (31:04):
That's a good question. I think our focus and
investment priority will alwaysbe on those specific
technologies that could addresscustomer pain points. So from
mapping customer problem totechnology perspective. I want
to summarise two major trends,two major areas. One is
(31:27):
customer's expectation, andquality of service we deliver to
them will continue to increase.
So that requires more efforts ondata science work. The other
observation is that a customerwould expect technology to
create more personalised andtailor made service to meet
their specific individual needs.
(31:49):
So those technologies that couldmove sophistication of
personalization into the nextlevel, will be hard. Of course,
you have to make sure personaldata privacy is not violated. So
getting understanding and deeperunderstanding with customer
problem as the guideline, as themajor driver for us to
(32:14):
prioritise our technology,strategy and focus. Basically,
we stay close with a lot ofcommunities and channels, both
internally and externally. Thatwe could constantly share the
most current advancedtechnologies of different kinds.
For example, we always keep aclose eye on global external top
(32:38):
conference and top papers out ofthose conference. Particularly
in AI industry, of course, inother technology industrys as
well. That helps a team to staycurrent. So it's just not just
me, I force people to learn thisand that. You have to build a
culture there, that cultureshould be everyone is eager to
(33:00):
learn. But that culture isalways driven. So in terms of
what we want to learn, thatshould be always driven by what
customer problem we want toresolve. So with that, because
there's so many technologies,it's very broad, right. There's
so many different layers,different levels of the things
you have to learn. You cannotlearn all of them. So, you we
(33:22):
use the customer problem as theguideline for us to decide which
area we want to do the deepdive.
YS Chi (33:29):
Well, you had a programme called getting outside
the building and getting yourtech people to listen to
customer firsthand?
Min Chen (33:36):
Yeah.
YS Chi (33:36):
Yeah. That's awesome.
Now, I cannot close withoutasking a follow up question,
because you know, tailor-madesolution is obviously something
that we all want to do. Buttailor-made has always been
associated with high cost, high,high price. So this technology
adoption, is meant to try tomake it tailor-made without high
(33:59):
costs. Is that right?
Min Chen (34:03):
Yeah. Put it this way, I would love to rephrase that
into a cost effective way. Butactually, it's the same thing.
You're right. It's the samething. We're going to want to do
the tailor-made solution in aefficient way, in a cost
efficient, effective approach.
YS Chi (34:23):
Yeah, I think that we can do what someone has coined
mass customization, right?
Min Chen (34:30):
Yeah.
YS Chi (34:31):
Well, I'm fascinated and I could stay on for three hours
talking to you Min. It's alwaysbeen fun to talk with you. Every
time I meet you, you are fivesteps ahead.
Min Chen (34:41):
Thank you
YS Chi (34:42):
Thank you so much Min.
Min Chen (34:43):
It's also an honour and I also feel pleasure talking
with you YS.
YS Chi (34:48):
Well, thank you for sharing your stories and
insights with us today. I thinkMin has shared with us not only
about the technology of naturallanguage processing or deep
learning. But really just abouther subtle leadership that is
not about just being a pioneerand bringing the entire team
(35:11):
along, and making this uniquecontribution to the legal
community. Thank you again.
Thank you to our listeners fortuning in. Please don't forget
to hit the subscribe button onyour podcast app to get the new
episodes as soon as they'rereleased. Once again, thanks for
listening and please stay welland healthy.
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