okay I’ll just kind of give you a glimpse of the future so what do we
glimpse of the future so what do we
glimpse of the future so what do we expect now you know ask the world is
expect now you know ask the world is
expect now you know ask the world is going become more and more digital and
going become more and more digital and
going become more and more digital and if you see all the big click companies
if you see all the big click companies
if you see all the big click companies we’re going to become a world where Adi
we’re going to become a world where Adi
we’re going to become a world where Adi is first so what does that mean a lot of
is first so what does that mean a lot of
is first so what does that mean a lot of companies are gonna start using AI to
companies are gonna start using AI to
companies are gonna start using AI to conquer lis gain competitive advantage
conquer lis gain competitive advantage
conquer lis gain competitive advantage you know it’s what I call weapon of mass
you know it’s what I call weapon of mass
you know it’s what I call weapon of mass disruption you know it’s one of those
disruption you know it’s one of those
disruption you know it’s one of those game changer if you will in fact it can
game changer if you will in fact it can
game changer if you will in fact it can also be used as a weapon of mass
also be used as a weapon of mass
also be used as a weapon of mass accelerate why is that because the
accelerate why is that because the
accelerate why is that because the company can use this AI to be able to
company can use this AI to be able to
company can use this AI to be able to catapult yourself in front and ahead
catapult yourself in front and ahead
catapult yourself in front and ahead over your competitors so how do we get
over your competitors so how do we get
over your competitors so how do we get here so what we did is we created this
here so what we did is we created this
here so what we did is we created this machine right and then what we did is we
machine right and then what we did is we
machine right and then what we did is we give it a brain you know we put in a
give it a brain you know we put in a
give it a brain you know we put in a processing chip what I call up chip and
processing chip what I call up chip and
processing chip what I call up chip and nowadays we have a specialty chip that’s
nowadays we have a specialty chip that’s
nowadays we have a specialty chip that’s that’s gonna be far faster that can
that’s gonna be far faster that can
that’s gonna be far faster that can handle a lot of artificial computation
handle a lot of artificial computation
handle a lot of artificial computation then what we did is that we trained this
then what we did is that we trained this
then what we did is that we trained this machine so we give it data we algorithm
machine so we give it data we algorithm
machine so we give it data we algorithm and then we thought yeah you know let’s
and then we thought yeah you know let’s
and then we thought yeah you know let’s just put in some senses throw in a
just put in some senses throw in a
just put in some senses throw in a biosensors now what we got so we got
biosensors now what we got so we got
biosensors now what we got so we got this machine now it’s recognized speech
this machine now it’s recognized speech
this machine now it’s recognized speech it understands languages and it can even
it understands languages and it can even
it understands languages and it can even translate from one word to different
translate from one word to different
translate from one word to different kind of forms okay and for the business
kind of forms okay and for the business
kind of forms okay and for the business world well it can boost performance and
world well it can boost performance and
world well it can boost performance and it does a great job doing that it
it does a great job doing that it
it does a great job doing that it improves productivity and quality and it
improves productivity and quality and it
improves productivity and quality and it drop costs substantially so now it does
drop costs substantially so now it does
drop costs substantially so now it does these things all these things this these
these things all these things this these
these things all these things this these outcomes
outcomes
outcomes achieve these outcomes and it does it
achieve these outcomes and it does it
achieve these outcomes and it does it beyond human capabilities I mean we’re
beyond human capabilities I mean we’re
beyond human capabilities I mean we’re the advances in machine learning
the advances in machine learning
the advances in machine learning artificial intelligence deep learning
artificial intelligence deep learning
artificial intelligence deep learning has gone
has gone
has gone not only it matches but it outperforms
not only it matches but it outperforms
not only it matches but it outperforms humans so that’s good and great and
humans so that’s good and great and
humans so that’s good and great and that’s where the problem is the problem
that’s where the problem is the problem
that’s where the problem is the problem is machines AI machines are coming to
is machines AI machines are coming to
is machines AI machines are coming to this world and there will be replacing
this world and there will be replacing
this world and there will be replacing human workers why well simple because
human workers why well simple because
human workers why well simple because they’re going to do the job and they’re
they’re going to do the job and they’re
they’re going to do the job and they’re doing the job better faster and cheaper
look at this look at customer service
look at this look at customer service that’s an example real these are the
that’s an example real these are the
that’s an example real these are the actual case it takes some AI machine
actual case it takes some AI machine
actual case it takes some AI machine four and a half minutes to handle a
four and a half minutes to handle a
four and a half minutes to handle a customer complaint well as humans it
customer complaint well as humans it
customer complaint well as humans it takes 18 minutes that’s four times
takes 18 minutes that’s four times
takes 18 minutes that’s four times longer and then on top of that it picks
longer and then on top of that it picks
longer and then on top of that it picks up a call picks up a call in two seconds
up a call picks up a call in two seconds
up a call picks up a call in two seconds it takes those few months 55 seconds to
it takes those few months 55 seconds to
it takes those few months 55 seconds to pick up a call and it can do all this at
pick up a call and it can do all this at
pick up a call and it can do all this at a substantially lower price to handle
a substantially lower price to handle
a substantially lower price to handle one customer it can cost about eight
one customer it can cost about eight
one customer it can cost about eight cents us what’s what four pesos
cents us what’s what four pesos
cents us what’s what four pesos it cost us humans $2.50 that’s 125 pesos
it cost us humans $2.50 that’s 125 pesos
it cost us humans $2.50 that’s 125 pesos I mean so if you really think about it
I mean so if you really think about it
I mean so if you really think about it that’s something that it’s just too
that’s something that it’s just too
that’s something that it’s just too compelling for any businesses so what do
compelling for any businesses so what do
compelling for any businesses so what do you expect well as expected job losses
you expect well as expected job losses
you expect well as expected job losses okay what this is going to be a global
okay what this is going to be a global
okay what this is going to be a global crisis yes there’s actually a lot of
crisis yes there’s actually a lot of
crisis yes there’s actually a lot of research done all around the world today
research done all around the world today
research done all around the world today where because close to half 50% of every
where because close to half 50% of every
where because close to half 50% of every workers in the world is going to lose
workers in the world is going to lose
workers in the world is going to lose their job to AI automation okay that and
their job to AI automation okay that and
their job to AI automation okay that and the way they did it they have a very
the way they did it they have a very
the way they did it they have a very methodical way as they look at every 800
methodical way as they look at every 800
methodical way as they look at every 800 occupations and they break it down to
occupations and they break it down to
occupations and they break it down to activities a vast majority of the
activities a vast majority of the
activities a vast majority of the activities they can be done today by AI
activities they can be done today by AI
activities they can be done today by AI machines we’re not talking about hey you
machines we’re not talking about hey you
machines we’re not talking about hey you know the technology that’s going to be
know the technology that’s going to be
know the technology that’s going to be created in the future now it’s today the
created in the future now it’s today the
created in the future now it’s today the technology Ori have today that that can
technology Ori have today that that can
technology Ori have today that that can perform what we’re going to do what we
perform what we’re going to do what we
perform what we’re going to do what we are doing today so what does that mean
are doing today so what does that mean
are doing today so what does that mean well it’s going to be 1.1 billion people
well it’s going to be 1.1 billion people
well it’s going to be 1.1 billion people in this world for the next 10 20 years
in this world for the next 10 20 years
in this world for the next 10 20 years whose job is going to be automated I
whose job is going to be automated I
whose job is going to be automated I know let’s look at an example of
know let’s look at an example of
know let’s look at an example of developed countries o United States look
developed countries o United States look
developed countries o United States look at us well us they’re saying that close
at us well us they’re saying that close
at us well us they’re saying that close to about 51 percent 51 it’s a developed
to about 51 percent 51 it’s a developed
to about 51 percent 51 it’s a developed country how can that be you know they’re
country how can that be you know they’re
country how can that be you know they’re skilled and well 51 percent of the jobs
skilled and well 51 percent of the jobs
skilled and well 51 percent of the jobs have the potential to be automated and
have the potential to be automated and
have the potential to be automated and you know that accounts for two point
you know that accounts for two point
you know that accounts for two point seven trillion dollars in wages every
seven trillion dollars in wages every
seven trillion dollars in wages every single year well how about a developing
single year well how about a developing
single year well how about a developing country let’s look at the Philippines
country let’s look at the Philippines
country let’s look at the Philippines over here and the Philippines well 47
over here and the Philippines well 47
over here and the Philippines well 47 percent we looked at contact center BPO
percent we looked at contact center BPO
percent we looked at contact center BPO in the Philippines that’s the driver of
in the Philippines that’s the driver of
in the Philippines that’s the driver of the growth of the Philippines for the
the growth of the Philippines for the
the growth of the Philippines for the last 17 years well 17 47 percent of the
last 17 years well 17 47 percent of the
last 17 years well 17 47 percent of the 1.2 million Filipinos in that sector
1.2 million Filipinos in that sector
1.2 million Filipinos in that sector also have the potential to be automated
also have the potential to be automated
also have the potential to be automated now if you look at that sector it has
now if you look at that sector it has
now if you look at that sector it has 1.2 million direct employment 3.2
1.2 million direct employment 3.2
1.2 million direct employment 3.2 million indirect employment combined you
million indirect employment combined you
million indirect employment combined you got 4.4 million Filipinos in that one
got 4.4 million Filipinos in that one
got 4.4 million Filipinos in that one industry given a 5v5 to one dependency
industry given a 5v5 to one dependency
industry given a 5v5 to one dependency ratio close to 20 million Filipinos 20
ratio close to 20 million Filipinos 20
ratio close to 20 million Filipinos 20 percent of the entire countries is
percent of the entire countries is
percent of the entire countries is dependent on that one industry so well
look at this machine this thing that we
look at this machine this thing that we created you know well they they colonel
created you know well they they colonel
created you know well they they colonel they work 24 hours a day seven days a
they work 24 hours a day seven days a
they work 24 hours a day seven days a week they don’t take
week they don’t take
week they don’t take breaks your antique nap and siesta they
breaks your antique nap and siesta they
breaks your antique nap and siesta they don’t even take a vacation okay with
don’t even take a vacation okay with
don’t even take a vacation okay with these things if they want to expand the
these things if they want to expand the
these things if they want to expand the brain capacity memory they can depend
brain capacity memory they can depend
brain capacity memory they can depend and anywhere I mean these kind of
and anywhere I mean these kind of
and anywhere I mean these kind of cognitive robots now they have fine
cognitive robots now they have fine
cognitive robots now they have fine motor skills that can actually do
motor skills that can actually do
motor skills that can actually do surgery for us even how delicate that is
surgery for us even how delicate that is
surgery for us even how delicate that is surgery and then on the other hand you
surgery and then on the other hand you
surgery and then on the other hand you can go they can move crates inventories
can go they can move crates inventories
can go they can move crates inventories and move them around warehouses without
and move them around warehouses without
and move them around warehouses without human supervision you could look at the
human supervision you could look at the
human supervision you could look at the cargo bay the shipping docks they can
cargo bay the shipping docks they can
cargo bay the shipping docks they can move containers without any supervision
move containers without any supervision
move containers without any supervision today and one day a couple years from
today and one day a couple years from
today and one day a couple years from now well not a couple years but they
now well not a couple years but they
now well not a couple years but they claim 2020 or something like that they
claim 2020 or something like that they
claim 2020 or something like that they can operate on motor vehicles for us so
can operate on motor vehicles for us so
can operate on motor vehicles for us so hey they can take us anywhere any place
mental capacities well they’re great in
mental capacities well they’re great in recognizing patterns they can retrieve
recognizing patterns they can retrieve
recognizing patterns they can retrieve information on the fly and they can
information on the fly and they can
information on the fly and they can optimize continuously what does that
optimize continuously what does that
optimize continuously what does that mean these machines are learning they
mean these machines are learning they
mean these machines are learning they are learning in fact these machines they
are learning in fact these machines they
are learning in fact these machines they can do a vast amount of intense
can do a vast amount of intense
can do a vast amount of intense computation it can even teach itself it
computation it can even teach itself it
computation it can even teach itself it can self-taught it can create its own
can self-taught it can create its own
can self-taught it can create its own simulation to teach itself to improve
simulation to teach itself to improve
simulation to teach itself to improve and then on top of that yes I mean
and then on top of that yes I mean
and then on top of that yes I mean besides that it can optimize and to make
besides that it can optimize and to make
besides that it can optimize and to make sure that it can predict see what’s this
sure that it can predict see what’s this
sure that it can predict see what’s this is a part with us humans in animals
is a part with us humans in animals
is a part with us humans in animals let’s not compare against that but
let’s not compare against that but
let’s not compare against that but because we can plan we can set goals we
because we can plan we can set goals we
because we can plan we can set goals we have the ability to predict well we
have the ability to predict well we
have the ability to predict well we create for the first time in the history
create for the first time in the history
create for the first time in the history of mankind we create this machine that
of mankind we create this machine that
of mankind we create this machine that does a better job in prediction that can
does a better job in prediction that can
does a better job in prediction that can actually predict in a fairly accurate
actually predict in a fairly accurate
actually predict in a fairly accurate manner so what is this machine
manner so what is this machine
manner so what is this machine were humans well the advances machine
were humans well the advances machine
were humans well the advances machine learning the advances deep learning
learning the advances deep learning
learning the advances deep learning artificial intelligence well not only
artificial intelligence well not only
artificial intelligence well not only have this so-called things that we
have this so-called things that we
have this so-called things that we created has matched but it can even
created has matched but it can even
created has matched but it can even outperform what we can do this is
outperform what we can do this is
outperform what we can do this is something that is Corvair be of great
something that is Corvair be of great
something that is Corvair be of great concern and yet at the same time it’s
concern and yet at the same time it’s
concern and yet at the same time it’s something that okay now how do we solve
something that okay now how do we solve
something that okay now how do we solve this problem on the things that we
this problem on the things that we
this problem on the things that we created well the good news and what the
created well the good news and what the
created well the good news and what the good news is well they have a weak spot
good news is well they have a weak spot
good news is well they have a weak spot they also they’re vulnerable okay now
they also they’re vulnerable okay now
they also they’re vulnerable okay now these machines
these machines
these machines well they lack creativity they lack
well they lack creativity they lack
well they lack creativity they lack empathy and morality I mean they can’t
empathy and morality I mean they can’t
empathy and morality I mean they can’t imagine things they can’t feel anything
imagine things they can’t feel anything
imagine things they can’t feel anything right and they can’t make any judgment
right and they can’t make any judgment
right and they can’t make any judgment okay whereas hey as humans well we can
okay whereas hey as humans well we can
okay whereas hey as humans well we can build something from scratch out of
build something from scratch out of
build something from scratch out of nothing we can conceive ideas creativity
nothing we can conceive ideas creativity
nothing we can conceive ideas creativity we can connect and relate with other
we can connect and relate with other
we can connect and relate with other humans okay and then we can make sound
humans okay and then we can make sound
humans okay and then we can make sound judgment based on morality and humanity
judgment based on morality and humanity
judgment based on morality and humanity so well so what we did what we did is
so well so what we did what we did is
so well so what we did what we did is that we we got the strength of these the
that we we got the strength of these the
that we we got the strength of these the power of this machine and then we get
power of this machine and then we get
power of this machine and then we get the strength of humans and we combine
the strength of humans and we combine
the strength of humans and we combine them we merge them we fuse them and what
them we merge them we fuse them and what
them we merge them we fuse them and what do we got
do we got
do we got augmented intelligence I mean if you
augmented intelligence I mean if you
augmented intelligence I mean if you think about it these machines are just
think about it these machines are just
think about it these machines are just raw computing power ok well that’s like
raw computing power ok well that’s like
raw computing power ok well that’s like the left side of our brain we can just
the left side of our brain we can just
the left side of our brain we can just get the machine to do that we can take
get the machine to do that we can take
get the machine to do that we can take care of their right side of their brain
care of their right side of their brain
care of their right side of their brain we can create
we can create
we can create we have emotions they don’t so what
we have emotions they don’t so what
we have emotions they don’t so what we’re going to do is we combine these
we’re going to do is we combine these
we’re going to do is we combine these things in order to come up with
things in order to come up with
things in order to come up with augmented intelligence so that’s why we
augmented intelligence so that’s why we
augmented intelligence so that’s why we culturally call it AI ^ – you know as
culturally call it AI ^ – you know as
culturally call it AI ^ – you know as they say it – brains is better than one
they say it – brains is better than one
they say it – brains is better than one so what have we done in the past well we
so what have we done in the past well we
so what have we done in the past well we spent eight years of research and
spent eight years of research and
spent eight years of research and development we’ve gone through about 87
development we’ve gone through about 87
development we’ve gone through about 87 trials and we tested among 26 different
trials and we tested among 26 different
trials and we tested among 26 different vertical industries and this is one of
vertical industries and this is one of
vertical industries and this is one of the trial we did so what we did is that
the trial we did so what we did is that
the trial we did so what we did is that we have a group of people and we got the
we have a group of people and we got the
we have a group of people and we got the top 20% doing its own thing
top 20% doing its own thing
top 20% doing its own thing fantastic performance great results and
fantastic performance great results and
fantastic performance great results and then we got the bottom 20% now the
then we got the bottom 20% now the
then we got the bottom 20% now the bottom 20% are the poor performers in
bottom 20% are the poor performers in
bottom 20% are the poor performers in fact they’re in the rehired list you
fact they’re in the rehired list you
fact they’re in the rehired list you know what that means they’re about to
know what that means they’re about to
know what that means they’re about to get fired and so what we did is that we
get fired and so what we did is that we
get fired and so what we did is that we get these out the ones who’s about to
get these out the ones who’s about to
get these out the ones who’s about to get fired and we gave them an Augmented
get fired and we gave them an Augmented
get fired and we gave them an Augmented intelligence system and what happened as
intelligence system and what happened as
intelligence system and what happened as you can see the results they can
you can see the results they can
you can see the results they can outperform even the top 20% okay and if
outperform even the top 20% okay and if
outperform even the top 20% okay and if you look at another this is trial number
you look at another this is trial number
you look at another this is trial number 83 and what happened here is that we got
83 and what happened here is that we got
83 and what happened here is that we got a fantastic US agents they got 20 years
a fantastic US agents they got 20 years
a fantastic US agents they got 20 years of experience so they’ve been running
of experience so they’ve been running
of experience so they’ve been running all these programs in campaign and then
all these programs in campaign and then
all these programs in campaign and then we try to get in the Philippines in the
we try to get in the Philippines in the
we try to get in the Philippines in the provincial not even Metro Manila these
provincial not even Metro Manila these
provincial not even Metro Manila these are the kolduny or higher you know what
are the kolduny or higher you know what
are the kolduny or higher you know what they knew your heart meant they’ve tried
they knew your heart meant they’ve tried
they knew your heart meant they’ve tried applying in five six and seven call
applying in five six and seven call
applying in five six and seven call centers nobody wants them there in the
centers nobody wants them there in the
centers nobody wants them there in the near higher because they’re not
near higher because they’re not
near higher because they’re not qualified so we got these people and we
qualified so we got these people and we
qualified so we got these people and we put them in the Augmented intelligence
put them in the Augmented intelligence
put them in the Augmented intelligence system and if you can see what happened
system and if you can see what happened
system and if you can see what happened is that they beat and outperform the
is that they beat and outperform the
is that they beat and outperform the agents who’s done this for 20 years
agents who’s done this for 20 years
agents who’s done this for 20 years versus the new hire who
versus the new hire who
versus the new hire who unemployable with zero experience and
unemployable with zero experience and
unemployable with zero experience and they actually outperformed those guys
they actually outperformed those guys
they actually outperformed those guys and on top of that what was very
and on top of that what was very
and on top of that what was very impressive was the customer that they
impressive was the customer that they
impressive was the customer that they acquired well they stayed a lot longer
acquired well they stayed a lot longer
acquired well they stayed a lot longer so instead of lasting comparing against
so instead of lasting comparing against
so instead of lasting comparing against the agents we’ve been doing it for 20
the agents we’ve been doing it for 20
the agents we’ve been doing it for 20 years these customers stayed with the
years these customers stayed with the
years these customers stayed with the company a lot longer by 24% in the
company a lot longer by 24% in the
company a lot longer by 24% in the business term that’s fantastic so where
business term that’s fantastic so where
business term that’s fantastic so where do we go from here well us business if
do we go from here well us business if
do we go from here well us business if our step is first three steps simple but
our step is first three steps simple but
our step is first three steps simple but difficult to do first of course is you
difficult to do first of course is you
difficult to do first of course is you dare to discover I know it takes risk I
dare to discover I know it takes risk I
dare to discover I know it takes risk I know people don’t like risk because of
know people don’t like risk because of
know people don’t like risk because of the fear of the unknown but you have to
the fear of the unknown but you have to
the fear of the unknown but you have to dare to discover you got to go out there
dare to discover you got to go out there
dare to discover you got to go out there and try it
and try it
and try it okay only do you explore can you
okay only do you explore can you
okay only do you explore can you eventually exploit afterwards the second
eventually exploit afterwards the second
eventually exploit afterwards the second step my mentor is Dada Benito and he
step my mentor is Dada Benito and he
step my mentor is Dada Benito and he basically said you got a dig deep why do
basically said you got a dig deep why do
basically said you got a dig deep why do I have to dig deep it’s because you want
I have to dig deep it’s because you want
I have to dig deep it’s because you want to make sure you can innovate a product
to make sure you can innovate a product
to make sure you can innovate a product that you’re the only one who can do this
that you’re the only one who can do this
that you’re the only one who can do this it’s very competitive differentiable
it’s very competitive differentiable
it’s very competitive differentiable it’s a product that’s for the global
it’s a product that’s for the global
it’s a product that’s for the global market so that’s basically what you do
market so that’s basically what you do
market so that’s basically what you do and when you dig deep you will only be
and when you dig deep you will only be
and when you dig deep you will only be the one to have the insight a special
the one to have the insight a special
the one to have the insight a special insight that Intel that nobody else has
insight that Intel that nobody else has
insight that Intel that nobody else has and the third step is well you gonna
and the third step is well you gonna
and the third step is well you gonna have to be able to iterate and
have to be able to iterate and
have to be able to iterate and continually iterate improvement
continually iterate improvement
continually iterate improvement improving on people you have a good
improving on people you have a good
improving on people you have a good product market fit that means to say you
product market fit that means to say you
product market fit that means to say you have to then get the innovative product
have to then get the innovative product
have to then get the innovative product you did and then you have to monetize it
you did and then you have to monetize it
you did and then you have to monetize it and so you can actually feed the whole
and so you can actually feed the whole
and so you can actually feed the whole cycle and continue R&D bigger dig deeper
cycle and continue R&D bigger dig deeper
cycle and continue R&D bigger dig deeper together to gather Intel insight and
together to gather Intel insight and
together to gather Intel insight and then to be able to monetize it and the
then to be able to monetize it and the
then to be able to monetize it and the company grows that way well
company grows that way well
company grows that way well options well you asked there’s three
options well you asked there’s three
options well you asked there’s three ways of using augmented intelligence
ways of using augmented intelligence
ways of using augmented intelligence well one is that you can use it to
well one is that you can use it to
well one is that you can use it to reassign your workers so to the higher
reassign your workers so to the higher
reassign your workers so to the higher value tasks or you can actually
value tasks or you can actually
value tasks or you can actually compliment your workers to do the harder
compliment your workers to do the harder
compliment your workers to do the harder to do work now if you decide to go to
to do work now if you decide to go to
to do work now if you decide to go to take the first option reassign well on
take the first option reassign well on
take the first option reassign well on the simple first year the simpler ones
the simple first year the simpler ones
the simple first year the simpler ones the repetitive ones you automate that
the repetitive ones you automate that
the repetitive ones you automate that that way you free up those workers to
that way you free up those workers to
that way you free up those workers to focus on the higher value tasks now if
focus on the higher value tasks now if
focus on the higher value tasks now if you want to complement then you have to
you want to complement then you have to
you want to complement then you have to empower these lower skilled workers so
empower these lower skilled workers so
empower these lower skilled workers so they can do the higher skilled work so
they can do the higher skilled work so
they can do the higher skilled work so you need to ask are you here to disrupt
you need to ask are you here to disrupt
you need to ask are you here to disrupt and when you disrupt the market to be
and when you disrupt the market to be
and when you disrupt the market to be winners or are you here to be disrupted
winners or are you here to be disrupted
winners or are you here to be disrupted and then if doing so you become I mean
and then if doing so you become I mean
and then if doing so you become I mean those are the things that you have to
those are the things that you have to
those are the things that you have to ask yourself and nowadays you can use AI
ask yourself and nowadays you can use AI
ask yourself and nowadays you can use AI to basically unlock the benefits wherein
to basically unlock the benefits wherein
to basically unlock the benefits wherein these benefits can be used as an
these benefits can be used as an
these benefits can be used as an competitive advantage to be able to
competitive advantage to be able to
competitive advantage to be able to drive your company your competitors out
drive your company your competitors out
drive your company your competitors out of business skills required a lot of
of business skills required a lot of
of business skills required a lot of people will you ask how what do we do
people will you ask how what do we do
people will you ask how what do we do where do we go from here well very
where do we go from here well very
where do we go from here well very simple you have to develop the skills
simple you have to develop the skills
simple you have to develop the skills where you know the activities are go out
where you know the activities are go out
where you know the activities are go out to get automated just like psychology
to get automated just like psychology
to get automated just like psychology sociology even sales because that’s
sociology even sales because that’s
sociology even sales because that’s gonna be freemium and on the other hand
gonna be freemium and on the other hand
gonna be freemium and on the other hand the higher skilled work that requires
the higher skilled work that requires
the higher skilled work that requires designing developing deploying these AI
designing developing deploying these AI
designing developing deploying these AI technology you will be in great demand
technology you will be in great demand
technology you will be in great demand as for the educational system well as
as for the educational system well as
as for the educational system well as the workforce evolve they have to
the workforce evolve they have to
the workforce evolve they have to emphasize and focus on creativity you
emphasize and focus on creativity you
emphasize and focus on creativity you have to emphasize on emotional
have to emphasize on emotional
have to emphasize on emotional intelligence as well as leadership in
intelligence as well as leadership in
intelligence as well as leadership in coaching others so anyways are you here
coaching others so anyways are you here
coaching others so anyways are you here racing with or
racing with or
racing with or against machine well you can you can
against machine well you can you can
against machine well you can you can race against them good luck because we
race against them good luck because we
race against them good luck because we haven’t seen a lot of people who can do
haven’t seen a lot of people who can do
haven’t seen a lot of people who can do that very good on the other hand you can
that very good on the other hand you can
that very good on the other hand you can beat them you can fight them or as they
beat them you can fight them or as they
beat them you can fight them or as they say if you can’t beat them join them
say if you can’t beat them join them
say if you can’t beat them join them thank you
thank you
thank you [Applause]
”
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